THE TOPOGRAPHY OF HEMISPATIAL NEGLECT:

BRAIN-BEHAVIOUR CORRELATIONS

WTH CT AND SPECT IMAGING M STROKE

by

Farrell S. Leibovitch

A thesis submiaed in confomity with the requirements for the degree of Master of Science

Graduate Department of Institute of Medical Science University of Toronto

@ Copyright by Farrell Stuart Leibovitch 1996

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Topography of Hcmispatid Ncglca

ABSTRACT

THE TOPOGRAPHY OF EIEMISPATIAL NEGLECT:

BRAIN-BEHAVIOUR CORRELATIONS WTH CT AND SPECT IMACLNG IN STROKE

Farrell S. Leibovitch Master of Science

1996 Institute of Medical Science

University of Toronto

Hemispatial neglect, characterized as failure to attend to contralesional space, is

hypothesized to result tiom damage to a network for duected attention which involves the

fiontal, parietal, and cingulate cortices, and the basal ganglia and thalamus. This study identified

the neural correlates of hemispatial neglect in 75 LHD and 120 RHD acute stroke patients using

structural (CT) and fimctional (SPECT) imaging. Multiple Linear Regression and Partial Least

Squares were used to identify the brain regions that predicted performance on the Sunnybrook

Neglect Banery. In LHD patients, the significant regions were the cingulate, frontal, parietal,

laterat occipital, and temporal regions. In RHD patients, the significant regions were the

cingulate, parietal, lateral occipital, and parietotemporal regions. Overall, the parietal region

emerged as the powerful predictor of neglect behaviour. A qualitative difference emerged

between the hemispheres on fùrther inspection of negiect subtest and brain region correlations.

The study shows the value of complementary structural and fùnctional imaging techniques and

neuropsychological tests of behaviour in elucidating brain-behaviour relationships.

Keywords: Hemispatial Neglect, Lefi-Sided Neglect, Right-Sided Neglecq Siioke, Left Hemisphere- Damaged, Right Hemisphere-Damaged, Theoretical Network for Directed Attention, Single Photon Emission Computed Tomography, Computed Tomography, Multiple Linear Regression, Partial Least Squares

Topography of Hanispatial Neglect

1 am very gratefül to many individuals whose help and fnendship has made this thesis possible.

1 want to thank my thesis cornmittee for their suggestions, support, and devoted efforts, specifically: Dr. Sandra Black. my supervisor, has been a great source of motivation both academically and personally. Her fkiendship and professional support has guided me throughout this project. 1 will be forever grateful to Sandy for encouraging, challenging and always having confidence in me. It has been a privilege and pleasure to know her and work for her. Many thanks to Dr. Curtis Caldwell, my other supervisor, for al1 his assistance during this project, especially regarding SPECT. 1 always benefited fiom his suggestions, challenges, and encouragement. My thesis has been greatly improved fiom the advice of Dr. A. Randy McIntosh, especially regarding PLS. To Dr. John Szalai who provided ongoing statistical guidance and Dr. John Wherrett who gave a clinical perspective and gave many helpfûl suggestions.

1 want to acknowledge my examination cornmittee for their careful reading and detailed recommendations, specifically: Dr. Frank Prato, my external appraiser, for al1 of his comments, both in the written appraisal and during the defense. The final version of this thesis has benefited greatly fiom his suggestions following his in-depth examination. Dr. Mary Pat McAndrews, my interna1 appraiser, for her helpful suggestions and recommendations during the defense and in her written report. Drs. Mary Lou Smith and Gordon Winocur for their insightfiil suggestions.

1 am also grateful to Cognitive Neurology Unit staff, who shared their experiences and expertise and provided constant support, guidance, and camaraderie. Special thanks to Patricia Ebert and Kira Barbour for helping with the CT data, Karen Ma for helping with the SPECT data, Joanne Lawrence, Nancy Blair, Jay Bondar, and Doug Martin for collecting the Behavioural data. 1 owe much gratitude to Dr. Lisa Ehrlich and the technicians of Nuclear Medicine for their willing cooperation.

This work was fùnded by the Ontario Mental Health Foundation and the Heart and Stroke Foundation.

Finally, 1 want to thank my wife, Kem Leibovitch, for her constant encouragement, patience and confidence in me and to the rest of my family, especially my parents and my extended family, for their ongoing support and words of wisdom.

TABLE OF CONTENTS

ABSTRACT - . * . ~ w . * - - - - ~ . m m n . m r t . u . - . * ~ - r . r - u . - . - . . - u u * - * * * . * = . * m . m U

ACKNOWLEDGEMENTS --.- ~ ~ . ~ , . ~ . ~ o . . . . ~ . m H I . . H . H . . W m ~ ~ . t ~ ~ ~ ~ . . . ~ ~ . o ~ . ~ ~ . ~ ~ H w n iÜ

LIST OF TABLES .-W.---.-.UI.UIU.nm.n-n... ""UI..tM..C..n-.um*.*..........----*.*-..*..*.." )Iji:

LIST OF FIGURES .,.,.,......~o.........oH..~~ ........ ~..*....*~**..~*.HHH...o.*..-..**...*~.~............. . .......... VU

LIST OF ABBREVIATIONS ~.........ltnuw. .... w..*o..H1...*.w.*.. ~.**..*.-.*~1,.*.~.-.-.*.*.*......-.-~.......*...-....... i~

LIST OF BRAIN REGION ABBREVIATIONS~lt~~~~nw~~~u~~*mw~~u-*~~m~~~**u~*u*~~~~~~~~***~u~o*u=*w*~~

1.1. NEUROP~YCHOLOGICAL MODELS OF NEGLECT ......................................................................................... 2

1 -2. NEUROANATOMICAL MODELS OF NEGLECT ................................................. - ...........-..- - ...................... . ..... 3

1 -3. EVIDENCE SUPPORTMG MODELS OF NEGLECT .......................................................... - ............................... 6

1.4. IN Vrvo NEUROIMAGWG IN NEGLECT ................. . ......................................... ............................................ 8

1 -5. STATISTICAL TECHNIQUES IN IMAGNG DATA .......................................... ......................................... 10

1.5.1. Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . I I

1.5.2. Partial Least Sipares.. . . . ... ... . ... ............. ... ........ . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I 7

1.6. HYPOTHESIS ...................................... -...- .......... - .--.---........... ..... ....-.. . -............ ..-.. ...-.. ...... ... ..... .. . . ..... ........ 20

1.6.1. Hypothesis: .......... . . . . ....... ..... .. ... . .... .. ...-.. .. .... . ... . . ...... ............. -.. . . ... .......- -. .. ... . .... . -- -.-.,-.. . .. ... .. .. . .... . ... 20

2.2. CT SCANS ............................................................................................................................................... 26

2.3. SPECT SCANS .... ............................. ................................................ ..... ........-.... ..... .... ............. ........ ....... 29

3.1. POPULATION ~NCLUSION CR~TUUA ...................... ... .............,.- 38

........ . 3 -2 CT INCLUSION CRITERIA .................... ...................... .................................. ........................... 4 1

................................. .....*............................. .. .......*..................... .. 3.3. CT VISUAL ANALYSIS ....... ..-. - .-. -41

3.4. SPECT INCLUSION CFUT~UA .......................... - ....................................................................................... 42

3 . 5. STATISIICAL NORMALIZATION PROCEDURE ..................................... .............................. -- -.---..- - ----... - --.-.. 43

3.5.1. Neglect Score and Subtest Log Transformation ......................................................... ..- -.---..-- - -...... 43

3- 5.2. CT Regional Arc Sine Transfomation .......................................................................... -- .--- - - - - .--...-- 44

3.6. M U L ~ C O L L ~ [N CT AND SPECT DATA ........................................................... -.-----.-.-.- --.-------.... 45

3.7. CT LINEAR REGRESSION ANALYSIS ................................................................... ..............-. -.---------- ---...--- 45

* .....*...-*-..-- 3.8. SPECT NORMAL~ZATION AND STANDARDIZATION ...nnnnnnnnnnnnnnnnnn.nn.. ......-........-..-.. ----- ------...... -46

3.9. CT-SPECT L~NEAR REGRESSION ANALYSIS .............................................. -- . .-. .........-... .--. . - - -------. -- ---. .... -47

3.1 0 . POWER CALCULATIONS FOR LMEAR REGRESSION ANALYSE ........................................ - --- - --------- ----.....- 48

3.1 1 . SPECT P A R M L LEAST SQUARES ANALYSIS .............................................................................. --.......- 48

4 . RESULTS ................ " ..wNH.tt.H..H.m........... ...... ..................... ".. .... - ............................................. S

............................................................................................................. . 4.1. DE~I~GRAPHIC DATA RESULTS 50

............................................................................................................ 4.2. CT VISUAL ANALYS~S - RESULTS 1

4.2.1. LND Croup ..................................................................................................................................... 51

4.2.2. RHD Group ..................................................................................................................................... 52

3.2.3. Summary ......................................................................................................................................... 53

............................................................. 4.3. MULTICOLL~NEARI-IY M CT DATA - RESULTS 54

4.4. CT LMEAR REGRESSION ANALYSIS . RESULTS ....................................................................................... 55

4.4.1. Summary ......................................................................................................................................... 56

4.5. M U L ~ C O L L ~ N ~ M SPECT DATA - RESULTS .................................................................................. 56

4.6. SPECT LMEAR REGRESSION ANALYSE - RESULTS ................................................................................ 57

4.6.1. Summary ........................................................................................................................................ -58

LIST OF TABLES

TABLE 1 : SUMMARY OF IMAGMG SNDIES M NEGLECT ....................................................................... ----.--8a

TABLE 2: DATA SUMMARY ........................................................................................................................ 24a

TABLE 3: ANALYSIS SUMMARY ................................................................................................................. 4 1 b

TABLE 4: POPULATION DEMOGRAPHICS SUMMARY ................................................................................... 50a

TABLE 5: CT VARIABLES SUMMARY ......................................................................................................... 50b

TABLE 6: SPECT PERFUSION RATIO SUMMARY FOR 20 REGIONS OF INIEREST ........................................ 5 1 a

TABLE 7: PERCEWAGE OF PATIENTS W ~ T H CT DAMAGE IN THE THEORETICAL NEfWORK FOR DIRECTED

A ~ E N T I O N .................................................................................................................................................. 5 1 b

TABLE 8: AVERAGE SPECT RATIOS OFTHE REGIONS IN TWE THEORETICAL NETWORK FOR D I R E ~ D

AITEXTION .................................................................................................................................................. 5 1 b

TABLE 9: PERCENTAGE OF PATIENTS WlTH DAMAGE TO THE FIVE REGIONS IN THE THEORETICAL NETWORK

FOR DIRECTED A'ITEMION .......................................................................................................................... 53a

TABLE 1 O: POWER CALCULATION SUMMARY FOR MLR ANALYSES ......................................................... 60a

TABLE 1 1 : RESULTS SUMMARY FROM PLS ANALYSES .............................................................................. 6 1 a

TABLE 12: SUMMARY OF THE RESULTS OBTAINED FROM THE MLR AND PLS ANALYSES ........................ 69a

vii

LIST OF FIGURES

........................................................................................ FIGURE 1 : DIAGRAM OF PARTIAL LEAST SQUARES 17a

FIGURE 2: COMPLETE NEGLECT BAT~ERY SCORE VS. COMPOSITE SCORE USING DRAWING ESTIMATE ....-..4la

FIGURE 3: COMPLETE NEGLECT BA-ITERY SCORE VS. COMPOSITE SCORE USCNG LINE BISECTION

ESTIMATE ............................. ........................................................................................................ -...--..----4 la

FIGURE 4: COMPLETE NEGLECT BA'ITERY SCORE VS. COMPOSITE SCORE USiNG LINE CANCELLAT~ON

..................................................................................................................................................... EST~MATE 4 1 a

FIGURE 5: COMPLETE NEGLECT B A ~ E R Y SCORE VS. COMPOS~TE SCORE USCNG SHAPE CANCELLATION

..................................................................................................................................................... ESTIMATE 41a

................................. FIGURE 6: SINGULAR IMAGE FOR THE FIRST LATENT VARIABLE IN THE LHD GROUP 62a

........................... FIGURE 7: SINGULAR IMAGE FOR THE SECOND LATENT VARIABLE m THE LHD GROUP -.63a

FIGURE 8: SINGULAR IMAGE FOR THE T HIRD LATENT VARIABLE IN THE LHD GROUP ................................ 63b

FIGURE 9: MAGE VS. SUBTEST SCORES FOR LV 1 iN THE LHD GROUP ........................................................ 64a

FIGURE 10: IMAGE VS. SUBTEST SCORES FOR LV2 IN THE L H D GROUP ...................................................... Wb

FIGGRE 1 1: IMAGE VS. SUBTEST S c O R f 3 FOR LV3 iN THE LHD GROUP ...................................................... 65a

.............................. FIGURE 12: SINGULAR IMAGE FOR THE FIRST LATENT VARIABLE IN THE RHD GROUP -67a

..................................... F I G ~ ~ R E 13: IMAGE VS. SUBTEST SCORES FOR LV1 IN THE RHD GROUP

LIST OF ABBREVIATIONS

mT~-HMPAO AC-PC ANOVA CI CT fMRI FWHM GE LHD LR LV MBq MCA MLR MR MRI OMHF PET PLS RHD ROI SI SLR SM3 SPECT SSCBC SVD TPO VIF VSB

9%-Tec hnetiurn Hexamethyl propyleneiunineoxUne Anterior Commissure - Posterior Commissure Analysis of Variance Confidence Interval Computed Tomography Functionai Magnetic Resonance Imaging Full Widîb Half Maximum General Electric LeA Hemisphere-Damaged Lin- Regression Latent Variable Mega-Becquerel Middle Cerebral Axtery Mu1 tiple Lioear Regression Magnetic Resonance Magnetic Reso~mce Imaging Ontario Mental Health Foundation Positron Ernission Tomography Partial Least Squares Right Hemisphere-Damaged Region of Interest Singular Image Simple Linear Regression Sunnybrook Neglect Battery Single Photon Emission Computed Tomography Summed Squared Cross-Block Correlation Singular Value Decomposition Temporal-Parietai-Occipital Variance Inflation Factor Visual Search Board

LIST OF BRAIN REGION ABBREVIATIONS

ACing AntWM BG CentWM Deep-TPO F F-Inf FLi FLS-Ant FLS-POS~ F-Mid FOF-Aat FOF-POS~ F-Sup IC-Ant IC-Post Lat0 MedO Motor O P P-Inf PostWM P-Sup PT Sensory SM T TH

Anterior Cingulate Anterior White Matter Basal Ganglia Centrai White Matter White Matter deep beneath the Temporal-Parietal Occipital Junction Frontal Cortex Merior Frontal Cortex Inferior Longitudinal Fasciculus Antenor Superïor Longitudinal Fasciculus Posterior Superior Longitudinal Fasciculus Middle Frontal Cortex Antenor Frontal-Occipital Fasciculus Posterior Frontal-Occipital Fasciculus Superior Frontal Cortex Anterior Intemal Capsule Posterior Interna1 Capsule Lateral Occipital Cortex Medial Occipital Cortex Pnmary Motor Strip or Pre-Central Gyms Occipital Cortex Parietal Cortex inferior Parietal Cortex Posterior White Matter Superior Parietal Cortex Parietal-Temporal Cortex Primary Sensory Strip or Post-Central Gyms Sensorimotor Cortex Temporal Cortex Thalamus or Thalamic Nuclei

INTRODUCTION

Hemispatial neglect is a cognitive disorder characterized by a failure to attend to

stimuli in one's personal or extrapersonal space contralateral to the side of brain damage,

when this failure cannot be attributed to either sensory or motor defects (Heilman &

Valenstein, 1979). Nthough it has k e n observed following damage to the left

hemisphere, hemispatial neglect is encountered most fiequently in association with a

lesion in the right hemisphere as a failure of patients to attend to stimuli in the lefi side of

space (Weintraub & Mesulam, 1987). In severe cases, patients may fail to dress the left

side of their body or rnay eat food only from the right side of their food tray. Male

patients may shave only the right side of their face and female patients might fail to put

makeup on the left side of their face. On clinical or experimental tests of neglect (Black,

Vu, Martin, & Szalai, 1990; Stone, Patel, Greenwood, & Halligan, 1992), patients ofien

draw spatially incomplete pictures, for example, omitting al1 the left-sided petals when

asked to draw a &isy. When asked to bisect a line, they may quarter it instead, ignoring

the lefi half (Schenkenberg, Bradford, & Ajax, 1980), or they may fail to cross out lines

distributed over a page on the side contralateral to a lesion (Albert, 1973). The disorder

can also be seen in reading; patients rnay read only the right side of words or sentences, a

phenomenon called neglect dyslexia (Behrrnann, Moscovitch, Black, & Mozer, 1990;

Riddoch, 1991). While hemispatial neglect has been observed in al1 sensory modalities, it

is most frequently tested using visual stimuli as was the case in this project.

1.1. Neuropsy~holo~~cd Models of Negleci

The rnechanisms underlying hemispatiai neglect are not completely understood.

Early in the 20th Centwy, neglect was attributed to an attentional disorder (Poppelreuter,

1 9 17), but in the mid-forties, sensory deficits (Bender & Furlow, 1944; Bender & Furlow,

1945) were thought to be the underlying cause, In the seventies, it was argued that sensory

deficits aione could not explain the neglect phenornenon, (Le., patients were found who had

neglect without sensory deficits) and an attentional-amusai mechanism (Heilman &

Valenstein, 1972; Heilman & Valenstein, 1979) was again favoured. A representational

theov of neglect (Bisiach, Luzzatti, & Perani, 1979; Ruzolatti & Berti, 1990) was also put

fornard which attributed neglect to an abnomai intexnal spatial representation, although

this theory was not sufficient to explain the neglect phenornenon. In the early eighties,

Mesulam (198 1) proposed a cortical network mode1 of directed attention, based on studies

with the macaque monkey, and postulated that hernispatial neglect was failure to direct

attention to the side opposite the lesion (Mesulam, 198 1 ; Mesulam, 1990).

Heilman, Watson & Valenstein (1993) described at l e s t three possible attentional

hypotheses used to explain hemispatial neglect: (1) inattention or unawareness; (2)

ipsilesional orientation bias; and (3) inability to disengage fiom ipsilateral stimuli. The

inattention hypothesis postulated that patients with left hemispatial neglect fail to orient

and respond to stimuli on their lefi side because they are unaware that any stimuli exist in

lefi hemispace. According to the proposal that there is an ipsilesional orientation bias, the

damaged hemisphere becomes hypoactive, thus releasing a bias toward stimuli in space

that activate the opposite hemisphere. Posner et al.. (1 984) proposed that when attention

is drawn to one side of space by a cue, three operations are required to shifi it towanfs a

target on the contraiateral side: (1) attention is disengaged h m the ipsilateral cue, (2)

attention is moved to the contralateral target, and (3) attention is engaged on the target

(Posner, Waiker, Friedrich, & Rafal, 1984). Posner et al. postulated that parietal damage

caused an impairment in the disengage proçess and that this couid contribute to the

neglect syndrome. In surnmary, the consensus is that unilateral spatial neglect is due to a

deficit in visuospatial attention, although opinions differ as to how this arises.

1.2. Neuroanatomical Modeis of Neglect

In his cortical network mode1 of attention (198 l), Mesularn proposed an

anatornical substrate of directed attention which explained why hemispatial neglect couid

arise fkom lesions in different matornical locations. He postulated that the neural

substrate for directed attention included the fiontal, parietal and cingulate cortices and

that dysfunction in this neural network caused the neglect syndrome (Mesulam, 198 1).

Mesularn defined each region of the network as being responsible for different

processes. The postenor parietal component was responsible for processing incoming

sensory information. The cingulate gym, king part of the limbic system, was

responsible for attaching a motivational value to sensory input and the dorsolaterai frontal

component was responsible for the motor output. Mesulam also included the thalamus as

the network component which was responsible for the overall arousal of the patient.

The tint three components of this network have cortical interco~ecticns with

each other as well as having corticoreticular (mesencephalic reticular fornation)

connections. Anatornical evidence showing the multitude of reciprocal connections

between ipsilateral cortical regions has been shown in monkeys (Pandya & Kuypers,

1 969; Ungerleider, Desirnone, Galkin, & Mishkin, 1984; Mishkin & Ungerleider, 1982;

Van Essen & Maunsell, 1980) and rats (Vogt, 1984; Vogt & Miller, 1983). Connections

between homologous contralateral cortical regions have also been shown (Pandya &

Vignolo, 1 969; Pandya, Karol, & Heilbronn, 1 97 1 ). Supporthg anatomical evidence can

also be seen in neurochemical and neurophysiological experiments by Mesulam (1990)

and Morecrafi et al.. (1993). Mesulam argued that the cortical network s u b s d n g

directed attention works in an intepteci and collective way, such that damage to any

node in the network could lead to neglect.

The cortical network theory for directed attention incorporates both a holistic and

brain localization approach to brain-behaviour relationships and Mesulam lists five

important corollaries associated with the cortical network: (1) A single complex function

is represented by a number of distinct anatomical sites that collectively act as an

integrated network for that fiinction. (2) Individual cortical areas contain the neural

foundation for components of several complex functions. (3) Lesions confined to a single

cortical region are likely to result in multiple deficits. (4) Severe and lasting impairments

will usually arise fiom damage in more than one component of the network. (5) The same

complex lùnction may be impaired due to a lesion in one of several cortical areas, each of

which is a component of an integrated network for that function.

in summary, this meam that a number of anatomically separate but intercomected

regions are collectively responsible for the complex function of directed attention.

Although al1 of the key anatomical ~ g i o n s are needed, according to this theory, these

regions have different fûnctions. Each key region is responsible for a different aspect of

attention and damage in a%- region in the network would impair its funftion. Maulam

describes the importance of each region in relation to a specific type of negiect. For

example, damage to the parietai region was important in causing sensory neglect whereas

damage to the fiontal cortex was related to motor neglect. According to the model,

neglect will occur if there is damage to any one of the critical components, but the

severity should increase in proportion to the nurnber of regions damageci.

Heilman and Valenstein (1985) have proposed a similar theory of negiect relating

it to an attentional-arousai disorder induced by dysfùnction in a corticolimbic reticular

formation network, except that their network includes a more comprehensive cortical-

subcortical loop (Heilman, Watson, & Valenstein, 1993). In their network, the thalamus

and basal ganglia also play an important role in mediating attention. The thalamus is

crucial as a relay site for information between the cortices, as there are nurnerous

reciprocal connections with the thalamus and each of the three previously mentioned

cortices (Shepherd, 1994). The basal ganglia, including the caudate nucleus, the putamen

and the globus paliidus, are connecteci with the fiontai cortex and thalamus and are

responsible for the programrning of motor movement (Shepherd, 1994). According to

Heilman and Valenstein, the anterior attention system (including the frontal lobe, basal

ganglia & the dorsal nuclei of the thalamus and the cerebellum) plays an important role in

the planning and execution of motor output while the posterior system (including the

parietal lobe and the lateral nuclei of the thalamus) is responsible for sensory input and

organization (Heilman, Watson, & Valenstein, 1993). Posner & Petersen (1990) posit that

the anterior system maintains control over the postenor system. In their explmation, the

postenor system is responsible for a l l o c a ~ g attention on the basis of spatial location.

The anterior system has a dual role involving the motor activity of shifting attention and

the monitoring of the postenor system (Posner & Petersen, 1990). A hierarchical mode1

of attention systems, with specific subprocesses (e-g. monitoring component) has been

suggested by Stuss et al., (1996 in press).

1.3. Evidence Suppotting Models of Neglect

Since its first formal recognition as a neurological deficit, hemispatial neglect has

been attributed to damage in the nght parietal lobe (Brain, 194 1 ; Critchley, 1966; McFie,

Piercy, & Zangwill, 1950), mainly from pst-mortem examinations. During the 1 s t two

decades, however, with the advent of non-invasive neuroimaging techniques, such as

computed tomographie (CT) scanning, there have been numerous studies suggesting that

other cortical regions outside the parietal lobe as well as purely subcortical damage rnay

also be associated with neglect (Vallar, 1993). These lesion studies can be divided into

single and group case studies in humans, and animal studies. (For comprehensive

reviews, refer to (Vallar, 1993; Heilman, Watson, & Valenstein, 1993; Heilman, Watson,

& Valenstein, 1994).)

Other lesion sites associated with negiect have included the fiontal lobe (Heilman

& Valenstein, 1972; Darnasio, Damasio, & Chui, 1980; Van der Linden, Seron, Gillet, &

Bredart, 1980), cingulate cortex (Watson, Heilman, Cauthen, & King, 1973), the basal

ganglia (Damasio, Damasio, & Chui, 1980; Hier, Davis, Richardson, & Mohr, 1977;

Vallar & Perani, 1986) and the thalamus (Cappa & Vallar, 1992; Ferro, Kertesz, & Black,

1987; Watson & Heilman, 1979). In fact, there are a number of articies that show that

neglect can occur following damage to either cortical or subcortical structures or both.

Recent studies with positron emission tomography (PET) on spatial attention in

normal human volunteers (Corbetta, Miezin, Shulrnan, & Petersen, 1993) have attempted

to identiS, the neural systems involved in shifting spatial attention. Corbetta et al., (1993)

examined s h i b in attention in relation to hemispace and direction, and found PET

evidence, in 24 subjects, showing that activation in the superior fiontai and superior

parietal cortex depended on the required response. Both the superior frontal and superior

parietal cortex were more active during overt shih of attention than during central gaze

fixation. The attentional s h i h involved peripheral movement toward cues and stimuli in

different hernispaces and directions, as well as covert shifts of attention where the

required overt response was to maintain central fixation. A covert shift of attention can be

described as an intemal preparatory response which will facilitate a muscular eye shifi in

an overt shifi of attention. Lefi visual field stimuli caused activation in the right superior

parietal lobe (near Brodmann's Area 7) mostly, although a weak activation was also seen

in the left superior parietal region more posteriorly. Right visuai field stimuli caused

bilateral activation in the superior parietal lobe, although the contralateral activation was

larger in magnitude. Both parietal regions responded to stimuli during both peripheral

(overt) shifts of attention and central (covert) shifts of attention, dependent on hemispace

and not direction of movement. On the other hand, the superior fiontal cortex (near

Brodmann's Area 6) was activated only for contralateral stimuli during peripheral shifts

of attention, provided an overt respoase was required and not during covert shifts when

gaze was centrally fixateci.

1.4. I n Yivo Neuroimaging I n Negleci

Most previous localization studies of neglect were based on analysis of subjects

with structural damage as demonstrated by CT scanning or pst-mortem examination (see

Table 1). With the advent of fùnctional imaging techniques in the 1980's such as PET or

single photon emission computed tomography (SPECT), examination of the functional

disruption of different anatomical regions became possible. With PET or SPECT imaging

of brain-damaged subjects, it is possible to investigate h c t i o n in relation to hemispatial

neglect using either glucose utilization (PET) or blood perfusion (SPECT) as an index of

function. In this way, it is possible to see the effect of structural damage on function, not

only at the directly-darnaged site but also at anatomkaliy-comected but structuraliy-intact

regions.

Neuroanatomical models of neglect can be better evaluated with functional

irnaging. In accordance with these models, hemispatial neglect could result when there is

direct darnage to important components or when there is indirect impairment of

intercomected but stnicturaliy-intact regions. Similarly, damage to connections between

regions, for example in the white rnatter connecting cortical as well as subcortical

regions, could also result in hemispatial neglect when these regions are important

components in the attentional network. Some studies have shown that neglect can result

from subcortical damage to the white matter, for example the intemal capsuie (Heilman,

Bowers, & Watson, 1983) and white matter deep to the temporal-parietal-occipital

TABLE 1: SUMMARY OF I M A G ~ c STUDIES IN NEGLECT

Authors I L H D I R B D I Negiect Tests 1 I m ~ ~ g 1 Findhgs Reported

- - to bilatëral stimuli Frontal damage.

Levine et al.. 2 1/29 LC, LB, Rey Figure, CT Regions affected incl. 14/2 1 TP, 7BG,

Watson et al.. ( 1979) Damasio et al.. (1980) Hier et al. .( 1983)

(1986) 1 1 1 1 1 1/47 had A ~ L . incl. F. 16/47 had both; 5/47

N+ -

-

-

ValIar er al..

N+ 11 1

1 / 1

4 1 /4 1

-

Ferro et al.. ( 1987) Ogden ( 1987)

Perani et al.,

Bedside Testing

Bedside Testing

Rey Figure, Extinction

1 sentence writing 47/110 1 Circle Cancellation

- 25/56

( 1986) Wameral..(1988)

-

Bogousslavsky et al., ( 1988) Vallar er al..

Moddities Post- Monem CT

CT

CT

IO/ 15 20145

-

( 1988) De la Sayette et al..

Thaiamic Infarct.

Lesion in Putamen, Cau&te & Ant IC.

Increased recovery in patients without Right

Neglect severity correlated with lesion size. 17/47 had Post. lesions incl. P M & SMG;

315

-

-

( 1989)

Fiorelli er al.. (1991)

LB, LC, Spatial map Drawings, LC, CIock

2 2

-

Vallar er al..

completion LC, Reading msk,

2/2

Y2

111

(1991) Binder (1992)

Weiller et al..

( 1993) I I I Circle ~ancellation I I decreased UR ratio in F & P, 9 normal contmls. Recovery requires improvement of

CT or MRI CT

Tactile ~ x ~ ~ o r a t i o n LC, Visual Search task

1/ 1

4/44

( 1993) Perani et al.,

1 1 1 1 1 undamaged R and L hemisphere regions. Note: Anr IC=antenor intemal capsule; Cenruntral; BG=basal ganglia; F=frontal; L=icft; LB=iiic biuction; LC=line cancellation; LHD=left

had TH & 10147 had BG lesions. Al1 had Subcortical incl. IC. WM. BG. RHD: Post damage (12) > Ant (9), (4 Both);

CT.

LC, LB, Drawings geographic orientation LC, reading, tactile

5/5

-

-

hemispherc-damageci; MCA=middlc ccrcbraI ancry; Med Occ=medial occipital; Mid T=middle temporal; N+=patitnts with ncglect; Occ=Occipital; PInf=inferior parictal; Posr lC=ponsior intemal capsuk; R=right; RHD=right hcmispherc-damageci; SM=sensorimomr. SMG=supnmarginal gynis; Sup Tsuperior temporal; TH=thalarnus; TP=tempmparietal; WM=white mancr.

LHD: Ant damage (12) > Post (9). (4 Bath)- CT-Subcortical incl. BG & TH; SPECT-

l-'"SPECT -*SPECT

exploration task Drawings, image

23/66

-

Decreased Cortex RfL ratio, 9 normal. Thaiamic recovcry correlated with recovery

CT, "13SPECT

description, ci& placement LB, Drawings, Lack of conua. movement, double simul taneous

21/34

12/3 1

of negfect at one year. CT-Right Ant. Choroidal Artery; SPECT- Decreased Parietal & Frontal.

1-"3SPECT CT, IJ3Xe

tactile stimulation LC, reading, tactile

2/2

CT, 1 CT-Subcortical; SPECT- Decreased in Ant & Cent Normal in Post; RfL ratios. CT-Ant IC -Motor neglec~ Xenon-

inhalation

fl, O "'-PET

LC,LB

Bedside examination

Decreased in Right ~ k r i o r Rolandic area.

3 FP CT lesions, 3 Subcortical CT lesions; Al1 had decreased FP, RL, hypometabolism of entire ipsilateral cortex suggestive of

CT

LC, Reading Task,

functional depression of a di% network. 2/44 with Ant lesion F in LHD; Other areas

CT

.

Ci, MRI, ""SPECT 1 Decreased Cortex lUL ratios.8 normals. fl, 1 CT-1 Subcortical, 1 MCA; PET-Both had

incl. WM, BG. TH. F & P. 7/2 1 Merior & Prefiontal, 312 1 BG, 1 I/2 1 Posterior incl. PInf, Mid T, Anterolateral Occ, Non N+ overlap inci. Putamen, Corona

. Radiata, Med Occ, Peri-rolandic, Sup T. CT-Subcortical; dual head SPECT-F,P,T

junction (Critchley, 1966; Heilman, Watson, & Valemtein, 1993). In these patients

neglect may result h m disconnection, and therefore hypoactivity, of relevant cortical

regions. In other words, neglect c m be more severe either because more than one region

in the cortical network for directed attention is damageci, or because one area and its

comec tions are severely disrupted so that intercomected, but not directly damaged,

regions in the network become dysfùnctional. This important concept of dysfunction at a

distance is called diaschisis, a tenn introduced by Von Monakow in 19 14 to refer to

fùnctional depression in a brain region that is at a distance fiom the site of direct darnage

(Von Monakow, 19 14; Von Monakow, 1969).

There are a number of studies that support the idea of cortical diaschisis as a result

of damage to either subcortical structures (Bogousslavsky, Miklossy, Regli, Demaz,

Assal, & Delaloye, 1988; Perani, Vallar, Cappa, Messa, & Fazio, 1987; Perani, Di Piero,

Lucignani et al., 1988; Vallar, Perani, Cappa, Messa, Lenzi, & Fazio, 1988) or other

cortical regions (Fiorelli, Blin, Bakchine, Laplane, & Baron, 1991; Perani, Di Piero,

Lucignani et al., 1988) or even homologous cortical regions in the contralateral

hemisphere (Dobkin, Levine, Lagreze, Dulli, Nickles, & Rowe, 1989). In these studies,

structural damage in a region leads to functional depression in the form of decreased

blood flow to anatomically connected, but structurally undamageci regions which are no

longer activated. As Vallar & Perani (1986) point out, a functional irnaging study "could

allow the correlation of neglect not only with 'anatomical' lesions, but also with site and

extent of functional damage" (Vallar & Perani, 1986).

An imaging technique such as SPECT can be a useful way of obtaining fbnctional

information about specific brain regions. One can examine interconnected but not

structurally-damageci brain regions which may be hypoperfused as a result of loss of

fûnctional input fiom directlydamaged regions. For example, by comparing the area of

dysfunction on SPECT to the damage seen on CT, it is possible to estimate the functional

depression caused by the stroke in addition to the infarction itself. Thus a cerebral blood

flow study might be useful in elucidating the fûnctional effects of a lesion and in

correlating brain ac tivity with behavioural outcorne.

Evidence for a cerebral network is also starting to emerge from functional studies.

Recently, Von Giesen et ai., (1994) studied motor hemineglect, wbich was described as

Iack of spontaneous activity in the side of space contraiateral to damage despite an intact

motor output system. They reporteci four such patients, who had decreased regional

cerebral glucose metabolism in the premotor, prefiontal, parietal and cingulate cortex and

thalamus on positron ernission tomography (PET), whereas regions in the sensonmotor

cortex, basal ganglia and cerebellum did not show functional depressions. They posit that

motor hemineglect results from interruption in a higher order cerebral network s u b s d n g

motor activity in the presence of an intact motor system (von Giesen, Schlaug, Steinmetz,

Benecke, Freund, & Seitz, 1994).

1.5. Statistical Techniques In Imaging Data

In this study, based on the previously described models of neglect, both cortical

and subcortical regions of subjects were examined for differences within as well as

between hemispheres. Lesions localized as structural damage on CT scanning and as

Topography of Hcmispaùal Ncglcct

functional damage on SPECT scanning were quantified in order to correlate regional

brain activity with behavioural outcome. By correlating both blood flow and structural

damage wi th behaviour on neuropsychological tests of hemispatial neglect i t was possible

to evaluate the influence of each of the different regions. Since a complex anatomical

network has been implicated in hernispatial neglect, it was anticipated that combinations

of brain regions could be important. For instance, functional depression in both the

parietal lobe and anterior cingulate could be required for neglect to be present.

There are a number of different methods that can be used to cornelate brain

irnaging data with behaviour, two of which are Linear Regression (LR) and Partial Least

Squares (PLS). LR is a classic, well known, and widely applied statistical tool, whiIe PLS

is a relatively novel and possibly more appropriate technique to use with large sets of

irnaging data. Depending on the type of analysis and particular questions king addressed,

either technique may be appropriate to use. The advantages and disadvantages of each

technique will be discussed in the following section.

1.5.1. Linear Regrcssion

LR analysis is a statistical technique that attempts to predict an outcome or

dependent variable, such as the score on a neglect battery, fiom a set of predictor or

independent variables, such as ratios pertaining to b l d flow through a brain region. One

important feature of regression anaiysis is that it is able to mathematically quanti@

relationships between variables (Stevens, 1986).

In its simplest fonn, Simple Linear Regression (SLR) involves only two variables,

a dependent (y) and an independent measure (x). In trying to predict a dependent variable,

the regression technique most often uses the least squares criterion in fitting an equation.

In SLR, a straight line is fit in Cartesian space through the data using the formula

y = Po + &x, where Bo is the coefficient for the intercept, pl is the coefficient for the

independent "x" variable, and an error term (e) associated with the fit is also calculated.

The line is made to fit so that the sum of the squared distances between the actual value

for the dependent variable and the predicted value (which f d s on the regression h e ) is

minimized (in other words, Ce = minimum). Stated more simply, SLR tries to find a

linear relationship between the two variables such that one can be used to predict the

other. in this way, regression analysis cm be used to test the relationship of two

measures, for instance, testing to see if there is a linear relationship between blood flow

in the parietal region with score on a neglect battery. In this case, SLR is testing to see if

counts in the parietal region corresponding to brain activity can be used to predict

performance on a test battery. Since the relationship between variables xnay not be linear,

a non-linear equation could also be calculated to explain the relationship between

variables.

For more complicated designs in which there is more than one predictor variable,

as is almost always the case in the biological sciences, Multiple Linear Regression (MLR)

is often utilized (Cohen & Cohen, 1983). For example, in ûying to predict an outcome

measure, such as neglect score, based on two brain regions, MLR would attempt to fit a

three-dimensional plane (or an equation such as y = P, + &x, + Bx, , where each brain

region (xi,&) would have a corresponding coefficient (Pt , P2)), instead of a 2-D line as

used in SLR, that minimized the mors, i-e., the distance between the predicted and actuai

values. A similar but more cornplex process would be used if there were ten independent

"x" variables. MLR can be used to select only those variables, fiom a larger set of

variables, that maximally predict the dependent variable. Here, MLR attempts to

eliminate redundancies beîween independent variables and to allow only variables with

enough unique contribution (based on some critical threshold) to enter the equation. In

geoeral, MLR tries to find a set of weights for the 'Y' variables which are maximally

correlated with the ''y " outcorne variable.

In using MLR (or SLR), there are a number of assumptions that must be satisfied

for the results of the analyses to be meaningful. Three cardinal requirements are as

follows. First, there is the assumption of nonnality which presumes that for each level of

a predictor variable, the dependent variable follows a normal distribution. In addition, the

errors (residuals) associated with each dependent variable should also follow a normal

distribution. Normality is important in deciding the significance of a variable. The most

cornmonly used criterion in MLR is to set the Type 1 Error to 5%, Le., a=0.05. A Type 1

Error refers to the probability of incorrectly rejecting a true nul1 hypothesis (i-e., a false-

positive), in contrast with a Type II error which refers to the probability of failing to

confirm a significant difference (Howell, 1982). This threshold is ofien used to decide if a

variable in the regression equation or the entire regression equation is "significant"

(which is ofien associated with meaningfulness). In the case of a=0.05, this means that

there is a 95% likelihood that a variable which enters a regression equation at that level or

lower does so not just by chance. This is only m e if the variables confom to the normal

distribution. in cases where the variables do not conforxn properly, there are

transfomation techniques which can aileviate such problems. For example, a log

transformation perfoms a non-linear transformation on a variable, which c m help if the

distribution is skewed. Tests of nonnality such as the Kolmogorov-Srnimov test can be

used to judge improvement (Winer, 197 1).

Second, a usefiil regression, in most cases, is a reproducible one. In order to

maxirnize generalizability and reproducibility a large sample size is usually needed,

especially in relation to the number of independent variables entered in the sarne

regression. This is important since MLR is a mathematical maximization procedure in

which there is opportunity for capitalkation on chance (Le., finding a mathematically

significant but clinically unimportant difference as a result of a large sample size). A

commonly used rule of thumb is that in any MLR analysis there needs to be between ten

to fi fteen subjects per independent variable (Stevens, 1 986).

Third MLR performs best when the independent variables are highly correlated to

the outcome variable but have no or low intercorrelations among independent variables.

MLR searches the predictor data to find a set of weights which optirnally predict the

dependent variable and in doing so assesses unique contributions fkom the independent

variables and elirninates redundancies between variables. With highiy correlated regions,

MLR may not be able to find the optimum set of predictor variables, since highly

correlated independent variables generally will not enter together into a regression

equation (Stevens, 1 986). This potential problem, known as multicollinearity, is due to

the fact that there is less chance for unique contributions with variables that are highly

coiIinear, for example, with r 2 0.8 (Stevens, 1986).

In the biological sciences, it is WNally impossible to find independent variables

with low or trivial intercorrelations. Most biological systems are complex and involve the

interaction of many diffeient regions workhg together. For example, in the brain, there

are in the order of ten billion neurons, each of which is connected to about ten thousand

other neurons (Shepherd, 1994). Sets of neurons in one region interconnect to many other

regions in complex ways and the hctional operation of a behaviour may involve the

interaction of many regions. Thus, it is ofien found tbat neurobiological data contain

highfy collinear variables,

In applying MLR to a biological system composed of numerous intercorrelateci

regions, MLR attempts to discem which of these regions are "most important" or

maximally significant in accounting for the outcome variable. MLR, uniess specific

constraints are impose4 examines the influence of each independent variable, in trying to

account for the variance of the equation, given that the variation explained by the other

independent variables has already been taken into account; Le., MLR searches only for

unique contributions. This is not to say that MLR cannot be used with biological data.

Depending on the variables used (and the intercorrelations therein) and the relationships

queried, MLR may be an appropriate statistical technique to employ. In hypothesis testing

in which specific variables are investigated, MLR can be an excellent tool, for instance, to

examine whether scores on a neurobehavioural test battery can be predicted based on

measures of brain damage or blood perfbsion in a number of relatively uncorrelated

regions. Multicollinearity is generally not a problem with MLR when the correlations

between variables are below 0.8 (Stevens, 1986).

With brain irnaging data, such as that measured with SPECT, the intercorrelations

between brain regions, whether on a pixel-by-pixel or regional basis, commody involve

predictor variables which are highly correlated, 1 4 . 8 and above. There is much

redundamy in the brain due to the parallel structure of most networks, and this

contributes to the high correlations between regions. In addition, imaging data fiom

patients with brain-damage may also contribute to highly collinear regions. For example,

brain images h m patients anlicted with swke may show highly collinear counts across

brain regioos as a result of blockage of a primary artery, such as the middle cerebral artery

(MCA), which supplies a large part of the brain, and affects many regions simultaneously

(Le., the MCA temtory) (Damasio, 1983). Lasly, damage to part of an interconnected

network of brain regions may cause M e r decrease to other regions in the network,

resulting in collinearity between those regions.

Using MLR with highly collinear data, as can be found in a SPECT imaging

dataset as a result of the cornplementary and overlapping underlying anatomy, and the

failure to incorporate small but cntical influences may lead to biologically uninterpretable

or uninteresting equations which, nonetheless, are mathematically significant and provide

good prediction within the sample. Thus, it rnay be difficult to discem the arnount of

variance that can be explained by multiple regions, due to their hi& intercorrelation, even

though each region may explain the associateci variance differentially. in ûying to explore

the relationships among highly comelated regions related to a behaviour of interest, such

as hemispatial neglect, it rnay be more prudent to use a statistical technique that does not

single out oniy unique contributions h m significant regions, but rather describes the

relative relationships of all regions in reference to the dependent variable.

1.5.2. Parttoi Least Squares

One relatively new statistical technique which can examine an image dataset in a

more holistic and explanatory approach is Partial Least Squares (Mchtosh, Bookstein,

Haxby, & Grady, 1996; Bookstein, 1994; Bookstein, Streissguth, Sampson, & Barr, 1990;

Nyberg, Mchtosh, Houle, Nilsson, & Tulving, 1996). PLS is a rnethod of data reduction

designed to extract relationships between two (or more) blocks of variables, for instance,

brain regions in one block and subtests on the neglect battery in the other. PLS searches

for the linear association between the blocks (while ignoring the associations within the

bIocks) by capitalking on the relationships and redundancies of the cross-correlation

matrix of the blocks in order to decompose the covariance between the blocks. One of the

assumptions PLS makes is that the relationship between blocks is linear, which is also an

assumption generally made in MLR The other assumption in a PLS analysis is that there

is a causal relationship between the blocks (e.g., poor performance as a result of brain

hypoperfusion).

One important aspect of PLS is that rather than be hindered by multicollinearity,

PLS actually takes advantage of the redundancy in image data by ignorùig the within-

block correlations and focusing on the between-block correlations. This approach,

explained in more detail in the following paragraphs, is especially usefûl when analyzing

biological systems since it takes advantage of the inherent redundancy while

simultaneously examiring many regions, and therefore may be more suited to exploration

with large datasets when there are redundant masures.

PLS operates by decomposing the covariation between two (or more) blocks of

data (Figure 1). A block corresponds to a matrix of the set of variables of interest (i.e.,

raw data for one set of variables). For example, in relation to exploring brain-behaviour

relationships, one block would contain the raw data matrix for the independent variables,

or brain regions as in the earlier MLR example, whiie the second block of data would

contain the raw data matrix for the neglect subtest scores. PLS computes a cross-bloçk

correlation matrix, which ignores the within-block comeIations, and analysis is performed

on this new matrix using a mathematical algorithm called Singular Value Decomposition

(SVw-

SVD computes sets of paired vectors, also refend to as latent variables (LVs),

that completely reproduce the cross-correIation matrix and relate to the covariance

between the btocks. Each LV is made up of a set of paired vectors, one vector for each

block. In addition, PLS computes a set of singular values, each of which corresponds to

each of the paired vectors mentioned above. The total number of LVs will be constant

across the new variable sets, with the actual nurnber dependent on the minimum number

of variables in the original data matrix. For example, suppose there were 4 neglect

subtests in data matrix A and 160 brain regions in data ma& B. PLS would then

compute 4 latent variables, each LV containing one vector for the neglect subtests and

one vector for the brain regions and a corresponding singular value.

Within each Iatent variable (Le., either the vector correspnding to image or

neglect battery data) are weights, referred to as saliences, that can be used to evaluate the

influence of different regions. The emphasis is on determining the relative influences of

brain regions. The vector comsponding to image data can be remapped into image space

into the Singular Image (SI), which contains the image of the whole dataset in relation to

the other vector.

Further, each vector can be used with the raw &ta rnatrix for analysis. Scores c m

be calculated that express the decomposed singular vectors aloag with the original data.

Image scores can be calculated, one for each subject ia the original dataset, by

multiplying the saliences fiom the image vector by the original pixel values and then

summing across brain regions for an individual. Similarly, a set of subtest scores for the

outcome measure cm be produced by multiplying the vector of subtest saliences by the

original subtest performance. Both of these scores can be placed in a scatterplot to

characterize the relation between blocks for a latent variable and the resultant plot can be

examined to see if any inc idental subgroup di fferences emerge.

To address the significance of the PLS output, a multiple linear regression

analysis and a permutation test (Edgington, 1980; G d , 1994) are used. MLR is used to

regress the subtest scores on the latent variables. Then, following random reordering of

the rows and columns of the original data matrices, thereby breaking the association

between the brain and behaviour blocks, a new SVD is computed with LVs. MLR then

regresses the subtest scores on the new set of LVs. The relative contribution for each

latent variable can be assessed by caiculating an R~ (corresponding to the amount of

variance explained on the LV by the subtest scores regressed) for each singular value. The

process is repeated 10,000 times and the likelihood of obtaining a value for R' or higher

fiom the regression on the original data, is computed. In this way, a probability of

significance can be computed base. on random manipulations of the a c t d dataset, rather

than relying on the distributional assumptions underlying most conventional parametrk

statistical methods.

1.6. Hypothesis

Based on the background described above and the use of both MLR and PLS approaches,

the following hypothesis was formulated:

1.6. 1. ffypothesk

a) Al1 patients with neglect will show structural damage in at least one of the key

regions, or its interconnections, proposai in the anatomical network for

directed attention, and patients with neglect will have more key regions

damaged, compared to a matched set of patients without neglect.

b) Reciprocally, on either MLR or PLS analysis, darnage in the five predicted

regions, namely the frontal, parietal, and anterior cingulate cortices, basal

ganglia and thalamic nucfei will emerge as significant predictors of

hemispatial neglect. This will be tested by predicting neglect score, as

measured by a battery of tests, by either using a measure of structural damage

on CT with MLR or using a measure of fuoctional depression on SPECT with

MLR or PLS.

2. METHODS

2.1. Negfect Battery

Al1 patients adrnitted to the Stroke Unit at Sunnybrook Health Science Centre had

the Sunnybrook Neglect Battery ( S m ) (Black, Vu, Martin, & Szalai, 1990) administered

as part of their routine initial clinical assessrnent as soon as they were able to be assessed

( ~ 2 4 0 , mean 13.2 f standard deviation 17.7 days pst-stroke, 95% confidence intervals

(CI) [10.9, 15.41, range 1-1 19). Testing was performed either in a t e s ~ g room or at their

bedside, depending on the physical status of the patient by a trained examiner from the

Cognitive Neurology Unit at Sunnybrook.

The battery was presented midline to the patient's head and body, in order to

decrease any bias toward side and ensure a standard administration. It consisted of four

subtests: spontaneous drawing and copying of a dock and &isy, l he cancellation, line

bisection, and shape cancellation (refer to Appendix A for examples). The drawing

subtest comprised four items: spontaneous drawing of a clock and a daisy; and copying of

a clock and a daisy. The patient was presented witb two blank white sheets of paper and

asked to draw a daisy and a clock on each sheet. In the copying subtest, the patient was

given a sheet of papa with a clock already drawn on (followed by a daisy) in the upper

half of the page and asked to copy the clock (or daisy) (Friedman, 199 1 ).

In the line cancellation subtest, the patient was presented with a sheet of paper

with 20 dark lines, approxllnately 3 cm in length, 10 on each side of the midline,

scattered across the page. With the paper midiine, the patient was asked to place a mark

through each line that was seen on the page (Le., cancel the line) and to put down the

pend when finished (Albert, 1973).

For the line bisection subtest, the patient was presented with four lines, two 15 cm

in length on one page and two 20 cm in length on another page. With the page midline to

their body, the patient was asked to place a mark on the line corresponding to the middle

of the line (Le., to bisect the line) (Schenkenberg, Bradford, & Ajax, 1980).

The shape cancellation subtest was that published in the Principles of Behavioural

Neurology (Mesulam, 1985). A syrnbol that looks like a sun with a line crossed through

was shown to the patient and described as the target shape. The patient was presented

with a sheet of paper with a scattered array of different shapes, including 30 of the target

shapes on each side of the midline of the page and was asked to find and circle al1

instances of the target shape on the page.

In order to examine nomal performance on the battery, each of the subtests was

given to 60 age-matched normal healthy volunteers. From their results, normal lirnits for

each of the subtests were calculated. No control patient made any omission on either the

drawingkopying or the line cancellation subtests. A slight deviation fiom the rnidline was

found for the normal age-matched controls in the line bisection test, more so to the lefl of

the midline. Finally, one omission, on either side of the page, was found to be within

normal limits on the shape cancellation subtest (BIack, Vu, Martin, & Szalai, 1990).

Following testing, each battery was scored according to omissions on the side of

the page contralateral to the side of their stroke (see Appendùr AS for scoring sheet). For

the drawingkopying subtest, omission of numbers or petals on the contralateral side of

the drawing was considered abnormal. The subtest score was calculated tiom the number

of abnormal drawings. If there were O, 1, 2 or more abnormal drawings, the patient's

drawing/copying subtest score was 0,20,30, respectively.

For the line cancellation subtest, the number of lines rnissed on the contralateral

side was summed and multiplied by 3 to provide a score that could range tiom O to 30.

For exarnple, if the patient rnissed 7 lines, their line cancellation score would be 2 1.

The line bisection score was based on the mean percentage deviation of the

patient's mark fiom the correct midhe for al1 four lines. Scores were based on the

number of standard deviations the patient's mark deviated to the ipsilesional side

calculated fiom the mean of the normal controls, separateiy determineci for the left and

right hand. Two points were given for each standard deviation above the mean, up to a

maximum of five, resulting in line bisection subtest scores that ranged fiom O to 10.

Finally, the scores fiom the shape cancellation subtest were the number of target

shape omissions on the contralesional side of the page. Each target missed was valued at

1 point, which meant that the shape cancellation subtest score could range fiom O to 30.

Addition of al1 subtest scores yielded a total out of 100. A score ranging fiom 0-5 was

within normal limits, based on performance of normal control subjects. A score fiom 6-

39 was classified as rnild-moderate neglect and a score of 40 and above was classified as

severe neglect. This classification, as well as the individual score weightïng, was based

on clinical intuition and experience with this battery in its early years of development

(Black, Vu, Martin, & Szalai, 1990)(refer to Table 2 for summary of variables).

TABLE 2: DATA SUMMARY

SPECT

k

Neglect Battery

Line Cancellation

Line Bisection

Shape Cancellation

Structural Lesion Volumes

Dichotomous Regions (1 3 regions)

% Regional Damage

Mean counts/ipsilateral cerebellwn from the cortical rirn

analysis & automated ROIs (188 segments)

Mean countdipsilateral cerebellum fiom the cortical rim

analysis & automated ROIs, grouped into 10 regions

depending on ROI, ratios

ranged fiom 0.07 to 1.89

depending on region, raaged

fiom O. 1 1 to 1.69

depending on ROI, ratios

ranged fiom 0.19 to 1.48

depending on region, ratios ranged fiom 0.45 to 1.26

Statistical tests were ernployed to examine the psychometric properties of the

Sunnybrook Neglect Battq, including internal consistency, redundancy of items, and

external content validity (Black, Ebert, Leibvitch, Szalai, Bon* & Blair, 1995). The

analyses were performed on a group of patients ( ~ 2 3 2 ) with SNB testing and Visual

Search Board (VSB) (Kimura, l986), within 1 day of each other. VSB testing requires

special apparatus that is not readily available for bedside testing, which was the intent of

this study. The group consisted of both right and left hemisphere-darnaged stroke patients

with and without neglect. Al1 subtests were significantly correlated with the total neglect

score (r-0.8, p<0.001) and with each other ( ~ 4 . 6 , p<0.001), thus demonstrating intemal

consistency within the battery for each individual subtest. Factor analysis was used to

assess redundancy of the subtests withh the neglect battery. .Ml four subtests conmbuted

to a single factor (eigenvalue = 2.78), accounting for 69.4% of the variance. Each subtest

was positively correlated with that factor, indicating that al1 four subtests were needed to

capture the visuoconstnictive neglect phenomenon. To assess external content validity,

the neglect battery, based on weighted scores, was compareci to performance on the VSB.

Chi-square test of independence showed that subject group identity, according to the

SN%, was in agreement with that on the VSB @<0.001). Shape cancellation was the most

sensitive subtest (76%) and drawkopy was the most specific (99%) (see Appendix A.6

for a complete listing). Logistic regression of the 4 subtests against the VSB was highly

significant (pc0.00 1 ) providing M e r evidence of the vaiidity of the neglect battesr and

i ts subtests against an external standard.

Stroke patients underwent CT scanning of their head usually within 48 hours post-

stroke. Since it is known that a lesion may not appear on a CT scan if it is done too early

post-stroke, patients with an initial negative scan (approximately 30% of patients) had a

repeat scan perfomed at a later date. Whichever scan maximally represented the lesion

was used as the CT scan for al1 data purposes (n=211, mean 7.6k16.4 days pst-stroke,

95% CI [5.4,9.9], range 0- 154). Scans were perfomed parallel to the orbitomeatal line, a

commonly used reference line which extends h m the canthus of the eye to the extemal

auditory rneatus. Unfortunately, it was not possible throughout the five year study, due to

financial restrictions as well as disk storage limitations, to store and quanti@ the original

digitized CT scans. Thus, for each CT scan, one centimetre thick slices in the traasaxial

plane, approximately 12 in total, were yrinted on X-ray film for fixther analysis and

interpretation.

The films were used to obtain both the structural lesion volumes and anatomical

localizations. To obtain the lesion volumes, the lesions were traced fiom the x-ray film

ont0 paper. With the aid of a digitizing scanner (Sigma ScanM Version 3.0, Jandel

Scientific, Sausalito, California), the are;? corresponding to the lesion for each slice was

digitized and then summed to arrive at a lesion volume for that scan.

Anatomical localization was performed by reference to a stereotactic atlas

(Talairach & Townoux, 1988). The lesion seen on each of the transaxial slices was drawn

on the best matched template from the Talairach-Toumoux atlas. Since each slice fiom

the atlas has an underlying grid, a detailed lesion localization was possible (refer to

Appendix B for examples). From the grid of the atlas, each region that was affected was

detailed on a checklist by region, Brodmann's area and x-y coordinate. The checklist was

entered into a database (Filemaker Prom, Claris Corp., California) on a Macintosh

Cornputer (Apple IISi). This technique allowed for dichotomous categorization, Le., the

particular region was denoted as damaged or not. A problern with this method is that if

the same area, region EZ, is damageci in two different patients (A & B), a '1'

(corresponding to a 'yes') wouid be entered into the &tabase corresponding to the

presence of damage Le., irrespective of its size or depth. Thus, patient A could have 75%

of the entire region R damaged (i.e., of its entire volume damaged) and patient B 25%

damage and yet both would show only a 'yes' response regarding the site of damage.

To allow an estimation of the degree of damage in each region, a quantification

procedure was devised, based on the number of transaxial slices in which that region

appeared in the Talairach-Toumoux atlas (1988). Specifically, the number of slices on

which the region of interest appears was first determined (maximum of 12 slices). For

example, the inferior parietal lobe is found on four slices (slice 3, 4, 5, 6; Appendix B.3).

A ratio of structural damage was calcutated to be the number of slices with damage to a

region divided by the number of slices that the region of interest appeared. In the case of

the inferior parietal lobe, for example, the ratio was taken over the 4 slices. Patient A

fiom above, for example, would show 3 of 4 slices damageci whereas patient B had only 1

of 4 slices damaged. The resultant variables would be coded as 0.75 for patient A and

0.25 for patient B, instead of 1 .O for both. In this way, the degree of region involvement

could be quantifieci on a =aie h m O to 1 for each region of interest. This quantification

approach allowed for a measure of the vertical depth of damage in a region.

Thkteen CT regions were used in subsequent analyses (please refer to Appendix

B.3 for stereotactic breakdown of regions), al1 of which came h m the ipsilesional

hemisphere (single lesions). There were 7 cortical regions, including the anterior

cinguiate cortex (Acing), the fiontai cortex 0, the parietal cortex (P), the temp~ral

cortex (T), the lateral occipital cortex (LatO), the primary motor strip (Motor), and the

primary sensory strip (Sensory). Since the frontal and parietal regions were of specific

interest in this study, each of these regions was M e r subdivided into smaller more

spec i fic anatomical subdivisions which were us ed in speci fic pst-hoc analyses. The

fiontal region was subdivided into the inferior frontal gyrus (F-id), the middle frontal

gyms @-Mid), the superior frontal gyrus (F-Sup), and the parktal region was subdivided

into the inferior parietal cortex (P-Inf) and the superior parietal cortex (P-Sup).

There were 2 subcortical nuclei, specifically the basal ganglia (BG) and the

thalarnic nuclei (TH). White matter regions comprised the remaining 4 CT regions

inc luding anterior white matter (AntWM), central white matter (CentWM), posterior

white matter (PostWM), and a subdivision in the PostWM deep beneath the parietal-

temporal-occipital junction (Deep-TPO). Each region was a composite of the white

matter tracts enclosed in the corresponding area defined by the Talairach-Tournoux

(1988) atlas. Damage to the AntWM region was calculated by averaging the amount of

damage in the following regïons: the antenor superior longitudinal fasciculus (FLS-Ant),

the anterior frontal-occipital fasciculus (FOF-Ant), the anterior intemal capsule (IC-Am),

and the anterior centmm semiovale (CS-Ant). Similady, the PostWM was a composite of

the posterior portion of the above regions (FLS-Post, FOF-Post, IC-Post, and CS-Post),

and also included the idenor longitudinal fasciculus (FLi). For certain pst-hoc analyses,

each of the above white matter subdivisions were used to refine matornical localization.

2.3. SPECT Scans

At the time this study was conducted stroke patients undenvent SPECT scanning

of the head as part of their clinical assessment. Two-hundred and twenty-one patients

were Maged on a GE single head gamma camera following injection of 740 MBq of

9 9 M ~ c - ~ ~ ~ ~ ~ in the Medical irnaging Department at Sunnybrook Health Science

Centre (see Appendix C.1 for an example of a SPECT scan). Although there was no

specific patient preparation prior to or during injection, patients were generally seated,

had their eyes open, in a quiet environment with normal iighting. Scans were acquired in

the first two weeks (n454, mean 7.Sf 15.2 days pst-stroke, 95% CI [S. 1, 9.91, range O-

187), and repeated when appropriate as part of the standard clinical protocol at two to

three weeks (n=84, mean l7 .8S5.5 days pst-stroke, 95% CI [12.3, 23.31, range 8-167).

For research purposes, scans were repeated at thirteen months (n=54, 406.W79.3 days

post-stroke, 95% CI [384.4,427.7], range 156-766) in a subset of consenthg survivors. In

addition, nineteen SPECT scans were performed on normal volunteers.

SPECT scans were acquired on a GE Mode1 400 AT single head gamma camera

with patients in the supine position (imaging tirne approximately 30 minutes). Using step

and shoot mode, 64 planar views were acquired over 360 degrees, with a 64 pixel x 64

pixel acquisition frame per view (25 seconds per view) and a magnification factor of

1.33. Following acquisition, each SPECT scan was reconstnrctd to correct for any head

tilt and to align each brain so it was parallel to the orôitomeatal (OM) line in the

transaxial plane, as delheated on the mid-sagittal slice. The reconstnic tion procedure

(ramp and Buttenvorth filter with a power factor of 15 and a cut-off fkquency of 0.4cm-',

attenuation correction p=û. 12cnf1 (Sorenson, 1974)) took approximately twenty minutes

per SPECT scan. A correction for non-linearity was applied (Lassen, Anderson, Friberg,

& Paulson, 1988). Recumtnicted image spatial resolution was approximately 1.2 cm full

width at half maximum (FWHM). Each brain was realigned in the coronal, sagittal, and

transaxial planes during the reconstruction procedure in order to correct for head tilt and

standardize individual brains to a set of standardized axes (Appendix C.2). Thus when

viewing the brain from the transaxial plane, ail slices were aligneci in the sarne

orientation.

Since each 0.96 cm thick transaxial slice (pixel size = 0.48cm x 0.48cm) may not

contain the sarne brain regions across subjects, depending on brain size, a linear scaling

technique was applied. In this technique, the length of each of the three major axes was

determined for each brain (lefi side-right side "x-axis", anterior-posterior "y-axis", dorsal-

ventral "2-ariis"). Prior to cdculating the axes lengths and in order to decrease noise in

the image, the image was tfvesholded and converted to binary format. The first step

involved padding the image with zems on al1 sides, for use with the erosion procedure

described later. Next, the median value of d l pixels in the image that were greater than

7% of the maximum value was calculated. A threshold was applied such that d l pixels

with a value p a t e r than 55% of the median were given a value of 1 while al1 pixels less

than or equal to 55% of the median were assigneci a value of O, thus converting the image

to binary. Finally, in order to make the edges of the brain more continuous and fil1 in any

holes that may have been caused by a stroke, which would lead to incorrect size estimates

of the axes, the image was dilated (24~24x3 pixel kernel) and eroded (25x25~3 pixel

kernel) (Russ, 1992). The dilation procedure set any outer background pixels to 1, that

touched an inner brain pixel with a value of 1. Conversely, the erosion procedure set any

inner brain pixels to O, that bordered on a background pixel with a value of 1. The

technique of using dilation then erosion is referred to as a closing morphological

operation (Russ, 1992). The height of each brain (dorsal-ventrai) was calculated on the

rnid-sagittal plane and the maximum length (anterior-posterior) and width (side-to-side)

were found using the transaxial slices. This procedure, including the threshold

percentages and morphological filter kemel sizes, was developed using a subset of fifty

SPECT images.

in addition, the image midline was found using a Stochastic Sign Change

algorithm (Minoshima, Berger, Lee, & Mintun, 1992). In cases where large asymrnetries

were found between the widths on opposite sides of the midline, probably corresponding

to a large hypoperfused ara, the width of the larger side was mirrored and a width was

subsequently recalculated. Based on the known width, height, and length of the brain,

each of the axes was rescaled to preset lengths ( x 4 8 y=54 pixels 2=12 slices), so that

each brain was compresseci or expanded into a predetermined volume with known axes

(therefore pixel size was altered and propodonal to each of the axes). Following scaling,

the resultant image was centered in the rniddle of the m y (image, or array, size = 64

pixels wide by 64 pixels long by 12 siices high). Once the brain had been rescded,

transaxial slices were able to be matched across subjects with improved accuracy. Each

slice could now also be more accurately rnatched to a stereotactic atlas for anatomical

localization.

To analyze the SPECT scans two different methods were employed: a cortical rim

procedure and an autornated region-of-interest (ROI) analysis (Appendices C.3 & C.4).

For the cortical rim analysis, the procedure, adapted from Hellman et al., (1989) and

written in Interactive Data Language (RSI, hc., Boulder Colorado) on a Sunm

workstation (Sun View Mountain, California), involved finding the b e r and outer brain

edges, and then dividing the corresponding rim into equal annular segments for

cornputation of counts. Prior to hding the edge of the brain, counts in the cerebellum

were found using an automatic algorithm, described in more detail in the automated ROI

procedure below. The cortical rim was performed on each of 6 transaxial slices separateiy

(corresponding to slices 2-7 of the 1inearIy scaled brain). To find the outer edge of the

brain for slice 2 the program searched for the first outside pixel above 22% of the counts

in the cerebellum and then zeroed aii pixels outside of that pixel. The thresholds for slices

2-7 were as follows: slice 3(26%), 4(30%), 5(33%), 6(36%), 7(42.2%). The thresholds for

each slice were found by optimizing the resultant image followiag thresholding on a set

of 50 brains. For five patients (out of 221) the threshold values required adjustment to

optimize edge detection in two slices per patient, for a total of 10 slices with adjusted

thresholds. The resultant images on each slice containecl only brain, with the first pixel

above O corresponding to the first pixel of brain.

Using a dilation and erosion procedure similar to the one described above, an 11

pixel thick rim was obtained (using an 1 lx1 lx1 pixel kemel), which corresponded

approximately to 3.5 cm of cortex. An automatic evaluation of the rim was made to see if

any large lefi-right asymmetries existed. The difference in distance behveen any two

pixels on each side (hemisphere) of the cortical rim should not differ by more than a few

pixels. If a large difference existed, possibly as a result of a stroke, the side with the

smaller difference was automaticdly mirrored across the midline on to other side.

Finally, the rim was divided into 24 equal annular segments, 12 per hemisphere.

This technique was used on 6 slices, autornaticaliy selected from the rescaled brain to be

slices 2-7 (out of 12) for a total of 72 segments per hemisphere. In addition, a similar

technique was used on slice 1, the most dorsal slice reliably identifiable as brain.

Segments on this slice were obtained in a different way since it generally contained bain

that was too small to use the previous methoâ. Here, the same outer rim that was found

for slice 2 was overlaid on slice 1 but instead of eroding the rirn, al1 of the brain intenor

to the edge was used, divided into 8 segments. Since this dorsal slice was smaller than the

slices beneath, the number of pixels in each segment remained similar to the lower

segments. In total then there were 152 segments calculated fiom the cortical rim

proc edure.

In order to measure counts in subcortical regions, an additional ROI analysis was

performed. On each of ten slices, preset regions of interest were placed, proportional to

slice size, over anatomical regions of interest including the basal ganglia, thalamus,

antenor cingulate gps, and cerebellum. There were 18 ROIS placed on each hemisphere

for a total of 36 ROI segments. Measurement of counts in the cerebellum was slightly

different than for the other ROIS, in that, initially the cerebellar ROI was placed over the

cerebellar region on slices 1 1 and 12, proportional to the length and width of each slice.

Final placement, however, involved a local search for the maximum pixel value and the

cerebellar ROI was then centered over it.

From both automated procedures described above, there were 188 segments, 94

per hemisphere, in which mean counts, standard deviation of mean cowts and the

number of pixels in each ROI were obtained and used in analyses. Al1 segments

corresponded to matched anatomical regions fiom the reference atlas previously

described. To reduce the data for certain analyses, segments from similar regions (e-g.,

parietal) were grouped and averaged. in total, there were 10 SPECT scan averaged

regions per side including 8 cortical regions as follows: the fiontal cortex (F), the anterior

cinguIate gyms (ACing), the parietal cortex (P) , the parietal-temporal region (PT), the

temporal cortex (Temp), the sensorhotor cortex (SM), the medial occipital region (Med

O), and the occipital cortex (O), and 2 subcortical regions which were the basal ganglia

(BG) and the thalamic nuclei (TH). Since the parietal and frontal cortices were of specific

interest in this study, each of these regions was subdivided into smaller regions (3 fiontal

and 2 parietal subregions per side) for more specific anatomical localization. The fiontal

cortical region was subdivided into the idenor frontal gyrus (F-Inf), the rniddle h n t a l

gyms (F-Mid), the superior fiontal gym (F-Sup) and the parietal region was subdivided

into the inferior parietal cortex (P-Inf) and the superior parietal cortex (P-Sup).

Two patients, who also had Magnetic Resonance Imaging for cli&al

reasons, were used ui an SPECT-MR superposition experiment to ver@ localization of

SPECT regions (Appendix CS). The two patients were imageû on a 1.5 Tesla MR

magnet (Signa, Version 4.7; General Electric Medical Systems, Milwaukee, USA). A

volumetric 3-D sequence was performed in the sagittal plane, covering the entire brain,

that resulted in 1 24 contiguous 1 -3 mm slices in thickness. The scans were acquued in

14.4 minutes using a Tl-weighted sequence, 192 phase-encoding steps, with a T M E of

3Y5 ms, flip angle of 35", and a field of view of 20 cm. m e superposition technique,

developed by Woods et al., (1993) which nins on a S u P workstation (Sun View,

California), compares voxels in the MR image to voxels in the SPECT image. For a

SPECT-MR superposition, where the SPECT scan is superimposed on the MR scan, the

algorithm first divides the MR brain into 256 separate components (nonbrain structures

aiready removed), which differ based on voxel intensity 0). This technique makes the

assumption that voxels with similar inteasity correspond to sirnilar brain tissues. For each

of the 256 voxels intensities in the MR image, the algorithm finds a weighted average of

the normalized standard deviations (a'') for the value of the corresponding voxels in the

SPECT image in the following way. First, the number of voxels (n,) corresponding to

each voxel intensity is tabulated (i.e., nj conesponds to the number of voxels with an MR

voxel intensity ofjl. Next, the mean (a;) and standard deviation (aj') of al1 SPECT voxels

in the same locations corresponding to the M . image are caiculated (prior to this step, the

SPECT image had already been linearly Uiterpolated to be identical in size to the MR

image). Finally, a weighted average of the normalized standard deviations is calculated

Topography of Hanispatial Ncglcct

using the following formula a" = (qïai)*(nh. At the start of the algorithm, it is assumed

that the reslice parameters, correspondhg to the x-, y-and z-axis rotations and translations

needed to register the two images, are set to zero. Following calculation of the weighted

average for each voxeI intensity, the program changes the reslice parameters and

recalculates new weighted averages. The assumption is that smaller weighted averages

correspond to more accurate registration of the two images. The algorithm minimizes a"

by adjusting the reslice parameters and recalculating d' iteratively.

These two superpositioned brains were used to aid anatomical localization. Since

the MR brain was aliped parallel to the anterior commissure-posterior commissure (AC-

PC) line, this ensured that the superimposed SPECT was also aligned with the same

angulation. Two stereotactic atlases (Talairach & Tournoux, 1988; Damasio, 1995) were

used to optirnize anatomical interpretation. By looking at the MR anatomy and the

SPECT overlay, it was possible to identiQ lobar anatomy on the SPECT scan more

precisely. Siace al1 SPECT scans were reconstructed, parallel to the OM line, and linearly

scaled, each scan should be standardized such that when viewing transaxial slices, slice 5

should correspond to very similar brain anatomy across al1 subjects (Appendix C.6). It

was then possible to use the two superimposed SPECT-MR scans as reference atlases for

anatomical matching with the rest of the SPECT scans in our population.

To ven@ that the OM line SPECT reconstruction was beïng estimated correctly,

the rotation between the MR and SPECT in the sagittai-plane was calculated. Using

output coordinates fiom the superposition program, the difference between the SPECT

rotated to the MR (rotated to the AC-PC line) and the SPECT prior to superposition

(estimated parallel to the OM line h m the reconstruction procedure) was found to be

-0.97" (Range -3.1,0.23 degrees) based on five SPECT-MR reconstructions.

ANALY SIS

3.1. Population Inclusion Criteria

In order to characterize the patient populations, prior to any subsequent analyses,

univariate statistics were performed on al1 datasets, including the calculation of means,

standard deviations, and simple correlations. The patients selected for analysis in this

study came fiom a combined population of patients (n=561) from three consecutive,

partly overlapping, prospective studies, the Stroke SPECT study ( ~ 4 6 9 ; funded by the

Heart and Stroke Foundation), the Spatial Attention Study ( ~ 1 7 0 ; fimded by the Ontario

Mental Health Foundation), and the Neglect Study (n=64; also fùnded by the OMHF),

over the course of six years (1988-94). Although some patients were in more than one

study at the same time, they were included only once in this analysis. Each population of

patients to be analyzed, e.g. lefi hemispheredamaged (Lm) patients in a CT analysis,

was compnsed of patients who conformed to a nwnber of inclusion criteria. For al1

anaIyses, al1 patients were right-handed, had at least 20/40 vision generally with

corrective glasses, were able to undergo SNE3 testing, had a single CT-confirmed lesion,

for a total of 297 patients from which to choose. Eighty percent of those patients also had

a SPECT scan. Exclusion criteria included patients who were too il1 or disabled to

undergo testing (loss of 156), bilateral acute stroke (loss of 10), previous brain i n j q

such as an earlier stroke (loss of 67), negative CT scans and no SPECT scan [and

therefore no useable imaging data] (loss of 13), no scans at al1 due to misplacement (loss

of 9, and lefi handedness (loss of 1 3).

ui addition, for al1 analyses, each patient must have had a wglect battery

administered to them within a specified period of time. Al1 patients were tested as soon as

they were able to sit up and undergo the testing procedure. Because neglect tends to

diminish over time rather than increase and because it can dîsappear quickly (Stone,

Patel, Greenwood, & Halligan, 1992), different temporal inclusion criteria were allotted

to patients with and without neglect. The requirement for patients without neglect was

that a complete neglect battery had to be administered to them within 14 days pst-stroke.

Fifty-four patients out of a possible 157 were excluded for these reasons (n=23 for

incomplete battery and n=3 1 for time restriction). For patients with neglect, the timeline

for testing was extended to 120 days pst-stroke; 3/140 patients were excluded because of

this restriction. This was doue in order to maximize inclusion of the patients exhibiting

neglect behaviour. Patients with severe neglect were frequently tw il1 from their stroke to

undergo neglect battery testing in a shorter tirne period. Despite this later testing,

however, they still showed neglect; thus, theoretically, even if their neglect had improved,

compared to performance which rnight have been anticipated had they been testable

earlier, they still demonstrated presence of neglect on the battery. A total of 5 1 additional

patients could thus be included by expanding this time window for inclusion.

To M e r increase the population of patients with neglect, those with partial

scores were also included if the score exceeded the neglect cut off (score L 6) despite the

fact that the battery could not be completed due to the severity of the patient's deficits.

For example, severe constructional apraxia excluded scoring of drawings in 14 patients; a

further 18 patients had difficulty with the visual discrimination of targets from non-

targets required for the shape cancellation task. In order to be able to use the neglect score

fiom patients with a partial score, such as 40/70, a composite score was extrapolated

based on the patient population who completed al1 subtests, as discussed below. For any

rnissing subtest, a score was calculated based on the mean neglect score in the population

of patients with neglect (n=112, mean neglect score = 38) multiplied by the average

proporîional ratio, with the calculated score not to exceed the maximum possible score

for the subtest. This mean ratio was calculated by finding the proportion of a subtest to

the total neglect score for an individual and then finding the mean proportion for the

population. For example, the score on drawings accounted for 17% of the total neglect

score (Le., 38) in the population and thus a missing value of 7 (0.17 x 38) was allotted for

patients for their drawing/copying subtest (n=14). In this way, composite scores for al1

subtests were calculated for those patients with incomplete batteries. Sirnilady patients

with a missing score on the line cancellation task (n=l), line bisection task (n=3), and

shape cancellation task (n=18) were given calculated missing scores of 4 (0.10 x 38), 10

(0.28 x 38), 15 (0.40 x 38), respectively. For al1 patients with incomplete batteries, the

corresponding calculated population-averaged subtest scores (7 for drawings, 4 for line

canceliation, 10 for line bisection, and 15 for shape canceliation) were substituted for

missing subtest scores, in order to calculate an overall neglect battery score. This may

have underestimated their performance, but it allowed a total score to be derived for each

patient (total n=25), for entry into the data analysis. To calculate the error associated with

each of the composite subtest scores, four linear regressions were used to predict the tnie

SNI3 score from the composite SNB score for those with completed SNBs (i.e., the

composite SNB score was tabulated using a composite subtest score (i.e., drawings)

summed with the true scores for the other three SNE! subtests). The correlations for al1

four composite scores were high (r4.94 or higher; Figures 2-5), indicating that the

composite scores were providing a reasonable estimate of neglect performance.

3.2. CT Inclusion CriteRa

From the above inclusion and exclusion criteria, there was a population of 240

potential patients on which CT analysis could be performed. From this popdation, al1

patients with negative CT scans were removed (n=27), since lesion localization could not

be performed. Demographic and descriptive (i-e., visual analysis) data was calculated on

the remaining population of 2 1 3 patients, 83 le A hemisphere-damaged ( L m ) patients (45

patients without neglect and 38 with neglect) and 130 right hemisphere-damaged (RHD)

patients (41 without neglect and 89 with neglect). Due to the fact that lesion volume was

found to be a confounding variable, al1 patients included in the CT linear regression

analyses required a CT Iesion volume measurement, which was not attainable in 18

patients because the original CT scans had been lost. In total, 195 patients were available

for CT analysis, 75 LHD patients (42 patients without neglect and 33 with neglect) and

1 20 REID patients (38 without neglect and 82 with neglect).

In order to address the hypothesis that al1 patients with negIect will show damage

to at least one of the predîcted key matornical regions in the directed attention network,

lesion locaiization data fiom the CT scans were investigated (refer to Table 3 for a

TABLE 3: ANALYSIS SUMMARY

Hypot hcsis Addrcsscd

Part A.

Patients with neglect will

have at least one key

rcgion or the

interconnected white

matter fibre bundles

damaged.

Part B.

The regions that will

emerge in MLR andlor

PLS will be the five

predicted t heoretical

regions.

Variables Examincd

--

a) Neglect Category(y1n) - dependent -

24 CT Regions (dainage yln) - indepcndent

b) Transformed Neglect Score - dependent

24 CT Transformed Regions - independent

c) Transformed Neglect Score - dependent

5 SPECT anatomicnl ratios - independcnt

5 CT Transformed Regions - independent

b) Transformed Neglect Score - dependent

24 CT Transformed Regions - indepcndent

c) Transformed Neglect Score - dependent

16 SPECT anatomical ratios - independent

d) 4 Neglect subtest scores - dependent

160 SPECT segment ratios - indepcndcnt

# of Patients

a) 84 LHD

a) 130 RHD

b) 75 LHD

b) 120 RHD

c ) 59 LHD

c) 89 RHD

b) 75 LHD

b) 120 RHD

c) 59 LHD

c) 89 RHD

d) 44 LHD

d) 68 RHD

Stutistic Employcd

a) Visual Data Inspection

Frequency Calculation

b) Linear regression with transformed

neglect scores & CT regions.

c) Linear regression wi th transformed

neglect scores & CT and SPECT

regions.

b) Linear regression with transfonned

ncglect scores & CT regions.

C) Lincar rcgression with transformed

neglect scores & SPECT regions

d) Partial Least Squares with transformed

subtest scores & SPECT segments

summary of al1 analyses). Al1 patients with neglect and a single lesion were exarnined. If

a patient displayed neglect and had a lesion that did not include the hypothesized regions

of interest, they were M e r anaiyzed anatomically in order to see if any other common

regions emerged. in addition, frequency tables were charted for ail regions, within each

subpopulation (i.e., RHD patients with neglect). For cornparison, lesion localization data

fiom the group of patients who did not show negiect were also investigated.

3.4. SPECT Inclusion Criteria

Since lesion volume was also an issue in the SPECT population, one limiting

factor in the selection of patients was that they must have had a CT scan with

measurement of lesion volume. In addition, due to the dynamic, functional nature of

SPECT, it was considered prudent to match the date of acquisition of the SPECT scan as

closely as possible to the date of administration of the neglect battery. However, the

criteria for inclusion in relation to this difference depended on which manoeuvre came

first. As explained above, neglect performance rarely detenorates in a patient yet it can

recover relatively quickly; in this population, 70% of patients, who initially showed

neglect, had normal performance on the SNI3 by three months. Therefore, if the SPECT

scan was performed prior to the neglect battery and the patient displayed neglect, it is

relatively safe to assume that the patient would have displayed neglect if tested on the

sarne day as the SPECT scan. On the other hand, if the patient was assessed prior to the

SPECT, it can not be assumed that the patient would have had neglect at the time of

SPECT scanning, unless a neglect battery was performed at a date some time after the

SPECT scan and the patient displayed neglect at that time. Based on these assurnptions,

patients without neglect had their date for inclusion restricted to -3 to 12 days Corn the

tirne of their SPECT to the time of their neglect battery (SPECT Date - Neglect Battery

Date). Patients with neglect were Iimited to -120 days to 7 days between the t h e of their

SPECT to the time of their neglect battery. In total then, 147 patients remained in the

SPECT population for analysis, 59 LHD patients (3 1 patients without neglect and 28 with

neglect) and 8 8 RHD patients (3 1 without neglect and 57 with neglect).

3 . Statistical Normalization Procedure

3.5.1. Negfect Score m d Subtest Log Tmnsformation

To investigate the importance of predicted key anatomical regions, linear

regression analyses were planned. In order to capitalize on the full range of neglect

behaviour, it seemed prudent to use the whole score from the neglect battery. However,

on visual inspection the distribution of neglect scores was markedly skewed to the right.

Univariate statistics showed that the distribution had a skewness value of 1.2, a kurtosis

of 0.25, and a significant value of 0.2220 (p<0.001) on the Lilliefors test of normality (a

modification of the Kolmogorov-Smirnov test). The transformation that best improved

the univariate statistics of normality was the log transformation. Since scores on the

neglect battery could have a value of zero and the logarittun of zero is not solvable, a

constant was added to the iog transformation equation, specifically loglo(neglect score +

2.1), which improved normality. The transformation improved skewness to 0.06, and

kurtosis changed to -1 -4. Although the Lilliefors test was still found to be significant

@<0.00 1 ), the value calculateci improved by a factor of two (value = 0.1 OS). Therefore

al1 regression (and other) analyses involving the neglect score were perfoxmed on the log

transformed scores. In addition, the same log transformation was applied to the individual

subtest scores, to be used in the PLS aoalysis. This improved normality in a similar

fashion for the subtest scores.

3.5.2. CT Regional Arc Sine Transformation

The CT data used in the regression analyses was the quantitative structural data,

as described in the methods section. Each anatomical variable was detennined first by

calculating the ratio of slices showing damage in a particular anatornical region to the

number of slices on which the region appeared. For example, a lesion in the inferior

parietal lobe appearing on 2 slices occupied 2 out of 4 possible slices for that region.

Since the measurements calculated are proportions based on different denominators,

regions with a small denominator, such as 2, have linle room to vary compared to a larger

region with a denominator of 10. To decrease this difference in variance potential

between regions, it was necessary to transform the ratios. A commonly applied

transformation to proportional data is the arc sine transformation of the ratio multiplied

by two (2 sin-' ( p ) ) (Winer, 1971). This was calculated for each CT variable and used in

the CT analyses. Transformation is also important since the power associated with finding

a significant difference between proportions based on different denominators decreases

and thereby increases the Type II error in analyses (Stevens, 1986).

3.6. Mufticollineatig in CT and SPECT Data

Prior to regression analyses, a Pearson product-moment correlation matrk was

produced to explore the issue of multicollinearity beniveen anatomical variables. In

addition, a test of rnulticollinearity of the variables enter4 into the regression equation

was aerformed. This was done for both the CT and SPECT regional variables.

For each of the individual regression analyses, a multicolhxuity test was

performed which cornputes both the tolerance of each variable, defineci as (1-~i') where

Ri is the multiple comlation coefficient when the ith independent variable is predicted

fkom the other independent variables, plus a variance inflation factor (VIF) score (which

is the inverse of the tolerance) (Belsley, Kuh, & Welsch, 1980). If the tolerance of a

variable is low (or if the VIF score is high), then its contribution to the regression

equation can probably be explained by a linear combination of the other variables and

thus is highiy collinear with them. Eigenvalues and condition indexes (defined as the

square-root of [the maximum eigenvalue divided by the eigenvalue for the ith

independent variable]) were computed fiom the scaled, uncentered cross-produçts matrix

of the independent variables. Cornparison of these values can also identiQ collinear

variables. For example, eigenvalues with corresponding high condition indexes are

probably the result of collinear variables.

3.7. CT Linear Regression Anaiysis

Two approaches were used in building regression equations. The first type was

exploratory in nature and involved stepwise regression analysis. Thirteen anatomical

regions, detailed earlier, were entered in a stepwise linear regression ushg the log

transfomeci neglect score as the dependent variable, and the arc sine transforrned CT data

as independent variables. Regional significance was set at a conservative F0.004

(Bon ferroni correction for 1 3 regions for a=0.05). The second approac h involved entering

into a mode1 the five regions from the hypothesized network for directed attention. The

analyses were performed on the LHD (n=75) and RHD (n= 120) populations separately

for these analyses. Prior to regression analysis, t-tests were performed and if any variable,

such as age or volume, was found to be significant between the two groups, it was added

as a covariate in the regression analyses. Al1 analyses were perfomed using the statistical

software package SPSS (SPSS inc. 1995).

3.8. SPECT Noma&ztion and Standardization

Prior to SPECT scan analysis, the data for each anatornical segment were

standardized. Mean counts obtained in each segment from the cortical rim and subcorticai

ROI programs were normalized by dividing by the mean counts in the higher of the two

cerebelli, which was the ipsilateral cerebellum in 90% of cases, on an individual basis.

The basis for using the higher cerebellum rather than the ipsilateral cerebellum was that

in a small subset of patients (3% of our population) with occipital damage, the

ipsilesional cerebellum was f o n d to have structural damage. Each segment ratio (X,J

was then standardized by subtracting fkom it a specified mean (x,,) and dividing the

result by the standard deviation of that mean (+p), using the formula xsa - P P .. The

specified mean and standard deviation values were calculated as the mean and standard

deviation of the homologous segment (same hemisphere) from the SPECT scans of the 19

normal volunteers, excluding values that were greater than 2 standard deviations from

that mean (Le., calculahg n o d meam based on the values with 95% codidence).

Stated differently, for each segment a mean and standard deviation were calculated fiom

counts in the normal population corresponding to the segment in the same hemisphere.

From these calculations, standardized values were calculated for each segment. Twenty

larger anatomical regions were obtained by averaging those standardized segments thar

corresponded to the region of interest. To correct for the size of each segment, a weighted

average of al1 segments based on the number of pixels in each segment was used in the

calculation of larger averaged regions.

3.9. CT-SPECT Linear Regression Analysis

Following the CT regression analyses above, two additional regressions were

performed using the both the CT and SPECT regions for the LHD and RHD populations

separately. First, the five hypothesized CT regions were entered into a model, along with

their corresponding covariates, and then stepwise regression was used on the

corresponding SPECT regions to see if any would enter in addition to the structural data.

Second, al1 ten regions were forced into a model to see if the sarne regions that were

significant in the independent analyses also emerged here or if a new combination of

structural and functional regions arose. The reason for this anaiysis was to see the effect

of entering both structural and functional information into the same model, and how both

modalities would predict neglect score.

3 m I U m Power Calculations for Linear Regression Analysis

In order to calculate the probability of failhg to confïrm a significant difference

(Type iI error), pst-hoc power calculations were perfomed for the CT regression

analyses. The power calculations were performed for a medium effect size (Cohen, 1988)

with two sets of independent variables, one for the covariates, R' of 0.25, and the other

for the variables of interest producing an iacremental R~ of . l O.

3. II. SPECT PartZal Least Squares Anaiysis

For the PLS anaiysis, which was performed usuig Matlab software (The

MathWorks hc. 1 994), the four dependent variables which were entered into the neglect

subtest block (matrix) were the log transformed neglect subtest scores. The image data

block (matrix) contained 160 segments fiom the cortical nrn and ROI procedures,

including 152 segments ffom the cortical rim program and 8 fiom the ROI program - 2

for each Iefi and right thalamic nuclei and 2 for each lefi and right basal ganglia. Pnor to

analysis and similar to the above regression analyses, the influence of any variables found

to be significantly different between the groups on univariate tests must be removed.

Removing the contribution of covariates was doue by regressing the volume on the

individual subtest scores and then using the calcuiated residualized scores ~ o m the

regression analyses as the new subtest scores.

Following analysis, the saliences from the PLS output were remapped into image

space. To test the significance of the sinplar values computed by PLS, a permutation test

was used to assess the extent to which the computed imaging latent variables characterize

performance on the neglect subtests. As descnbed earlier, the test involved reordering the

rows of the subtest matrix, breaking the association between the blocks, and cornpuMg a

new cross-correlation matrix. MLR was then used to regress the raw subtest scores on to

the imaging latent variables produceci. This process was repeated 10 000 times in order to

test the signïficance of the output Grom the original dataset in a large enough sample.

Topography of Hanispatial Ncglect

4. RESULTS

4.1. Demographic Data - Resu&s

Popdation demographics were analyzed for each subgroup (Table 4). For the

LHD group, in both the CT and SPECT populations, there were no significant differences

found between the population of neglect patients and non-neglect patients with regard to

age, sex, and education. There was a significant difference found with regard to CT lesion

volume between the two groups; patients with neglect had larger lesions. For the RHD

group, there were no differences for sex or education but both lesion volume and age

differed significantly; patients with neglect were older and had larger lesions (This age

difference was not significant for the smaller SPECT population, but the trend was stitl

present). Based on these results, lesion volume was entered in al1 regression analyses for

the LHD population regression analyses as a covariate, and both age and lesion volume

were entered in a11 regression analyses for the RHD population as covariates. Table 5

contains a suMnary of the mean and standard deviation of percent damage, number of

patients with damage and the percent of patients with damage for al1 CT regions,

separated according to group and presence of neglect. Nine regions were found to have

sustained significantly more damage, assessed by percentage of the number of slices with

lesions, between the RHD groups with and without neglect, afier conecting for multiple

cornparisons with an a=0.05 (i.e., Bonferroni correction for 27 regions). The nine regions

were the parietal cortex, antenor white matter, posterior white matter, supramarginal

gyms, motor strip, sensory strip, FLi, posterior FOF and postenor FLS.

Posterior White Matter (PostWM)

hiean Percent Damage + Standard Drvhak i n 4 ofpople with damage f % ofpropde uith èamegeJ

ANTEFUOR CINGULATE (ACING) 1

BASAL GANGLIA (Be) FRONTAL (F)

PAIUETAL (P) r

Deep Temporal-Parietal-Occipital Junction (Deep-TPO) 9.8 ' I 9 a 0 18.6 + 28.1 10.8+312 20.9k31.8 n=/1/?4%] , n=I5 (38x1 n=8 [ lOO/o l n=35 f39%j

Motor Strip (Motor) 4 2 5 10.1 10.4 2 22.5 3.9 f l 2.4' 17.7 f 253' n=8 ( /8%j n= IO f X % j n-5 /12%/* r J I /46%/*

Sensory S trip (Sensory) 6.3 2 14.4 10.4 r 20.6 4 i 2 6 20.8 f 28.4+ n=8/16%1 n=/O(26%] a d / I 5 % / * r42/47%/*

I

Occipital (O) 5.0 f 18.0 112 + 18.8 2.1 i 8 2 7.4 2 16.9 n=7(/676] n=l3(33%] n=4f1O0%J n=25[18%J

1

Temporal (T) 4.7 i 10.6 8.6 r 12.4 5.0 I 9.6 11.5 k 14.4

n=/f?%j n =4 /1 O%] n=O [PA] n=2 (2%j

hferior Frontal (FM) 3 2 r 9.6 9.5 I 19.8 7.0 i 17.0 14.7 f 23.4 - n=5 11 1%j n=IO [26%j n=8 /2O0Aj n=34 [38%]

N- (0-45) 0.5 123

n=2 f4%J

14.8 + 19.9 n=?O (44x1

1.4252 n=6 fI3%]

2 3 15.1 n=/ l f24%]

15.1 + 25.0

n=l[??G. , n=6 /15%] n=5 /12%] n=34 [38%] 1

Superior Frontal (FSup) 0.9 2 5.8 o s f 2.1 1.6 I 6.8 1.8 + 7.3 . . n=/ [2%j n =4 (1 Ph] n=3 /7%] n=7 [PA]

Anterior Superior Longitudinal Fasciculus (FLS-Ant) 1-4 20-1 20.0 32-8 13.2 i 34.2 34.2 i 47.7 n=l2/27%] n= l2 f3I%J r-8/20%/* ~ r l6 /52%/ *

L

Anterior Frontal-Occipital Fasciculilc mnF-Ant\ 8.2 2 14.8 I 1.7 r 2.0 11.1+.205 21.1f26.5

N+ (1138)

1.96 fiî n =6 f I5%]

14.8 f 20.0 n=19 f49°%]

3.6 f 7 5 n = I I [?PA]

10.9 f 262 n=ll[?8%1

10.4 2 24.5

Anterior Interna1 Capsule (IC-Ant) I

Posterior Superior Longitudinal Fasciculus (FLS-Post) 1

Posterior Frontal-Occipital Fasciculus (FOF-Post)

Posterior Interna1 Capsule (IC-Post)

Inferior Longitudinal Fasciculus (FLi)

N- ( m 4 i)

2.75 11.1 n 4 ( /PA ]

3 8 f 8 n 4 . 2 (54%]

3.5 k 9 2 n=9 f 2 % ]

1.2 f: 5.7' a-3 /7%/'

1 1.8 f 24.7

N+ (i-89) 2.8 2 95

n=13 (IS%] 21 -8 f 23.0 n=57 f64%J

6.0 5 10.5 n=40 /45%j

9.6 i 21.2' II-37[42%/* 22.6 f 34.8

Note: N- refers to patients without ncglcct and N+ rcfcrs to patients with ncglcct Rcgions in bdd correspond to the fivc prcdictcd regions in the thcorctical nework for directcd attention. Numbcrs with an astcrisk w m found to be statistically d i f fmn t h m each other. af ia comcting for multiple cornparisons (p<O.OOZ).

6.5 I 15.3 n=8 (18%j

7.9 I 183 n=8 (18%/

7.0 + 17.7 n = 7 /16%J

7.3 k 193 n=6 /13%]

5.3 + 15.1 n=5 (13%j

n2:c, i 8.9 I 3 5 s

n=/3 f33%]

1 1.6 2 26.8 n=7/18%j

10.0 f 21.6 n=9 (23%] -

5.8 + 15.2 n=6 /I5%]

4.9 I 16.2, a 4 /IO%/*

9.6 I 19.6 n=9 f22Sq

5.7 f IM+ n=7[17%j _

6.9 + 14.7 n=18 /,'PA]

::yzK" I

24.6 *«).IO

a-35 /39%/*

12.7 .C 25.2 n=2/ (24%] 19.4 + XII* n=29 (33x1

Similar to the table above, Table 6 displays the mean count ratios for the SPECT

regions. On visual examination, it can be seen that for the LHD and RHD groups, patients

with neglect generally had lower average ratios, in the lefi and right regions, respectively,

compared to the group of patients without neglect. The only statistically significant

difference, after correcthg for multiple cornparisons with an a4.05 (i.e., Bonferroni

correction for 10 ipsilaterai regions), was in the basal ganglia of the RHD group ( ~ 3 . 2 ,

p<0.001). Table 7 shows the percentage of patients with damage to each of the five

predicted theoretical regions and Table 8 shows the average ratios (over the cerebellum)

for those same regions.

4.2. CT Ksual Analysis - Resufts

4.2.1. LHû Group

Examination of the CT data revealed that in the LHD neglect group, 29 of 38

patients (76%) had damage to at least one of the key theoretically predicted matornical

regions, which included the fiontal, parietal, cingulate cortical regions, as weH as the

basal ganglia and thalamus. By cornparison, however, 32 of 45 patients (71%) without

neglect also had damage which included darnage to at least one predicted region, and the

difference between the two groups was not significant &,2=0.29, n.s.). Cornpanson of

the average extent of damage to the theoretically predicted regions also showed no

statistical difference between the patients with neglect (6.8 f 7.8% average percent

damaged of the five predicted regions) and the patients without neglect (8.4 f 10.7%

average damage; t,+-0.73, m.). The group with neglect had damage to two or more of

Topognphy of Hcmispatial NegIcct

TABLE 6: SPECT PERFUSION RATIO SUMMARY FOR 20 REGIONS OF INTEREST

-

SPECT SPECT SPECT SPECT LHD LHD MD RHD

Mean corresponding Average Ratio f SD

Note: N- rcfcrs to patients without ncglcc an osrcrisk wcrc found to be strrtisticilly diffcrcnt h m cûch ohm. after corrccting for multiple compmisons (p-zO.005).

N- N+ N- ( 1 ~ 3 2 ) 1 ( ~ 3 0 ) 1 ( ~ 3 2 ) f ~ 6 0 )

Avcraged Segmcnlsl Averaged Segments! Avcraged Segments/ Averaged Segments/ Cmbellum Ccrrkllrun Ccrckllurn Ccrckllum

N+ 1

0.706 f .O66 :t and N+ rcfcrs to F

0.721 t .O79 1 0.735 f .O88 1 0.692 f -081 1 0.645 f -140 nits with neglcct Numbers rcfcr IO average ratios ova ccrcbc11um. Numbers with

TABLE 7: PERCENTAGE OF PATIENTS W T H CT DAMAGE IN THE THEORETICAL NETWORK FOR DIRECTED ATTENTION

Note: Values refer to percemage of patients with damage on CT.

TABLE 8: AVERAGE SPECT RATIOS OF THE REGIONS IN THE

Group

LHD N-

LHD N+ I I I I I I I

Frontal

13 %

28%

Note: Values correspond to average ratios over cerebellum on SPECT.

Anterior

Cingulate

4 %

15 %

Group

LHD N-

LHD N+

S l b

Basal

Gang lia

44 ./.

49 Y.

Parietal

24 %

28 %

Thalamus

31 %

18 74

Anterior

Cingulate

0.774

0.786

Parieîal

0.674

0.669

Frontal

0.669

0.662

Basal

Ganglia

0.934

0.901

Thalamus

0.871

0.û45

the predicted regions in 1 5 of 3 8 (39%) patients, cornpared to 13 of 45 (29%) patients in

the group without neglect This fincihg was also not significant (~~, '=0.27, n.s.).

investigation of the 9 LHD patients with neglect but without damage to the

predicted theoretical regions revealed that al1 had darnage to posterior white rnatter fibre

bundles including the superior longitudinal fasciculus (FLS; n=3), the frontal-occipital

fasciculus (FOF; n=5), the infenor longitudinal fasciculus (n i ; n=4), and the interna1

capsule (IC; damaged in only 1 patient - although darnage was almost exclusive to the

postenor IC). Another area commonly affected in those 9 patients was the deep white

rnatter area beneath the parietal-temporalecipita1 junction @cep-TPO), which was

affected in 7 of the 9 patients. Al1 patients with neglect had damage which included at

Ieast one of the above regions. By cornparison, examination of the 13 LHD patients

without neglect showed 12 subjects with damage to at least one of the white matter fibre

bundles including the FLS (Ant., n=2; Post., n=3), the FOF (Ant., n=2; Post.. n=3), FLi

(n=3), the IC (Post., n=l), and the Deep-TPO (n=5). One of 13 patients had darnage

outside of the above regions, located in the antenor insular region.

4.2.2. RHD Group

In the RHD neglect group, 79 of 89 of patients (89%) had damage to at least one

of the key theoretical regions. By cornparison, 30 of 41 patients (73%) without neglect

had darnage to at least one predicted theoretical region, and the difference between the

groups was statistically significant (X$=5.04, pcO.05). Funher, the group of patients

with neglect tended to have damage that included more than one of the key predicted

regions. The group with neglect had 55 of 89 (62%) patients with darnage to two or more

key theoretical regions, compared with only 12 of 41 (29%) patients in the group without

neglect, a significant hding (X&11.9, p4.001) (Table 9). Patients with neglect also

had more overall darnage to the predicted regions (1 2.6 f 12.4% average damage to the

five regions) than the patients without neglect (6.6 + 8.1 % average damage; t1 1,=-3.3,

p<O.OOS, two-tailed).

Investigation of the 10 patients with neglect but without damage to those regions

revealed that al1 had damage to white matter fibre bundles that connect the key regions,

inchding either or both the antenor and posterior branches of those fibre bundles. The

fibre bundles affected most ofien in this population included the FLi (n=7), the posterior

FLS (n=5), the anterior FLS (n=4), and the Deep-TPO ( ~ 6 ) . Al1 patients with neglect

had damage which included at least one of the above regions. Examination of the 1 1

RHD patients without neglect with lesions outside the five predicted regions revealed that

9 of 11 patients also had damage to at least one white matter fibre bundle including the

FLS (Post., n=2), the FOF (Ant., n=l; Post., n=2), FLi (n=3), the IC (Ant., n=2; Post.,

n=l), and the Deep-TPO (n=5). Two of 1 1 patients had damage outside any of the above

regions; one patient had anterior insular damage and the other patient had postenor

temporal and occipital damage.

The purpose of examining the fiequency of damage to the key predicted regions

was tu address the hypothesis @art A) which posited that all patients with neglect will

have damage located " W i n " the network for directed attention (Le., damage to either

one of the key regions or the interconnections between them).

TABLE 9: PERCENTAGE OF PATIENTS WITH DAMAGE TO TRE F m REGIONS IN THE TC~EORETICAL NETWORK FOR DIRECTED ATTENTION

# of predicted theoretical 1 LHD Patients without Neglect I # of Patients

(% of total)

- 1 - - - - . - . - - - Y

3 1 Frontal-Parietal-Anterior Cingulate (l), Fmntal-Parietal-Basal Ganglia (1). 1 3 (7%)

regions damaged O 1

# of predicted theoretical

Combination of Regions (# of patients with damage on CT) - Frontal (2). Parietal (3), Basal Ganglia (8), Thalamus (6)

4 5

LHD Patients with Neglect

total n=45 13 (29%) 19 (42%)

2 1 Parietal-Basal Ganglia (3), Basal Ganglia-Thalamus (6)

l # of Patients (% of total)

9 (20%)

Frontal-Basal Ganglia-Thalamus (1) - Frontal-Parietal-Antcrior Cingulate-Basal Ganglia-Thalamus (1)

- -

O (0%) 1 (2%)

I -

1 r Anterior Cingulate (l), Parietal (7), Basal Ganglia (6) 1 14(37%)

regions damaged O

1 X of predicted theoretical

Combination of Regions (# of patients with damage on CT) -

RHD Patients without Neglect

total n=38 9 (24%)

1 0 (26%) 3 (8%)

O (0%) 2 (Sm

2 3

4 5

# of f atients (% of total)

- --

Frontal-Basal Ganglia (3, Basal Ganglia-Thalamus (5) Frontal-Parietal-Anterior Cingulate (2)-

Frontal-Anterior Cingulate-Basa! Ganglia (1) -

Frontal-Parietal-Anterior Cingulate-Basal Ganglia-Thalamus (1)

1 regions damaged O 1

# of predicted theoretical

3

4 5

RHD Patients with Neglect

Combination of Regions (# of patients with damage on Cl') -

Frontal (4). Parietal (l), Basal Ganglia (1 1). Thalamus (2)

# of Patients (% of total)

total n=41 11 (27%) 18 (44%)

2 1 Frontal-Anterior Cingulate ( 1), Frontal-Basal Ganglia ( 1 ), Basal Ganglia-Thalamus (6)

Frontal-Basal Ganglia-Anterior Cingulate (2), Parietal-Anterior Cingulate-Basal Ganglia ( 1) Frontal-Parietal-Basal Ganglia-Thalamus ( 1 )

-

8 (20%)

3 (7%)

1 (2%) O (0%)

regions darnaged O 1 2

3

4

Combination of Regions (# of patients with damage on CT) -

Frontal (4), Parietal (1 1), Basal Ganglia (7), Thalamus (2) Frontal-Parietal (4), Frontal-Basal Ganglia (8), Parietal-Basal Ganglia (2),

Anterior Cingulate-Basal Ganglia (1 ), Basal Ganglia-Thalamus (1 2) Frontal-Parietal-Anterior Cingulate ( i), Frontal-Parietal-Basal Ganglia (5).

Frontal-Anterior Cingulate-Basal Ganglia (2).

5

total n =89 10 (1 1%) 24 (27%) 27 (30%)

16 (18%)

Frontal-Basal Ganglia-Thalamus (4). Parietal-Basal Ganglia-Thalamus (4) Frontal-Parietal-Anterior Cingulate-Basal Ganglia (1 ), Frontal-Parietal-Basal 6 (7%)

Ganglia-ïhalamus (3). Frontal-Anterior Cingulate-Basal Ganglia-Thalamus (?) Frontal-Parietal-Antehr Cingulate-Basal Ganglia-Thalamus (6)

* .

6 (7x1

In the LHD group, there were no statistical& sign$cant findings between the

group of patients with and without neglecz, but there was a tendency for greater damage

in the group with neglect. AR patients with neglect (38/38) had damage "within" the

theoretical network, olthough almost al1 patients without neglect (44145) also had

damage "within " the predicted network

In the RHD group, patients with neglect had more individuais with damage to the

five predicred regions in the network for directed attention as well as more damage

within those regions, compared to patients without neglect. Al1 patients (89189) with

neglect had damage "within " the predicted theoretical network, but almosr all patients

without neglect (39/41) also had damage "within " the predicted network.

4.3. Multicollinearity in CT Data - Results

Pnor to regression analyses, the issue of multicollinearity was addressed with

respect to the CT data. Cross-correlation matrices were computed using Pearson bivariate

correlations for the LHD and RHD groups, separately (Appendices D. 1 & D.2). On visual

examination, as expected due to the vascular territorial supply of the middle cerebral

artery, which was involved in approximately 85% of patients with stroke, most of the

variables correlated with each other positively, mostly amund 1-0.4. The largest

correlation was ~ 0 . 8 2 6 between the sensory and motor strips. Although there were a few

highly collinear regions, rnulticolinearity did not appear to be a factor with the CT data.

Further, the multicollinearity diagnostics produced with the regression analyses did not

reveal any evidence of multicollinearity between regions.

4.4. CT Linear Regression Analysis - Results

On linear stepwise regression analyses of 13 regions in the LHD group (n=75), no

variables entered into the regression equation after lesion volume, which alone accounted

for 26% of the variance (F,,.-, = 26.9, p(0.001, ~~=0.25916), was partialled out. A

hypothesis-driven model was therefore built by adding brain regions to the equation,

based on the newoanatomical model for directed attention (refer to Appendices E. 1 to E.4

for complete analysis of variance (ANOVA) tables). A model with al1 5 predicted regions

(ACing, BG, F, P, TH) and the covariate was found to be significant ( F ( 6 . B ) = 5.54,

p<0.001, ~'=0.3285). This model accounted for 33% of the variance, an increase of 7%

over the model with volume alone. Of the regions entered in the model, none of the

variables were significant at the 0.05 level.

Linear regression analyses of the nght hemkphere group ( ~ 1 2 0 ) revealed that

upon stepwise regression of al1 13 regions, no variable entered into the regression

equation following volume and age which together accounted for 20% of the variance

(F,r, ,;, = 1 4.7, p<O -00 1, R*=O -2009). Although no region signi ficantly entered the equation

using a critical p-value cutoff of 0.004 (Bonferroni correction for 13 regions with an

a=0.05), the postenor white matter region (p=0.0068) and the lateral occipital regions

@=0.0095) s howed trends toward signi ficance. For the hypothesis onented regression

analysis, the model with al1 five predicted regions produced a significant mode1 (F<7.1121 =

6.5, p<0.001, ~~=0.2889). The anterior cingulate and parietal regions were the most

significant regions @<O.OS), with the thalamus showing a trend toward significance

@=0.087). Exploration of the parietal subdivisions found that the supramarginal gyms

was the most significant parietal region @<O.OS). This is supported by the finding that

13% of RHD patients with neglect had damage to this region compareci to the RHD

patients without neglect, none of whom had damage to the supramarginal gym.

4.4.1. Summary

The purpose of using MLR wirh CT data was to see if the regions involved in the

theoretical network for directed attention entered an equation to predict neglect

perfarmance on the SNB, addressing part of the hypothesis @art B). The exploratory

approach was used to examine additional regions that might be of interest, such as the

white matter regions, and a hypothesis-drïven model, in which each predicted region was

forced in, was used more specijcally to test the theoretical anatomical network for

directed attention. Results fiom these analyses provided evidence based on structural

damage ro the regions of interat. In the LHD group, no regions entered signijicantly into

a regression equation in either the exploratoq or hypothesis-driven analyses. In rhe RHD

group, no region entered the exploratory regression equations, but the right anterior

cingulate and the right parietal, specz#cafZy the supramarginal gym. entered the

hypothesis-driven equations.

4.5. MulticoUinearity in SPECT Data - Results

On the basis of the output fiom the regression analyses, the issue of

multicollinearity was addressed with respect to the SPECT variables. Similar to the CT

anal ysis, cross-correlation matrices were computed using a Pearson multiple bivariate

correlation analysis for the LHD and RHD groups, separately (Appendices D.3 & D.4).

On visual examination as compared to the CT data, SPECT variables were more

correlated with each other than were CT variables. Most correlations were positive, as

was seen in the CT data, and the average correlation was 14-60. The largest correlation

was ~ 0 . 8 9 9 between the temporal and parietotemporal regions. Regression d y s i s was

performed for the SPECT data; however, examination of the collinearity diagnostics,

described earlier, produceci evidence of multicollinearity. It was therefore inappropriate to

use MLR to explore the SPECT &ta. Thus, MLR was ody used with the SPECT data for

hypothesis-driven testing, and exploratory analysis was perfonned ushg PLS, as detailed

Iater.

4.6. SPECT Linear Regressiion Anaiysis - ResuUs

Linear regression analyses of the LHD group (n=59) showed that the covariate CT

lesion volume accounted for 29% of the variance (Ft im = 23.6, p<O.OOl, ~?=0.2930). The

hypothesis-driven model with al1 five regions was found to be significant (F,asz, = 4.85,

p<O.OOl, ~~=0.3592) and accounted for 36% of the variance, an increase of 7% over the

model with volume alone (refer to Appendix F.l for complete ANOVA table). Of the

regions entered in the model, only the left parietal was significant @<0.05), although the

left thalamus showed a trend for significance @-0.071). As Table 8 shows, both of those

regions had lower ratios in the group of patients with neglect. Further exploration of the

parietal region revealed that the infetior parietal w .02 ) was most significant.

Linear regression analyses of the RHD group ( ~ 8 8 ) showed that the covariates,

CT lesion volume and age, accounted for 25% of the variance (Fm5, = 13.8, p<0.001,

~~=0.2448). For the hypothesis-driven model, the five regions entered into a signi ficant

model (F,,,, = 5.8, p<0.001, ~'=0.3377) (refer to Appendix F.2 for complete ANOVA

table). Of the regions entered in the model, only the nght parietal region showed a trend

to significance @-=.OS) and further investigation found that the superior parietal

subregion was the more significant parietal reaon (p-0.058).

4.6.1. Summriry

nie purpose of uring MLR with SPECT data was to see if the regions involved in

the theoretical neîwork for directed attention entered an equation to predict neglect

performance on the SNB. addressing part of the hypothesis @an B). A hypothesis-driven

approach was used to etamine the ability of the five regions implicated in the d i ~ e d

attention nenvork to predict neglect. Due to multicolZinearity between regionr, an

exploratory approach was inappropriate to use with U L R and instead w u later cam-ed

out with PLS. Results fiom these analyses provided evidence based on functional damage

zo regions. In the LHD, the le3 inferior parietal region signzfîcantly predicted neglect

score. In the M D group, no region entered the equation at a pcO.O.5 level of

signzjkance, alrhough the right parietal came close at p = 0.08.

4.7. CT-SPECT Linear Regression Anaiysis - R e d

For the first regression analysis, al1 five predicted CT regions were entered along

with the appropriate covariate into a regression equation. Then stepwise regression was

used to see if any additional SPECT variables entered @-value 0.05). For the LtlD, the

result was that following the CT variables, no SPECT variables entered (refer to

Appendices G.l to G.4 for complete ANOVA tables). For the RHD gmup, the nght

parietal entered and the resultant equation accounted for 28% of the variance (Fm, = 3.7,

pc0.0 1, ~'=.2765).

The second regression involved forcing al1 ten variables fiom both modalities into

the regression equation, in order to examine the effect of both modalities simultaneously.

For the LHD group, the rnodel produced was not significant (F,I1x, = 1-97, p=0.062,

~ ' = 0 . 3 7 6 1). For the RHD group, the mode1 produced was significant (T;,,, = 3.5 1,

p<0.001, ~*=0.4048, accounting for 40% of the variance. Aithough no regions emerged

significantly, the right parietal on SPECT (p=0.06), the thalamus on CT (p=0.09), and the

anterior cingulate @-O. 10) showed trends toward signi ficance.

4.7.1. Summav

The purpose of using MLR with CT and SPECT data was to see LY a stronger

equation could be built based on both stmcîural and functional information. 13ie

equations oniy used regions involved in the theoretical neiwork for directed attention to

predict neglect pet$ormance on the SNB, addressing part of the hypothesis @art B). Two

h?,pothesis-driven approaches were used io see zfany SPECT regions added to the rnodel,

following CT regions. In the M D group, no SPECT regions entered following CT

regions. In the RHD group, the right pan-etal count ratios entered, following the CT data.

In the second approach. al1 ren regrgronsI five fiom each irnaging rn~dali@~ were entered to

see whether a srronger model could be built. No regions signifcantly entered an equation

in either the LHD or RHD analyses, although the RHD did produce a significant model

that accounted for approxirnateiy 40% of the variance.

4.8. Power Calcu&àtions for Linear Regression Anulyss - Resulfs

For each of the regression equations in the LHD and RHD group, power

calculations were performed in order to calculate the probability of detecting a significant

difference, in our dataset, if one existed. Power calculations were performed using an

a=0.004 for the exploratory approach (as explained above) and an a=0.05 for the a priori

hypothesis approach. The e f k t size used was 0.15, a medium effect size according to

Cohen (1988), with the covariate(s) accounting for 25% of the variance and the test

variables accounting for an increase of 10%. The results of the power calculations cm be

seen in Table 10 and show that there was more power for the hypothesis-driven

regression analyses. For the exploratory analyses with the CT data in both the LHD

@ower=O. 14) and RHD (power-0.40) groups, there was little power associated with the

regression equations, suggesting that strong conclusions should not be based upon the

results of these analyses. Similarly, the LHD in the hypothesis-driven approach with

SPECT data had small power (powe~0.56) associated with its regression equation and

one should be careh1 not to infer any strong conclusions based on this analysis. For the

remaining analyses, the power associated with each regression equation did not appear to

be a limiting factor.

4.9. SPECT Pclrtrài L e m Squares Anabsis - Results

Multicollinearity between regions is common with fùnctional imaging data, a

finding which poses a problem for MLR as described above. Thus PLS, which is not

adversely affected by multicollinearity, was utilized for cornparison with the results fiom

TABLE 10: POWER CALCULATION SUMMARY FOR MLR ANALYSES

# of

Covariates

Cumulative

R*

# of Test

Variables

Cumulative

R'

Effect Sizc Power

LHD - Explorrtory

LHD - Hypothesis-Driven

RHD - Exploratory

RHD - Hypothesls-Driven

SPECT ANALYSES

LHD - Hypothesis-Driven

RHD - Hypothesls-Driven

variables, covari~tcs ant nc for thc variabl ; of interest, and an

the MLR analyses. Table 1 1 shows the output for both the LHD and RHD patients, which

will be discussed separately.

4.9.1. PLS Findings in the LHD Croup

There were four singular values computed for the LHD patients. The first singular

value was 3.3363 and accounted for 84% of the summed squared cross-bloçk correlation

(SSCBC). As outlined earlier, multiple linear regression of the subtests on the latent

variables was used in conjunction with a permutation test to assess the statisticd

significance of the PLS output. MLR analysis of the subtests on the associated latent

variable for the imaging data was significant @=0.0142) with a model that accounted for

17% of the variance explained by the siibtests. The MLR performed here was testing to

see how much of the variability of the imaging latent variable was explained by the

subtest scores.

The second singular value was 1.209 and accounted for 12% of the SSCBC. MLR

analysis of the subtests on the second latent imaging variable produced a model which

accounted for 23% of the variance (p=0.0816). Similarly, the third singular value was

0.6307 accounting for 3% of the SSCBC. MLR on its related latent variable produced a

mode1 which accounted for 22% of the variance @=O. 1 0 10). The fourth singular value

accounted for less than 1% of the SSCBC and was less reliable given the high p-value

(p=0.8 163). Thus, it was not coasidered fbrther.

The top saliences, which were negatively correlateci with the first singular value,

can also be seen in Table 11 (Appendices H.l to H.3 contain a complete listing). For the

purposes of the discussion, a threshold based on a Scree Plot (Tabachnick & Fidell, 1989)

TABLE 11: RESULTS SUMMARY FROM PLS ANALYSES

1 Latent Variable

Singular Value

1 - p-value

1 Subtest Saliences -0,2828 0.8788 -0.0557 0.3803

LF ln f 7-21 -0.2861

LF ln f 638 -0.2288

L FInf 6-11 4.2249

L F ln l 7-22 -0.2 143

R Flnf 7-2 4.1888

1, Flnf 7-20 -0.11186

LTemp 6-19 -0.IfUS

R Tcrnp 7-5 0.0980

LACing 1-7 0.1063

L ACing 2-23 0.1 103

LTernp 7-17 0.1 113

R Tcmp 7-6 0.1456

Draw Line Biseciion

0.6864 -0.0 1 75 0.3968 0.6092

L PSvp 3-14 4.1379

L PSup 4-14 4.1332

L Lat0 4-12 4.1 292

LPSup 3-13 4.1224

L Plnf 3-15 4.1 168

L Lat0 4-13 4.1 139

L Psvp 2-12 -0.1 I l 6

L Plnf 4-15

0.493 1 -0.6799

LAClng 2-23 -0.2294 R BG

-0.2053 R SM 2-4

-0.1921 R Plnf 5-8

4.1671 R TH

4.1609 L Tcmp 6-1 8

O. 1337 R Flnf 7 3

0.2436 L FSup 2-21

0.2628 L ACing 733

Top Image Saliences

according to Scree Plot

4.1526- R Plnf 6-8

4.14% R Plnf 3-8 -

-0,1476 R Plnf 4-8

-0.1475 n,s,

. . m

bottom, rcspcctivcly. L = Lcfl, R = Right, Rcfcr to abbrcviation list for codc to rcgions. L

Notc: For LV2 t LV3, lhe iKg le (in h l d ) and p s i c salicnccs arc in ordcr from thc top and

was used to impart "significance" for saliences. By plotting the saliences in descendhg

order, it was possible to locate the point (or salience) on the graph that departed fiom the

slope of the initial points. Stated differently, if a straight line was fit through the data

starting with the h t two highest saliences, then the salience that would change the slope

of that line was considered the threshold point. Al1 saliences above that point were

considered "significant" (Appendices H.5 to H.7 contain Scree Plots for LV1 to LV3).

Although the corresponding brain regions within this range can be regarded as influentid,

regions outside of this range should not necessarily be regarded as meaningless. Perhaps a

more appropriate method of evduating the significance of these saliences is to compare

the relative saliences of surroundhg regioas.

In addition, based on the saliences for the first singular value, a singular image

(SI) was produced (Figure 6). The singular image is displayed in a linear gray colour-

coded scale in which saliences can range fiom negative to positive and corresponds tu a

gray scale with shades going fiom black to white. Thus, regions that have high negative

saliences appear black and regions with positive saliences appear bright white. (This is

more pertinent for the second and third singular images.) Interpretation of the latent

variables requires knowledge of the relationship between the imaging and subtest

saliences. For example, a negative irnaging salience and a positive subtest salience means

that higher scores on the subtest battery will be associated with lower blood flow ratios.

From both the table and the singular image for L W , it can be seen that the left

parietal and lefi occipital regions emerge as the most salient regions associated with the

first singular value. In a similar way, the top saliences c m be viewed in Table 1 1 for both

the second and third singular values, with one exception. In contrast with the f h t singular

value, these two variables have both negative and positive saliences. Thus, the top

saliences for these two variables correspond to the "significant" negative saliences (h

bold), in descendhg values h m the top, and the top positive saliences, in descending

values fiom the bottom.

Singular images were also remapped for the second (Figure 7) and third (Figure 8)

singular values. For the second singular value, the lefi and right inferior frontal regiofls

emerged as the most wgatively salient regions whereas the lefi and right temporal md

lefi antenor cingulate regions emerged as the most salient in the positive direction. For

the third singular value, a number of different regions emerged, including the lefi anterior

cingulate, the right basal ganglia, the right sensorimotor, right inferior parietal, and the

right thalamus that al1 correlated negatively with the third singular value. On the other

hand, the left anterior cingulate, left supenor frontal, right inferior frontal, right supenor

parietal, and the lefi temporal regions al1 had positive saliences with the third singular

value.

The image and subtest scores for the first latent variable were placed in a

scatîerplot to visualize the relation and to see if any patterns emerge. The advantage of

this visual approach is that it facilitates identification of specific relationships within the

PLS output by graphically portraying the data. Detailed examination of each plot can lead

to the classification of specific subgroups or patterns within the dataset. By identimg

patients located at the perimeter of each graph, it is possible to recognize both cornmon

and unusual pattern trends within the dataset. The image (or subtest) scores, as described

Figure 7: Singular Image for the Second Latent Variable in the LHD Group Note: Regions with high ncgative s~liences look black, segments with psi l ive saliences apperir bright white, and near zero saliences appcnr gray.

Figure 8: Singular Image for the Third Latent Variable in the LHD Group Note: Regions with high ncgative saliencçs look black, segments with positivc saliences appcar bright white, and ncar zero saliences appcar gray.

earlier, were caicuiated by muitiplying the image (or subtest) saliences by their original

raw value in a patient and then summing the products for a given individuai. The resuitant

plots for the f k t , second, and third latent variables can be seen in Figures 9, 10 and 11,

respective1 y.

Examination of Figure 9 revealed a separation of patients with and without

neglect. The majority of patients with neglect (dark circles) were found on the right side

of the page while the majority of those without neglect (light squares) were found on the

lefl side of the page. The three points at the far right side of the page were patients with

higher neglect scores, who had abnormal subtest scores on the drawings, lule

cancetlation, and shape cancellation tasks. Thus, a pattern of neglect severity could be

seen across the page, extending fiom the rightmost side of the page with the most severe

patients, decreasing in a gradient fashion, to the lefi side of the page, which mostly

contained patients who had normal performance on the neglect battery.

Examination of Figure 10 revealed interesting subtest and image patterns for the

second LV, identifjmg specific subgroups. In a circular pattern around the penmeter of

the plot, the type of subtests on which patients were found to show abnormal performance

varisd. The two patients in the top right corner (group A) had abnormal scores on line

bisection and shape cancellation. Proceeding in a ciockwise direction, the next three

patients (group B) also had poor performance on line bisection and shape cancellation,

while the bottom of the three had an abnonnal line cancellation as well. The next two

patients (group C) were abnormal only on the shape cancellation task. The next patient

(group D) had poor performance on the drawhg, line and shape cancellation tasks.

, l . opogriipliy 01' I-iciiiisliiitinl Ncglcci

Figure 9: Image vs Subtest Scores for LV1 in LHD Group

Category

Cl No Neglec t -2 - 1 O 1 2 3 4 5

Siibtest Scores for the First Latent Variable

, l . opognipliy of I Icriiispoiiiil Ncglcci

Figure 1 0: Image vs Subtest Scores for LV2 in LHD Group

Group E

Group D

Group B

Category

Neglect

No Neglect -1.00 -. 50 0.00 .50 1 .O0 1.50

Subtest Scores for the Second Latent Variable

Finally the patient at (group E) only showed poor performance on line bisection. Thus the

pattern of abnormal subtest performance was line bisection in the upper quadrant, shape

cancellation in the lower right quadrant, line cancellation in the middle to lower part of

the page, and drawing in the middle to lower lefi quadrant.

With regard to the SPECT data in Figure 10 of the second LV, the top half of the

page contained patients with primarily anterior hypoperfbsion including the fiontal cortex

and basal ganglia, relative to the normal control data. The bottom half of the page

inc luded patients with primarily posterior damage.

Similady, examination of Figure 1 1 revealed distinct image and subtest patterns

for the third LV. Starting at the top right quadrant, the three patients (group A), one with

neglect and nvo without, had hyperperhion in both the right and left hemispheres,

compared to normal controls. The patient with neglect had abnormal performance on the

line bisection and the shape cancellation tasks. In a clockwise fashion, the patients (group

B) at the rightrnost side of the middle part of the page had poor performance on drawings

and line bisection, although their SPECT ratios were in the normal range. At the bottom

lefi of the page, the patient (group C) was identified as having poor performance on the

shape cancellation task and had decreased flow in their right occipital region. The next

patients above (group D) also had poor performance on the shape cancellation task and

had decreased perfiision in the occipital and parietal regions. Finally, the patient above

(group E) had poor performance on the shape cancellation and line bisection. This patient

had perfusion ratios on the left side within the normal range, but increased ratios in al1

regions on the right side. Thus, the pattern of abnormal subtest performance was line

bisection in the middle to upper quadrant, shape cancellation in the left side of the page,

and drawing in the lower right quadrant. The image pattern that seemed to ernerge was

hyperperfbsion in the upper quadrants, and hypoperfbsion in the lower quadrants.

In summav* a number of distinct patterns were seen in the three graphs above.

Figure 9 revealed a pattern of neglect per$omnce severiiy. Figure IO illustrated hvo

relationships berween imaging ratios and peflorrnance on the neglect subtests; one

between anterior hypope~i ion on SPECT and poorer performance on zhe Iine bisection

task, and the other between posterior hypoperjion and poorer performance on the

shape cancellation fask. Figure 1 II revealed a relationship of posterior ipsilateral

hypoperjusion with the drawing task. and an association of righf hemisphere

hyperpe&sion and the line bisection and shape cancellation tasks. Thus, examination of

the scatterplots of the image scores and subtest scores for each latent variable enabled

distinct subtest relationships to be idenrifed within the SN& despite the fact that the

subtesrs are highly correlated.

4.9.2. PLSFindings in the RHD Group

There were also four singular values computed for the RHD patients, but only the

first singular value emerged as significant. The fint singular value was 4.334 and

accounted for 95% of the SSCBC. Multiple linear regression of the subtests on the

associated latent variable for the imaging data was significant (p=0.0188) with a mode1

that accounted for 47% of the variance explained by the subtests. It is interesting to note

that compared with the LHD results, in which each of the three latent variables were

found to be significant, only one latent variable emerged in the RKD analysis.

The top saliences, which were al1 negatively correlateci with the first singular

value, c m be seen in Table 11 (Appendix H.4 contaias a complete listing). A singular

image was produced and can be seen in Figure 12. The most saiient regions included the

right lateral occipital, nght idenor and supenor parietal, and right parietal-temporal (see

Appendix H.8 for Scree Plot).

The scatterplot of the image and subtest scores for the first latent variable in the

RHD group can be seen in Figure 13. Similar to the LHD plots, a circula pattern around

the perimeter of the graph was seen with abnormal performance on the subtests. The

upper right quadrant contained patients (group A) with severe neglect who had poor

performance on al1 subtests. The bottom middle part of the page (group B) contained

patients with poor performance on the line bisection and shape cancellation tasks. Some

of these patients also had abnormal drawing or line cancellation scores. The patient (dark

circle) at the bottom left of the page (group C) had normal SPECT ratios, relative to the

normal controls, and an abnormal line bisection (score=6, mild neglect). The upper lefi

quadrant contained the majonty of patients who did not show abnomal performance on

the battery (group D). In sumrnary. Figure 13 revealed a relatioruhip of severity of

neglect peflormance and showed the heterogeneity of patient graups, even in severe

patients with respect to perjonnance on the subtests.

4.9.3. Summav

Partial L e m Squares was used with the SPECT data to explore the relationrhip

between counr ratios in the cortical rim and other regional segments and peflonnance on

the SM. The goal war to assess whether regions postulated to be pan of the network for

Figure 12: Singular Image for the First Latent Variable in the RHD Group Note: Rcgions wiih high ncgaiivc salicnccs look black, and segments with neat zero salienccs look white, with thc rcmaining segments in shades of gray.

Topograpliy of t-lciiiispiitial Ncglcci

Figure 13: Image vs Subtest Scores for LV1 in RHD Group

Group A

Group C

I I 1 w I w

Category

si No Neglect

Subtest Scores for the First Latent Variable

directeci attention wouid emerge as strong predictors of neglect, and whether any

additional regions not previousfy considered would emerge as well.

In the LHD group, three latent variables were produced that could be rcsed to

explore the brain-behaviour relationships. The first L V ident~ped the le3 superior and

inferior parietal regions and the lep lateral occipital cortex as the most salient regions.

mese regions were involved in predicting 3 of the 4 subtests, drawings. line and shape

cancellarion tash. Line bisection emerged as the most salient subtat in the second latent

variable. which was associated with lefC and right inferior fiontal regions, and the le9

temporal region. m i s second LV also revealed a second relationship behÿeen poor

pe~formance on the drawings with the lefi and right temporal cortex, and the left anterior

cingulate. Finalfy, the third L V revealed a relationship between poor performance on

drawings, line bisection and line cancellation with lower segment ratios in the lefi

anterior cingulate, rïght basal ganglia, nght sensorimotor, righ t inferior parietal. right

thalamus, and le9 in ferior fiontal regions. m e third L V also revealed an association

between shape cancellation and the left anterior cingulate. temporal and inferiorfiontal

regions. In addition, each of the LVs showed distinct subtest peflormance and imaging

ratio patterns, idennfling specl$c subgroups, when the imaging saliences were plotted

against the subtest saliences.

In the RHD group, on& one L V emerged as strongly associating aZl four subtests

wirh decreased jlow in the right Zateral occipital, and parietal and parietotemporal

regions. In addition, specifc subgroups, according to either subtest or imaging patterns,

were identified by examining the plot of irnaging vs. subtest salience for the jirst latent

variable. Table 12 contains a surnmary of the resuZ~fiom the PLS and MLR analyses.

TABLE 12: SUMMARY OF THE RESULTS OBTAINED FROM THE MLR AND PLS ANALYSES

I LHD Patients

MLR

CT & SPECT

CT - Exploratory 1 --- (Left Anterior Cingulate),

(Lefi Frontal [Superior])

SPECT-Hyp. 1 Le f t Parietal [Inferior], (Lefi Thalamus)

CT then SPECT 1 ---

CT-SPECT Forced 1 --- PLS I

SPECT

RHD Patients

Latent Variable 1

Latent Variable 2

Latent Variable 3

(Right Posterior White Matter), (Right Occipital)

Left Superior Parietal (-1, Left Lateral Occipital {-), Left lnferior Parietal (-1

Left Inferior Frontal (-1, Right lnferior Frontal 1-1, Left Temporal {-,+}, Right Temporal {+),

Left Anterior Cingulate {+) , Lefi Anterior Cingulate {-,+) , Left Temporal {-), Right Basal Ganglia (-1, Right Sensorimotor (-1, Right Inferior Parietal (-1, Right Thalamus {- ) ,

Left Inferior Frontal {+), Right Superior Frontal {+}

Right Anterior Cingulate,

Right Parietal [Supramarginal Cyrus), (Thalamus)

(Right Parietal)

Right Parietal (SPECT) , ( K i n g {CT) ), (Thalamus (CT))

(Right Parietal { S P E C ~ ) , (ACing {CT)), (Thalamus (CT))

Right Lateral Occipital (-1, Right Parietotemparal {-), Right Inferior Parietal (-1, Right Superior Parietal {-)

Note: Round IO variables showing a trend toward signi ficance. Square brackets [ ] cornspond Io spccific subdivisions, image

modality or positive or negativt salicnccs in thc PLS section. Arcas in bold rcfcr to significant rcgions in thc MLR (pCO.05) or PLS (according to a Scm Plot).

5. DISCUSSION

5.1. Intetpreîrtion of Resufts

5.1.1. General Overvitw

The structural and fiinctional results h m this study were supportive of an

anatomical network underlying hemispatial negiect and indicate that different neural

components may be important for each hemisphere. Overall, the parietal cortex emerged

as the brain region most correlated with hemispatial neglect in both hemispheres. Data

fiom the LHD group of patients supported an anatomical network whicti included the left

parietal, antenor cingulate, lateral occipital, temporal, and froatal cortical regions.

Evidence fiom the RHD gr ou^ analyses were supportive of an anatomical network

including the right parietal, anterior cingulate, lateral occipital, and parietotemporal

cortical regions.

In the LHD group, evidence fiom the hypothesis-driven MLR analyses of the

SPECT functional irnaging data demonstrated that the left parietal region was a predictor

of neglect, as measured by performance on the Sunnybrook Neglect Battery. Further

support came from the PLS analysis of the SPECT data which also revealed additional

distinct relationships. A strong relationship emerged between decreased perfusion in the

left inferior and supenor parietal, and lateral occipital regions and greater neglect as

indexed by an increased SNB score on 3 of 4 subtests. A relationship was also shown

between decrease in the lefi inferior frontal and temporal regions with higher scores on

the line bisection task. The drawing task was shown to be more highly associated with

decreased perfusion in the anterior cingulate and lefi and right temporal regions.

Simfiarly, the shape canceHation task was shown to be more associated with decreased

perfusion in the left antenor chgulate and inferinr fiontal, and right superior fiontal

regions. Results fiom the PLS anaIysis also revealed an unanticipated bilateral hding in

the LHD, which was in support of the theory of right hemisphere dominance for attention.

In the second and third latent variables, regions in both the Lefi and right hemisphere

emerged as negatively associated with performance on the subtests of the SNB. For

example, in the third LV, the left and right infenor fiontal cortices were negatively

associated with higher scores on the shape cancellation task.

in the RHD group, greater structural damage on CT in the nght parietal and

antenor cingulate cortical regions comelated with poorer performance on the SNB.

Damage to the posterior white matter fibre bundles, specifically, the FLi, posterior FLS

and FOF, also correlated with negkct. A combination of structural and fimctional data

from the same patients revealed that only decreased perfùsion in the parietal lobe on

SPECT was a significant predictor of SNB score, when combined with the CT data in a

MLR analysis. Further support came fiom the PLS analysis of the SPECT data which

revealed a strong relationship between decreased perfusion in the right infenor and

superior parietal, lateral occipital, and parietotemporal cortical regions and increased

score on the four subtests of the SNB.

Because different relationships emerged for the right and lefi hemisphere-

damaged populations, they will be discussed separately and then compareci. The

matornical network for directed attention (Mesulam, 198 1 ; Mesulam, 1 990; Heilman,

Watson, & Valenstein, 1993), as described in more detail in the introduction, is composed

of three cortical and two subcorticd regions. The frontal, parietal, and antenor chp la te

cortices have reciprocal connections with each other as well as with the basal ganglia and

thalamus. Damage to either the cortical nodes of the network or the subcortical

comections between them has k e n postulated to be the neural substrate of hemispatial

neglect. The results of this snidy provide support for an important role for two of the

cortical regions but none of the subcortical regions in the theoretical network emerged as

significant.

Hemispatial neglect is a complex disorder, which can have many expressions,

such as, sensory, motor, personal ancVor extrapersonal neglect (Heilman, Watson, &

Valenstein, t 994; Halligan & Marshall, 199 1 ; Halligan & Marshall, 1992; Binder,

Marshall, Lazar, Benjamin, & Mohr, 1992). While the battery used in this study mainly

assessed visuoconstnictive extrapersonal hemispatial negiect, each patient may aIso have

had other types of neglect in combination. Al1 of the regioas in the theoretical network for

directed attention may be important for al1 subtypes of neglect, although in differing

degrees. For instance, the frontal lobe rnay be more important in motor neglect while the

panetal lobe may be more instrumental in sensory neglect.

To assess each subcomponent in isolation is problematic. For example, it is

difficult to assess the motor aspect of neglect without having the patient react to a

stimulus involving a sensory prwess. Lack of spontaneous movement in the

contralesional side of space has been amibuted to motor neglect. However, it is often

untestable due to concomitant weakness. Mirrors (Tegner & Levander, 199 1) and videos

(Coslett, Bowers, Finpatrick, Haws, & Heilman, 1990) have been used to decouple input

and output modalities to examine sensory and motor components separately, but these

techniques require special apparatus and are not suitable for testing at the bedside, which

was the original intention of this study. Although not the focus of this study, assessrnent

of sensory extinction to bilateral stimulation was obtained in most of the patients in this

series. The results, which h2ve not yet been analyzed rnay be able to address the

incidence of sensory neglect in association with extrapersonal neglect- In addition, other

measures of neurological function such as hemiparesis (Adam's Hemispheric Stroke

Scale) and hypoarousal (computer reaction time testing) were also obtained in a majority

of these patients for future analyses. Moreover, isolated cases of the specific subtypes,

such as personal neglect, without extrapersonal neglect are rare and when present

generally are transient (Guariglia & Antonucci, 1992). Since al1 the different subtypes of

neglect were not assessed in al1 patients in this study and some could have k e n

overlooked, this may explain some of the inconsistencies found across patients, but it is

doubtful that this has seriously affected the siatistical inferencing.

The results from this study generally came fiom three separate analyses involving

the CT data and the SPECT data in the MLR and PLS analyses. One major attribute of

the study was that most patients had information pertaining to both structural and

functional measures of damage from their CT and SPECT scans, respectively. CT

imaging provided information on the direct physicai damage to the brain in critical

regions and the white matter fibre bundles connecting brain regions. The latter is not seen

well in SPECT images because of the relatively low blood flow and decreased tracer

iiptake typically seen in white matter. Blood fiow rates in the grey rnatter have been

estimated to be in the range of 75 mi/midlûûgraxn of brain whereas in the white matter

the average blood flow is 30 mi/min/lOOgram, which is close to the rate that is seen in

ischemic brain, i.e., below 20 ml/min/lOOgrarn (Amersharn International Place, 1987).

SPECT imaging enabled an assessrnent of the remote effects of direct damage on brain

fùnction. The ability to measure regional fuactional deficits in the absence of direct

damage facilitated the testing of the fhctioning of the theoretical anatomical network.

The results obtained from both imaging modalities provided complementary information

which could be exploited to explore brain-behaviour relationships in a new and

innovative manner. For example, in the CT-SPECT MLR analysis, a combination of the

data fiom both imaging modalities in the sarne regions made it possible to see whether

any additional information could be used in predictïng performance on the SNB. MLR

anal ysis was important because it allowed the neuroanatomical hypothesis of directed

attention to be scrutinized, by testing the prediction of a dependent variable (scores on the

SNB) based upon a set of independent variables (brain regions). PLS was used as an

exploratory approach, which enabled examination of many regions simultaneously,

without compromise to statistical significance. As a reminder, regions were considered

signifiant if they entered the MLR analyses at the p<.OS level and saliences from the

PLS output were considered to be important if they resided in the top crest of a Scree

Plot. If converging evidence for a region was found fiom al1 three analyses, i.e., from

MLR with CT, MLR with SPECT, and PLS with SPECT, it was taken as a stmng

indication for involvement of that region. If only one analysis found the region of interest

to emerge as significant, the result was still considered to be important, but less robust.

5.1.2. Cornparison of the MLR and PLS Approaches

Results fiom both the MLR and the PLS analyses overall produced converging

evidence, although each technique provided unique contributions. One reason was that

most of the MLR analyses involved structural data fiom CT whereas the PLS analysis

was most appropriate for the SPECT data. Another reason may be that the MLR analyses

compared larger SPECT regions (groupeci segments corresponding to a lobar region), due

to the need for data reduction, in an attempt to predict a single neglect score. The PLS

analysis, on the other hand, examined the individual segments and found relationships

between al1 four neglect subtests and not a single neglect score. A more direct cornparison

would have been to compare PLS against a canonical correlation analysis (Tabachnick &

Fidell, 1989). This multivariate approach correlates one set of independent variables with

another set of dependent variables, as opposed to MLR which only allows a single

dependent variable. However, this approach has similar assumptions to MLR and would

likewise suffer fiom poor power due to the relatively small sarnple size and

multicol linearity.

A major limitation for the MLR analysis of SPECT data was the problem of

multicollineanty. MLR analysis with many, often collinear, variables and few subjects

relative to independent variables, as in this study, bas little power for an exploratory

approach. Hence, it was primarily used in hypothesis-driven equation models. On the

other hand, the PLS analysis was able to take advantage of the redundancy of the SPECT

data, and used it to extract latent variables, which revealed relationships between

perfusion ratios and performance on the four subtests of the SNB. PLS may also be a

more sensitive technique in determining îhe influence of variables when there are many

independent variables of interest. Another approach called path analysis (McIntosh &

Gonzalez-Lima, 1993), also known as structural equation modeling, can use a priori

knowledge of relationships between regions to build a model and test the relationships

between regions, although it generally requires a large subject population and can be

hampered by the sarne problems as in MLR (e-g., multicollinearity). Analysis of the

fünctional data could have also been performed using an artificial neural network (ANN).

A supe~so ry ANN could have been trained to distinguish between patients with and

without neglect, based on irnaging chia. One advantage of ANNs, similar to path anstlysis,

is that it is possible to build a model based on a priori assumptions. However, ANNs are

also limited by small sample sizes and the resultant output (Le., weights) c m be difficult

to interpret. PLS analysis is primarily exploratory in nature and is not as dùectly useful in

testing a priori hypotheses. For these reasons, a combination of approaches was useful in

ascertaining the influence of different regions on hemispatial neglect. Regions that

surfaced in both analyses can be regardai as reliable predictor variables.

In the lefi hemisphere, it is unknown whether the anatornical network for directed

attention can be used to explain hemispatiai neglect. There are far fewer studies that have

examined hemispatial neglect arising from damage to the left rather than the right

hemisphere (Ogden, 1985; Vallar, 1993; Cappa, Perani, Sressi, Paulesu, Franceschi, &

Fazio, 1993). This is likely due to the fact that the neglect is less m u e n t with LHD and

is generally milder and may go unnoticed unless systematically assessed. in fact, many

earlier studies assumed that language difficulties, which are common following LHD,

made the patients unassessable. Of the studies that commented on right-sided neglect

following LHD, the regions thought to be important were mainiy the parietal and frontal

cortex. Ogden reported that hemispatial neglect more fkquently followed anterior lesions

in the lefi hemisphere, in contrast to the greater fiequency of postenor lesions seen in

RHD patients (Ogden, 1985; Ogden, 1987). This is the first study to obtain both structural

and functional infornation in a large consecutive population of LHD that enabled

complex statistical hypothesis testing. For these reasons it was thought tbat this snidy

could illuminate the neural components underlying LHD patients with hemispatial

neglect.

Examination of the structural damage in the group of patients with and without

neglect revealed that al1 LHD patients with neglect had damage that either included one

of the five theoretical regions involved in the neuroanatomical network for directed

attention or a white matter fibre bundle connectiag these regions, although almoa al1

patients without neglect also had darnage to these regions. Cornparison to the LHD group

of patients without neglect indicated that the neglect group sustained more extensive

damage overall, which is consistent with the literature (Ogden, 1987). Although 39% of

the LHD patients with neglect more often had lesions that included two or more of the

predicted anatomical network regions, ihis did not differ significantly fiom the group

without neglect (29%). Therefore, the results fiom this analysis did not provide strong

Topography of HcmispatiaI Ncgkct

evidence for part A of the hypothesis regarding the anatomical network for directed

attention.

By contrast, on examination of tùnctional damage in the LHD group, the lefi

parietal region emerged as the most reliable and significant region in predicting acute

hemispatial neglect in both the SPECT MLR and PLS analyses, although not as strikingly

as with the RHD groups (Le., it did not enter any of the CT analyses). Although the group

of patients with neglect tended to have more structural damage to their parietal Iobe than

the group without neglect (10.9% of slice in the parietal lobe vs. 2.3%), the number of

patients with parietal damage was small and did not differ between the groups (n=l1/38

with neglect, n=I 1/45 without neglect). For these rasons, it was not surprishg that the

parietal region did not enter into any regression equations involving structural data. On

the other hand, both the regression analyses and the PLS results showed that there was

greater hypoperfusion in the lefi parietal lobe in the patients with neglect than in the

group without neglect. Specifically, the left infenor parietal lobe emerged as the most

significant subregion in MLR analyses. In the PLS analysis, both the inferior and supenor

parietal regions had high saliences (-0.1 3 79, -0.1 1 68, respective1 y) which were associated

with 3 of 4 subtests (drawings, line and shape conçellation tasks) of the SNB. The

findings from the SPECT analyses, therefore, are supportive of a role for the left parietal

lobe in nght hemispatial negiect.

An unanticipated fhding was the dissociation between brain regions and subtests

of the SNB suggestive of a qualitative difference in the LHD patients. Line bisection

emerged strongly in the second latent variable, associated with decrease in the left and

right infenor frontal and lefi temporal regions, but it did not emerge in the first latent

variable. This dissociation may reflect the different information proçessing requirements

of this particular task. Line bisection requins cognitive estimation as well as perceptual

processing. Cognitive estimation has k e n reported to be associated with the fiontal lobe

(Shallice, 1988). Thus, it rnay be that abnormal performance on the line bisection task is

compounded by cognitive estimation problems due to a dysfunctional fiontal lobe. The

drawing task also emerged separately in the second latent variable, associated with

positive saliences in the left and nght idenor frontal regions, perhaps reflecting the role

of the frontal regions in planning and execution of visuoconstructive tasks (Sniss, Eskes,

& Foster, 1994).

Decrease in the lefi and nght frontal regions, and anterior cingulate was associated

with poor performance on the shape cancellation task in the third latent variable of the

PLS analysis of the SPECT data. This task requires a visual search of the feature array to

locate the target of interest, which involves orienting, working memoiy, and a search

strategy, al1 of which are thought to involve frontal lobe executive functions (Stuss,

Eskes, & Foster, 1994). Further, the shape cancellation task was the most tirne consuming

and demanding subtest for our patients. Motivation was needed to complete the task.

Since the anterior cingulate is presumed to be involved in motivational aspects of any

task (Mesulam, 1981), this could explain the finding that decreased flow in the lefi

anterior cingulate was associated (0.2697) with poorer performance on the shape

cancellation task (subtest salience of third LV -0.6799). Further, the left antenor cingulate

was found to be damaged in more patients in the neglect group (15% compared to 4%).

Although the fiequency of damage to lefi anterior cingulate was low, its occurrence was

important and therefore entered the MLR analysis of the CT data. Thus, the data from this

study support a role for the iefi frontal and antenor cingulate cortical regions in the

theoretical neuroanatomical network for directed attention in association with right

hemispatial neglect.

Other regions that entered either the MLR or PLS analyses in LHD patients

included the lateral occipital and temporal regions. The lateral occipital region ernerged

as a significant region in the PLS analysis. Although it was not expected to emerge as a

significant region, according to the theoretical mode1 for directed attention, this fhding

was not surprising given that the lateral occipital cortex is part of the visual association

cortex and borders on the temporoparietooccipital (TPO) junction, a region considered to

be important in neglect (Cntchley, 1966; Vallar & Perani, 1986). In the PLS analysis, the

lefi lateral occipital region emerged with a high negative salience (-0.1292) and thus

decreased blood perfùsion, and was associated with poorer scores on the drawings, line

and shape cancellation tasks of the SNB. Thus, the fiadines of this study suggest a role

for the laterai occipital and temporal regions in LHD patients with ngbt hemispatial

neglect.

No evidence was found to support a role for the left thalamus and basal ganglia in

reference to the neuroanatomical network underlying hemispatial neglect. The thalamus

was one of the regions expected to be a significant predictor of hemispatial neglect,

according to the theoretical network for directed attention. It was stnicturally damaged to

a greater extent in the group of patients without neglect (15% of slice in the thalamus in

3 1% of patients) as compared to the group of patients with neglect (10.4% in 18% of

patients). Thus, it was not surprishg that it did not enter into any of the regression

equations. Although the thalamus did not enter the MLR analyses, it showed a trend for

significance w0.07 1) in the analyses with SPECT regions. Patients with neglect had less

thalamic activity, as measured by Tc-HMPAO uptake, compared to the patients without

neglect (0.845 vs. 0.87 1 mean ratios), although this finding did not achieve statistical

significance. Sirnilarly, although patients with neglect had more decreased perfusion in

their basal ganglia (0.901 vs. 0.934 mean ratios) compared to patients without neglect,

this finding was likewise not significant Although the basal ganglia and thalamus are pan

of the neuroanatornical mode1 for directed attention used to explain hemispatial neglect,

functional and structural results from this study did not produce supportive evidence.

In sumrnary, this is theJrst study to do a detailed in-depth examination of the

ropography of nght hemispatial neglect in a large population of LffD patients. Of the few

previously published large group studies (Ogden, 198 7; Hecaen, l962), the etiology of

damage in the populations examined was rnixed, comprising tumors. as well as strokes.

One large study was conducted in the pre-CT era (Hecaen, 1962). Localization was

iirnited to either lobar descriptions (Le., anterior/posterior lesion) from CT (Ogden) or

pst-rnortern examinution (Hecaen). This is the first study to provide both structural and

funcfional data from the same population of s ~ o k e pcltients, and to correlate behavioural

measures of neglect with both types of imaging data. Since this study was comprised of a

large consecutive patient population, we were able to test the theoretical network for

direcfed attention to see whether the same predicted regions would emerge in Our

analysis, which utilized statistical techniques such as MLR and PLS. The regions in the

left hemisphere which ernerged in accordance with the theoretical network for directed

attention were the parietal, anterior cinguhte, and inferior fiontal cortical regions, but

nul the basal ganglia or thalamus. In addition, the Iateral occipital and temporal cortical

regions were also shown to be predictive.

Examination of the CT evidence for structural damage in the group of patients

with neglect revealed that al1 RHD patients with neglect (89189) had damage that either

included one of the five theoretical regions involved in the neuroanatomical network for

directed attention or a white matter fibre bundle comecting these regions. However,

alrnost al1 patients without negiect (39141) had involvement of at least one of these

regions as well. Although the white matter fibre bundles have been implicated previously

in relation to hemispatial neglect (Mesulam, 1930; Heilman, Watson, & Valenstein, 1994;

Vallar & Perani, 1986), previous studies have either based these frndings on small sample

sizes, or on global CT lesion overlays that did not detail the specific white matter fibre

bundles involved. This is the first study which provided actual empirical evidence that

damage to them is associated with neglect in a large consecutive senes of patients. In

cornparison to patients without neglect, it was also f o n d that patients with neglect

sustained a larger volume of damage overall, which is in confonnity with the literature

(Kertesz & Dobrowolski, 198 1; Hier, Mondlock, & Caplan, 1983; Vallar & Perani,

1986). Further, 55 of 89 (62%) patients with neglect had lesions involving two or more of

the predicted attention network regions, compared to only 12 of 41 (29%) patients in the

group without neglect. Since patients with and without neglect had damage to at least one

predicted region in approximately equal percentages in both groups, the results cannot be

used as support for the predicted neuroanatomical network for directed attention in the

right hemisphere as inferred fkom left hemispatial neglect and thus is not in agreement

with part A of the hypothesis. On the other hand, the correlation of increased lesion size

with neglect is consistent with the literatwe (Levine, Warach, Benowi tz, & Calvanio,

1 986; Vallar, 1 993). However, whether neglect occurs due to larger volume of damage to

the right hemisphere, or as a result of damage to multiple regions in an underiying

network subserving directed attention cannot be inferred fiom these results. In order to

further understand the neuropathology of hemispatial neglect, evidence fiom the MLR

and PLS analyses was deployed.

From the imaging &ta analyses in the RHD groups, the right parietal area

emerged as the most reliable significant region in predicting hernispatial neglect. In al1

analyses, including the CT and SPECT MLR and PLS analyses, the right parietal cortex

surfaced as a region significantly related to performance on the SNB battery. This

conforms with expectations from the clinical literature. The parietal cortex has been

associated wi th hemispatial neglect since the earliest clinicopathological correlations

(Brain, 1941; Cntchley, 1966) and fonns a key part of the theoretical network for directed

attention. The infenor parietal lobe, specifically, the supramarginal gyrus, which is

sirnilar to dorsolateral PG in the macaque monkey described by Mesulam (1981),

emerged as the more influentid subregion. The parietal lobe in patients with neglect had

significantly more structural damage on CT (9.6% of slices in the parietal lobe in 42% of

Topography of Hmispatial Ncgiect

patients) and lower blood flow on SPECT (0.616 mean ratios) as compared to patients

without neglect (1.2% in 7% of patients and 0.651). However, the SPECT ratio differed

only with a p<O.OS, and this lost its significançe once a correction for multiple

cornparisons was made. In MLR analysis of the CT data, the supramarginal gyms

emerged as the most significant subregion. interestingly, no patients had damage on CT to

this region in the group without neglect and 12 of 89 (13%) patients with neglect had

structural damage to this region. in the PLS analysis of the SPECT data, the inferior

parietal region had a negative salience (-0.1522), that is, lower b l d flow, and the

superior parietal region had a slightly smaller negative salience (-0.15 13) associated with

a higher neglect score (since the latent variable for the neglect subtests were al1 positively

correlated). The results fiom both analyses strongly support the involvement of the nght

parietal lobe, specifically the inferior parietal, in hemispatial neglect. Although the

parietal lobe has been recognized as an important neural cornponent associated with

neglect for almost a century, single-case reports and the theoretical network proposeci in

the eighties (Mesulam, 198 1 ; Mesulam, 1990; Heilman, Watson, & Valenstein, 1993;

Heilman, Watson, & Vaienstein, 1994) indicated that darnage to other areas could also

produce negiect, and may have consequently de-emphasized the hierarchical nature of

this network. This reaffms the primacy of parietal damage and is convergent with the

group study of Vallar and Perani (1 986). The current study is the largest consecutive

group ever studied and the first group study to analyze both structural (CT) and functional

(SPECT) imaging in association with hemispatial neglect.

Other regions that emerged inc luded the right antenor cingulate, lateral occipital,

and parietotemporal regions. The anterior cingulate was expected to be involved

according to the theoretical corticaVsubcortical network for directed attention. The nght

anterior cingulate is thought to add the limbic system component to the network for

directed attention by ataibuting a motivational value to incoming stimuli. Despite the fact

that it was damaged in approximately qual percentages of patients (15% vs- 10% in the

group with and without neglect, respectively), this region entered in the CT MLR analysis

as a significant predictor of SNB score. Thus, the positive CT MLR results support the

involvement of the anterior cingulate in hemispatial neglect. Aithough this region did not

emerge in any of the SPECT analyses, this fact may have more to do with the resolution

of SPECT and the way in which we tried to quanti@ hypoactivity in that region. (See next

section on limitations for more details.)

The lateral occipital cortex and the parietotemporal region also emerged as highly

predictive regions. Although not expected by the anatomical mode1 for directed attention,

this finding was not surprishg since these regions border on the temporal-parietal-

occipital (TPO) junction, a region previously found to be important in neglect (Critchley,

1966; VaIlar & Perani, 1986). Vallar (1993) States that "the more fiequent cortical

correlate in humans is a retro-rolandic lesion involving the temporo-parieto-occipital

junction." The fibre bundles deep to the TPO junction are also at a cnticai point

connecting the lobar regions, locaiiy (Pandya & Yeterian, 1990) and anterior-posteriorly

(Seltzer & Pandya, 1984). Damage to this area has been shown to affect both nearby

areas, such as the parietal lobe, and distant areas, such as the fiontal lobe. In humans, the

TPO junction has connections with the visual, tactile and auditory unimodal sensory

association areas and is considered to be a polymodal sensory region. This is consistent

with the results from our study of multimodal sensory extinction in the same patient

population (Ebert, Black, & Lee, 1996). The fact that significantly more patients with

neglect had damage to the Deep-TPO region in our study compared to patients without

neglect (39% vs. 20% of patients, ~ 0 . 0 5 ) provides empirical support for the important

role of the temporoparietooccipital region in directed attention.

in the PLS analysis, the right lateral occipital region had the highest negative

salience (-0.1579), and the parietotemporal region also had a high salience (-0.1526) in

the first latent variable, which were both associated with higher scores on al1 four subtests

of the Sm. Thus our data are in support of a role for these regions in impaired

functioning of the attention network as manifested in hernispatial neglect. A M e r

reason that the nght lateral occipital region may have emerged was that the subtests of the

SNB used in this smdy were visuospatial in nature and might be expected to correlate

with damage in the visual association cortices.

The three regions in the RHD group that did not emerge as significantly impaired

in neglect as predicted in the mode1 for directed attention were the thalamus, the basal

ganglia and the frontal region. Although the right thalamus on CT was not found to be a

significant predictor of neglect in the MLR analyses, it showed a trend toward

significance @=0.087) in combination with the anterior cingulate and the parietal regions.

In the MLR analyses with both imaging rnodalities, the nght thalamus also showed a

trend toward significance. Smicturally, the thalamus was darnaged in more patients with

neglect (36%) compared to those without neglect (22%), but this showed oniy a trend

toward signi ficance (p=0.09). The resul ts revealed weak and unreliable correlations wi th

the SNB score and did not therefore confirm the predicted role of the thalamus in neglect.

The thalamus is a major relay site for both incoming sensory information fiom the

periphery and feedback loops fkom the cortex, but it also plays a key role in general

arousal. Thalamic connections with the mesencephalic reticular activating system in the

brainstem contribute to the maintenance of overall arousal (Heilrnan, Watson, &

Valenstein, 1993). Robertson et al. (1995) has shown that the ability to maintain arousal

and sustain attention is an important requirement for rehabilitation of patients with

neglect and is a predictor of recovery. Activation of the thalamus is therefore important in

maintaining arousal and decreased thalamic activity could contribute to neglect. Although

the group with neglect had a lower mean ratio (0.750 mean ratio) in the thalamic region

compared to the group without neglect (0.8 1 1 mean ratio), it did not emerge in any of the

SPECT analyses as a strong predictor. This may reflect the fact that SPECT was

performed with a single head carnera and the relatively poor spatial resolution may have

precluded an accurate measure of thalamic perfusion (refer to SPECT limitation in

section 5.2.4). Although the results of this study do not provide evidence for thalamus in

acute neglect, there may be a more important role for the thalamus in recovery from

neglect (Wam, Gini, Tucker, Roeltgen, & Holmes, 1988).

Although the fiontal cortex was found to be damaged to a greater extent on CT in

patients with neglect compared to patients without neglect (45% of patients compared to

22%, p<0.05), it did not surface in the MLR analyses. This is consistent with a previous

group study which showed that lefi hemispatial neglect was associated with right

posterior lesions and not anterior darnage (Vallar, 1993). Our data show4 thar al1

regions, with the exception of the anterior cingulate, that emerged in the MLR and PLS

analyses were pst-rolandic. This is aiso supported by the fact that 90% of patients with

neglect had darnage to posterior brain regions. Although the frontal lobe may have been

expected to show diaschisis on SPECT as a result of posterior damage (Perani, Vallar,

Paulesu, Alberoni, & Fazio, 1993), our data did not support this suggestion in relation to

the topography of hemispatial neglect. In the PLS analysis, the supenor frontal region did

show a negative salience (-0.0983) with the neglect battery, as did the regions above;

however, its salience was well below the designated threshold for distinction, and was not

regarded as supporting evidence.

The right basal ganglia also did not emerge as a significant predictor of neglect

performance on the SNB, as expected fiom the theoretical mode1 for directed attention.

The basal ganglia are involved in the neural programming of movement and have many

reciprocal connections with the fiontai lobe. Given that the frontal lobe regions did not

emerge strongly in any of the analyses, it is perhaps not surprising that the basal ganglia

did not enter as well. It was anticipated to emerge in the SPECT analyses, since it was the

only region in RHD patients to show a statistical significance @<0.002), afier correcting

for multiple comparisons, between the group with (0.793 mean ratio) and without (0.898

mean ratio) neglect. This finding may have been incidental, though, as a result of the fact

that patients with neglect had larger lesions. in the PLS analysis, the nght basal ganglia

on SPECT did show a negative salience (-0.0787) with the neglect latent variable,

although this value was well below the range considered to be significant. Thus, the data

fiom this study do not support a role for the basal ganglia in relation to hemispatial

neglect.

Although the frontal lobe, basal ganglia, and thalamus did not emerge fiom these

analyses, this does not necessarily mean that they are not involved in the anatomical

network for directed attention. The data for this study were derived fiom patients with

bnindamage, whose functional network for directed attention was disrupted. The fact

that a region did not emerge in our analyses cannot be used as evidence that those regions

are not involved in the network for directed attention in a normal functioning brain.

Recent studies by Gitelman et al. (1996) and Nobre et al. (1996) have provided

supporting evidence for the postulated cortical network for directed attention in the

normal bchaving adult human. Using functional MRI (MRI) in normal subjects, they

have s h o w that the frontal, parietal and cingulate cortices were activated in tasks

requiring directed attention and spatial orientation. The results fiom the current study are

based upon lesion localization of hemispatial neglect. Using such lesion data, inferences

may be drawn about regions necessary for the disruption of normal function, Le., the

regions that when darnaged cause abnomal fùnction of this network. The fact that a

region does not emerge in such analysis suggests it may not be critical for this fûnction,

but not that it does not participate in normal performance.

Another reason these regions rnay not have emerged rnay be that their influences

may be more subtle. The fact that these regions show smaller relationships (Le., smaller

saliences) does not mean that those relationships are meaningless. One way in which to

test the stability, and hence reliability, of a salience is to use a bootstrap technique. Using

this approach, by resampling the data and recalculating the saliences. an estimate of the

error associated with each salience can be determined. In this way, saliences which are

found to have small standard errors can be regardeci as reliable and probably reflect

regions with minor but significant influences, whereas those saliences with a large

standard error may be more unreliable. Mesulam (1990) postulated that the more regions

in the theoretical network damageci, the greater the severity the resultant neglect. It rnay

be that damage to the frontal, basal ganglia, and thalamus may not be in and of itself

sufficient to cause neglect but rather the combination of regions damaged may be

important both to the initial occurrence and to the persistence of neglect over time.

In summary, this is the first study to do a detailed in-depth examination of the

topograp hy of le) hemispatial neglect in a large population of consecutive stroke patients

rtirh unilateral lesions and to provide both sntctural and functional imaging cotrelates

of neglect from the same population of stroke patients. Since this study was comprised of

a large patient population. we were able to test the theoretical network for directed

attenriorz to see whether the same predicted regions would emerge in our analysis, which

utilized powerful statistical techniques such as MLR and PLS. The regions in the right

hemisphere rvhich emerged in accordance with the theoretical network for directed

attention were the parietal and anterior cingulate regions, but not the basal ganglia,

rhalamus or frontal regions. In addition, the lateral occipital and parierotemporal

cortical regions were also shown to be predictive.

5.1.5. Comparison of the Right and Lefi Hemisphere Networks

The results of this study supported the concept of a neuroanatomical network for

directed attention in both hemispheres, damage to which correlated with the measures of

hemispatial neglect. In addition, evidence was s h o w that the white matter fibre bundles

comecting these regions were of importance. The specific regions involved in this

network differed for each hemisphere. The inferior and superior parietal, anterior

cingulate, lateral occipital, and the parietotemporal regions were implicated in the rïght

hemisphere. The basal ganglia, thalamus, and frontal regions were not. For the lefi

hemisphere, the inferior and superior parietat, anterior cingulate, lateral occipital, and

inferior frontal regions, but not the basal ganglia and thalamus were irnplicated.

One interesting feature of the PLS analysis in the LHD groups was that the latent

variabIes produced shared the majority of variance across three latent variables, as

compared to the RHD analysis, which mainly loaded o d y on the first latent variable. One

possible reason for this may have to do with right hemisphere dominance for attention

(Weintraub & Mesularn, 1988; Posner & Petersen, 1990). The right hemisphere,

specificaliy the parietal lobe, has been demonstrated to activate in relation to both lefi-

and right-sided stimuli, although more so for contralateral stimuli, whereas the left

hemisphere is activated only by right-sided stimuli (Corbetta, Miezin, Shulman, &

Petersen, 1993; Heilrnan, Schwartz, & Watson, 1978). As discussed earlier, the parietal

lobe emerged as the most reliable and significant predictor of neglect performance, more

strongly in the right compared to the left hemisphere. in the case of RHD, there is little, if

any, compensation from the left hemisphere, so the neglect deficit is more severe, and the

Topopphy of Hemispatial Negiect

strong influence of the right parietal lobe could be discemed. It is Iikely that the parietal

region is a crucial region within the theoretical anatomical network for directed attention,

such that dysfunction in that region causes a more severe deficit. For LHD, on the other

hand, the ability of the right hernisphere to cornpensate, either completely or partially, for

the lefi-sided damage may have been the reason that right hemisphere regions emerged in

relation to neglect. For example, in the second latent variable in the LHD group, the lefi

inferior frontal regions had a high negative saliences (-0.2861), which is expected for

right sided neglect. In addition, the homologous infenor frootal region on the right

hemisphere also had a high negative salience (-0.1886), which is consistent with the

theory of right hemisphere dominance for attention.

Another intriguing finding, as briefly described above, concemed the first latent

variable From the PLS analysis and identified possible qualitative differences of neglect

within each hemisphere. In the RHD group, the four subtests positively correlated in

approximately equal arnounts in deriving the first latent variable. In contrat, ody three

(drawings, shape and line cancellation) of four subtests loaded on to the first latent

variable in the LHD group. Line bisection did not contribute to the first latent variable,

but it was the main contributor in the derivation of the second latent variable in the PLS

analysis. It may be that line bisection task is probing a different subcomponent of neglect

fiom the other tasks, or that a different combination of brain regions are cntical for the

line bisection task. For instance, on the first latent variable the regions on SPECT with

the highest negative saliences were the posterior ones while in the second latent variable

the regions with the highest negative saliences were more anterior, specifically the

inferior fiontal region. The fiontal region was related more to the line bisection task while

the parietal-occipital region came out as more related to the other three tasks. One reason

for this differentiation could be that the line bisection task requires more judgment, Le., it

is a cognitive estimation task, which is known to involve the fiontai lobe, while the other

three subtests may be more influenceci by perceptual discrimination processes primarily,

involving the parietal lobe. in addition, the anterior cingulate and temporal regions had

high positive saliences (0.1 103, 0.1456), on the second latent variable, which were

associated with omissions on the drawing task. The drawing task involves feature

detection in objects, which should require temporal lobe functioning (üngerleider &

Mishkin, 1982). This may be the reason that the drawing task did separate fiom the other

more visuospatial perceptual tasks (Goodale & Milner, 1992).

In summav, structural and functional data from this smdy provided evidence for

distinct anatomical networks underlying hemispatial neglect in the le@ and right

hemisphere. Evidence was put fomard to suggest a qualitative d i f l e n c e nof on&

regarding fie neural components of hemispatial negfect in each hemisphere, but also

t-egarding the measures used to capture the neglect phenornenon. m i s siudy h a provided

evidence from s~?rc~uraf and functional imaging in the same population and has clearly

s h o w distinct differences between the le> and right hemisphere in relation to the

ropography of hemispatial neglec f ,

Topography of Hcmispatial Ncglccf

5.2. Study Limitations and Future Directions

One limitation of this srudy was that the battery used to assess neglect was

composed only of visuoconstructive tasks. Each subtest captures a different component of

the neglect phenomenon, as shown by factor anaiysis. For exarnple, the spontaneous

drawing requires an interna1 representation of that object as well as complex

constmctional praxis skills. Shape and line cancellation tasks are prirnarily target

detection, visual search tasks, requiring sustained attention as well. Line bisection,

mentioned earlier, is a cognitive estimation task, and requires making a judgment about

distance. While many patients were impaired on al1 tasks, some showed dissociations,

even though analysis of the psychometric propenies of the battery revealed that ail four

su btests were al1 required in the factor analysis.

For these reasons it is certainly important to assess neglect on multiple tasks, a

shortcoming of many previous studies of neglect. By using four subtests to assess neglect,

this study was able to identiQ minor degrees of neglect, but as a result the underlying

brain correlations may have been more difficult to deconstruct. Another source of error

with regard to the battery was the fact that not everyone had a complete battery and a

composite partial score was exmpolated which probably underestimated the hue score

because a conservative formula was used. It was not always possible to obtain scores on

al1 subtests of a battery, which would have been preferable for correlation analysis. In this

study, only a smali nurnber of patients required interpolated scores. Aithough the

composite scores were shown to be highly correlated with the actual scores on the SNB,

linear regression analysis could be used to formulate an equation that could be used to

calculate better composite scores.

5.2.2. Testimg Dates

Although testing dates for this study were optimized as best they could, an

additional source of error could arise due to the tirne differences between procedures. For

example, although the difference in time between neglect testing and SPECT scanning

was minimized, not al1 patients could be scanned as desired during the same week that

neglect was tested. Only a minority could be scanned on the same &y. In many other

situations, such as PET activation studies, the subject actually perfonns the task of

interest d u h g scanning. in a traditional lesion study, the behavioural deficits are assessed

O ff-1 ine and correlated with structural andor functional damage documented within a

reasonable tirne interval. Because scanning was fiequently performed for clinical

purposes, this time interval was difficult to control. This is of less concem in the chronic

stable phase after recoveiy from stroke, but is an issue in the acute, evolving stage afier a

suoke.

Hemispatial neglect, as described previously, can recover quickly. Thus to

meaningfûlly correlate the hinctional imaging data with absence of neglect, the battery of

tests must be adrninistered within a few days of scanning, as was done in this study. For

the other situation, Le., severe persisting neglect, it would be possible to be more lenient

about the tirne interval between the behavioural testing date and scanning onset, on the

assumption that neglect was present throughout the interval. However, the greater the

time period between testing procedure and scanning, the greater the likelihood of adding

e m r to a correlation between brain and behaviour. The testing t h e interval used for this

snidy was not always ideal, but was a reasonable tmdesff that allowed for the inclusion

of many additional patients.

S. 2.3. CT Scan Limitations

CT scans were performed for clinical reasons generally within the first 48 hours

post-stroke. Negative scans occurred in a subset of individuals, and as part of the routine

investigation at the tirne of this study, many of these patients were rescanned. in general,

the scan which best captured the full extent of damage was used, if there were more than

one scan fiom which to select. it is known that the lesion on CT probably best represents

necrotic tissue alone, without the effects of edema, if the scan was done a few months

later. Scans a few months afier the stroke could not be done in our patients, however, for

financial, pragmatic, and ethical reasons. Thus, although the most ideal scan

demonstrating the true extent of the CT lesion was not available to us, the use of scans

obtained in the acute stage of stroke provided a reasonable index of the resultant

structural darnage.

Another source of error was that tracing and subsequent lesion localization was

performed on templates fiorn a stereotactic atlas, which required the assumption that scan

orientation was parallel to the orbitomeatal line. However, this was known to be off by a

few degrees in 42% of patients (CT scan tilt was known fiorn scout film). This was taken

into account by the lesion tracer as much as possible, but added some error, although, on

the whole it probably did not affect the results substantially.

Topography of Hemispatial Ncgicct

Finally, the method used to assess the extent of structural damage was not ideal, in

that it was able to capture the sire of the lesion in each region of interest in the verticai

but not horizontal plane. As described in the methods section in Chapter 2, damage in

each CT region was quantified as a percentage of the number of slices in which damage

was evident. While this approach provided more information than simply dichotomizing

into damage present or absent in a region, it would be preferable to estimate the tnie

percentage of damage in the volume occupied by that region in each patient. Some studies

have used a subjective estimate of damage on each slice, e.g., less than 10 percent, 1 1 -

49%, or greater than 50 percent (Ferro, Kertesz, & Black, 1987). The difficulty of this

approach is the subjectivity of such judgments. Generally CT does not show sufficient

anatomical detail to define different regions reliably. Magnetic resonance imaging would

be much more reliable in this context, but it is still not available for routine investigation

of stroke patients in most Canadian centres.

5.2.4. SPECT Scan Resolution

Perhaps a larger problem with this study was the resolution of the imaging

rnodaIity and of the regions-of-interest therefore available to us. This study used a single-

headed gamma camera with an inherent resolution of about 12mm (FWHM). It is known

that structures at the edge of an image will have better resolution than intemal structures,

as a result of the back filtration algorithm used to reconstruct the SPECT images

(Masdeu, Brass, Holman, & Kushner, 1994). Structures such as the basal ganglia and the

thalarnic nuclei, which are difficult to resolve on single-headed SPECT scans under

normal circumstances, may be even more difficult to see if perfusion is reduced. Thus, it

is possible that the counts measured in those regions were significantly higher than the

m e counts, but not necessarily different by the same proportion in al1 patients. Noise is

therefore added to the image, making it more difficult to differentiate between groups.

The fact that the thalamic nuclei and basal ganglia did not emerge significantly in the

analyses in this study may have more to do with poor resolution that with the absence of

differences between the groups. Currently, there are dual and triple head SPECT carneras

that can be used. which have much higher resolution and would improve accuracy

(Devous, 1995).

5.2.5. Regional Measurements

The regions used to measure brain activity also had inherent noise as a result of

being an automated procedure. Ideally, it would have been better to have had MR scans

on each person and then trace the regions of interest with the MR anatomy as a guide.

This, of course, was not possible in this study for a number of reasons. For one, the

financial expenditure would have been enormous for a study of this size. The time

necessary to trace each scan, and the software and hardware needed to store this

information would have made this project much more expensive and complex. More

importantly, even if fhding had been available, it would not have been feasible in many

of Our patients to require them to undergo an MRI in addition to CT, which was the

clinical procedure of choice. Many of the patients were il1 and would not have been able

to undergo the additional procedure.

To reduce observer

was used to capture counts

error and increase

in segrnented brain

tirne efficiency, an automated procedure

regions, afier correcting for brain size by

linear scaling, standardking the angle of orientation, and correcthg for head tilt. An

alternative approach would be to use a template with preset regions-of-interest

comesponding to anatomy, rather than placing ROIs that are not anatornically guided.

This study utilized both techniques in an effort to measure brain activity. For the cortical

regions, an automated circular annulus was used, without specific regard to lobar

divisions. The segmental divisions were then localized anatomically. This allowed the

segments to be combined in an objective fashion prior to analysis. In addition, preset

ROIs were used to capture activity in subcortical as well as cerebellar regions.

To ven@ the anatomical designation of the regions we used a template fkom the

MR-SPECT superposition in a few subjects, where both MR and SPECT were available.

It would have been desirable, if t h e had permitted, to base the template on a larger

sample of ten to fifteen individuals. Even with a larger sarnple though, some e m r would

still be present as a result of the individual differences in brain size and lobar

differentiation between people. No single template could ever be perfectly matched for

every brain. An alternate approach could be to use a nonlinear deformation technique that

would warp each brain into a standardized stereotaxic space (Friston, Frith, Liddle, &

Frackowiak, 199 l), which could be used to reduce intersubject variation.

5.2.6. SPECT Reference Region

Another source of error using the irnaging data was the amount of within group

variation. As a result of the characteristics of SPECT, the counts measwed are not

absolute measures of blood flow, but are only relative to that particulru reference region.

Xenon inhalation techniques (Masdeu, Brass, Holman, & Kushner, 1994) can be used to

provide absolute blood flow measures on SPECT, but with marked Ioss of resolution. To

deal with this issue, SPECT counts fiom each segment were made into ratios by dividing

them by the counts in a region of the brain thought to be the least affected in the majority

of individuals in the group. in this study the cerebellar hemisphere with the higher counts

was used. This was the ipsilesional cerebellum in over 90% of patients, because it is

unaffected by direct or indirect effefts in the majority of hemisphenc stroke patients. The

cerebellum with higher counts was used rather than the ipsilesional cerebellurn as a rule

because it was determined that in association with occipital lesions, fiom postenor

circulation stroke in this study, approximately 3% of patients also had direct darnage to

the ipsilesional cerebellwn.

Although there was no mean difference between the counts in the cerebelli

benveen groups, examination of the intragroup variation found that it was high compared

to the intergroup variation, since high standard deviations were assoçiated with each

SPECT region. Part of the reason for the high intragroup variation stems fiom the fact

that each group was composed of stroke patients with lesions of varying size and location.

Another source of variation may corne fiom the fact that the cerebellum was not an ideal

reference region. A different reference region that was considered was the average counts

in the undarnaged hemisphere, which would have the advantage of comparing

homologous regions with each other. This is not an ideal approach in acute stroke,

however, since transhemisphenc diaschisis is present especially in larger lesions (Dobkin,

Levine, Lagreze, Dulli, Nickles, & Rowe, 1989). To compensate for this, it may be

possible to calculate the mean average in the undamaged hemisphere, remove any pixel

values that were greater than two standard deviations from the mean and then recalculate

the average value. This could result in a less biased and more reliable reference source.

Finding an ideal reference is not an easy task and is one of the inherent limitations of

current SPECT tracer technique.

6. CONCLUSIONS AND FUTURE DIRECTIONS

This study supports the idea that the neural correlates of hemispatial neglect

involve a network of anatomical regions subse~ ing directeci attention including the

frontal, parietal, and anterior cingulate cortices, basal ganglia and thalamus. The regions

actually found in this CT-SPECT study to be correlated with hemispatial neglect were

different in the left and right hemispheres. In LHD patients, the significant regions were

the frontal, parietal, antenor cingulate, Iateral occipital and temporal cortical regions. Ln

RHD patients, the significant regions were the parietal, anterior cingulate, lateral

occipital, and parietotemporal cortical regions. It was M e r determined that the role of

each region may not be equally important. This study reaf fms the primary role of

damage to the parietal lobe in hemispatial neglect, as suggested in the earliest clinical

pathological case reports. The shidy also provided evidence suggestive of a qualitative

difference of the neglect phenomenon in each hemisphere. Further investigation of the

nature of this difference between the hemispheres may provide further understanding of

qualitative differences of neglect and the neural components responsible. The need to

adopt different statistical techniques depending on the nature of the data and the questions

posed has also been illustrated. Both conventional MLR and a new technique PLS were

used to test hypotheses that guided the study design. Finally, this study has shown the

value of complementary structural and functional imaging techniques, such as CT and

SPECT, in conjunction with neuropsychological tests of behaviour in attempting to

elucidate brain-behaviour relationships.

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A P P E m K : TABLE OF CONTENTS

A . SLNNYBROOK NEGLECT BATïERY APPENDIX." ...H...HH........ ................................. -115

A . 1 . DRAWMG AND COPYING OF A DAISY AND A CLOCK ............................................................................. 115 A.2. LWE BISECTION TA% .......................................................................................................................... 116 A.3. LWE CANCELLATIONTASK ................................................................................................................... 117 .4.4. SHAPE C~LVCELLATION TASK ................................................................................................................ 118 A.5. SWYBROOK N E G L E ~ BAITERY SCORMG SHEET ........................................................................... 119 A.6. SUMMARY TABLES FOR SurwYBROOK NEGLECT BATTERY vs . VISUAL SEARCH BOARD ANALYSES .. 120

B . 1 . CT SCAN OF A P A T I ~ T WH HEMISPATIAL NEGLECT ......................................................................... 121 B.?. CT TEMPLATE OF A PAT~EXT m HEMISPATIAL NEGLECT ............................................................. 1 2 2 B.3. BREAKDOWN OF CT REGIONS BY STEREOTACTIC SLICES . QUANT~~ATION APPROACH ........................ 123

C . SPECT APPENDCX ................... ............. .............. ...................................................................... 124

C . 1 . ACUTE SPECT SCAN OF A RHD PATIENT WITH HEMISPATIAL N E G L E ~ ............................................ 124 C.2. SPECT SCAN ANGLE ROTATION .......................................................................................................... 125 C.3. EXAMPLE OF A CORTICAL RIM METHOD ............................................................................................... 126 C.3. EXAMPLE OF THE ROI METHOD ........................................................................................................... 127 C.5. MR-SPECT SUPERPOSITION ................................................................................................................ 128 C.6. BREAKDOW OF SPECT LOCALLZATION APPROACH ................................................. ................... 129

D . CORRELATION MATRICES .................................................................................................. 1 3 2

D . 1 . CT CORRELATION WTRIX . LHD ....................................................................................................... 132 D.2. CT CORRELATION U4TRIX - RHD ...................................................................................................... 133 D.3. SPECT CORRELATION WTRIX - LHD ................................................................................................ 135 D.3. SPECT CORRELATION ~ T W X - RHD ................................................................................................ 136 D.5. CT-SPECT CORRELATION ~ T R I X - LHD .......................................................................................... 137 D.6. CT-SPECT CORRELATION MATRIX - RHD .......................................................................................... 138

E . AXOVA TABLES FOR CT REGRESSION ANALYSES ................................................................. 139

E . 1 . CT ANOVA TABLE . EXPLORATORY METHOD . LHD .................................................................. 1 3 9 E.2. CT ANOVA TABLE - HYPOTHES~S METHOD . LHD ............................................................................ 140 E.3. CT ANOVA TABLE - EXPLORATORY MHOD . RHD ........................................................................ 141 E.3. CT ANOVA TABLE - HYPOTHESIS METHOD - RHD ............................................................................ 142

F . AYOVA TABLES FOR SPECT REGRESSION ANALYSES .......................................... .............. 143

F . 1 . SPECT ANOVA TABLE . HYPOTHESIS METHOD . LHD .................................................................. 1 4 3 F.Z. SPECT ANOVA TABLE . HYPOTHESIS METHOD - RHD .......................................................... , 1 4 4

G . ANOVA TABLES FOR CT-SPECT REGRESSION ANALYSES .................................................. 145

G . I . CT-SPECT ANOVA TABLE . CT FORCED +SPECT . LHD ............................................................... 145 G.2. CT-SPECT ANOVA TABLE - CT-SPECT FORCED - LHD ................................................................. 146 G.3. CT-SPECT ANOVA TABLE - CT FORCED +SPECT - RHD ............................................................... 147 G.4. CT-SPECT ANOVA TABLE - CT&SPECT FORCED . RHD ............................................................... 148

H . COiMPLETE TABLE OF PLS SALXENCES .... ........................... .......................... ........................... 149

H . 1 . THE FIRST LATENT VARJABLE OF THE LHD GROUP ............................................................................ 149 H.Z. THE SECOND LATENT VARIABLE OF THE LHD GROUP ........................................................................ 150 H.3. THE THIRD LATE~T VARIABLE OF THE L m GROUP .......................................................................... 151

cxüi

H.4. THE FIRST LATM VARIABLE OF THE RHD GROUP ............................................................................. 152 H.5. SCREE PLOT FOR IMAGE SALIENCES LHD LV1 .................................................................................... 153 H.6. SCREE PLOT FOR IMAGE SALIENCES LHD LVZ ................................................................................... 153 H.7. SCREE PLOT FOR IMAGE SALIENCES LHD LV3 .................................................................................... 153 H.8. SCREE PLOT FOR IMAGE SALIENCES RHD LVl ...................... ... .................................................... 153

A. 5. Sunicybrook Neglect Battery Scoriig Sheet

f figuru Ornlsslon of any flpure on contralateral slde of DW. &ncdIation l a I

Normal performance:I l omlsslon 1 Scorc:I 1 pl. per omllted figure > I (mer.40 flgs.)

1 I 1 1-

A

A. 6. Surir nt a ry Tablcs For S m iiybrook Neglect Battery vs. Usual Search Board Aiaulyses

Scnsi tivi ?y, Specificity, Posil ive Predictive Value, Ncgativc Predictive Valuc, Prcvalcnce

1 Test vs. VSB Sensi t ivi ty Specifici ty PPV NPV Prevalence - Total Total Total Total Total

Neglect Battery

n= 105 lcAs & 138 ri hts 8 1 Line Bisection

n=110 lcfis& 141 ri his 1 ( Line Cancellation

n = I l l IcfSs& 142 ri hts + n=i05 Icfts & 138 ri hts L

B. CT Appendix

B. 1 CT Scan of a Patient with Hemispatial Neglect

Page 121

Topography of Hemispatial Ncglm

B.2. CT Tempiate of a Patient with HemLspotiai Negiect

Page 122

Topography of Hemispatial Ncglcct

B.3. Breakdown of CT Regions by Stereotactic Sfices - Quantitation Approach

I2 Talairach slices to be used:

Page 123

1

2 3 4 5 6 7

Region (N-25) Anterior Cingulate 115

.Medial Frontal 11 1 Inferior Frontal I9 middle Frontal 111

Superior Frontal /I2 Inferior Parietal 14 Superior Parietal I2

In& Ci-Ant GC-Ant

GFd GFi

GFm GFs LPi LPs GTi GTm GTs GOi GOm Gus GL GF

GPrC GPoC NL NC Th Ro FLi

FLS-Ant FLS-Post FOF-Ant FOF -Post

IC-Ant

2

i

8 9 10 1 I 12

13

14 15 16 17 18 19 20 21 22 23 24 25

inferior Temporal 14 -Middle Temporal I8

Superior Temporal I7 Inferior Occipital I3 Lateral Occipital I6

- -

-Medial Occipital 15

Primary Motor Strip I8 Primary Sensory Strip I7

Lenticular Nucleus I4 Caudate Nucleus I6

Thalamus I3 Optic Radiations /4

Fli /4 Anterior FLS I3 Posterior FLS I3 Anterior FOF 16 Posterior FOF I3

Anterior Internal Cap I3

Y

Y

3 5

Y Y Y

Y Y Y Y

Y Y Y Y

I.

y

Y -

8-9.

Y Y

Y Y

4

Y Y

Y -

y

Y

Y Y Y Y Y

Y Y Y Y Y

6

Y Y

Y Y

Y Y Y

Y Y Y

Y Y Y

Y Y Y

Y

Y Y Y

Y Y

Y

Y Y

98

Y Y Y

Y Y Y

Y Y Y

Y Y Y

Y

y

6-78

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y Y Y

Y

9-10

4 , 8

1

7 3 5 1

3

Y

Y

Y

y

Y

Y Y

Y

6-7b

Y

Y

Y

Y

Y

Y

Y

Y

Y

7-8b

Y

Y

Y

Y

Y

Y

Y

10-11.

Y

Y

y

y

Y Y

Y Y

Y Y

Y

Y

#

7 8 11 9 11

3 4 3 , 3

I

6 3 3

Y 2 8 7 4 6 3

Y

Y

Y

Y

Y

Y

I 2 4 2

1 Y

Y

Y

Y

Y

Y

Y Y

Y Y

Y

Y Y

Y

Y

Y

Y

Y

Y

Y

Y

C. SPECT Appendix

C. 1. Acute SPECT Scan o f a RHD Patient with Hemispatial Neglect

2. SPECT Scarj Arrgk Rotation

Transverse Tilt Correction Coronal Tilt Correction

Sagittal Linc Parallcl to AC-PC Line

Page 125

0.2. CT Correlation Matrh - M D

Correlation, 1-tailed Sig:

P i 3 LOG

AGE

VCLUME

ANTCIIIG

MJTWM

BG

c m

DEEPTPO

FRONTAL

MOTOR

OC: P ITAL

PAR 1 ETAL

POSTirlM

SENSORY

T E M P O U L

T H A W Ç

NB LOG

1.000

.203

.O13

- 3 3 1 . O00

. O2 3 -402

-219 .O08

-218 . O08

-259 . O02 -217 . O09

-128 .O81

-230 . O06

- 2 8 3 . O01 -299 .O00

- 3 74 . O00

.261

.O02

- 3 16 . O00

- 2 3 6 . O05

AGE VOLUME ANTCING

Page 126

Topography of Hcmispatiai Neglect

0 .2 CT Correlation Matrk - RHD - c0n.t

. * m .

FIS LOG

AGE

VOLUME

ANTC 1 NG

ANTwM

BG

CR;cD

DEEPT W

FROIJTAL

MOTO R

OCIPITAL

PARI ETAL

POSTWM

SENSORY

TEMPORAL

THMAMUS

M U L T I P L E R E G R E S S I O N * + * *

FRONTAL

. i28

. O8 1 - -128 . O82 .560 - O00

.727

. O00

.67 3

. O00 -186 .O21

-348 . O00

- .O95 -151

1.000

.573

. O00

- -164 . O36 -181 .O24

.O87 -172

- 4 04 . O00 -120 . O95

- . G70 -225

MOTOR OCIPITAL PARIETAL

Page 127

C. 6. Breakdown of SPECT Localization Approach

Segment Area Region ' Are8 1 Region Area 'Brodmana, Region Region Side SI-0 SFG. MFG F SFG F GFs 8 F A c h e R i ~ h t s 1-1 ReCG M Pre&PostCG : SM ZFm.GRC.GPâ 6.4.3.1 2 SM SM Mght s 1-2 POKG S SPL c P LPi M P P-Inf ' Right S 1 3 SPL. K U P SPL KU P LPS.PCU 7 P P_Suo Rinht SI3 SPL. K u P SPL. K u P LPs.PCu 7 P P-Sup Ltft SI-5 POSICG S SPL P LPi M P P I n f Left Si0 R K G M Pre&PostCG SM SFm.GPiC.GPoC 6.43.12 SM SM Left SI-7 SFG. MFG F SFG F GFs 8 F ACinn Left S 2 0 SFG F SFG F GFs 8 F ACing Rigbt S2-1 MFG F MFG F GFm 8 F F-Sup Rigbt s2-2 MFG F MFG ' F GFm 8 F F-Sup Right S 2 3 MFG F RcCG M GFm 9 F FSup Right S 2 4 M G M PostCG S GFm 9 F S M Rieht S2-5 PostCG S SMG P GnC 6.4 M S M Rigbt s2-6 SMG P SMG P GPoC 3.12 S P_hf R i ~ b t s2-7 SX1G P SMG P LPi 10 P PJnf Rigbt s2-8 SMG P SMG P LPI 40 P P 1nf W ~ b t s2-9 SMG P ' SMG P LPi 40.19 P P S U P Right s2 10 SPL P SPL P 0 . K u 19.7 PO P SUD R i ~ b t s2-11 SPL. K u P K u P 0.PCu 19.7 PO P S U P Wgbt S2-12 SPL. PCU P K U P O.KU 19.7 PO P-Sup Lefî S2-13 SPL P SPL P O .Ku 19.7 PO P s u p Left s2-14 SMG P SMG P LPi 40.19 P P-Sup , Left s2-15 SMG P SMG P LPi 40 P P-In f Left S2-16 SMG P SMG P LPi 40 P P-hf Lefî s2-17 SXtG P SMG P GPoC 3 - 1 2 S P Inf Left s2-18 PostCG S SMG P GPrC 6.4 M SM Left S2 1 9 Pr& G M P O ~ G s G F ~ 9 F SM Le f î S2-20 MFG F M G M GFm 9 F F-Sup Left s2-2 1 hlFG F MFG F GFm 8 F F SUD Left sz-22 M FG F MFG F GFm 8 F F-Sup Left S 2 2 3 SFG F SFG F GFs 8 F ACing Le ft

-

S 3 0 SFG F SFG F GFd 9 F AChg Right S3-1 hl FG F SFG F GFs 9 F F-Sup Rigbt S3-2 MFG F ,MFG F GFs 9 F F-SUP Rigbt S3-3 I FG F M G M GFm 9 F SM R i ~ h t s3-4 PrcCG M P m C G S GF i 44 F S M Rigbt S3-5 PostCG S SMG P GRC 6.4 M P h f Mgbt S 3 6 SAMG P SMG ' P GPoC 3.12 S P I n f Rigbt s3-7 SMG P A G 1 P LPi 40 P P I n f Rigbt s3-8 SMG P AG P LPi. Gsrn 40 P P S U D R i ~ b t s3-9 AG P AG P ' Ga 39 P P-sup Rigbt

S3-1 O SPL P SPL P I G O S 19 O MedO Rinbt S3-11 SPL. PCU P CU O 1 G0s.c~ 19 O MedO Le ft S3-12 sPL.PCu P CU O ' G0s.c~ 19 O P SUD Left S3-13 SPL P SPL ' P i G O s 19 O 1 P-SUP Left s3. -14 AG P I A G ' P I Ga 39 P P Inf Lefî s3-15 SMG P AG P LPi.Gsm 40 ' P 1 P-hf Left S3-16 SMG P ~ A G P I L P ~ I ~ O ~ P , P-Inf Left s3-17 SMG P SMG P ' GPoC 1 3 . 1 2 s SM Left S3-18 P O K G S , SMG i P GRC 6.4 M 1 SM Left s3-19 P d G M PostCG 1 S 1 GFi 44 I F 1 F-Sup Left

C.6. Breakdown of SPECT Localization Approach - conf.

Segment Area Region Are. ! Region Are. Broâmann~ Region Region Side s3-20 ! FG F ReCG !bl GFm 9 F F SUD Le ft S3-2 1 .M FG F MFG I F GFs 9 F F-Sup 1 S 3 2 2 MFG F SFG F GFs 9 F ACing 1 S3-23 SFG F SFG F GFd 9 F F-Sup R - 0 SFG F SFG ' F GFs 10 F ACing Il s4-1 MFG F SFG. MFG F GFm 1 O F F-Sup R 53-2 hl FG F MFG F GFi 46 F F-Sup Il S4-3 I FG F M G M GFÎ 45 F F-Sup Il 9 - 4 ReCG M PosCG S GFi 44 F SM R s4-5 PostCG S SMG P GK-GPoC 6-4-53 SM SM R

eft

igbt

ight

5 4 6 SMG P SMG P LPi 40 P P-hf Rigbt 53-7 SMG P AG 1 P GTs -- 7 7 T P-hf Right s4-8 SMG P AG P GTsLPi 22.39 Pl- P-hf Right a-9 AG P AG P GTm 19 TO P-Sup Right 54-1 0 SPL P SPL P GO^ 19 O Lat0 Right 54-1 1 CU O CU O GO~,CU 18 O MedO Right S4-12 CU O CU O G O ~ C U 18 O MedO Left S4-13 SPL P SPL P GOm 19 O Lat0 Left S C 1 4 AG P , AG P GTm 19 TO P SUD Left 54-1 5 SMG P AG P GTS.LP~ 22.39 PT PJnf . Left a-16 SMG P AG P G T s -- 17 T P-hf Le ft S1-17 SMG P SMG P L P ~ JO P P-1nf Left s3-18 PostCG S SMG P GfK.GPoC 6.1.13 SM S M Left S4-19 PreCG M PosiCG S GFi -44 F SM Left s4-20 1 FG F M G M GFi 45 F FSup Left

MFG F MFG , F GFi 46 F F S U D Left S4-22 MFG F SFG-MFG F GFm 10 F F-Sup Left !M-23 S FG F SFG F GFs 10 F ACing Lefi SS-0 SFG F SFG F GFs 1 O F ACing Mgbt S5-1 hl FG F MFG F GFm 10 F F-hf Rigbt SS-2 1 FG F .MFGJFG : F GFi 10.16 F F-In f Right SS-3 PrcC G M [FG.ReCG . M GFi 45 F F-hf Rigbt SS-4 PosrCG S PostCG S G K 44.6 M SM Rigbt SS-5 STG T STG T GTT 6.42 T Temp Rigbt SS-6 STG T STG T GTS 4222 T Tem p Right ss-7 AG P AG P GTs - 7 7 T P-1 nf Right ss-8 AG P AG P GTm 39 PT P-hf Mgbt SS-9 LOG O lat OG O GOm 19 O Lat0 Right

S5-10 LOG O fat OG O GOm 18 O Lat0 Right SS-Il CU O , LG.CU O Ci0m.C~ 18 O MedO Rieht ss-12 Cu O LG.Cu O GOm,Cu 18 O MedO S5-13 LOG O lat OG O GOm 18 O Lat0 S5-14 LOG O ' latOG O GOm 19 O Lat0 s5-15 AG P AG P GTm 39 PT P-hf ! s5-16 AG P AG P GTs 22 T P-In f

.-

S5-17 STG T SïG T , GTs 4222 , T Temp

ss-18 STG T STG ' T GTT 6.42 . T r e m p S5-19 PostCG S PostCG S i GRC 44.6 ' M SM '

1 SS-20 RCG M IFG.R~CG' M ! GFI i 45 ' F , F-1.1 Left 1

C- 6. Breakdown of SPECT Localkation Approach - cont.

Cori Rim Darnasio O De1p1ees : Damasio 15 Degrees Tdaimch-Toumow O k e e s , Sclectcd 1 Segment Area Region Area Region Arta Brodmanni Region - Region SMe

S5-2 1 IFG F MFG.iF G F G E 10.46 F F-Inf Left SS-22 MFG F MFG F GFm 10 F F-Inf Left s5-23 SFG F SFG ' F GFs 10 F ACing Lef i S6 0 FPolc F SFG F GFs 10 F ACinn R i ~ h t s6-1 MFG F MFG F GFm 10 F F-hf Right S6-2 1 FG F MFG.iFG ' F GFi 46 F F Inf Rinht S6-3 1 FG F 1FG.RcCGi M GFi 45 F F-hf Right S6-4 STG T POSCG S GTs 7 7 T Temp Right S6 5 STG T STG T GTs - 7 7 T Temn Rinht S6-6 STG T STG T GTm - 7 7 T Temp Right s6-7 MTG T MTG T GTm 2 1 T T e m ~ Rinht S6-8 AG P MTG T GTm 37 PT E T Right S6-9 LOG O IÎL OG O GO^ 19 O L a t 0 R i ~ h t S6-10 LOG O ~ a t OG O ciom 18 O L a t 0 Right s6-11 LG. Cu O LG. CU O GOmCu 17 O bled0 Wght S6-12 LG. CU O LG. CU O GOmCu 17 O MedO Loft S6-13 LOG O ht OG O GOm 18 O L a t 0 Left s6-1 4 LOG O Iat OG O GOm 19 O L a t 0 Left S6-15 AG P MTG T GTm 37 PT Temp Left , s6-16 MTG T blTG T GTm 2 1 T Temp Le ft S6-17 STG T STG T GTm 22 T Temp Left s6-18 STG T STG T GTs - 7 7 T Temp Le f t s6-19 STG T PostCG S GTs - 17 T T e m ~ Left S6-20 IFG F IFG.PreCG : M GFi 45 F F-hf Le f t s6-2 i IFG F MFGJFG F GFi 46 F F h f Left S6-22 MFG F ,UFG F GFm 10 F F-hf Left s6-23 FPolc F SFG F GFs 10 F A C i n g k f t s7-0 FPok F ' SFG F GFs 10 F ACing Right S7-1 IFG F MFG F GFrn 1 O F F-1 n f Wght s7-2 I FG F iFG F GFi 1 O F F-1 n f Right S7-3 STG T 1 FG.PrcCG M G Fi 47 F F-hf Right s7-4 STG T STG T GTs 22 T Temp Right S7-5 STG T STG T GTs 2 1.22 T T e m ~ Rinht S7-6 MTG T MTG T GTs 21 T Temp Right

LITG T MTG T GTrn 2 1 T Temp Right s 7-8 MTG T MTG ' T GTi 37 PT Temp Right S 7-9 LOG O Lat (Xi 0 ci01 19 O L a t 0 Right

s7-10 LOG O Lat OG O GOi 18 O L a t 0 Right S7-11 LG. Cu 0 Cu 0 G0i.c~ 17 0 MedO Right S7-12 LG. CU O CU O G01.cu 17 L O MedO Left S7-13 LOG O ht OG O GO1 18 O L a t 0 Le ft

LOG O Iat OG O GOi 19 O L a t 0 ~ e f t MTG T MTG ' T GTi 37 PT T e m ~ k f t

S7-16 MTG T MTG T , GTm 2 I T Temp Le ft s7-17 X1TG T MTG : T GTs 2 1 T Temp Left s7-18 STG T STG T GTs - 71 - 77 T Temp Left S7-19 STG T I STG T ' GTs 22 T Temp Left s7-20 STG T ' 1FG.PreCG ' M ' GFi ' 47 F F-1 n f kft S7-2 1 IFG F , IFG F GFi ' 10 F F-t nf Le ft S7-22 IFG F MFG F 1 GFrn 1 10 F F-hf k f t s7-23 FPole F SFG F 1 GFs 10 , F ACing k f t

Topognphy of Hemispatial Beglcct

D. Correlation Matrices

D. 2. CT Correlation Mat& - LHD

CIU3

-265 -0::

.378

. OGO

.O94

.2 12

- 502 .O00

-527 .O00

:.cou

-. 052 -217

. $07 -000

-680 .O00

-101 .O61

.?O4 -00;

.6 11

.O00

- .28$ .O01

-.136 -122

-356 -00:

S N S O R Y O C I P E A L TEKPüRAL E S L A R U S

CT Correlation Matth - M D

Correlation, 1 - tailed Sig:

N a M G

AGE

VOLUME

ArL'ITC I N G

iINTWM

BG

CRAD

DEEPTPO

FRONTAL

MOTOR

OCIPITAL

PAR 1 ETAL

P0SrnvW

SENSORY

TEM W RAL

THALAMUS

AGE

-203 .O13

1.000

- -267 .O02

- -116 -103

- -127 . O83

- -248 . O03

- -130 .O78

- .O18 -4 24

- -128 -082

- .O54 -278

- .O37 -346

- -125 . O88

- -186 . O21

- .O56 -27 1

- . O83 -184

- - 059 -262

Page 133

D.2 CT Correlation Munir - W . - c0n.t

. * . .

PJaLOG

AGE

VOLUME

M i T C 1 NG

A P m

BG

CRAD

CEEPTPO

FRONTAL

MOTOR

O C I PITkL

PAF1 1 =AL

POSrn'M

SZNSORY

TEMPORAL

TFSUA-WS

M U L T

FRONTAL

-128 .O81

- -128 .O82

- 560 . O00 -727 . O00 -673 .O00

-186 .O21

-348 . O00

- .O95 -151

1.000

-573 . O00

- -164 . O36

.181

. O24

-087 .172

.404

. OOG ,120 . O9 5

- . 070

I P L E R E G R E S S I O N ' * * *

MOTOR O C I PITAL PARIETAL

Page 134

SPECT Correhtion M a h - LHD

=:CG

l .CO0

- 5 4 1 - 0 0 0

- . 094 .2CO

- .O13 . I 6 2

- . cg7 -233

- .12E . :6E

- - 1 5 5 - 2 7 0

- .O71 - 2 9 7

- . O44 . 2 I C

. . C I € .365

- . 128 - 1 6 7

. ! < G . - d d . -. . - L - L ?T

- . 094 - 2 4 0

- .299 -0::

.513

.OC0

.62E

. ûPO

- 6 2 1 - 3 0 0

.;O1 - 0 0 0

-7:: . OC0

- 8 4 6 . O O O

1. CO0

.64E

.O00

- 7 4 7 - 0 0 0

-73; . GCO

V O L L ?

- SC :. . 000

:. O00

- .O95 - 2 3 7

.O88 -253

- .O87 - 2 5 6

- - 037 - 5 9 0

- .306 . O09

- - 1 5 5 . O70

- - 2 9 9 .O11

- - 163 . I O 9

- - 2 9 6 - 0 1 1

- . i 0 5 .215

Lsn

- . OC6 .365

- .163 . 109

-703 .O00

. 7 4 7

. OOC

.870

. O03

- 6 6 6 . O00

- 4 0 8 .O00

.a05

.O00

- 6 4 8 .300

1 .000

.763

.O00

,726 .O00

I Z G

- - 0 9 4 - 2 4 O

- .O95 - 2 3 7

1.000

.77 8

. O00

- 8 0 5 -009

- 4 6 9 . O00

. 2 5 7

.O25

- 5 9 6 .O00

- 5 1 3 .O00

- 7 0 3 .O00

- 7 0 0 .O00

.a02

.O00

LTEK?

- . 1 2 8 . 1 6 7

- - 2 9 6 . O 1 1

- 7 0 0 . O00

.61C

.O00

- 7 4 8 . O00

- 5 6 7 . O00

,466 . O00

.685

.ooc

- 7 4 7 .O00

- 7 6 3 .O00

1 .000

.696

.O00

SCING

- .O13 .CO2

. 088 - 2 5 3

- 7 7 8 .O00

1.000

- 8 6 6 .O00

- 7 7 5 .O00

- 4 9 1 - 0 0 0

- 7 6 1 .O00

- 6 2 8 .O00

- 7 4 7 .O00

.6 14

. O00

.862

.O00

LTH

- - 1 5 5 . 1 2 1

- - 1 0 5 .215

- 8 0 2 .O00

- 8 6 2 .OOG

- 7 7 5 .O00

- 7 8 2 .O00

- 5 3 0 .O00

- 8 0 9 .O00

.734

.O00

.726

.O00

.696

.O00

L.000

SO

- - 1 9 5 .070

- .!O6 . 009

- 2 5 7 - 0 2 5

- 4 9 1 .000

- 4 8 2 -306

- 794 . 000

1.000

.656

.000

.7 11

. O00

.468

. O00

-468 . OOC

- 5 3 0 - 3 0 0

Li?

- - 6 7 1 - 2 5 7

- -195 . O70

- 5 5 6 .000

.76 1

.000

.752

.c00

- e 17 .000

. € 5 6

.000

1.000

.846

.000

. eos

. O00

- 6 8 5 .000

.a09 - 0 0 0

Page 135

SPECT Correlation Mu& - RHD

C c r r e L a t ~ o n , l-talle& S i g : NE LOG

1 - o o c

. 197 - 3 3 3

- -- - 2 0 ,

- OOC)

- - 3 8 2 - 3 0 0

- .19E .O32

- - 2 3 3 - 3 14

- - 2 6 3 . GO7

- . ? 4 2 -90:

- -232 .O01

. .?GO . O00

- .264 . O07

- - 3 3 9 - 2 3 1

- .323 -89:

3 P

- - 3 3 2 . O01

- 1 9 7 .O33

- - 2 8 6 . CO3

-6 15 - 0 0 0

.0:7

. GO0

- 7 3 7 .O00

- 6 4 5 . o o c

- 6 3 6 .O03

1 - 0 0 0

- 7 2 2 . û00

. E 1 5

.O00

- 7 6 6 . OC0

- 6 4 7 .O00

AGE

.197

.O33

1 .000

- ,321 . g o 1

.O26

. iOS

.O50 - 3 2 2

. OS4

.?O8

- 1 3 4 .LE7

-238 .O26

. '9 7

.O33

- 0 4 8 - 3 2 7

- 1 1 5 . .. . -..-. .O02 - 4 9 4

.O78 - 2 3 6

RPT

- - 3 8 0 .O00

. O48 - 3 2 7

- - 1 7 2 - 0 5 5

- 4 5 6 - 2 0 0

- 4 0 0 - O00

- 5 14 .O00

- 4 2< .O00

.598

. O00

* 7 2 2 . O00

1 .000

- 4 7 3 . O00

- 8 9 9 . O00

- 4 7 8 .O00

VOLCJIiO

- 3 6 7 .O30

- -32: .O01

i . 0 0 0

- . 346 . O 00

- .O53 - 3 1 3

- - 1 0 6 - 1 5 8

- -111 - 1 5 2

- - 1 5 5 .O75

- - 2 6 6 .O03

- - 1 7 2 - 0 5 5

- - 3 7 0 ,000

- - 2 5 1 . O09

- - 2 4 4 .O11

RSM

- .264 - 5 0 7

.115

.TC4

- - 3 7 0 . O00

- 5 2 7 . O00

- 5 1 0 .O03

- 6 7 5 .O00

. 4 5 1

. O00

. 4 0 1 * O00

. a 1 5

. O00

- 4 7 3 .O00

1 .000

.621

.O00

.O88 . O00

RC ING

- , 1 9 8 - 0 3 2

- 0 5 0 - 3 2 2

- .O53 - 3 1 3

- 6 6 9 . o a o

1 .000

- 7 7 6 .O00

- 7 2 8 . O00

- 5 5 4 . O00

- 6 1 7 . O00

- 4 0 0 .O00

- 5 1 0 .O00

- 3 3 2 .O0 1

- 7 4 6 ,000

XTH

- - 3 2 3 .O01

. O76 -236

- - 2 4 4 -0::

- 7 7 5 .O00

- 7 4 6 .O00

- 6 3 9 .O00

- 7 6 3 . O00

-521 .O00

- 6 4 7 - o c 0

- 4 7 8 .O00

- 4 8 6 - 0 0 0

.cc:

.O00

1.000

Page 136

D. 5. CT-SPECT CorreIation Mut* - LHD

C o r r e l a t i c c , 1 -:azled Sig:

NJLOG

1- O00

-541 . OOC

- -091 ,279

- -303 -491

- -116 -216

- .O75 -257

- -358 -347

.25e

. O2G

.2C3

. O83 -425 .O01

-302 . a 19

-053 -265

CTSG

.20?

. CE3 -235 . O10

- -492 .O00

- -225 .O62

- .293 . 3 22

- -25.4 . O1 1

- .226 . C6 1 -164

7 -3-3

1.000

. ? 05

.O16

.2i5

.O25

.529

. go0

LBG K I N G

Page 137

VOLUME

-332 .O0 2

'-000

. 5 64 -000

.392

.O00

.590

.O00

-560 .OC0

.144 -199

- -305 .O04

.Ot7 ... .....-. - . O34 -386

- .227 .O25

- .19C . o;e RF

- .123 -147

- .O34 -386

- .oe5 .234

-205 .O39

- -139 -116

- .O33 -391

.O71

.272

.674

. O00

-736 . O00

1.000

.67G

.O00

.553

.O00

CTaG

.2e7

.O06

-392 -000

-245 . 0 17

1-000

-229 -02;

-145 -107

-435 .O00

- -212 .O34

-134 .226

.205 -039

.O74 -265

- .O36 -378

RTH

- -254 .O14

- -194 -048

- .O23 -423

- .O36 -378

- .O60 .3 04

- -105 - 2 8 5

- -204 .O40

.731

.O00

.667

. O00 -553 . O00 -537 -300

1.OGO

Page 138

E. ANOVA Tables for CT Regression Analyses

E. 2. CT A N 0 VA Table - Exploratory Metliod - LHD

* * * * M U L T I P L E R E G R E S S I O N " ' *

Multiple R -51882 R Square .26917 Adjusted R Square -25916 Standard Error -4 129 1

Analysis of Variance DF Sum of Squares Mean Square

Reçress ion 1 4 -58401 4.58401 Residual 7 3 12.44622 -17050

F = 26.88630 Signif F = -0000

Variable B SE B 95% Confdnce Intrvl B Beta

'10 LUME . 006389 . 001232 -003933 -008845 .SI8815 (Cons tant -660756 . 062337 -536518 -784994

- - - - * - - - - - * Variables in the Equation - - - - - - - - - - -

Variable Tolerance V I F T Sig T

VOLUME 1.000000 1.000 5.185 .O000 (Cons tant ) 10.600 .O000

"" M U L T I P L E R E G R E S S I O N + * * *

Equation N m t b e r 1 Dependent Variable.. NBMG

Variable Beta In Partial Tolerance V I F Min Toler T Sig T

m c I t l G Am'hM BG c m DEEPTPO FRONTAL MGTOR PAR 1 ETAL PO S m SENSORY GCI PITAL TEMPORAL THALAMUS

Collinearity Diaqnostics

Number Eigenval Cond Variance Proportions Index Constant VOLUME

1 1.64420 1.000 -17790 -17790 2 -35580 2.150 -82210 .a2210

Page 139

CT AN0 VA Table - Hypothesis Method - LHD

* * - . M U L T I P L E R E G R E S S I O N ""

Multiple R -57312 R Square -32846 Adjustea R Square -26921 Standard Error .41010

Analysis of Variance D F sum of Squares Mea. Square

Reqress ion 6 5.59380 .93230 Res idual 68 11.43642 -16818

* * * * M U L T I P L E R E G R E S S I O N

Equation Number 1 Depenaent Variable.. NBLOG

Variable B SE B 95% Confdnce Intrvl B Beta

VOLUME . 004826 . 001977 8.803468-04 -008771 -391860 ANTCIPIG . 016805 . 012250 - -007639 . 04 1249 -146791 SG - -002013 . 002742 - .O07485 . 003459 - .O83000 FROPTAL -015408 . 011184 - -006909 -037726 -184570 PARIETAL 9.00401E-04 . 003780 - -006642 -008442 -032607 TPALAKU S .O01201 . 002371 - -003531 -005932 -056908 (cons tant) .667287 . 072830 -521958 -812617

- - - - - - - - - - - Variables in the Equation - - - - - - - - - - - Variable Tolerance VIF T Sig T

trOLüME -383122 2 -610 2.441 -0173 N i T C 1 NG -862506 1.159 1.372 -1746 BG -772485 1.295 --734 .4654 FROPdTAL -550212 1.817 1.378 -1728 PARI ETAL .527148 1.897 -238 -8124 TIiALMWS .78i922 1.279 -506 -6142 (Cons tant i 9 -162 .O000

Collinearity Diagnostics

Number Eigenval Cond Index 1.000 1.719 1.983 2.122 2.565 3.069 4.925

Variance Cons tant

-02421 -01244 -0101-9 . O0320 -41825 -16099 -37071

Proportions VOLUME ANTCING -01537 -01547 -00777 -17663 -01654 -14716 . O2840 ,53940 -00439 ,00922 -00438 -01920 -92315 -09291

FRONTAL -01903 . O6463 . O5940 -12946 -23682 -03815 -45251

PAR 1 ETAL .O1628 -01553 -34363 . O3754 -00794 . O3091 -54816

THALAMUS 1 . 01961 2 .la834 3 -00036 4 -11545 5 .28241 6 .32685 7 -06698

Page 140

Topopphy of Hernispatid Neglect

3 CT AN0 VA Table - Exploratoy Metlrod - RHD

t t t t M U L T I P L E X E G R E S S I O N * * * *

M u l t i p l e R .50000 R Square -25000 Adjusted R Square -23060 Standard Error -50446

iinalysis o f Variance D F Sum of Squares Mean Square

Reqress ion 3 9.83975 3 -27992 Residual 116 29.51998 -25448

F = 12.88857 Siqnif F = -0000

- - - - - - - - - - - - a - - - - - - - - - Variables in the Equation - - - - - - - - - - - - - - - Variable B SE B 95% Confdnce I n t r v l B

AGE -012618 -003296 . 006089 -019146 V O L W -002256 9.9583E-O4 2.83599E-04 . 004228 P O S m i . 006997 . 002539 . 001968 . 012027 (constant . 096707 -246385 - -39 1290 - 584705

- - - - - - - - - - - Variables in the Equation - - - - - - - - - - -

Variable Tolerance VIF T Sig T

AGE -928126 1.077 3 -828 -0002 VO L W -584457 1.711 2.265 -0253 POSLdM -607.149 1.646 2.755 -0068 (Constant -393 -6954

t t t * M U L T I P L E R E G R E S S I O N '*

- - a - - - -

Beta

-3 19477 -238272 -284281

* t

Equation Number 1 Dependent Variable.. tTBLOG

m c I I J G ANru'M BG c w DEEETPO FRONTAL MOTO R PARI ETAL SENSORY OCIPITAL TEMPORAL T!iALAMüS

Collinearity Diagnostics

Number Eigeiwal Cond Variance Proportions Index Constant AGE VOLUME

1 3.14434 1.000 -00308 -00328 -02455 2 .62259 2.247 -01072 -01735 .15686 7 - .21376 3.836 -00022 .O0000 -75945 4 -01881 12.932 -98599 -97936 -05914

Page 14 1

E.4. CTANOVA Table - Hypotikesis Mdhod - RHD

" * * M U L T I P L E R E G R E S S I O N * * * *

Multiple R .53758 R S q u a r e .28899 Adjusted R S q u a r e -24456 Standard E r r o r -49987

Analysis of Variance DF Sum of Squares M e a n Square

R e g r e s s i o n 7 11 -37470 1.62496 Res idual 112 27.98503 -24987

F = 6 -50331 Signif F = .O000

" ' O M U L T I P L E R E G R E S S I O N " . *

Zquation Number 1 Dependent Variable. . NBLOG

V a r i a b l e B SE B 95% C o n f d n c e Intrvl 5 Beta

AGE VOLUME ANTC I N G BG FRONTAL PARI ETAL THALAMUS ( C o n s tant)

. - - - - - - - A - - Variables i n the Equation - - - - - - - - - - -

Variable T o l e r a n c e V I F T Sig T

hGE -902932 1.108 VOLUME -404849 2.470 ANTCING .455631 2.195 - BG -635877 1.573 FRONTAL .372302 2.686 PARI ETAL -697376 1.434 THiILAMUS .745865 1.341 (Cons tant

Collinearity Diagnostics

Numbe r

1 2 3 4 5 6 7 8

Eigenval

4.38919 1.34014 .79529 .69649 -36395 .24 146 .15542 . O1807

PAR 1 ETAL 1 -01018 2 -00984 3 .S7489 4 -02685 S -03911 6 .O5900 7 .28013 8 .O0000

Cond Index 1.000 1.810 2.349 2.510 3.473 4.264 5.314

15.586

V a r i a n c e Cons tant

-00127 . O0201 -00139 . O0772 . O0114 . O0078 . O0029 -98540

Proportions AGE VOLUME

-00134 . 00953 . O0268 -00339 -00213 -01139 -01134 .O0042 . O0410 -07773 .O0100 -17890 . O0000 -68559 .97741 -03305

ANTCING -00734 .Il022 -02530 .O2356 -18131 -43776 -21418 -00033

FRONTAL . O0767 -05377 . O2857 -00096 -00953 .26061 .63886 . O0003

Page 142

F. ANOVA Tables for SPECT Regression Analyses

SPECT AN0 VA Table - Hypothesis Method - LHD

* * * * M U L T I P L E R E G R E S S I O N

Multiple ?. -59941 R Sware -35929 kdjusted R Square -28536 S:andard ErrOr -41052

hnalysis of Variance D F S m of Squares Mean square

Regress ion 6 4 -91417 -81903 Rss i dua 1 5 2 8.76338 -16853

F = 4.85994 Siqnif F = -0005

* * * * M U L T I P L E R E G R E S S I O N * * "

Equation Number 1 Dependent Variable.. NBMG

Variable B SE B 95% Confdnce Intrvl B Beta

VOLUME .O07007 . 001568 . 003860 -010153 -578861 LBG -035623 -029967 - .O24511 -095756 -275037 LC 1 ElG . 001837 . 022298 - -042908 -046581 -025924 L F - . 003983 . 005021 - . 014059 . 006093 - -215695 LP . 010635 -005211 1.79055E- 04 -021091 -456935 LTH - -073405 -039812 - .153294 -006484 -.539517 i Conscant i -653306 .O80 167 .492439 -814173

- - - - - - - - - - - Variables in the Equation - - - - - - - - - - -

Variable Tolerance VIF T Sig T

VOLUME -734154 1.362 LBG -230167 4.345 LC IPIG -1244 10 8.038 L F -166659 6.000 LF -245833 4.068 LTH -143907 6.949 (Coxxitanc)

Collinearity Diagnostics

Eigenval

4.18223 1.65014 ,42186 ,37054 -20753 -10961 ,05808

Cond Index 1.000 1.592 3.149 3.360 4 -489 6.177 8.486

Variance Proportions Constant V O L W LBG

-00363 .O0091 -00941 .IO415 .13777 .O0004 -43499 -54539 -02233 -11394 -00406 -23014 . O7852 -00057 .O3250 . O0000 -20702 -51785 -26477 -10427 -18773

LCING LF .O0493 -00758 .O0122 .O0030 . O0152 -00185 . O0036 -00631 . O0227 -40826 -27953 -01475 -71017 -56095

LTH 1 .O0646 2 .O0477 3 .O1484 4 .O0029 5 -25956 6 -13036 7 -58373

Page 143

Topography of Hemispatial Neglm

SPECT AN0 VA Table - Hypothesis Metlrod - RHD

t . . . M U L T I P L E R E G R E S S I O N O * * *

M u l t i p l e R - 5 8 1 1 5 R S q u a r e - 3 3 7 7 4 A d j u s t e d R S q u a r e - 2 7 9 7 3 S t a n d a r d Error - 5 2 8 0 4

A n a l y s i s of V a r i a n c e D F Sum of Squares Mean Square

R e g r e s s i o n 7 11.37579 1 . 6 2 5 1 1 R e s i d u a l 8 0 22 .30651 -27883

. * * * M U L T I P L E R E G R E S S I O N

Equation IJumber 1 D e p e n d e n t Variable. . NBLOG

V a r i a b l e B SE B 95% C o n f d n c e Intrvl B Beta

AGE 'K2LUME RBG RC 1 PIG R F RP RTH ( C o n s t a n t i

- - - - - - - - - - - V a r i a b l e s i n the Equation - - - - - - - - - - - Variable Tolerance VIF T Sig T

AGE -844546 VOLUME .677138 RBG -260722 RC I IJG -279229 R F -232523 RP -364727 RTH -268839 !Cons t a n t }

C o l l i n e a r i t y Diagnostics

Cond index 1 .000 1 . 4 1 5 2 .664 3 .357 4 .132 4 - 4 0 2 6 . 6 7 5

19 .277

RTH . O0806 . O0003 . O0324 .O5258 . O5350 .17202 .69959 . O1098

Variance Cons tant

. O0047

. O0251 - 0 0 3 3 8 - 0 0 0 1 3 . O0008 . O0230 . O0217 - 9 8 8 9 6

Proportions AGE VOLUME

-00049 -00888 -00281 .O2103 . O0580 -47027 . O0000 -00082 -00110 -08039 .O0251 - 2 6 2 6 1 . O0612 -02714 -98116 -12885

RBG - 0 0 9 2 4 . O0399 - 0 1 7 8 8 - 1 2 6 8 2 - 3 1 0 1 3 - 0 5 9 3 2 - 4 2 5 3 4 .O4727

RCING . O0585 .O2626 - 0 2 4 1 6 - 0 4 5 8 1 - 3 6 5 1 1 - 1 7 8 2 4 - 3 5 4 5 7 . O0001

Page 144

G. Anova Tables for CT-SPECT Regression Analyses

G. 1. CT-SPECT ANO VA Table - CT Forced +SPECT - LHD

Kx1::ple R - 5 6 8 2 1 F. ;.q.sre - 3 2 2 8 6 À&;zs:ed 4 Square - 2 2 3 7 7 S:a-Car6 Error . 4 ? 8 1 4

Azzalysrs of Varza,-.ce SF S a of Squares Me- Square

Regresszon 6 3 -75264 -62544 Resicua l 4 1 7 - 8 7 0 5 2 -19196

. . . . . . . V a r r a b l e s rn the Equation - - - - -

'Jar r a b l e To lerazce VIF T Sig T

3e:a :- P a r t i a l To lerance VIF Min T a l e r T Sig T

: : i ~ . e z Ezgerival Cond Varrance Proportrons Index Constant VOLUME J4NTCING CT9G FROFiiAL PAnIETAL

: 3 .551:7 1 . 0 0 0 -3199s -0142; . o l e 5 7 .02122 .0:606 -01752 2 .38016 1 .90; - 0 3 8 6 2 - 0 1 5 2 0 .15930 -05749 - 0 6 2 3 5 .O4201

.O3527 1 . 9 8 7 .O2713 - 0 1 2 7 5 .19972 -00166 - 1 7 9 3 3 -13225 w . 62439 2 .385 -12024 - 0 0 5 6 5 -54612 .O0006 .O1105 -29849 5 -50764 2 .645 -37366 - 0 3 0 5 5 .O7599 .O7948 -18483 - 0 8 0 2 5 6 . 28553 3 - 5 2 7 - 0 2 5 6 5 - 0 0 0 2 1 .O0021 .a3669 - 1 1 9 3 3 - 0 0 4 2 7 - .15154 4 - 8 4 1 .39496 - 9 2 1 4 2 .O0009 .O0340 - 4 2 7 0 5 - 4 2 0 2 5

Page 145

G. 2. CT-SPECT ANO VA Table - CT-SPECT Forced - LHD

- - H U L T T P L E R E G R E S S I O N * O . *

i\-,alysrs of Varrarrce DF Sizn of Squares Kean S-are

neçressrcn 1 1 C -37192 -39745 Resld-a: 3 6 7 -25124 -20142

. . a . M U L T I P L E R E G R E S S I O N - - - - Zqdat:cn G~Tker 1 Dependent Variable. . NBUXi

VIF 2.839 7.385 8.678 L2.336 - 7 -974 4 -647 1.372 3.096 2.491 2.501 2.523

Variarrce ?roportic Co~scanc VOLUME

-00356 -00162 -00561 -00992 . 00003 .Cl964 -02860 -00127 . O0207 .O1676 -14439 .O0473 -02668 -00615 -04454 .O3411 -22802 .80086 -07445 .O0146 .O0001 .O1371 -44207 -08978

CTBG . O0036 . O0999 -00379 -02852 . O0513 . OC817 -11649 -20762 .O0015 .O0360 .23238 -34378

LCING -00430 .00000 -00079 -00027 .O0080 . O0069 -00166 -00097 .OC013 .12949 .522S4 .33836

FRONTAL PARIETAL THALAHUS .O0004 -00228 .O0070 .O1174 .O0925 -00965 -13639 -01393 -09808 -00135 -10233 -01909 . O0273 -15466 .O0003 -02966 .OC047 .0149C -24408 .O5427 .32965 -00024 .O1706 .O0080 -30955 -28937 -03787 -07395 -14891 -00074 - 12470 -16567 -22271 -06550 -00179 -26579

LTW . O0277 .O0153 -00335 .O0001 .O0015 -00591 . O0003 -09155 .O0003 .O0603 -02251 -86612

Page 146

Topography of Hernispatial Ncglm

CT-SPECT AhV VA Table - CT Forced +SPECT - RHD

-- X T J ~ T I P L E R E G R E S S I O N * * * -

S k l t - p l e R -63255 1 S q ~ a r e .<O012 Aè]ïs:ed Fi Squaze - 3 2 7 4 1 S:azèarC E r r o r - 4 8 8 8 7

A r a L y s r s of Var:k?ce 3 C S u of Squares M e c S c p a r e

Reçress t o n 8 10.52094 1.31512 R e s rdual 6 6 15 -77334 - 2 3 8 9 9

'+'ar:abLe

AGE 'JO LLyE 2 4 Y C : ?:G --- - C ;s'a 3 0 x z Fk?.IS'=AL -... . . z ~ h Y U I RI ( C c - 5 car,: )

Sig T

-0005 -0612 . O970 -2704 -2685 -511: -0590 .O122 .2969

E,~a:ror. N m b e r : Dependen: V a r i a b l e . . NBLOG

':ar:ab:e 3e:a I n ? a r z i a l Tclerance V I F Hi= T o l e r

Cond I n d e x 1 .000 1.813 2.283 2.635 3 .O78 ? .go8 4 .572 5 - 7 3 7

18 .8 iO

V a r i a n c e ?ropo:cio C o a s t a n t A G I

.O0080 .O0081 - 0 0 1 9 3 - 0 0 2 6 1 . O0325 -00022 - 0 0 3 8 2 .O0466 - 0 0 2 8 5 -00365 - 0 0 2 4 8 -00375 -00134 -00287 - 0 0 1 2 7 .O0001 -98528 -98140

T Sig T

-475 . 6 3 6 1 -232 - 8 1 7 2 -117 -6760

-. IO5 .9166

CTBG FRONT= -01344 -00580 -01259 -03120 -05320 -02600 .O5121 .04C62 -00490 .CO001 -68024 .O0002 -17600 -07263 - 0 1 1 ~ 2 .eo799 -3OCOO -01473

Page 147

Topography of Hemispatial Ncglea

CT-SPECT ANOVA Table - CT&SPECT Forced - RHID

.k?alys:s of Varrance 3 F Süm cf Squares H e m Square

X e ç r e s s :oz :2 10.64309 -88692 .=.es r dra l 62 15.65120 -25244

Variables Tclerance

.837617 -311678 .298222 -537669 -297391

iz the Equation - - - - - VIF T

1.194 3 -623 3 -138 1.750 3.353 -1.679 1.860 1.110 3.363 1.210 1.904 - 5 15 1.395 1.712 4.569 -486 3 .O46 -252 4.326 .O36 2-61.: - 1.926 3.265 - .487

-1.182

2cl1:zear:cy D:aqnost~cs ::x~ber Erçer.val Cond

Index 1 5.92615 1.000 2 2.37419 1.580 3 1.53726 1.963 - .8:772 2.692 5 .66721 2.980 6 .45E90 3.594 7 -33715 4.193 E -27927 4.607 5 -21842 5.205 10 . - -15250 6.226 - - .13950 6.516 12 . - .C7951 8.662 - - .CL231 21.942

Variance C o n s c a t

. O004 3

.O0024

. O0112

. OC006

. O0697

. O0003 - O0036 - O0014 . CO164 -00046 .O0015 -0036E .9@472

. - * - . -

Sig T -0006 -0651 -0982 -2714 .2310 -6087 .O9 19 -6264 .8800 .9716 -0587 .6282 .2C 17

Proporcio AGE

.O0047

. O0026

.O0168

. O0004

. O0875 -00001 .O0051 .OC023 .O0495 .O13038 -00223 .O1393 .96657

RCI NG .O0121 .O3739 . O0125 -00998 -00268 -00938 -43469 -07327 . O0927 .O6779 .12511 -22771 .O0007

%TC ING .O0216 .O0394 .O5503 -02368 .O0040 .O7035 .O0007 . O0897 -02433 -59765 -02878 .la464 .00001

Beta .38783C -303665

- -302282 . L48290 -217350 .O69556 . 198127 .iO1856 .O25917 -007295

- -305G58 - -086434

Page 148

Appendix H. 1: Complete Table of PLS Saliences for the First Latent Variable of the LHD Croup

sUbTe8t S~ôte8î -1. Une Bire«ion -0-0 175 UnetCincelktion O. 396û -Sb- C8ncdktiori 0.6092 A

Dmwlngs O. 6864 Segment Side Anatomy Image Saliencar- Seg. Skie Anatomy Image 9 1 . çs0. Side * An8tomy- lm Sa

Lefî P-SU~- Lefî P-Sup Lefî Lat0 Left P-Sup Left P-lnf Lefi Lat0 Lefî P-Sup Left P-lnf Left Medû - Left Me& Right P-Sup Left P-Sup Right P-Sup Right M e d û Right SM Right P-Sup Lefi P-lnf

Right Medû Right Temp Lefi Temp Left P-Sup Right P-lnf Right P-lnf Right ACing Left LatO

Right Temp Right F-lnf Left P-lnf Left TH .

Rig ht P-Sup Left P-lnf Left P-lnf Left Temp Left TH

Right ACing Left LatO Right P-Sup Left P-lnf

Right ACing Right Temp Right P-lnf Left ACing

Right F-Sup Right F In f Right MedO Right SM Left ACing Left F-Sup Left Temp

Right F-Sup Rig ht F-lnf Right Temp Right SM

9-10 Right LatO -0.0835 S6-11 Right MedO - -0.0683 - S4-7 ~ i g h t - P-lnf -0.0821 S6-23 Left ACing -0.0669

S4-2 Right F-Sup -0.0821 S3-18 Left - SM -0.0669 S5-8 Right P-lnf 4.082 52-1 8 Left SM -0.0669 S6-17 Left Temp -0.082 S2-21 Left ~ 3 u p -0.0664 : S2-6 1 ~ i g h t -

- 55-3 Right' S7-3 - Right. : S7-0 : Right

- S5-9 Right; S7-4 Right

j S5-22 Left : S6-8 Right- S2-1 Right SI-1 Right - 55-23 - Left -

SS-1 Right - S c 1 ' Right- - 54-9 - ~ i g h t - S2-23: Left '

52-2 Right- S3-19 Left S2-7 . Right' S5-10- Right S6-14- Left S2-16 Left : S7-8 ' Right S2-19 Left -

bg r l Right : S5-4 : FIight- S3-7 Right. th rO 1 Right. S2-3 . Right

' S5-2 Right . S5-17 Left

1 S6-7 ~ i g h t - S2-20, Left S6-O Right

- S6-9 ' Right . : S4-5 ' Right 1 9 - 8 Right S2-4 Right

- S7-6 . ~ i g h t ' . 53-2 . Right' S6-22 Left .

S4-6 , Right. th r l Right'

' ~6-12' Left S6-18 L e y

' S7-19. Left S3-5 : Right. S4-19, Left S5-5 Right

- . P-lnf F-lnf F-lnf

ACing

Lat0 Temp F-lnf p-T

KSUP . F-Sup - ACing F-lnf F-lnf

P ~ U P ACing F-SUP

SM P-lnf - LatO LatO . P-lnf Temp SM BG SM

P-lnf TH

F-SUP A

F-lnf Temp Temp F-Sup ACing LatO ,

SM P-lnf SM

Temp .

ESup - F-lnf P-lnf TH

MedO Temp Temp SM SM

Temp

S2-22 1 Left : S3-1 Right

' ~7-16 1 Left 1 S7-2 Right S3-6 Rig ht S6-4 ~ i g h t -

' ~7-17 Left 54-17 Left S6-13 Left

1 51-3 1 ~ i g h t ' Si-O Right

- Sl-1 ~ i g h t ; S I 4 . Left -

S3-23 Left SI-2 ~ i g h t . S2-17 Left S4-18 Left si-22. Lefi S3-17 Left _ Si-7 Left S7-9 . ~ i g h t - S3-21 Left S7-7 Right : bg rO : Ftight : bg 10 Left

53-20 Left S3-22 Left SI-5 Left S5-18 Lefl S7-12 Left Si-6 . Left S7-21 Left S7-15 Left

1 SS-i 9 : Left I S7-13 Left . S6-21 Left bg II Left

' ~7-14 Left 2 1 Left . 85-21 Left S7-10 Right S4-20 Left S7-20- Left : S6-19. Lefl .

S5-20 Left -

S7-11 Right S6-20' Left -

ESUP : F-Sup . Temp F-lnf P-lnf Temp Temp P-lnf Lat0 .

P-Sup - ACing SM

P 3 J p ACing P-lnf P-lnf SM

F-lnf P-lnf - -

ACing LatO

F-Sup Temp BG BG

F-SUP. . F S J P P-lnf Temp MedO . SM

F-lnf - -

Temp SM

LatO F-lnf =G .

LatO F-Sup F-inf LatO

F-Sup F-1 nf Temp F-lnf MedO F-I nf

. -

S5-O ~ i g h t ACing -0.0837 S6-10 Right Lat0 -0.0683

Page 149

Appendix H.2: Complete Table o f PLS Saüences Cor the Second Latent Variable of the LHD Croup

Left Le ff Left Left

Right Leff Le fi Le fl Lefl Left Lefl Left Left Left Left Righ t Left Left Left Right Left Left

Right Lefl Le fi

Right Lef? Le ft Left Left Left Left Left Left Left Left Left Left Left Left Left

Right Left Le ft Le ft

Right Le ft Right Left Left Left

Right Right

F-lnf F-lnf F-lnf F-lnf F-ln f F-lnf femp LatO -

SM LatO F-lnf BG

Temp SM

F-lnf ACing SM BG

ACing F-lnf F-SUP Temp F-lnf ACing LatO F-lnf Temp F-lnf F-lnf SM

MedO .

F-S~P LatO SM TH

MedO KSup . F-sup .

ACing P I nf Temp F-Sup F-Sup p-sup Tem p F-lnf

p-Sup ESup P-lnf SM

P-lnf F-lnf ACing

SubTert A S u M W SII. Dirwings -0.2828 - Une Cincelktion 4.0557

- Shape Cancelktion 0.3803 Une Biseaïon 0.8788

Segment Side ~natomy-image Satiences- Seg. Si& Anatomy Image al. Seg. Side Anatomy Image 5.1 S5-6 Right Ternp -0.0194 S6-12 Left MedO 0.0417 th 10 Left - TH

:ç3_i6 Lef t P-lnf . S c 4 Right SM S5-O Right. ACing S6-13 Left - LatO ~3-14 Left P-Sup S c 6 'Right, P-lnf

1 S4-2 Right F-Sup , bg r l Right] BG S2-16 : Left P-lnf -

SS-1 Right F-lnf S4-5 ~ igh t : SM :

- S<I 6 Left - P-~nf - ~2-18 1 Left SM .

S6-10 Right- LatO 1 S5-4 Right SM

S4-1 Right F-Sup .

S7-10:~ight1 LatO 53-1 2, Left , Medo 53-21 Left F-Sup S5-10Right LatO SC23 - Left _ ACing S7-14- Left LatO S2-15 Left . P-lnf

:s~-Is: L e t p-~nf 1 Sô-4 Right Temp . S5-9 Right MedO .

S5-5 ~ i g h t Temp S7-18 Left Temp S6-9 - Right . LatO S5-15 Left P-lnf bg rû Right BG

' S4-û Right ACing S2-14. Left . P-Sup -

S3-6 Right P-lnf S2-5 Right SM S3-4Right SM . 57-8 ' Right Ternp ~ 3 - 2 -Right F-Sup thrû Right TH -

S2-4 Right SM S2-21 Left F-Sup S2-3 Right. F-Sup ,

S5-11 Right- MedO Si-1 Right SM S3-11' Right ] MedO

. 52-6 Right P-lnf .

S6-11 Right MedO . : S6-15 Left : Temp -

S6-5 Right- Temp - S5-16 ' Left . P-lnf

S i 3 Right S3-O Right S6-8 Right S7-9 - Right SS-7 Right S7-3 Right S4-9 Right S6-6 Right S c 7 ' Right : ~ 1 5 : Left j S2-2 - Right S2-10. Right 1 S7-12 Left S3-9 . Right S5-8 Right SI-5 Left S4-11 Right 53-22 Left : S3-5 Right 3 Right - S6-16 Left : S2-7 : Right - S7-11 Right' S4-10 Right 1 S2-13 Left - S7-13 Left 57-15 Left ,

S7-7 Right S2-22 Left S3-7 Right S6-7 Right . S3-10 Right SI-6 Left Si-2 Right S2-9 Right : SI-4 Left SI-O Right -

S3-8 Right S2-1 Right S7-4 Right

S7-16 Left . S2-12 Left S4-8 Right S2-O , Right ,

S3-23 Left S2-8 . Right , S7-5 Ri'ht SI-7 , Lely S2-23. Le/t 57-17 Left S7-6 Right '

p-sup ACing p-T ,

LatO P-i nf F-lnf

P-SUP Temp P-lnf P-l n f ESup - P-Sup Medo - P-Sup P-lnf P-lnf MedO F-Sup

SM F-Sup .

Temp P-l nf MedO Lat0

p-sup LatO Ternp Temp E S u p P-I nf Temp p-sup .

SM P-I nf

P-Sup -

p-sup ACing P-lnf F 3 u p - Temp Temp F S u p .

P-lnf ACing ESup P-1 n f T-P- . ACing

- T-P .

T ~ P S2-11 Right P-Sup 0.0389 ,

. -

S5-2 Right F-lnf ' th r l * ~ i q h t - TH 0.0408

Page 150

Appendix H.3: Complete Table of PLS Saliences for the Third Latent Variable of tbe LHD Group

SubTe8î SubtW SII. Shaw Cancelktion -0.6799 Dmwings 0.3294 Une Bisection 0.4315 Lino Cmcellrtion 0.493 1

Segment Side Anatomy Image ~aliencas- Seg. Side AMtomy Image SaI. Seg. Si& ' Anatomy I m ~ e Sal 52-23 Left ACing bg rl Right BG

~ i s h r Right Right Left Left Left Left Left Left Left

Righ t Righ t Right Right Right Right Left

Right Le ft Le ft

Right Right Right Le ft Leit

Right Le fi Le ft Left

Right Right Left

Right Left

Right Right Right Right Left

Right Left Le ft Left

Right Left Le ft Left Le ft Left Le ft

Right

SM P-lnf TH

Temp Temp LatO SM

P-SUP SM

Temp P-lnf

P ~ U P F-s u P P-1 nf F-Inf F-I nf LatO

F-sup P-I nf

p-sup Tem p LatO P-I nf Temp FSJp p-T P-lnf ACing P-lnf SM

p-sup p-sup LatO F-I nf Temp P3up ACing

SM Temp F-I nf

F-SUP P3up

SM P-lnf p-s u p F-lnf SM SM SM

Temp Temp

] S&l l j Right MedO # S7-21 Left F-tnf

] 53-8 : Right P-lnf S2-1 , Right. F-Sup S3J , Right ' ACing -

- S5-5 .Right- Temp S3-9 Right- P-Sup

- S2-3 1Right- F-Sup -

S2-8 .Right- P-lnf -

bg 11 Left BG 1 S6-16- Left 1 Temp S c 1 0 Right Lat0 - S2-O I ~ i g h t - ACing S5-4 Right- SM S5-22 Left F-lnf S5-6 - Right Temp S7-15 Left Temp S2-2 Right. F-Sup - S2-19 Left , SM S3-17 Left P-lnf S5-23 Left ' ACing : S5-10 Right - LatO : ~5-13: ~ e f t LatO S3-5.Right SM

-S4-f 1 Right- MedO -

S4-3 Right F-Sup S2-5 R i g h t SM S3_6 ~ i g h t P-lnf -

S7-13. Left LatO S6-13, Left , LatO S6-21. Left F-lnf : SI-2 Right P-lnf -

S c 6 1Right' P-lnf -

S5-2 Right. F-lnf bg IO Left . BG

S2-10. Right P-Sup S7-8 ~ i g h t Temp S7-19. Left ' Temp S7-3 ~ i g h t ' F-Inf '

S5-16 Left ' P-lnf '

1 ~6-22. Left ' F-lnf .

~ 7 - 6 . Right Temp ' ~ 1 2 ' Left : Lat0 . S2-16 Left , P-lnf SS-21 Left , F-lnf S c 2 ' Right F-lnf - S5-O ' Right: ACing S2-7 , Right P-lnf S7-5 ~ i g h t ' Temp S2-9 ' Right P-Sup S4-21' Left F-Sup ~5-20' Left , F-lnf S5-7 Right P-lnf

-0.0126 ~5-11 . Right -0.0126 S4-5 Right -0.0122 S3-23' ~ e f t -0.01 18 S-20 : Left : -0.0114 S3-16- Left -0.01 13 S U - Right 1 -0.01 1 th I l Left -0.01 03 S6-5 - Right 1 -0.0094 54_16- Left -

-0.0083 S4-2 Right -0.0063 S7-9 Right 4.0057 S6-6 Right -0.0057 S5-3 , Right -0.0057 S4-19 Left -0.0051 S7-18 Left -0.004 S I 4 . Left -0.0001 S7-12 Left .

0.0004 . S4-17 Left 0.002 S3-2 Right 0.0036 , S3-22 Left 0.0057 S4-O Right 0.0064 S5-1 Right 0.064 52-17 Left 0.0089 S5-9 Right 0.0097 ' S3-12 Lefî 0.0109 S7-17 Left 0.0136 56-1 7 Left 0.01 37 bg rO - Right ' 0.01 56 53-3 Right 0.0168 S7-4 Right 0.01 82 55-12 ' Left . 0.0194 S7-14, Left -

0.0203 S3-14 Left 0.0205 S7-11 Right 0.0216 S3-11 Right- 0.021 8 S6-14 1 Left 0.0222 . S4-22 . Left 0.0224 thrO .Right 0.0245 S6-23. Left +

0.0266 th lO . Left ,

0.0267 S4-15 Left . 0.0288 S3-4 Right 0.0298 SZ-22 Left '

0.031 7 S6-12, Left - 0.032 57-20 Left -

0.0342 S3-21 . Left 0.0344 . S7-1 Right 0.0356 SI-7, Let! 0.036 . s2_ r i aight

0.0378 S7-2 Right j 0.038 S2-21 Lefl 0.0385 S7-23 Len 0.0389

MedO SM

ACing F-sup P-lnf ACing

TH Temp P-lnf F3up LatO Temp F-lnf SM

Temp P-SUP MedO P-l n f

F ~ U P F S J P ACing F-lnf P-lnf LatO

MedO Temp Temp BG

F-Sup Temp MedO LatO

t s u p MedO MedO Lat0

F-Sup TH

ACing TH

P-lnf SM

F B J p MedO F-I nf

ESup F-I nf ACing p,Sup F-/nf

F,Sup A Cing

S6-9 ~ i g h t ~ a t 0 -0.0201 S2-15 ' Left P-lnf 0.041 6

Page 151

Right Right Right Right Right Right Right Right Right Right Right Right Right Right Right Right Right Right Right Right Right Righ t Right Right Left Right Right Right Right Right Right Le ft

Right Right Right Left

Right Right Right Right Right Le fî

Right Left Left Left

Right Right Right Right Right Right Left

Lam Lat0 ,

p-1 .

P-lnf p-sup P-Sup Lat0 P-lnf P-lnf LatO Temp Lat0 MedO MedO LatO Temp P-lnf T @ ~ P LatO Temp P-lnf P-lnf

P 3 J p MedO ACing Tem p AC i ng Temp Temp AC i ng Temp ACing F-lnf

F-Sup F-Su p MedO ACing p-sup P-I nf ACing P-lnf MedO .

P-I nf ACing p-sup ACing

BG P-lnf F-lnf BG

F-Sup SM

LatO S5-1 Right F-lnf -0.0752 S6-14 Left Lat0 -0.0566

Page 152

56-13 Left LatO : Sô-4 a Right S3-3 . Right. S6-23. Left . a 4 Right

157-13 Left S5-4 Right S3-5 ~ i g h t - S5-5 Risht

- S2-5 ~ i g h t - th rO Right

: ~ 3 _ i 4 Lett S2-10 Right S6-2 , Right

. SC3 , ~ i g h t : th r i Right S2-4 , Right

' S3-1 Right. : ~3-13: Left : . S2-14 - Left .

56-3 - Right S2-12 Left S I 4 Left S6-12 Left S6-0 Right 3 -6 Right- 57-4 'Right

. S7-11 ~ i g h t S3-11 ' ~ i ~ h t '

1 5 2 2 ~ igh t . S3-20 Left

- S4-1 ' Right Si-O ~ i g h t - S4-19 Left S2-22 Left S4-20 Left S7-3 Right S2-3 Right SI-7 Left S3-15 Left S4-13 Left

Temp F-Sup ACing SM

LatO SM SM T

SM TH

P-SUP P-Sup F-l n f

ESup TH SM

F-Sup P-sup p-Sup F-lnf

p-sup p-sup LatO

ACing P-lnf Temo MedO MedO F-Sup ESup ESup ACing

SM ESup F-Sup F-1 nf

ESup ACing P-I nf LatO

. s4-14 1 Left 1 SI-5 Left : S6-1 ~ i g h t - S6-15 Left Si-1 Right S3-12 Left -

S7-14 Left S4-5 Right. S3-22, Left S4-15- Left ,

S2-11 Right - S4-22' Left

Appendix H.4: Complete Table of PLS Saliences for the First Latent Variable of the RHD Croup

SubTert - Subtesî Sal. Olrwings 0.6382 -

Une Biisction 0.4836 _ Cine Clncellrtion 0.4426 -

Shrpe CInc+tion 0.4036 -

Segment Side Anatomy . Image Saliant& Seg. S i g Anatomy image 5.1 .

1

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Seg. ' Side - Anatomy - Image Sa1 S3-19 Left SM -0.0565 S3-18 Lef! SM -0.0562 S5-22 Left F-lnf -0.0558 S3-17: Left P ln f - -0.0555 SI-2 Right- P-lnf - -0.0535

S2-15 Left P-lnf -0.0534 S3-16. Left P-lnf -0.0532 S5-15 Left . P-lnf -0.0526 S2-21 Left . F-Sup , -0.052 ~5-20- Left F-lnf -0.052 SS-19' ~ e f t SM -0.051 9 S5-14 1 ieft ' LatO . -0.051 5 S2-18 Left SM -0.051 1 S I - Left SM -0.0509 S2-20. Left F-Sup -0.0505 SI-3 .Right' P-Sup ' -0.0499 S7-15, Left Temp -0.0488 S4-16, Left P-lnf -0.0486 S4-21. Left , F-Sup -0.0486 S3-21. Left F-Sup -0.0479 52-17 Left P-fnf -0-0461 S4-17 Left P-lnf -0.0458 55-21 Left F-lnf -0.0457 S5-16 Left P-lnf -0.0456 56-22 Left F-lnf -0.0439 57-1 2- Left MedO , -0.0424 52-1 6 - Left P-lnf -0.042 52-19. Left ' SM - -0.042 S4-18.Left' SM - -0.041 4 bg IO , Left BG - -0.0412 S6-21,Left F-lnf -0.0398 S6-16 Left Ternp , -0.0397 S7-23 ' Left ACing -0.0393 S7-2 Right i l n f -0.0379 th 10 Left TH -0.036

56-20 Left F-ln f -0 .O349 35-17, Left Temp -0.0346 36-19 Left Temp -0.0322 35-1 8. Left Temp - . -0.0306 57-16, Left Temp -0.03 56-1 8- Left , Temp -0.0292 bg11 Left. BG -0.0255 S7-1 . Right F-lnf -0.0254 37-20' Left : F-lnf ' -0.0254 37-19 Left Temp -0.0227 36-17 Left Temp -0.0215 th11 Left- TH -0.01 94 37-18: Left Temp : -0.0156 S7-O , Right, ACing -0.01 25 57-17. Left Temp -0.0062 57-22 Left F-lnf ~. -0.0046 S7-21: Left F-lnf 0.0039