1
Department of Neurology
University of Helsinki
Functional imaging of peripheral vision and dorsal visual
stream in the human cerebral cortex
Linda Stenbacka
Brain Research Unit and Advanced Magnetic Imaging Centre,
Low Temperature Laboratory,
Aalto University
Academic Dissertation
To be publicly discussed by the permission of the Faculty of Medicine of the University of
Helsinki in the Lecture Hall S1, Aalto University, Otakaari 5A, on May 6th 2010 at 12 noon
2
ISBN 978-952-92-7258-7 (nid.)
ISBN 978-952-10-6256-8 (PDF)
Picaset Oy
Helsinki 2010
3
Supervisor:
Docent Simo Vanni
Brain Research Unit and Advanced Magnetic Imaging Centre,
Low Temperature Laboratory
Aalto University
Espoo Finland
Reviewers:
Dr. Iiro Jääskeläinen
Department of Biomedical Engineering and Computational Science
Faculty of Information and Natural Sciences
Aalto University
Espoo Finland
Docent Jyrki Mäkelä
Biomag Laboratory
Helsinki University Central Hospital
Helsinki, Finland
Opponent:
Professor Yves Trotter
Centre de Recherche Cerveau et Cognition
Faculté de Médecine de Rangueil
Toulouse France
4
List of Contents
Abstract 6
Abbreviations 8
List of publications 9
1. Introduction 11
2. Review of the literature 14
2.1. Overview of neural transmission in central nervous system 14
2.2. Vision system 16
2.2.1. Visual information processing starts already in the retina 16
2.2.2. Large portion of cortex is sensitive primarily to visual stimulation 18
2.2.3. Properties of a neuron in primary visual cortex depends on
the signal and brain state 22
2.2.4. Extrastriate visual cortices are specialised 24
2.2.5. Information from peripheral and central visual field serves
partially different purposes 26
2.2.6. Functional visual areas V6 and V6A are related to visuomotor
processing 27
2.2.7. Guidance of saccades and spatial attention share a network
of brain regions 30
2.3. Magnetoencephalography 34
2.3.1. Principles 34
2.3.2. Source modelling 35
2.4. Functional magnetic resonance imaging 37
2.4.1. Principles 37
2.4.2. Data analysis 39
2.5. Retinotopic mapping 41
3. Aims of the study 43
4. Materials and methods 44
4.1. Subjects, stimuli, and tasks 44
4.1.1. Subjects 44
4.1.2. Visual stimuli and tasks 44
4.2. Measurements 46
4.2.1. fMRI measurements 46
4.2.2. MEG measurements 47
4.3. Data analysis and visualisation 48
4.3.1. Analysis of fMRI data 48
4.3.2. Analysis of MEG data 49
4.3.3. Statistics 49
4.4. Eye movement recordings 50
5. Experiments 51
5.1.Comparison of minimum current estimate and dipole
modelling in the analysis of simulated activity in
the human visual cortices (Study I) 51
5.1.1. Methods 51
5.1.2. Results 52
5.1.3. Discussion 53
5.2. fMRI of peripheral visual field representation (Study II) 54
5.2.1. Methods 54
5.2.2. Results 55
5
5.2.3. Discussion 58
5.3. Central luminance flicker can activate peripheral retinotopic
representation (Study III) 59
5.3.1. Methods 59
5.3.2. Results 60
5.3.3. Discussion 61
5.4. Peripheral visual field representation activates during saccades
in darkness (Study IV) 63
5.4.1. Methods 63
5.4.2. Results 64
5.4.3. Discussion 66
5.5. Topography of attention in the primary visual cortex (Study V) 68
5.5.1. Methods 68
5.5.2. Results 69
5.5.3. Discussion 70
5.6. Motion sensitivity of human V6: A magnetoencephalography
study (Study VI) 71
5.6.1. Methods 71
5.6.2. Results 71
5.6.3. Discussion 72
6. General discussion 74
6.1. Controlling methodological confounds in MEG and fMRI 74
6.2. Human visual area V6 75
6.3. Top-down modulation of V1 77
6.4. Peripheral visual field representation in human parieto-occipital
sulcus 78
7. Conclusions 81
8. Acknowledgements 84
9. References 86
6
Abstract
Visual information processing in brain proceeds in both serial and parallel fashion
throughout various functionally distinct cortical areas. These areas can be organised
hierarchically according to neurons’ response profile and interareal connections.
Feedforward signals from the retina and hierarchically lower cortical levels are the major
activators of visual neurons, but top-down and feedback signals from higher level cortical
areas have a modulating effect on neural processing.
Modern imaging methods enable in vivo studies of human brain function. This thesis utilises
magnetoencephalography and functional magnetic resonance imaging for the measurement
of cortical responses during visual stimulation and oculomotor and cognitive tasks from
healthy volunteers. Magnetoencephalography measures the electromagnetic signal of neural
activation and provides high resolution in the temporal domain, whereas functional magnetic
resonance imaging detects the hemodynamic response to neural activation and is spatially
accurate but temporally bound to the hemodynamic delay. The use of both methods provides
temporally and spatially accurate knowledge of visual processing but also forces to consider
the limitations of the methods, the first aim of this work.
This thesis concentrates on hierarchically low level cortical visual areas in the human brain
and examines neural processing especially in the cortical representation of visual field
periphery. Previous evidence suggests that the visual field location could be one basis for the
division of visual encoding into the functionally segregated streams of cortical areas. The
second objective of my work was to develop methods for the stimulation of peripheral visual
field, to map the cortical representations of peripheral visual field in retinotopic visual areas,
and to study the functional properties of peripheral vision. The stimulation of peripheral
visual field enabled delineation of the putative human homologue of monkey visual area V6,
the third aim of this thesis. Substantial knowledge of brain function comes from animal
studies and the question of interspecies differences in visual cortical organisation arises
when the evidence from human neuroimaging is interpreted in the context of animal studies.
It is argued that homologous visual areas should have similar relative position and response
profile. This thesis aims to study the putative human V6. The fourth aim of my work was
examine the top-down modulation in hierarchically low cortical levels by measuring the
7
effect of attention and voluntary movement on retinotopic visual areas in the medial surface
of occipital lobe.
Visual cortex forms a great challenge for the modelling of neuromagnetic sources because
multiple neighbouring visual areas have temporally highly overlapping responses. This
thesis shows that a priori information of source locations are needed for neuromagnetic
source modelling in visual cortex (study I). In addition, this work examines other potential
confounding factors in vision studies: The optical properties of the eye can lead to light
scatter which may result in erroneous responses in cortex outside the representation of the
stimulated region (study III), and eye movements and attention increase responses and thus
confound the quantitative interpretations of the BOLD signal if not controlled (studies IV
and V).
This thesis demonstrates that the peripheral visual field representation of low-level visual
areas extends to the anterior part of calcarine sulcus and to the posterior bank of parieto-
occipital sulcus and the peripheral vision is functionally related to eye-movement processing
and connected to rapid stream of cortical areas that encode visual motion (studies II and IV).
My results identify the putative human V6 region in the posterior bank of parieto-occipital
sulcus (study II) and show that human V6 activates during eye-movements and responds to
visual motion at short latencies (studies IV and VI). These findings contribute to the
evidence that the human homologue of monkey V6 is located in parieto-occipital sulcus and
suggest that human V6, like its monkey homologue, is related to fast processing of visual
stimuli and visually guided movements. In addition, my work demonstrates two different
forms of top-down modulation of neural processing in the hierachically lowest cortical
levels. First, I found responses during eye-movements that are related to dorsal stream
activation and may reflect motor processing or resetting signals that prepare visual cortex for
change in the environment (study IV). Second, I show local signal enhancement at the
cortical representation of the attended visual field region that reflects local feed-back signal
and may perceptionally increase the stimulus saliency (study V).
8
Abbreviations
ANOVA Analysis of variance
BALC Brain ála Carte
BEM Boundary element model
BOLD Blood oxygenation level dependent
DLPFC Dorso-lateral prefrontal cortex
ECD Equivalent current dipole
EEG Electroencephalography
EPI Echo planar imaging
FEF Frontal eye field
FDR False discovery rate
fMRI Functional magnetic resonance imaging
FWE Family wise error
GLM General linear model
HRF Hemodynamic response function
IPS Intraparietal sulcus
IPS-1 – 4 Visual areas in intraparietal sulcus
LGN Lateral geniculate nucleus
LIP Lateral intraparietal area
LO-1 & 2 Visual areas in lateral occipital cortex
MCE Minimum current estimate
MEG Magnetoencephalography
mf Multifocal
mffMRI Multifocal fMRI
MIP Medial intraparietal area
MNE Minimum norm estimate
MNI Montreal neurologic institute
MRI Magnetic resonance imaging
PCC Posterior cingulate cortex
PET Positron emission tomography
PHC-1 & 2 Visual areas in parahippocampal cortex
PO Parieto-occipital
RF Radiofrequency
ROI Region of interest
SEF Supplementary eye field
SNR Signal-to-noise ratio
SPM Statistical parametric map
TMS Transcranial magnetic stimulation
V1 Primary visual area, striate cortex
V2–V7 Extrastriate visual areas 2-7
V6A, V3A, V3B Visual areas 6A, 3A and 3B
VIP Ventral intraparietal area
VO-1 & 2 Visual areas in ventral occipital cortex
9
List of publications
This thesis is based on following six publications which will be referred with roman
numerals I – VI
I Stenbacka L, Vanni S, Uutela K and Hari R. Comparison of minimum current estimate and
dipole modeling in the analysis of the simulated activity on human visual cortices.
NeuroImage 2002 Aug;16(4):936-943
II Stenbacka L and Vanni S. fMRI of peripheral visual field representation. Clinical
Neurophysiology 2007 Jun;118(6):1303-1314
III Stenbacka L and Vanni S. Central luminance flicker can activate peripheral retinotopic
representation. NeuroImage 2007 Jan1;34(1):342-348
IV Stenbacka L and Vanni S. Peripheral visual field representation activates during saccades
in darkness. Submitted
V Simola J, Stenbacka L and Vanni S. Topography of attention in the primary visual cortex.
European Journal of Neuroscience 2009 Jan;29(1):188-196
VI von Pföstl V, Stenbacka L, Vanni S, Parkkonen L, Galletti C and Fattori P. Motion
sensitivity of human V6: A magnetoencephalography study. NeuroImage 2009
May1;45(4):1253-1263
The author’s contribution:
I was a principal author in studies I-IV. In study I, I was responsible for the analysis of the
data and the interpretation of the results. I had a major role in writing the manuscript and I
collaborated with the other authors on the study design and data collection. In studies II-IV, I
was responsible for the measurement and the analysis of the data and had a major role in the
writing of the manuscript. We interpreted the results and designed the studies together with
the second author. In studies V and VI, I collaborated with the other authors on the study
design, measurement of the data and writing of the manuscript.
10
11
1. Introduction
Vision sense enables recognition of an object already at far distance and creation of spatially
accurate model of both the close and far visual environment. Seeing is essentially a cognitive
process. Brain builds a three-dimensional model of the visible world from the two-
dimensional light distribution that the relatively simple optic system of the eye has projected
onto the retina. This model and the resulting percept depend on the visual environment, input
from other senses, state of mind, and previous knowledge. The brain is able to renew the
model according to environmental changes, predict these changes, and create relevant
actions to them.
Visual processing in the central nervous system has traditionally been viewed as a diverse
and a hierarchical process. Light activates photoreceptors in the retina and the resulting
neural signal is processed in several consecutive steps first in various subcortical nuclei and
then in numerous cortical areas (Grill-Spector and Malach, 2004). The processing starts from
simple features such as contrast borders (Ferster and Miller, 2000) and finally primarily
visual processing turns into multisensory and guidance of motor actions (Andersen and
Buneo, 2002). In addition, different components of the visual signal are, to an extent,
processed in parallel (Ungerleider and Mishkin, 1982; Livingstone and Hubel, 1988; Lennie
and Movshon, 2005). However, accumulating evidence suggests that feedback from “higher-
level” visual areas and top-down signals from cognitive, multisensory, and motor systems
modulate the visual signal even in the earliest levels of the processing hierarchy and parallel
streams of visual processing are highly interactive (Bullier and Nowak, 1995; Albright and
Stoner, 2002; Gilbert and Sigman, 2007).
The visual system is the focus of large amount of research from cellular level to cognition
studies with wide range of animals from simple life forms, such as drosophila, to humans.
Previously examination on the impact of brain lesions as well as psychophysical
investigations have provided much information on the organisation of the human visual
system, but most knowledge on its neural basis has been acquired from electrophysiological
studies in nonhuman primates and cats. However, modern brain imaging methods developed
within the last decades enable detection of neural activity in the intact living brain, and
extend studies of human vision from detection of perception and action to measurement of
neural response. The imaging methods are based on different principles.
12
Electroencephalography (EEG) and magnetoencephalography (MEG) measure the
electromagnetic signal from postsynaptic potentials directly, whereas functional magnetic
resonance imaging (fMRI) detects the hemodynamic response to neural activation, and
positron emission tomography (PET) measures either hemodynamic response or uptake of
labelled molecules by activated neurons.
All imaging methods have limitations. EEG and MEG are temporally very accurate but the
localisation of neural activation is ambiguous due to non-unique inverse problem. In
contrast, fMRI is spatially accurate but its temporal resolution is compromised by the
hemodynamic delay. In addition, neurovascular coupling is not fully understood (Logothetis
and Wandell, 2004). Studies I, II, and III explore some of the limitations which need to be
considered in experimental work. Study I compares two methods for estimating the neural
source in MEG and shows that a priori information of neuromagnetic sources is needed for
localisation of close simultaneous sources in visual cortex. Study II aims to overcome the
limitation of narrow visual field in fMRI and describes a method for wide visual field
mapping. Stimulation of peripheral visual field is a challenge in a narrow magnet bore where
visual display setup has limited the field of view. Study III suggests that light scatter in the
eye can form a significant confounding factor with high luminance contrast stimuli.
The visual cortex can be divided into functionally separate regions, even though the exact
delineation is still under debate (Wandell et al., 2007). Functional areas can be arranged
hierarchically on the basis of their interareal connections (Felleman and Van Essen, 1991).
Functional areas at the bottom of the hierarchy show clearest retinotopic organization and
retinotopic mapping can be used for their localisation. Study II mapped the medial occipital
retinotopic areas and their representation of the peripheral visual field. The use of peripheral
visual stimuli enabled the localisation of human visual area V6, whereas study III showed
that, in contrast to previous hypothesis, a central luminance flicker stimulus is not an
adequate functional localiser of V6. Studies IV and VI investigated V6 region and showed
activation of V6 during saccades (study IV) and demonstrated motion sensitivity and short
response latency of V6 (study VI). These results suggest that human V6, like its monkey
homologue, belongs to dorso-medial processing stream that controls visually guided
movements (Rizzolatti and Matelli, 2003).
Studies IV and V utilise the relatively good spatial resolution of fMRI to examine the impact
of top-down modulation on the hierarchically lowest cortical visual area, V1. In study V, the
13
mapping of representations of multiple visual field locations shows the spread of visually
evoked responses in primary visual cortex during spatial attention to stimulus location. The
width of the response spread is in line with the top-down signal (Angelucci and Bullier,
2003). Study IV located eye movement –related neural activation to peripheral visual field
representations in the hierarchically earliest visual areas and showed different neural
responses in central and peripheral representations. Distribution of the responses and
simultaneous activation of dorsal stream cortical areas suggest a dorsal stream origin of the
responses.
14
2. Review of the literature
2.1. Overview of the neural transmission in central nervous system
The following chapter is based on the book of Kandel, Schwartz, and Jessell (2000) and
reviews of Bullier (2004a), Callaway (2004), Gilbert and Sigman (2007), and Saalmann and
Kastner (2009). Neurons and supporting glial cells mainly form the central nervous system,
the former being responsible for signal transmission. Within neurons signals are transmitted
electrically whereas the signal of presynaptic action potential is transferred chemically
across the synaptic cleft to the postsynaptic neuron. Like in all cells, a potential difference
exists across the neural cell membrane, but in neurons the membrane potential can be
modulated and reversed. The membrane potential builds up from concentrations of ions
inside and outside of the neurons that aim to reach equilibrium between chemical and
electrical forces. The ions can move only through protein channels because the lipid bilayer
of the cell membrane is impermeable for the charged particles. The number of open ion
channels can change according to neural state and environment, resulting in
hyperpolarisation and depolarisation of the cell membrane. Temporal and spatial summation
of these potentials may result in action potential i.e. depolarisation of the axonal cell
membrane. In contrast to postsynaptic potentials, action potentials are discrete and the signal
is embedded within the frequency of action potentials.
The signal is processed in a network of neurons. Neural networks in the brain integrate
signals from multiple sources. On the other hand the signal diverges in cortical networks into
separate streams, functional areas, and local intra-areal neural circuits. According to the
classical feedforward model, visual processing begins from simple attributes and the input to
subsequent steps of neural processing is a result of the previous steps, with the response
properties of a neuron at the higher level reflecting a combination of those at the previous
levels. However, recent evidence has challenged this model. Currently it is believed that
response properties of a neuron result from an interaction between local circuits and
feedforward and feedback connections. Feedforward connections are thought to be the main
driving force of the neurons whereas feedback signal and top-down information have a
modulatory role. Thus, accumulating evidence suggests that initial brain state can modulate
neural processing. Analysis of visual information in turn modifies the brain state which
provides top-down source for the signal processing.
15
Glial cells have an important homeostatic role. Astrocytes support neurons by providing
lactate molecules as fuel for citric acid cycle of neurons and by being part of
neurotransmitter cycle. Oligodendrocytes form myelin sheet around the axons to increase the
conduction velocity. Brain tissue has a strong local autoregulation of the blood flow. The
increased activation of the nerve cells results in increased demand of glucose and oxygen.
The blood flow normally responds to increased demands. Cells’ energy storage (adenosine
triphosphate) is utilised in synaptic transmission and maintenance of ion concentration, and
largest portion of energy consumption is attributed to the post-synaptic effects of
neurotransmitters. Chemical vasodilatators including adenosine and neurotransmitters
glutamate and GABA are released from the activated neurons. Nitric oxide mediates
vasodilatation that propagates in retrograde fashion into upstream arterioles. In addition,
local neural circuits regulate the diameters of arterioles and thus the perfusion rate.
16
2.2. Vision system
2.2.1. Visual information processing starts already in the retina
Light enters the eye via pupil and is refracted when it travels through the cornea and lens. In
emmetropic eye the refraction focuses light onto the retina. However, the refraction power of
cornea and lens and the length of the eye may be imbalanced, and lens inhomogeneities
affect the light refraction. A large pupil increases these distortions, whereas a small pupil
aperture results in light diffraction. These mechanisms result in blurred retinal image
(Wandell, 1995). In addition, light is scattered when it passes cornea and lens and hits the
retina (Vos, 2003). As a consequence of intraocular scattering of light the retinal
illumination does not optically correspond to the direction of light (Ijspeer et al., 1990).
Scattering of bright light causes a phenomenon called disability glare; a veil of light and
lower contrast elsewhere. Intraocular scattering and disability glare increase with age and
can be noticed for example when driving a car in the dark.
Neural visual signal is generated in the retina. The retina contains a complex network of
cells; photoreceptor cells are located in the outermost layer and the inner layers contain
neurons. Thus, light travels through neural layers before it reaches photoreseptors. Light
changes conformation of visual pigment molecules of photoreceptors, which results in neural
signal (Kandel et al., 2000). Human retina contains four types of photoreceptors and visual
pigment molecules. Pigment molecules in the rods are sensitive to all wavelengths within
visible light spectra already at very low intensities. Three types of pigment molecules in the
cones have a different wavelength sensitivity profile which enables colour perception. All
photoreceptors make distinct connections and they are unevenly distributed across the retina.
Rods are relatively more common towards the periphery and are totally absent in the fovea,
whereas cones are more abundant in central retinal regions. S-type cones are sensitive to
shortest wavelengths and are located relatively sparsely outside the foveal region, whereas l-
and m-type cones which are sensitive to longer wavelengths compose the fovea and are
unevenly distributed outside the fovea (Wandell, 1995; Dacey, 2000; Gegenfurtner and
Kiper, 2003).
Retina belongs to central nervous system. It forms a network in which signals pass through
bipolar cells to ganglion cells. The retinal network converts input from the photoreceptors
into information on the spatial and temporal contrasts of the light intensity (Field and
17
Chichilnisky, 2007). Bipolar and ganglion cells have circular receptive fields consisting of a
centre and a surround that oppose each other (Kuffler, 1953). Ganglion cell responses show
several non-linearities which represent for example as light adaptation (Purpura et al., 1990).
Retina comprises of multiple ganglion cell types and the relative density of different
ganglion cells varies according to retinal eccentricity. Each ganglion cell type has its own
characteristic structure, connectivity, physiological properties, and central projections and
each type feeds anatomically and functionally distinct parallel visual pathways (Dacey,
1994, 2000; Field and Chichilnisky, 2007).
From the retina the neural signals travel via the visual nerve, formed from the axons of the
retinal ganglion cells. Axons from the nasal hemiretina cross, at the optic chiasm, to the
contralateral hemisphere, resulting in a representation of the contralateral visual field.
Retinotopy is defined as orderly presentation of visual representation that follows the
topography of the visual field. Retinotopy is present in the retina and it is conserved later in
visual system where the information from different parts of the retina is processed in parallel
(Wandell, 1995).
The next synapse in the afferent visual pathway is in the lateral geniculate nucleus (LGN) of
the thalamus. The neurons in the LGN have similar circular centre-surround receptive fields
as ganglion cells, and they behave non-linearly which can, for example, dampen responses to
increases in stimulus luminance in order to maintain neural sensitivity even at a wide range
luminance values (Garandini et al., 2005). The LGN is anatomically separated as magno-,
parvo-, and konio-cells are arranged into distinct layers. Receptive fields of parvo-cells are
smaller and they are more sensitive to high spatial frequencies. Parvo-cells are also sensitive
to red-green contrasts whereas the magno-system, responds with shorter delays and more
transiently to visual stimulation and is more sensitive to low stimulus contrasts (Livingstone
and Hubel, 1988). The third, less studied cell group, the konio-cells, relay both low-acuity
visual information and blue-yellow contrast and have extrastriate projections and
connections with superior colliculus (Hendry and Reid, 2000). Importantly, the feedforward
signal is modified in the LGN, indicating that it is not a mere relay nucleus. In fact, the
majority of LGN input comes from feedback sources and cortical feedback signal have been
shown to modulate neural responses in the LGN (Saalmann and Kastner, 2009).
LGN receives approximately 90 % of the retinal signal. From the LGN the visual input is
transmitted to the cortex. The main destination is the primary visual cortex, V1, in the medial
18
part of the occipital lobe. The remaining 10 % of the retinal signal is projected to subcortical
structures such as the superior colliculus, suprachiasmatic nucleus, nucleus of the optic tract,
pretectum, and the nucleus of the accessory optic tract (Bullier, 2004a).
2.2.2. Large portion of cortex is sensitive primarily to visual stimulation
The human visual cortex covers approximately a fourth of the neocortex, and it includes
occipital lobe, significant portions of parietal and temporal lobes, and regions in frontal lobe
(Van Essen, 2004). Cortex can be divided into separate functional visual areas. The primary
visual cortex, V1, receives visual input from the thalamus and feeds the signal to extrastriate
cortical areas. At some extent extrastriate visual areas are specialised for the encoding of
specific stimulus attributes and they form separate streams of processing (Grill-Spector and
Malach, 2004). The dorsal stream is primarily devoted to visuo-motor transformations and
spatial processing whereas the ventral stream is devoted to the analysis of form, colour and
objects (Ungerleider and Mishkin, 1982). This concept of functional specialisation is
supported by neurological cases in which a lesion in a cortical region results in specific
visual deficits and from observation that stimulation of a cortical region specifically affects
behaviour and perception. A lesion of the dorsal stream areas may cause simultanagnosia,
meaning inability to detect more than one object at time, deficiency in visuomotor tasks such
as optic ataxia and apraxia, and deficiencies in spatial perception such as neglect. In contrast,
a ventral stream lesion may result in visual agnosia, a disorder of object recognition (Pisella
et al., 2006). However, several areas can be essential for a given visual function and one
region can participate in a wide range of functions.
Abundant connections link cortical functional areas. Feedforward and feedback connections
originate from and terminate to different cortical layers and they are determined according to
laminar pattern of the axon terminals (Barone et al., 2000). In addition to cortico-cortical
links, connections can operate via cortico-thalamo-cortical circuits through lateral geniculate
nucleus, pulvinar, and reticular nucleus and via superior colliculus (Guillery and Sherman,
2002; Shipp, 2002; Callaway, 2004; Cappe et al., 2009). Visual areas of the macaque are
believed to be hierarchically organised according to the laminar pattern of inter-areal
connections (Felleman and Van Essen, 1991). The majority of neurons in the higher levels of
the hierarchy are sensitive to visually complex and even multisensory or motor stimuli and
19
they are activated at relatively long latencies (Bullier, 2004b). However, some V1 neurons
also seem to be sensitive for higher-order computations (Lee et al., 1998) and the latencies of
lower and higher order areas are highly overlapping (Schmolesky et al., 1998).
Because visual areas serve partially different functions, they are sensitive to different
stimuli. These different response profiles are utilised in localising and mapping of visual
areas. Localisers often use contrasts between responses to different stimuli such as visual
motion and stationary stimuli to determine stimulus preference of neurons. However,
preferential sensitivity to a specific stimulus attribute does not provide a complete view to
the function of the area and the localiser response may originate from several functional
areas. Retinotopic mapping provides additional means for delineation of different areas. It is
well established that the early and intermediate steps of the visual processing hierarchy in the
human brain are organized in a retinotopic manner while the retinotopy of higher level areas
is under debate. Malach and colleagues (Levy et al., 2001) suggested that object areas on
high level of the hierarchy are organised according to eccentricity whereas Wandell and co-
workers (2005) proposed that these regions also have a retinotopic organization. Lately,
several higher-level topographically organized areas have been delineated with mapping
protocols that combine visual stimulation with attention or eye movements and these studies
suggests that topographic organization extends to high levels in visual cortex.
Figure 1 shows a map representing the current view of visuotopically and spatiotopically
organised areas in human cortex. Topographic areas form clusters in which the
representations of the same visual field eccentricities are next to each other in neighbouring
areas and the areas serve similar functional purposes (Wandell et al., 2005). Magnification
factor describes the relationship between visual field coordinates and cortical locations by
providing an approximation of the spatial extent of the cortex devoted for every degree of
visual field eccentricity (Daniel and Whitteridge, 1961; Virsu and Rovamo, 1979). The
proportions of visual field representations vary across visual areas but typically the foveal
representation is magnified. For example, Duncan and Boynton (2003) estimate that the
representation of the central 10 degrees covers 50 % of the cortical area of V1.
20
Figure 1. Visuotopically arranged functional areas in human cortex according to current knowledge.
The areas have been drawn at approximate locations according to previous literature and results of
study II. Functional areas V1, V2, V3 and V6 in medial occipital cortex are defined according to
mappings in study II. Ventral occipital areas hV4, VO-1, VO-2, PHC-1, and PHC-2 are drawn
according to results of Brewer and co-workers (2005) and Arcaro and co-workers (2009). Areas
V3A, V3B, LO-1 and LO-2 are placed according to mappings of Larsson and Heeger (2006). Area
V7 is placed according to data of Tootell and co-workers (1998). Location of V5 is adopted from the
results of Tootell and co-workers (1995). Parietal areas IPS-1, IPS-2, IPS-3, and IPS-4 are located
according to results of Swisher et al. (2007), Schluppeck et al. (2005), and Silver et al. (2005).
Proposed human VIP is drawn according to Sereno and Huang (2006). Saygin and Sereno (2008)
located spatiotopic map approximately at PRR? (parietal reach region?). PRR? region activates
during reaching (Filimon et al., 2009). Locations of FEF and dorsolateral prefrontal cortex (DLPFC)
are adopted from the results of Hagler and Sereno (2006).
21
Table 1. Retinotopic and spatiotopic visual areas presented in FIG 1.
Cortical region Areas
Medial occipital cortex V1, V2, V3, V6
Cuneus V3A, V3B
Ventral occipital and temporal lobes hV4, VO-1, VO-2, PHC-1, PHC-2
Lateral occipital cortex LO-1, LO-2, V5
Parietal lobe V7, IPS-1, IPS-2, IPS-3, IPS-4, PRR?, VIP
Frontal lobe FEF, DLPFC
22
2.2.3. Properties of a neuron in primary visual cortex depends on the signal and brain state
Primary visual cortex located around calcarine sulcus is the first stage of visual cortical
information processing and the first region in which the signal from both eyes is integrated.
V1 has been extensively studied in both animals and humans and the similarity between the
species is well established. The following presents results from electrophysiological
recordings, most of them from the V1 of the macaque monkey.
V1 has a clear retinotopy. In addition to this retinotopic organization, cells with similar
functional properties are to some extent grouped together (Hubel and Wiesel, 1963; Hubel
and Wiesel, 1968). Optimal stimulus varies between neurons, and V1 neurons are tuned for
stimulus features such as orientation (Hubel and Wiesel, 1959, 1962), direction of motion
(Hubel and Wiesel, 1959; Hubel and Wiesel, 1968), spatial frequency (Campbell et al.,
1969), wavelength (Gouras, 1970), and vertical and horizontal binocular disparities (Durand
et al., 2002; Durand et al., 2007). Majority of V1 neurons are jointly tuned for multiple
stimulus features (Grunewald and Skoumbourdis, 2004).
Neurons in the primary visual cortex have been divided into simple and complex cells
(Hubel and Wiesel, 1962). By definition, the receptive field of the simple cell contains a
region sensitive for increments and decrements of light and there is signal summation within
and antagonism between these regions. The response of a simple cell is linear and
predictable from the stimulus. Other cells are classified as complex cells. However, even the
responses of simple cells show nonlinearities such as modulation of the response by the
stimulus outside the classical receptive field (Blakemore and Tobin, 1972). Because of such
nonlinearities, a clear dichotomy between simple and complex cells may not exist at all;
rather, there might be a continuum of cells with varying degree of receptive field complexity
(Garandini et al., 2005).
In contrast to LGN, most receptive fields in V1 are not circular. Angelucci and co-workers
(2003) reviewed the receptive field properties of V1 neurons. The classical receptive field is
defined with a small high-contrast flashing or moving stimuli. However, visual information
is summated over a region extending beyond the classical receptive field. This summation
area depends on the stimulus contrast and it is larger for low-contrast stimuli. High contrast
summation area is considered as the centre of the receptive field, whereas the surround of the
receptive field consists of an additional low contrast summation field and a modulatory
23
surround. The extent of summation field for low contrast stimuli is in line with the horizontal
connections in V1 whereas the width of the whole modulatory region equals the receptive
field size in higher order areas such as V2, V3 or V5.
A stimulus presented far from the classical receptive field can modulate responses of V1
neurons by adding significant non-linearity to the neuron’s response. This modulation of V1
responses depends on stimulus context and it can be either facilitatory or suppressive. Such
contextual modulation has been linked to various perceptual phenomena (Albright and
Stoner, 2002) such as figure-ground segregation (Lamme, 1995), feature pop-up (Kastner et
al., 1997), and brightness perception (Rossi et al., 1996; Rossi and Paradiso, 1999). In
addition to modulation related to stimulus context, information from other sensory
modalities or top-down modulation from higher areas affect neural processing in V1 by
providing modulation related to the behavioural context. Behavioural context may modulate
neural processing even before the onset of a target stimulus (Li et al., 2004), but stimulus
context also affects responses rapidly, already less than ten milliseconds after the start of the
response (Hupé et al., 2001b).
Behavioural contexts which enhance neural signals include cognitive processes such as
attention and learning (Motter, 1993; Gilbert et al., 2000; Sharma et al., 2003; Li et al.,
2004). Perceptually, detection of salient stimuli and spatial working memory correlate with
contextual modulation (Supèr et al., 2001a, 2001b). In addition, oculomotor activity is
reflected in V1 responses. Neural activity in V1 is increased before the execution of saccades
at the location of saccade target (Supèr et al., 2004) and viewing distance and gaze direction
also modulate V1 responses (Trotter et al., 1992; Trotter and Celebrini, 1999). This
modulation is utilised in spatial perception; both gaze direction and vertical disparity provide
frames for horizontal disparity signal to generate 3D egocentric coordinates (Trotter et al.,
2004).
Both local horizontal connections and interareal connections may mediate centre-surround
interaction but cortico-cortical feedback connections are crucial for differentiation of low
salience stimuli from the background (Hupé et al., 1998; Hupé et al., 2001a). Bullier and
colleagues (2001; 2004b) hypothesised that the visual signal is transmitted to higher-order
areas via fast conducting fibres (Bullier and Nowak, 1995) for a first-pass analysis. This is
followed by feedback signals which can guide the ongoing feedforward signal processing in
lower level areas. Bullier et al (2001) proposed that feedback connections interact with
24
feedforward and horizontal connections in non-linear fashion to control the response gain
and that different stimuli utilise different sets of connections. Schwabe and co-workers
(2006) presented a feedforward feedback network model that could explain surround
suppression and facilitation and thus contextual modulation in V1. In their model a higher-
level neuron which provides feedback to lower-order areas is monosynaptically connected
with interneurons with a high threshold and gain. These interneurons are both excitatory and
inhibitory, and excitatory feedback connections can suppress responses via inhibitory
interneurons.
2.2.4. Extrastriate visual cortices are specialised
The following chapter provides a brief summary of the organisation of the human
extrastriate visual cortex. Adjacent to V1 are areas V2 and V3 (Sereno et al., 1995). Both V2
and V3 contain discontinuous contralateral hemifield maps. The maps are divided
approximately along the horizontal meridians. The lower and upper visual field quadrants
are represented dorsally and ventrally of the calcarine sulcus, respectively. The functional
role of V2 and V3 remains unsettled, but most likely they contribute V1 in processing of
local visual features (Boynton and Hegdé, 2004; Sincich and Horton, 2005).
Ventral occipital cortex responds to colour stimuli (Lueck et al., 1989). The division of this
cortical region into functional areas has been under debate. Hadjikhani and co-workers
suggested that adjacent to ventral V3 is a representation of the upper visual field quadrant
belonging to V4 and adjacent to which is a representation of the whole contralateral visual
field, named V8 (Hadjikhani et al., 1998). In contrast, Wandell and colleagues located a
whole contralateral representation next to ventral V3; this was named hV4. Next to hV4 they
located two additional ventral areas, VO-1 and VO-2 (Brewer et al., 2005). Ventral occipital
cortex and temporal cortex around fusiform and parahippocampal gyri are related to
processing of objects, faces, and scenes (Grill-Spector and Malach, 2004), and the optimal
stimulus becomes more complex further up the hierarchy (Lerner et al., 2001).
Representations of different object categories have been suggested to form local clusters of
highly specialised neurons (Kanwisher et al., 1997). Such clusters may be distributed and
overlapping (Haxby et al., 2001) or arranged according to eccentricity (Levy et al., 2001). It
is possible that responses to different objects are distributed over several retinotopic areas
25
that have different sensitivity profiles and eccentricity weightings. Recently, two new
retinotopically organised areas (PHC-1 and PHC-2) which are particularly sensitive to
scenes but respond to other objects as well were found near parahippocampal gyrus (Arcaro
et al., 2009).
Lateral occipital cortex contains regions that are sensitive to visual motion and objects. Zeki
and co-workers located a motion-sensitive area in the lateral occipital cortex of humans
which was named as the human homologue of the monkey area V5 (Zeki et al., 1991;
Watson et al., 1993). V5 has a role in integration of points moving to the same direction and
in discriminating a moving target from the background (Born and Bradley, 2005). Posterior
and medial to V5 is the LO-complex which is preferentially activated by objects than
texture-stimuli (Malach et al., 1995) and responds to object structure from a wide variety of
cues (Kourtzi and Kanwisher, 2000). Two retinotopic areas, named LO-1 and LO-2, have
been located in the region of LO-complex (Larsson and Heeger, 2006).
Visual areas in the dorsal occipital cortex are selective for stimulus orientation and they are
also sensitive to motion. Dupont and co-workers (1997) localised sensitivity to kinetic
contours in the lateral occipital lobe but the relationship of these responses to retinotopic
areas is unsettled. Lateral cuneus contains the retinotopic area V3A (DeYoe et al., 1996;
Tootell et al., 1997) and its adjacent area V3B. Anterior to V3A is area V7 (or IPS-0)
(Tootell et al., 1998). Visual motion sensitivity has also been detected in the human parieto-
occipital sulcus in a region that supposedly contains the human homologue of the monkey
area V6 (Pitzalis et al., 2006; Pitzalis et al., 2010).
Parietal lobe contains several functional areas which are related to visuospatial and
visuomotor processing, which are arranged in eyecentric coordinates, and which are strongly
connected with frontal cortex (Andersen and Buneo, 2002). Colby and Goldberg (1999)
reviewed functional properties of the monkey parietal cortex. Medial intraparietal area (MIP)
of monkey functions in reaching and grasping movements and lateral intraparietal area (LIP)
is related to control of eye movements. In contrast, ventral intraparietal area (VIP) may play
a role in the guidance of head movements. Sereno and colleagues (2001) were the first to
locate a a spatiotopically organised area in the human intraparietal sulcus which they
proposed to be a homologue of LIP in the monkey brain. Subsequent studies have revealed
several contralateral visual field maps along the intraparietal sulcus using visual stimuli
(Swisher et al., 2007) as well as tasks involving delayed saccades (Schluppeck et al., 2005)
26
and covert shifts of attention (Silver et al., 2005). The superior part of the postcentral sulcus
contains maps for tactile and near-face visual stimuli, suggestive of a homologue of monkey
area VIP (Sereno and Huang, 2006). In addition, several areas in frontal lobe are related to
visual signal processing. Dorsolateral prefrontal cortex is activated during visual working
memory tasks and the frontal eye fields which are involved in eye-movements and saliency
maps are both topographically arranged (Hagler and Sereno, 2006).
2.2.5. Information from peripheral and central visual fields serves partially different
purposes
The fovea, covering 1-2 degrees of the visual field, and the macula, covering the central 5
degrees, form the retinal part of the detailed central vision. Retinal parts exterior to the
macula are considered peripheral. Peripheral and central vision can be partially separated.
Structural and functional differences between central and peripheral vision begin at the
retina. Distribution of photoreceptors varies according to eccentricity and receptive fields of
bipolar and ganglion cells are the smallest in the fovea and increase in size in the periphery
(Dacey, 2000; Field and Chichilnisky, 2007).
Response profiles in early retinotopic areas directly reflect photoreceptor distribution
(Hadjikhani and Tootell, 2000). The sizes of receptive fields and the distributions of parallel
processing streams are also reflected in the properties of V1 neurons. The receptive field size
in striate cortex increases towards the periphery (Hubel and Wiesel, 1974) and neurons
become more selective for low spatial frequencies (Xu et al., 2007; Henriksson et al., 2008).
Even the properties of stereopsis have been adapted to retinal position. In the periphery,
neurons are sensitive to both vertical and horizontal disparity whereas neurons in the central
representations encode only horizontal disparity (Durand et al., 2007). In addition to
response tuning, modulations related to stimulus and behavioural context differ according to
eccentricity. The response gains of peripheral neurons show stronger suppressive
modulation; the surround facilitation is diminished and the suppression is less orientation
and frequency specific (Xing and Heeger, 2000). Attention increases spatial integration in
the periphery but decreases it in the central representations (Roberts et al., 2007).
27
The distinction between central and peripheral vision is also manifested in the extrastriate
cortex. In the monkey, dorsal stream areas receive relatively more projections from the
peripheral visual field representations whereas ventral stream areas are more densely
connected to the central representations (Ungerleider and Desimone, 1986; Colby et al.,
1988; Boussaoud et al., 1990; Baizer et al., 1991; Lewis and Van Essen, 2000; Gattass et al.,
2005). In fact, the input to peripheral and central representations can significantly vary even
within the same functional area (Palmer and Rosa, 2006). Furthermore, the peripheral
representations receive multisensory input. For example, peripheral V1 is connected with
auditory cortex (Falchier et al., 2002) and dorsal stream areas in parietal and frontal lobe
receive multisensory connections (Schall et al., 1995; Lewis and Van Essen, 2000). In
addition, relative visual field representations vary between functional areas in the human
brain. For example, the dorsal stream area V6 is strongly biased towards the periphery
(Pitzalis et al., 2006) whereas the ventral stream area hV4 has an extended central visual
field representation (Wade et al., 2002). Lesion studies support the association between the
peripheral vision and the dorsal stream. Lesion to the parieto-occipital cortex containing
dorsal stream areas results in optic ataxia which manifests in the peripheral vision (Pisella et
al., 2006) and impairs perception in the periphery (Pisella et al., 2009).
The connections and the response profiles suggest different functional role for the central
and the peripheral vision. The central vision in the ventral stream is important for object
recognition whereas the peripheral vision in the dorsal stream may be more important for
detecting sudden changes in the environment. A large field of view integrated with
multisensory input (Wang et al., 2008) would provide information and motor connections
would provide the means for controlling visually guided movements. However, the central
and the peripheral visions are segregated also within the ventral and dorsal stream. For
example, in humans reaching towards peripheral and central visual field activate different
cortical regions (Prado et al., 2005) and cortical regions sensitive to different object
categories may have different bias towards the centre and periphery (Levy et al., 2001).
2.2.6. Functional visual areas V6 and V6A are related to visuomotor processing
In the eighties a new area, PO complex, was defined on histological grounds in the anterior
bank of parieto-occipital sulcus of macaque monkey (Colby et al., 1988) and a functional
28
visual area V6 was discovered in the same location (Zeki, 1986). Later Galletti and co-
workers examined the function of the macaque’s parieto-occipital cortex and showed visual
responses, eye-position related modulation and oculomotor activity in that cortical region
(Galletti et al., 1991; Galletti et al., 1995). They defined two densely interconnected
functional areas in the anterior bank and at the bottom of the parieto-occipital sulcus of the
macaque, V6 and V6A, and argued that PO includes both V6 and V6A in the macaque brain
(Galletti et al., 1996; Galletti et al., 2005). Histologically V6 is an occipital area whereas
V6A belongs to the parietal cortex (Luppino et al., 2005). Following chapters review
literature concerning the visual areas V6 and V6A of the macaque monkey, studies of the
human homologue of V6/V6A, and the functional properties of the human parieto-occipital
region.
Monkey V6 is a retinotopically organized visual area with smaller emphasis on central
visual field than in areas V1-V5 leaving more cortex sensitive for peripheral stimulation
(Galletti et al., 1999b). Its neurons are especially sensitive to stimulus orientation and
motion, and eye position modulates the responses (Galletti et al., 1996). Monkey V6 receives
direct connections from the layer IVb of the primary visual cortex, thus it has a relatively
strong afferent connection from the magnocellular system (Galletti et al., 2001). Anatomical
tracing studies have shown that connections between V1 and V6 are concentrated in V1
periphery (Shipp et al., 1998; Galletti et al., 2001). In addition to V1, V6 has strong
reciprocal connections with areas V3, V3A, V5 and V6A and weaker connections with V2
and parietal lobe (Galletti et al., 2001).
In contrast to V6, area V6A is not retinotopic. The receptive fields of neurons are large and
may differ several degrees from each other between neighbouring neurons (Galletti et al.,
1999a). Area V6A contains both visually responsive and non-responsive neurons. As in area
V6, visually-driven neurons are sensitive to orientation and motion whereas visually non-
responsive neurons respond to saccades and eye and hand position (Galletti et al., 1996).
Area V6A receives connections from V6 and sends projections to dorsal stream areas in
parietal lobe and premotor cortex in frontal lobe (Shipp et al., 1998). Neurons in V6A have
been shown to respond during reaching and grasping (Fattori et al., 2004), and these neurons
are coded both according to the retinotopic position of the target of reaching or the spatial
direction of reaching (Fattori et al., 2005; Marzocchi et al., 2008).
29
Rizzolatti and Matelli (2003) presented a hypothesis of the division of dorsal stream into two
distinct functional systems. Ventro-dorsal (dorso-lateral) stream extends from area V5 to
lateral parietal lobe and its function concerns spatial perception and both the guiding and the
understanding of motor actions. Area V6 is a central node in dorso-dorsal (dorso-medial)
stream which extends to medial parietal lobe and frontal lobe and controls visually guided
actions. V6 is rich in cells that are able to distinguish the real motion of stimulus from self-
induced motion signals which can result from eye movements (Galletti and Fattori, 2003),
and some neurons in V6A code information in ego-centric coordinate frame of reference
which remains anchored in the subject despite eye movement (Galletti et al., 1995).
Functionally, V6 is involved in detecting motion in the whole visual field, the analysis of
flow fields resulting from self motion, and selecting peripheral targets in during visual search
(Galletti et al., 1999b; Galletti and Fattori, 2003). V6A has an important role in controlling
visually guided hand movements (Galletti et al., 2003).
The first proposals of the human homologue of monkey V6 came from MEG studies. Visual
and saccade-related responses in the posterior bank of parieto-occipital sulcus were
suggested to originate from human V6 (Jousmäki et al., 1996; Portin et al., 1998).
Luminance flicker stimulus produced especially strong responses with no foveal
magnification (Portin et al., 1998; Portin and Hari, 1999). The first parieto-occipital MEG
responses emerged early, less than 100 ms after the stimulus onset (Tzelepi et al., 2001;
Vanni et al., 2001) in line with fast magnocellular input to the area.
Pitzalis and co-workers (2006) located a new retinotopic area in the posterior bank of the
dorsal part of human PO-sulcus with fMRI. This area had a complete contralateral visual
field representation and it was located anterior to the peripheral representation of dorsal V2
and V3. On the basis of location and retinotopy they referred to this area as the human
homologue of monkey V6. Other groups suggested a location of human V6 and V6A in PO
sulcus on the basis of visual (Dechent and Frahm, 2003; Stiers et al., 2006), visuomotor (de
Jong et al., 2001), and oculomotor (Law et al., 1998; Bristow et al., 2005) responses.
However, they did not use retinotopic mapping in the localisation of V6 and responses from
peripheral visual field representations in V1-V3 may have confounded their results. In
addition, the separation of V6 and V6A may be complicated due to dense connections and
partially similar response profile. I use the term V6/V6A complex whenever the separation
of areas is ambiguous.
30
The functional roles of human PO cortex suggested by imaging and lesion studies are in line
with the properties of monkey V6 and V6A. A lesion in dorsal PO sulcus results in motion
blindness (Blanke et al., 2003) and in an impairment to detect frequency-doubling stimuli
(Castelo-Branco et al., 2006), suggestive of motion sensitivity and magnocellular
connections. Electrical stimulation of PO cortex evokes the perception of motion (Richer et
al., 1991). Visual motion activates human PO sulcus (Dupont et al., 1994; Mercier et al.,
2009) and human PO sulcus is activated when the perception of stimulus motion changes to
self motion (Kleinschmidt et al., 2002). PO responses have also been associated with the
calibration of visual motion signal during eye movements (Galati et al., 1999; Tikhonov et
al., 2004) which would require a contribution from real-motion cells. In addition, gaze
direction and vergence angle modulate PO responses (Deutschländer et al., 2005; Quinlan
and Culham, 2007), PO responses have been detected during saccades (Bodis-Wollner et al.,
1997; Dejardin et al., 1998) and visually guided pointing (de Jong et al., 2001), all
homologous response properties to monkey V6/V6A complex.
Posterior parietal cortex in the region of parieto-occipital junction may contain the human
homologue of V6A. This region is activated during visually guided reaching towards a
peripheral target (Prado et al., 2005; Filimon et al., 2009) and its lesion causes optic ataxia.
Ataxic symptoms are worst when pointing towards periphery and less marked when pointing
to the centre of the visual field or body parts (Karnath and Perenin, 2005). The symptoms
originate from deficiency in transformation between dynamic gaze-centered and arm-
centered coordinates (Khan et al., 2005). In monkeys V6A has a role in coordinate
transformation during reaching and grasping movements (Galletti et al., 1997; Galletti et al.,
2003) and lesion in V6A of the monkey produces short term impairment in visually guided
reaching (Battaglini et al., 2003). This may connect V6A lesion with optic ataxia in humans.
2.2.7. The guidance of saccades and spatial attention share a network of brain regions
Attention and saccades share many common features. A salient visual stimulus draws
attention in a bottom-up manner, and attention can also be directed in endogenous and
voluntary fashion (Kastner et al., 2001). Stimulus-driven and voluntary attention are guided
by different cortical networks (He et al., 2007). Competing stimuli show mutual suppression
demonstrating a bottom-up mechanism whereas top-down control may direct attention
31
towards spatial location, features, and objects (Kastner and Ungerleider, 2000). Like
attention, saccades are evoked with both salient stimulus in bottom-up manner and voluntary
top-down mechanisms. Stimulus driven pro-saccades aim to place a target-of-interest at
foveal vision whereas volitional saccades actively explore the environment. These saccade
types are mediated with slightly different network of brain areas (McDowell et al., 2008).
According to the premotor theory of attention, target selection for eye movements and
spatial attention are coupled (Rizzolatti et al., 1987). The activation of the similar network of
cortical regions during both saccade and attention tasks supports the hypothesis (Corbetta,
1998; Corbetta et al., 1998). Figure 2 shows approximate cortical regions activating during
saccades and the covert shifts of attention. Parietal cortex shows robust responses during
both conditions. Parietal cortex has a role in coordinate transformation (Merriam et al., 2003)
and it supposedly provides spatial information for saccades and attention shifts (Gaymard et
al., 1998). In addition, both saccades and attention activate frontal lobe in the region of
frontal and supplementary eye fields. Gaymard et al (1998) and McDowell et al (2008)
reviewed the roles of brain regions in guidance of saccades. Frontal eye field (FEF) is
located in precentral sulcus anterior to primary hand area. Electrical stimulation of FEF
elicits saccades at low threshold and this region is involved in the preparation and triggering
of saccades. Supplementary eye field (SEF) belongs to supplementary motor area. It is
located in superior frontal gyrus and it is involved in the temporal control of action
sequences. Dorsolateral prefrontal cortex (DLPFC) around inferior frontal sulcus inhibits
stimulus-driven saccades and is related to the coordination of voluntary action and spatial
working memory (Faw, 2003). Superior colliculus is a central node in saccade control and it
is connected to saccade generator in the brainstem. In addition, striatum, the part of cortico-
basal ganglia-cortical loop, and cerebellar vermis function in the motor control of saccades.
Both attention and saccades modulate visual processing in the low and intermediate levels of
the visual cortical hierarchy. Top-down attention affects neural signals in the visual cortex in
various ways. Spatial attention enhances neural responses in the corresponding retinotopic
representations (Tootell et al., 1998; Watanabe et al., 1999) whereas attention to a specific
stimulus attribute increases activation selectively to the corresponding stimulus feature
(Watanabe et al., 1998). In addition, attention may result in an increase of baseline activity
(Kastner et al., 1999) and response sensitivity (Reynolds and Chelazzi, 2004). Activity in the
primary visual cortex is also influenced by saccade processing. Despite the temporal
32
limitation of fMRI, previous studies suggested that BOLD responses to visual stimuli are
suppressed at the time of saccades even though saccades in darkness results in positive
responses (Sylvester et al., 2005; Sylvester and Rees, 2006; Vallines and Greenlee, 2006).
Response decrement is associated with the suppression of perception during saccades (Burr
et al., 1994; Vallines and Greenlee, 2006) and signal increases have been suggested to
represent efference copies from the movement planning and execution (Sylvester and Rees,
2006). In monkeys, signal increases are associated with sensory memory for visual
continuity during eye movements (Khayat et al., 2004), spatially triggered update signals
after eye position changes (Nakamura and Colby, 2002), and disparity related response
changes which are utilised in 3D vision (Trotter and Celebrini, 1999; Trotter et al., 2004).
Several studies have found connections between visual cortex and the fronto-parietal
attention and saccade network. These suggest a strong physiological top-down connection
from FEF and parietal cortex to retinotopic visual cortex. In monkeys, electrical stimulation
of FEF modulates response gain in extrastriate cortex (Moore and Armstrong, 2003) and
increases visually evoked responses in V1 (Ekstrom et al., 2008). In humans transcranial
magnetic stimulation (TMS) of FEF caused increased blood flow in parieto-occipital cortex
(Paus et al., 1997). Recently, TMS of right FEF and intraparietal sulcus resulted in BOLD
signal changes in the low-level retinotopic areas even in the absence of visual stimulation
(Ruff et al., 2006; Ruff et al., 2008; Ruff et al., 2009).
In addition, visual cortex has a role in saccade generation. Electrical microstimulation of the
primary visual cortex facilitates or interferes saccades depending on the stimulated layer
(Tehovnik et al., 2005). Saccadic eye movements increase neural responses in the saccade
target representation before the actual eye movement (Supèr et al., 2004). V1 has also been
suggested to serve oculomotor structures in target selection for saccades depending on
whether the target is part of the figure or background (Supèr, 2006). In humans, signal
increase in the representation of saccade target may be related to the spatial guidance of
saccades (Geng et al., 2008).
33
Figure 2. The network of cortical regions related to the processing of saccades and the covert shifts
of attention in human brain. The coloured regions are placed at the approximate positions of the
activations reported by Corbetta and co-workers (1998). In their review Corbetta and co-workers
presented the activations during saccade and attention tasks, but they did not delineate neighbouring
functional areas. Thus the activations may overlap with the neighbouring functional areas. Yellow
indicates parietal and frontal regions that were activated during both saccades and covert shifts of
attention. Green colour shows occipital regions that were more strongly activated with covert
attention task and red indicates regions activated with eye-movement task. In original data by
Corbetta et al, saccades activated lateral occipital cortex in left hemisphere but not in right
hemisphere.
34
2.3. Magnetoencephalography
2.3.1. Principles
Magnetoencephalography (MEG) is a noninvasive brain imaging method. It measures
neuronal activity directly and provides excellent, in the range of millisecond, temporal
resolution. Following overview summarises the basic principles of MEG method and MEG
source modelling. It is primarily based on the reviews by Hämäläinen and co-workers (1993)
and Hari (1999).
Electrical responses of neurons create magnetic field that can be measured with MEG.
Postsynaptic potential changes result in small electric currents called primary currents within
dendrites. Volume currents that flow passively through the whole conducting medium (e.g.,
the brain) complete the current loop. MEG signal results from postsynaptic currents most
likely in the cortical pyramidal cells. Apical dendrites of pyramidal cells are parallel to each
other and are oriented perpendicular to cortex surface. Thus simultaneous postsynaptic
potentials in several dendrites form dipolar current field perpendicular to cortex surface.
Neuromagnetic signals are typically 50-500 fT, which requires activation of approximately
10 000 -50 000 neurons (Murakami and Okada, 2006). Action potentials result in two
current dipoles and thus quadrupolar field which decreases more as a function of distance
than dipolar field. In addition, longer lasting postsynaptic currents summate temporally more
effectively than fast action potentials.
Measurement of weak magnetic fields requires sophisticated technology and
superconducting SQUID based sensors. Measurements are run in a magnetically shielded
room to prevent artefacts resulting from the earth’s magnetic field, radio-frequency fields
and ferromagnetic objects. In addition, physiological artefacts hamper MEG measurements.
Electric activity of the heart, muscle activity, eye movements and blinks create strong
magnetic signals, and during the measurements the subject must be still and avoid eye
movements and blinks. Eye movements are measured during the measurement and the
contaminated epochs are rejected. The configurations of neuromagnetic sensors help to
control artefacts. In planar gradiometers, the figure-of-eight construction, with the two loops
in opposite directions; result in sensitivity to sources near the coil. Homogenous fields
resulting from distinct far-away sources induce similar opposite current in both loops that
35
attenuate each other. Evoked neural responses are differentiated from spontaneous brain
activity and random noise by averaging several hundred responses.
2.3.2. Source modelling
Forward solution refers to the calculation of magnetic fields from electric currents. Cell
membrane level phenomena are discarded form the electromagnetic model and the whole
brain is considered as a conductor for the forward model. Moreover, because all tissues are
almost equally transparent to the magnetic field, a single layer conductor model is often
sufficient. The brain can be approximated with a homogenous sphere when the sphere radius
has been fitted to the curvature of the brain surface. For spherically symmetric volume
conductor only tangential component of the primary current produces magnetic field outside
the conductor due to symmetry reasons. In this model, the activities in brain sulci are
oriented tangentially to surface and create the neuromagnetic signal. The spherical conductor
model is computationally simple and reasonably accurate. Brain-shaped piecewise
homogenous conductor, boundary element model (BEM), is also used and it provides better
approximation in cortical regions where the brain surface is not spherical.
Neuromagnetic inverse problem refers to the estimation of underlying current sources on the
basis of the measured magnetic field. Due to non-uniqueness of the inverse problem the
current distribution inside the conductor cannot be uniquely defined from the
electromagnetic field outside. Nevertheless, a reasonable source model can be formed on the
basis of constrains, which may include the source distribution and statistics, sensor statistics,
and functional and anatomical a priori information. The source model aims to minimise the
difference between the measured magnetic field and the field obtained with the forward
calculations. Several methods of source modelling have been developed and they either
assume distributed or point-like sources. However, it is important to remember that all
methods provide only a model of brain activation. Moreover, the distribution of the modelled
sources reflects the chosen method rather than the actual extent of brain activation.
The electric source can be assumed to be point-like and modelled with an equivalent current
dipole (ECD)(Williamson and Kaufman, 1981). ECDs are defined with a fixed location and
usually fixed orientation and variable amplitude. When the noise is assumed to have
36
Gaussian distribution, dipole parameters can be approximated with a least square search i.e.
the minimization of the difference between the measured and calculated magnetic field
(Tuomisto et al., 1983). Complicated magnetic field pattern can be explained with several
ECDs in time-varying multidipole model (Scherg, 1990). ECD models require the definition
of source number and approximate location and are thus dependent of the educated guess of
the user. Number and location of ECDs can be approximated on the basis of the magnetic
field pattern or a prior knowledge of likely sources. In addition, BOLD responses can be
used as seeds for ECDs (Vanni et al., 2004).
Another approach to source modelling, based on general linear model, is to make minimal a
priori assumptions and to find the smallest current distribution at each time point that can
explain the data. Minimum norm estimate (MNE) was the first method based on that
principle (Hämäläinen and Ilmoniemi, 1984, 1994). It assumes that currents are normally
distributed and selects the current distribution with the smallest Euclidean norm. MNE
favours superficial sources, but this can be opposed with depth weightings. In addition,
MNE produces smooth and extended responses. Minimum current estimate (MCE) assumes
exponential a priori distribution of currents, minimises the currents as L1 norm and produces
more focal source estimate (Matsuura and Okabe, 1995; Uutela et al., 1999).
Current methods provide activity time course estimates for every cortical location and have
some advantages over the ECD method. Whereas ECDs are defined on individual basis,
current distribution estimates provide opportunity for normalization of the responses and
group level analysis. By normalising the current estimates with the noise, current
distributions can be treated like statistical parametric maps and displayed as dynamic
statistical parametric maps (Dale et al., 2000). In addition, anatomical constrains are used to
improve the results of the analysis (Dale and Sereno, 1993). Because neural currents are
oriented perpendicular to cortex, a cortex surface model provides an anatomical constraint to
the inverse problem. In practice, a loose orientation constraint provides better results because
it is less sensitive to segmentation and coregistration errors than a strict constraint (Lin et al.,
2006).
37
2.4. Functional magnetic resonance imaging
2.4.1. Principles
Functional magnetic resonance imaging (fMRI) is the most widely used functional brain
imaging method. It provides safe and relatively non-invasive way to measure hemodynamic
response to neural activation. It is spatially very accurate but temporally bound to
hemodynamic delay. The following overview is mostly based on the book of Huettel and co-
workers (2004).
Nuclear magnetic resonance of paramagnetic matter forms the basis of magnetic resonance
imaging. Atoms with odd number of protons form small magnet dipoles. These magnet
dipole vectors can also be explained as the vector of precession of a nuclear spin. When
exposed to a strong magnetic field a small part of randomly oriented magnet dipoles align
parallel to the field and start to precess around the direction of the field. The frequency of the
precession, Larmor frequency, depends on the matter and the magnetic field strength. The
1H isotope of hydrogen creates the most of the magnetic resonance signal from living
tissues. The orientation of magnetic dipoles parallel to the external magnetic field requires
lower energy and the system emits energy when it reaches new equilibrium. This equilibrium
and thus the magnetisation depend on the magnetic field strength and the temperature.
The creation of magnetic resonance signal requires application of the second
electromagnetic field. This radiofrequency (RF) pulse oscillates at the Larmor frequency and
tilts the magnet dipoles and thus the magnetization vector. When the second pulse is off and
the magnetization is subject to static field only the magnetization vector returns to
equilibrium and emits energy. The time the system requires to reach the equilibrium is called
T1 relaxation time. In biological tissue T1 relaxation time roughly corresponds to water
content. In the end of the RF pulse all magnetic dipoles are precessing in synchrony
producing strong signal, but interactions between spins results in de-phasing. T2 relaxation
time describes the time of de-phasing and it varies between tissues. Magnetic field
inhomogeneities affect the de-phasing of the rotation described with T2* effective relaxation
time.
A change in transverse magnetization results in measurable MR signal. Magnetization along
the main field depends on T1 relaxation whereas transverse magnetization depends on initial
38
magnetization, loss of magnetization due to T2 decay, and the phase of magnetization vector.
A MR image shows the spatial distribution of one spin related property that changes
transverse magnetization and it consists of discrete volume samples, voxels. However, a
measured MR signal is the sum of transverse magnetization within the whole excited sample
and spatial encoding schemes are required for the calculation of signal from each particular
voxel. A MR image formation is based on the introduction of spatially varying gradient
magnetic fields that alter the precession frequency and the accumulated phase of
magnetization vector depending on spatial location, thus producing different MR signal from
each location. Three orthogonal gradients are often used in anatomical images, whereas two
dimensional imaging sequences with one-dimensional excitation pulse to select a slice and 2-
D encoding scheme to resolve spatial distribution within a slice are used in fMRI.
Any signal changing in time or space can be constructed from the series of components in
temporal and spatial frequency domain. The spatial frequencies of a MR image are described
in k-space, which is a Fourier transformation of the image space. One point in k-space
represents the MR signal under corresponding gradient fields. At the centre of the k-space
magnetization vectors of all image voxels are at the same phase and it determines the signal-
to-noise ratio of the image, whereas high spatial frequencies of the image are described in
the periphery of k-space corresponding to MR signal when the phase of magnetization vector
differs between voxels.
Discovery of blood oxygenation level dependent (BOLD) signal enabled utilization of MR
in functional brain studies (Ogawa et al., 1990). Deoxygenated haemoglobin has higher
magnetic susceptibility than oxygenated haemoglobin, which causes dephasing of spins, and
thus T2* weighted MR pulse sequences show more signal where blood is highly oxygenated.
Both changes in blood flow and oxygen consumption of tissue affect the deoxyhemoglobin
content. Hemodynamic response function (HRF) describes the change of MR signal
triggered by neural activation. In the beginning, a local transient signal decrease has been
detected which probably reflects increased oxygen consumption. Later, compensatory blood
flow increases more than oxygen consumption resulting in decreased deoxyhemoglobin
content and increased MR signal. Signal starts to increase approximately two seconds after
the onset of neuronal activity and rises to plateau six to nine seconds after the start of
continuous activity. Finally, after the cessation of neural response, blood flow returns to
normal more quickly than blood volume and signal decreases under baseline.
39
Logothetis and co-workers (2001) showed in simultaneous electrophysiological and fMRI
measurements that local field potentials correlate with BOLD response more than the spiking
activity of neurons. The local field potentials reflect mainly synaptic potentials but also
membrane oscillations and spike after-potentials, and thus local field potentials and BOLD
signal reflect the aspects of input signal and local intracortical processing (Logothetis and
Wandell, 2004). However, the relationship between neural activity and BOLD response
varies in different brain regions due to local anatomic and neural circuit properties. In
addition, BOLD signal can fluctuate without correspondence to local neural activity (Sirotin
and Das, 2009). These variations of signal implicate that BOLD signal should only be
compared within a voxel during one experiment and balanced design.
2.4.2. Data analysis
Following chapter describes the analysis of fMRI signal and is based on the book by
Frackowiak and colleagues (2003). Preprosessing of measured raw data is essential for a
good signal-to-noise ratio, but the required preprocessing steps depend on the chosen method
for response analysis. Correction of the subject motion is most often recommended. For the
motion correction, translation and rotation parameters are defined for all volumes compared
to one. In addition, the timing differences between slices can be corrected. Statistical testing
with open anatomical hypothesis requires spatial smoothing. Data is smoothed by
convolving it with three dimensional Gaussian kernel to render the error distribution more
normal, to enable use of Gaussian random field theory for the correction of multiple
comparisons, and decrease the variance between subjects for the analysis of multiple
subjects. In addition, spatial normalisation is required for multisubject analysis. Spatial
normalisation includes both linear and nonlinear transformations to fit the volumes to a
template volume, such as Montreal Neurological Institute (MNI) brain.
Several approaches to data analysis have been developed. Correlation analysis calculates
covariance and correlation coefficients between the stimulus and the data. Anatomically
closed method examines BOLD signal within a region-of-interest (ROI). General linear
model (GLM) based methods are perhaps the most widely used for anatomically open
hypothesis. These methods explain the measured data as a linear combination of explanatory
variables and error term. To obtain explanatory variables in practice, a design matrix is
40
created according to study protocol and convolved with a hemodynamic response function to
take the time difference between the stimulus and the hemodynamic response in to account.
Additional regressors can be included into the design matrix to model confounding elements
and long- and short-range temporal correlations are controlled. The time-course of the model
is fitted to the data with a least square method separately for each voxel. The fitting
procedure provides parameter estimates for each condition at every voxel within the volume
and parameter estimates are combined to give contrast estimates for the effects of interest.
Voxel values of the contrast estimates are assumed to follow a statistical distribution
(student-t or F distribution) under null hypothesis. Statistical testing is conducted separately
for each voxel and the results are displayed as noise-normalised statistical parametric maps.
These statistical parameric maps show the significance of the results as the probability that
they could happen under null hypothesis. Correction for multiple comparisons controls type I
error due to very large number of statistical tests. Gaussian random field theory takes spatial
dependence of voxel values into account for Family wise error (FWE) correction of false
positive whereas False discovery rate (FDR) correction controls the probability of type I
error (Benjamini and Hochberg, 1995; Genovese et al., 2002). GLM approach allows
statistical inference of multiple subjects. Random effect analysis controls variability both
within and between subjects and provides inference of population (Friston et al., 1999)
41
2.5. Retinotopic mapping
Visual field mapping has many applications both in research and in clinical practice. Most
importantly retinotopic mapping provides a way to define the borders of a subset of
functional cortical areas. Because functional borders do not typically follow anatomical
landmarks and locations of functional areas varies between subjects, the knowledge of the
exact location of a functional area enables more accurate comparisons across subjects and
data analysis in group level (Wandell et al., 2007). Retinotopic mapping has implications
also in studies concerning other sensory modalities as some multimodal areas are organized
in visuotopic coordinates (Sereno et al., 2001). The relative representations of different parts
of the visual field within an area provide non-direct information about the function of the
area. The knowledge of the functional area borders, its neighbouring areas and the internal
representation enables the interspecies comparison. Finally, retinotopic mapping is useful in
clinical practise as quantitative analysis of visual field maps can be used in studies of visual
system pathologies for example in case of retinal injury, hemianopia, and preoperative
cortical mapping (Wandell et al., 2007).
Modern imaging methods provide means for retinotopic mapping in human cortex even
though the need for good spatial resolution limits the choice of usable methods. Even though
some mappings have been done with PET (Fox et al., 1987; Shipp et al., 1995), fMRI is the
most used method for retinotopic mapping (Engel et al., 1994; Sereno et al., 1995; DeYoe et
al., 1996; Warnking et al., 2002; Dougherty et al., 2003). Several ways for stimulus
presentation have been developed. The earliest approach was simply to stimulate different
parts of visual field one after another (Schneider et al., 1993). Later, the widely used phase-
encoded method was developed (Engel et al., 1994; Sereno et al., 1995). Method uses two
types of stimuli. A rotating wedge attached to fixation point provides information about the
visual field representation in polar direction and expanding or contracting ring around
fixation point about the representation in eccentricity direction. Stimulus phase is Fourier
transformed into visual field location, which in turn corresponds to cortex regions according
to retinotopic organization.
In my work I use mainly multifocal (mf) mapping method for determination of visuotopic
maps. It is based on the simultaneous stimulation of multiple visual field regions and on the
measurement of multiple local visual field responses in parallel. The timing of each region is
linearly independent from the other regions and thus the correlation between the stimulus
42
and the response pattern reflects directly the underlying neural system. The principle of
parallel stimulation of several regions was presented in maximal length shift register (m-
sequence) stimuli (Sutter, 2001), Andrew James (2003) introduced a mf-design with pulses
of pattern reversal resulting in large visually evoked potential amplitudes, and Vanni and co-
workers (2005) developed a multifocal stimulus for fMRI (mffMRI, Figure 3). In contrast to
phase-encoded retinotopic mapping, where a travelling wave of activation along the cortex
gives a continuum of phases in the response, mffMRI produces multiple discrete local
retinotopic BOLD responses that can directly be used as regions of interest for further
analysis. In addition, the standard general linear model based tools can be used in mf-data
analysis and the localization and the quantification of the signal is more straightforward even
though non-linear neural interactions may also result in signal loss (Pihlaja et al., 2008).
Compared to block designs, mf-technique enables simultaneous stimulation of several
locations, and therefore it requires considerably shorter measurement time.
Figure 3. One image from multifocal stimulus
Correct localization of close retinotopic areas or different visual field representations form a
challenge for EEG and MEG due to relatively poor spatial resolution of the methods.
Excellent temporal resolution would enable separation and localisation based on latency
differences. However, timings of early and intermediate retinotopic areas are highly
overlapping (Schmolesky et al., 1998) which hampers the separation. Both anatomical and
functional MRI results has been utilised as constrains for MEG and EEG source localisation,
in order to improve signal separation originating from different retinotopic areas (Di Russo
et al., 2001; Vanni et al., 2004; Hagler et al., 2009).
43
3. Aims of the study
This work concentrates on visual processing on human cerebral cortex in functional areas
which are at low-level in the hierarchy of visual cortices. Especially, the following
experiments examine the neural processing around parieto-occipital sulcus which
corresponds to human V6 and peripheral visual field representation in low-order visual
areas. Brain signals are measured with fMRI and MEG. In addition, this work approaches
some of the methodological problems of MEG and fMRI.
The aim of the work was to:
examine some of the limitations of MEG and fMRI methods in studies of human visual
processing (Studies I, II, and III).
map the peripheral visual field representations in retinotopic areas (Study II).
localise human V6 and study the properties of its neurons (Studies II, III, IV, and VI).
examine the effect of motor and attention tasks on early visual processing, which should
reflect top-down modulation, and especially to detect the spatial distribution of the
responses in the retinotopic cortex, including visual area V6 (Studies IV and V).
44
4. Materials and methods
4.1. Subjects, stimuli and tasks
4.1.1. Subjects
In study I ten volunteers from laboratory staff analysed the simulated MEG data with two
different methods. Studies II - VI comprised healthy adult volunteers. Some of the subjects
were measured in several experiments. For example, cortical surface maps of retinotopic
areas were made for six subjects in study II, and these maps were used also in studies III and
IV. Table 2 summarises number of subjects attending in different studies and experiments.
All subjects gave their written informed consent before participation in the studies, and the
studies were accepted by the Ethics Committee of Hospital District of Helsinki and Uusimaa.
Study Aim of the study Exp
1
Exp
2
Exp
3
Exp
4
I compare MEG source localisation with ECD and MCE 10
II map peripheral visual field representation, locate V6 12 5 6 2
III find cortical regions sensitive to luminance flicker 11 4
IV examine the effect of saccades in retinotopic areas 11 6 5 2
V examine the effect of spatial attention in V1 19
VI locate and study V6 with MEG 10
Table 2. The number of subjects in different studies and experiments and the aims of the studies
4.1.2. Visual stimuli and tasks
Visual stimuli were presented in studies II, III, V, and VI. All stimulus images were
generated with MatlabTM
(Mathworks Inc), and their timing was controlled with
PresentationTM
(Neurobehavioral systems Inc.). All visual stimuli were gray scale images at
photopic luminance levels. In study II, we developed a method for mapping the retinotopic
representations of wide visual field, 100 degrees and 40 degrees diameter for horizontal and
vertical dimensions, respectively. To achieve wide field in the narrow magnet bore, the
subjects viewed the stimuli at very short distance with the help from an optical aid. The
multifocal stimuli comprised five to 48 discrete regions. In study III the stimulus was
designed for comparison of the responses in retinotopic areas to same stimulus with different
45
surround. We showed luminance flicker and reversing checkerboard stimuli with dark and
light surround. The stimuli were viewed through small apertures in front of the eyes. The
surrounds of the stimuli were constructed with two masks, one black, coarse and dark and
another white and brightly illuminated. Study IV explored the effect of saccades on early
visual cortices. Activations were examined together with retinotopic mapping data from
study II. No visual stimuli were used, but the subjects were asked to perform predefined eye-
movement tasks in darkness. The darkness was complete, none of the subjects reported any
light after the full dark adaptation. In study V we used both event related fMRI, attention
task, and 60-region mffMRI, which served as an efficient functional localizer for V1
regions-of-interest. The attention task contained four regions from the mapping stimulus, one
in each visual field quadrant. Study VI was designed to describe the temporal properties of
human V6 with MEG. Peripheral and moving stimulus in upper visual field quadrant
enabled differentiation of neuromagnetic signal from V6.
Figure 4 visualises the stimulus systems used in fMRI and MEG experiments. Stimulus
images in fMRI studies were produced by a data projector with three micromirrors
(VistaPro, Electrohome Ltd) outside a magnet room. A customized objective with 350 mm
focal length was covered by 0.6 log unit neutral density filter to reduce the excessive
luminous flux. Images went through a RF-shield tube and were reflected by a surface mirror
into the magnet bore. In studies III and V a semitransparent back-projection screen was
located in the back of the head coil at 30 to 35 cm distance from the eyes, whereas in study II
the screen was located over the subjects’ foreheads at eight cm distance from the eyes. In all
fMRI studies the subjects lay on their backs and viewed the screen via a surface mirror.
Similar stimulus projection system was also used in MEG study (study VI). A data projector
with three micromirrors was located outside a magnetically shielded room. Stimulus images
were projected to a semitransparent screen via two surface mirrors. The screen was in front
of the subjects at 30 cm distance.
46
Figure 4. A) The stimulus presentation system used in the fMRI measurements (studies III and V,
The projection screen was above the subjects forehead in study II). B) The head coil and the surface
mirror used in fMRI measurements. C) The stimulus presentation system used in the MEG
measurement (study VI)
4.2. Measurements
4.2.1. fMRI measurements
All fMRI measurements were performed in Advanced Magnetic Imaging Centre (AMI-
centre) with a 3-tesla MRI scanner (SignaTM
, General Electric Ltd.) using a standard GE
single-channel head coil or an eight-channel head coil with exciteTM
that provides better
signal-to-noise ratio, especially at a brain surface. The functional imaging sequence was
T2*-weighted gradient-echo echo-planar imaging (EPI). Magnetic field inhomogeneities
were corrected with the default shimming for the entire brain. The imaging parameters
varied in different studies. Table 3 shows the measurement parameters for studies II – V.
Data was always acquired from the dorsal part of the brain. The measurement slices were
situated approximately orthogonal to PO sulcus. In studies II, III and IV the slices covered
the whole occipital and parietal lobes and the dorsal parts of the frontal and temporal lobes
and the cerebellum whereas in study V the slices fully covered only the occipital lobe. This
partial coverage of the brain enabled a better spatial resolution and faster sampling rate in the
focus areas.
47
At the end of each functional session, a 3-min anatomical T1-weighted image was acquired.
This image enabled the normalisation of the data into a standard anatomical space and
coregisteration of the different data sets within one subject. The parameters for all these
anatomical images were 1.5 mm slice thickness, field of view 23 cm, 128 x 128 matrix
resulting in 1.8-mm in-plane resolution. The segmentation of cortex for the surface oriented
analysis required higher resolution anatomical images, and the images with 0.9 mm slice
thickness, 256 x 256 matrix and 0.9 mm in-plane resolution were measured for six subjects.
Study TR
(ms)
FOV
(mm2)
Voxel
(mm3)
n.o. slices TE
(ms)
flip angle
(deg)
II 2000 190x190 3x3x3 27 30 60
III 2000 190x190 3x3x3 27 40 90
IV 2000 190x190 3x3x3 27 40 & 30 90 & 60
V 1800 160x160 2.5x2.5x2.5 24 30 60
Table 3. Measurement parameters in studies II - V
4.2.2. MEG measurement
MEG data for study VI were measured in Brain Research Unit of Low Temperature
Laboratory with a 306 channel Vectorview neuromagnetometer provided by Elekta
Neuromag Ltd. The measurement channels are composed from three channels in 102
locations covering the whole head. In each location are two orthogonally oriented
gradiometers that measure the spatial gradient of magnetic field and one magnetometer
detecting the strength of the magnetic field. The head position was measured in respect to
measurement helmet. The subjects sat in a magnetically shielded room. The remaining
artefacts were removed with the signal-space separation method. The neuromagnetic signals
were filtered to 0.1 – 200 Hz and sampled at 600 Hz. Vertical and horizontal eye movements
were recorded and the epochs contaminated with saccades or blinks were rejected on line.
We measured and averaged 200-300 epochs for each subject to obtain good signal-to-noise
ratio.
48
4.3. Data analysis and visualization
4.3.1. Analysis of fMRI data
Data in fMRI studies were analysed with Matlab based SPM2 software provided by the
Wellcome department of Imaging Neuroscience, London, UK (Frackowiak et al., 2003).
Head movements and timing errors between slices were corrected with spatial and temporal
realignment functions. The examination of the results in individual level required the co-
registration of functional and anatomical images, and the data were normalized into a
standard anatomical space before the group analysis. We used both FWE and FDR
corrections for multiple comparisons, and generalization of the results from the sample to the
population with the second level statistics. The resulting statistical t-maps were visualized
either over the anatomical images of the subjects or over a standard anatomical image.
We used Matlab-based Brain ála Carte (BALC) toolbox provided by INSERM unit
594/Universitè Joseph Fourier, Grenoble, France, for surface oriented analysis (Warnking et
al., 2002). BALC segments cortex along the border of white and grey matter and creates 3-D
model cortex. The statistical t-maps obtained from SPM-analysis were projected on this 3-D
model which was then unfolded onto 2-D surface.
Signal changes in a given brain location were calculated in studies II, III, IV, and V. In
studies II, III, and IV the regions-of-interest (ROI) for signal calculations were marked on
the cortex surface of V1 and V2. For study IV, we determined additional ROIs covering
statistically significant activations in brain from the group data. In study V the ROIs were
defined with multifocal sequences i.e. responses to one multifocal region in V1 corresponded
to one ROI. The location of ROI in V1 was confirmed with the restrictions of size and
distance from the local maximum. SPM calculates regression coefficients, β-estimates, for
each voxel separately, which are saved as 3-D volumes for each regressor of the design
matrix and for mean signal. The global mean signal is normalised during the estimation but
the mean signal in any particular voxel likely deviates from the global mean. We calculated
the percentage signal change by dividing the parameter value for the effect of interest with
parameter value for the constant term for all voxels within ROI separately and multiplying
with 100. Then the signal changes in all voxels within ROI were averaged. The calculation
of an average signal between voxels assumes that behaviours of the voxels are homogenous.
49
This assumption is reasonable when we measure retinotopic representation or other rather
inaccurate parameter.
4.3.2. Analysis of MEG data
In study I volunteers analysed simulated visual MEG data with Vectorview XFit toolbox for
ECD modelling and MCE toolbox for distributed modelling. They reported location,
orientation, timing and amplitude of the modelled sources and these parameters were
analysed further. Analysis of MEG data in study VI was performed with MNE toolbox
utilising surface orientation constraints provided by Athinoula A. Martinos Center for
Biomedical Imaging, Harvard University/MIT/MGH, Charlestown, Massachusetts. The
analysis was performed at both individual and group level. Activation ROIs were selected
from the individual data, and the timing of activations within ROIs were examined. The
group level analysis was utilised to confirm the individual results.
4.3.3. Statistics
A reletively small sample size forced us to use non-parametric methods for statistical testing.
In study I we used Wilcoxon signed-rank test for paired data to see whether the results
obtained with two methods differed significantly. The Wilcoxon signed rank test allows non-
parametric testing of the order of data points in paired samples, without need to assume
normal distribution of the variables. In studies II and III the BOLD responses were
quantified with statistical parametric mapping and the results were displayed at statistical t-
maps. No additional statistic was applied on the description of the results. In study IV we
examined the regional BOLD signals in addition to SPM analysis. We identified the
functional ROIs in V1 and V2 and quantified the signal change. Statistical significance of
the treatment across the subject group was evaluated with non-parametric Wilcoxon signed
rank test. In addition, we determined ROIs from the group data and calculated the correlation
coefficients between different ROIs. In study V we did not calculate statistical parametric
maps, but the whole analysis relied on the detection of signal changes at predefined ROIs.
Repeated measures of analysis of variance (ANOVA) were used for response quantification.
Conditions, visual field regions, and locations were used as regressors in the analysis of the
50
strength and the extent of response modulation. Post hoc paired samples t-tests examined the
difference between conditions. ANOVA was utilised also in the analysis of eye movement
measurements. In study VI the MEG data were presented as dynamic statistical parametric
maps and the functional ROIs were selected on the basis of these maps. The differences in
onset latencies between ROIs were tested with ANOVA, and post-hoc Tukey-Kramer test
revealed the significance of the differences between ROIs.
4.4. Eye movement recordings
Eye movements were recorded in studies IV and V with MRI compatible iViewXTM
MRI-
LR video-oculography system, provided by SensoMotoric Instruments (SMI) GmbH,
Germany. In study IV, complete darkness during the fMRI data measurement was essential
and therefore the eye movement recordings were performed before or after the measurement
in separate runs. In study V the eye movement recordings were performed simultaneously
with the fMRI measurements. The eye was illuminated with infrared light and recorded with
charge-coupled device camera, sensitive for infrared light. The eye positions were sampled
at 50 Hz or 60 Hz with an accuracy of approximately 1 degree. The positions of pupil and
corneal reflections indicated the position of the eye in relation to coordinate frame. This
coordinate frame was defined before the measurement with calibration of five to nine eye
positions.
51
5. Experiments
5.1. Comparison of minimum current estimate and dipole modeling in the analysis of
simulated activity in the human visual cortices (Study I)
The aim of the study I was to compare two different approaches to MEG source modelling.
Even though the localisation accuracy and source separation has been studied extensively in
different conductor models and source and noise conditions, the performance of data
analysis methods applied by different users had not been compared before.
5.1.1. Methods
Ten volunteers analysed the simulated MEG data with both multidipole modelling and
minimum current estimate methods. Figure 5 summarises the simulations 1-4. We created
four simulations; three with six sources of different amplitude and temporal activation
envelope and one with ten sources aiming to imitate better real measured data. Six sources
were situated in the average positions of human retinotopic areas V1-V5 (Hasnain et al.,
1998), and three more anterior sources were placed in well-known positions of MEG
responses. Background noise was obtained from a real measurement. We gave instructions
for source selection in both dipole modelling and MCE and, because the subjects were not
familiar with MCE, a short lesson of the method before the analysis. Subjects performed the
data analysis independently and reported the location, orientation, and timing of the
modelled sources. We examined the proportions of the modelled sources from the simulated
sources and the distances and the latency differences between the modelled and the
simulated sources within each simulation. The results were considered against the properties
of the simulations. In addition, we examined the number of false positives i.e. modelled
sources that did not correspond to any of the simulated sources.
52
Figure 5. Time-courses and amplitudes of simulated sources in study I
5.1.2. Results
Table 4 summarises the number of found sources and the distances and the timing
differences between the modelled and simulated sources in simulations 1, 2, and 4. Subjects
found approximately the same number of sources with both methods and defined the sources
with both good temporal (< 8 ms) and spatial (8 mm) accuracy, even though none of the
subjects was able to locate all sources in any of four simulations. The deep location and
radial orientation diminished the signal-to-noise ratio and thus hampered the source
localisation. In addition, the other simultaneously active sources affected source separation
and localisation. When the sources were not active simultaneously in simulation 1, dipole
modelling was more accurate both in time and space. The differences between methods (2
mm and 1 ms) were small even though they were statistically significant. When sources were
more overlapping in time in simulations 2 and 3 the dipole model and MCE performed
equally, but MCE analysis resulted in larger number of sources emerging from noise than
ECD analysis. In simulation 2, where the sources were more overlapping in time and every
second source had double amplitude, the stronger sources were located with both methods,
but the localisation accuracy was lowered by the weaker partially simultaneous sources. In
simulation 3, where the sources were strongly overlapping in time, the six sources merged
into two or three clusters according to source orientations. This merging of sources was seen
with both methods and it made impossible to connect a modelled source to a particular
simulated source. In simulation 4, both methods were equal in the analysis of the data that
imitated real visual evoked response.
53
Dipole modelling MCE
Simulation located
sources (%)
localisation
error (mm)
timing
error (ms)
located
sources (%)
localisation
error (mm)
timing
error (ms)
I 65 ± 12 4.4 ± 1.9 0.8 ± 0.3 62±13 6.2 ± 1.1 1.6 ± 0.9
II 58 ± 8 7.8 ± 1.8 7.8 ± 3.0 55±12 7.6 ± 2.5 5.4 ± 1.2
IV 54 ± 8 7.2 ± 1.0 5.7 ± 3.1 60± 2 7.2 ± 0.9 7.7 ± 1.5
Table 4. The results from simulations 1, 2, and 4. Mean number and standard deviations of reported
sources and localisation and timing errors. The results of simulation 3 are not presented because the
merging of the overlapping activations did not allow the identification of one simulated source from
the responce pattern.
5.1.3. Discussion
An ideal method for MEG source modelling would be fast, accurate and require as little user
interaction as possible. Dipole modelling relies on subjective selection of the detector
channels whereas MCE is sensitive to false positives. Especially subjects who localised more
simulated sources were prone to select also noise sources. As expected, the orientation and
the strength of the simulated sources strongly affected the responses, and the sources
overlapping in time form a challenge for MEG. Both dipole model and MCE performed well
when sources were active separately, but neither of the methods was able to differentiate
close simultaneous sources, a situation which is typical in visual cortex. Thus neither of the
methods was able separate the components of the real visually evoked response i.e. to find
and localise temporally overlapping sources in different visual areas. Functional or
anatomical a priori information would be required before MEG signals can be associated
with a correct functional area. However, our simulated sources were local current dipoles
which may have provided advantage for dipole modelling over MCE method.
54
5.2. fMRI of peripheral visual field representation (Study II)
The aim of study II was to map peripheral visual field representations in the medial surface
of occipital lobe and delineate the medial retinotopic areas as completely as possible. We
developed a method to stimulate wide visual field in a narrow magnet bore and tested
different multifocal mapping stimuli.
5.2.1. Methods
The projection screen was placed 8 cm from the subjects’ eyes, above their foreheads. The
subjects viewed the stimuli with + 10 diopter lenses. To diminish the need of converge
Fresnel prisms were applied onto the lenses. This stimulus presentation system resulted in
100 degrees of horizontal and 40 degrees of vertical visual field. According to Duncan and
Boynton (2003), 50 degree radius visual field corresponds to about 90 % of the surface area
of the primary visual cortex whereas typical 15 degree corresponds only to 60 % of the
primary visual cortex. Two sources of aberration emerged in our setup. Due to spherical
aberration the lenses increased the stimulus size nonlinearly, resulting in increase of the
image towards the periphery. However, the increment was easily measured with a light from
a point source (laser pointer) revealing the true extent of the stimulated visual field. In
addition, the prisms reflected the minor part of the light also to the hemiretina ipsilateral to
the stimulation.
We constructed four different multifocal stimuli and compared the results with historical
phase-encoded mapping from the same subjects with stimuli subtending up to 30 degrees of
visual field. The retinotopic visual areas in medial occipital lobe were delineated with a
stimulus covering horizontal and vertical meridians, and two stimuli covering 45 degrees of
polar angle in each visual field quadrant and full polar cycle gave information about
structure of the areas. A monocular stimulus, where the prisms were not necessary, was
constructed to control the reflections from the prisms. The data analysis was made with
SPM2 and with separate analysis algorithms and the results were examined in both group
and individual level, in 3-D and on cortical surface. The cortical surface analysis was made
with BALC.
55
5.2.2. Results
Our simple and relatively comfortable optical method allowed stimulation of the visual field
up to 40-50 degrees of eccentricity and delineation of the peripheral representation of the
retinotopic areas in medial occipital surface. Multifocal technique produces multiple discrete
local responses and thus the individual results can easily be examined both in 3-D and 2-D.
In contrast to mffMRI, phase-encoded mapping requires segmentation of cortex because it
utilises visual field sign for area delineation and visual field sign is defined from the polar
and radial phase gradient separately for each cortical location. 3-D analysis is a great
advantage especially in clinical conditions as the segmentation of grey matter or white and
grey matter border for cortical surface model is laborious. In addition, group level statistics
could be calculated from the 3-dimensional data. Because the multifocal technique relies on
the general linear model, the data from predefined retinotopic representations can be easily
averaged across subjects. However, the intersubject variability of area locations hampers the
group level analysis. New methods with more robust surface segmentation and inter-subject
analysis in 2-D surface might provide better results.
Figure 6 visualises the results of group level analysis. The group level analysis showed that
occipital retinotopic areas extended dorsally to parieto-occipital sulcus and ventrally
throughout the posterior brain to anterior calcarine sulcus up to brainstem structures. In
addition to responses in the medial surface of occipital lobe representing areas V1, V2, and
V3, we found responses in the lateral part of occipital lobe likely corresponding to human
V5 (Zeki et al., 1991; Watson et al., 1993). Separate upper visual field representations were
found in dorsal lateral cuneus and in medial cuneus. In lateral cuneus the most central
stimulus evoked responses, whereas medial cuneus activated only for the most peripheral,
more than 30 degrees of eccentricity, stimulus. The lateral cuneal activation is likely to
correspond to V3a (Tootell et al., 1997). The Talairach coordinates of the medial cuneal
responses were x = -12 y = -77 z = 37 and x = 18 y = -77 z = 34, which were close to the
proposed location of human V6 (Pitzalis et al., 2006).
56
Figure 6. The group level results projected on the right hemisphere of a template brain. The
multifocal stimulus comprised a checkerboard wedge in each visual field quadrant. The most central
part of the stimulus (visualised with yellow) extended approximately from one to 12 degrees of
eccentricity, the middle part (red) subtended degrees from 12 to 30 and the most peripheral one
(green) subtended to approximately 50 degrees of eccentricity. On the left are the results from
stimulation of the left upper visual field and the image on the right shows responses to the lower left
visual field stimulus.
The results of individual analysis were in line with the findings of the group level statistics
and multifocal and phase-encoded method yielded similar responses on the cortical surface.
Figure 7 visualises the results of one representative subject on her right occipital surface.
Wide multifocal stimuli activated medial cortex at larger extent than the narrower phase-
encoded stimulus. V1 responded extensively to all multifocal stimuli, but extrastriate cortex
showed less activation when the multifocal stimulus covered the whole field. In addition, a
stimulus in horizontal meridian did not activate border between dorsal V2 and V3. Only the
most peripheral stimulus along the horizontal meridian activated the dorsal extrastriate
cortex and this activation overlapped with the separate upper visual field response in parieto-
occipital sulcus.
Other subjects, whose data were analysed on cortical surface, showed similar responses as
the representative subject. On cortical surfaces the responses extended to anterior calcarine
sulcus, lingual gyrus, and PO sulcus with some individual variability. The same separate
upper visual field representations in lateral and medial cuneus emerged in individual and
group analysis. The separate representations of peripheral upper visual field in medial cunei
were located in posterior bank of PO-sulcus anterior and lateral to peripheral V2d and V3.
The mean Talairach coordinates of the medial cuneal responses were x = -12 ± 6 y = -74 ± 7
z = 30 ± 8 and x = 12 ± 6 y = -71 ± 5 z = 28 ± 5. Calculation of mean magnification factor in
V1 showed that the inverse relationship between cortical magnification factor M and visual
field eccentricity E was 1/M = 0.0592E + 0.0310. In addition, the surface analysis revealed
57
that when the stimulus regions were next to each other extrastriate areas did not activate but
the activations were abundant when the stimulus regions were separated. In addition, the
results showed asymmetric activation in dorsal and ventral V2 / V3 as the horizontal
meridian stimulus failed to activate dorsal V2/V3 border. This result suggests that the
division of the visual field to upper and lower representation for ventral and dorsal V2 and
V3 is below the horizontal meridian.
Figure 7. The responses to central phase-encoded stimulus and wide field multifocal (mf) stimuli for
one representative subject projected over the segmented and unfolded cortex surface of her right
occipital lobe. The upper row visualises results of phase-encoded mapping and stimulation of
horizontal and vertical meridians. The area borders are drawn according to meridian data. Lower row
shows responses to different multifocal stimuli. On the left are results to “full field” stimulus at
different polar angles and in the middle at different eccentricities. On the right are the responses to
left upper visual field stimulus showing an additional activation in the dorsal part of parieto-occipital
sulcus (marked with V6).
58
5.2.3. Discussion
We mapped retinotopic visual areas up to 50 degrees of eccentricity both in 3-D for group
and individual level analysis and on cortical surface for individual analysis. Compared to
classical methods with eccentricities from 10 to 15 degrees (Engel et al., 1994) we cover
almost the whole extent of V1. In line with previous histological results (Rademacher et al.,
1993; Amunts et al., 2000), peripheral visual field representation in V1 extended to PO-
sulcus and anterior calcarine sulcus. Almost complete delineation of retinotopic areas
enabled localization of parieto-occipital peripheral visual field representations.
We found an additional response to peripheral upper visual field stimulus in the posterior
bank of the PO sulcus in both individual and group data. The location of this response
maximum was approximately at one centimeter distance from previously proposed position
of the human homologue of area V6 (Pitzalis et al., 2006) and it was clearly separate from
the other upper visual field representations in medial and lateral occipital surface. In line
with previous MEG and fMRI results (Jousmäki et al., 1996; Portin et al., 1998; Vanni et al.,
2001; Pitzalis et al., 2006), we suggest that the peripheral upper visual field representation
belongs to human V6.
Surface oriented analysis enabled calculation of human magnification factor which was in
line with previous calculations from more central data (Grüsser, 1995; Sereno et al., 1995;
Engel et al., 1997; Duncan and Boynton, 2003). The lack of horizontal meridian responses in
dorsal V2 and V3 may indicate the division of dorsal and ventral retinotopic areas below the
horizontal meridian as suggested previously (Vanni et al., 2004) whereas the lack of
extrastriate responses related to stimulus regions next to each other suggests the existence of
nonlinear interactions in extrastriate areas. Pihlaja and co-workers (2008) recently showed
that neural surround modulation in V1 attenuates response to central stimuli when the
surround is stimulated simultaneously. Large receptive fields in extrastriate areas could
predispose to this modulation.
59
5.3. Central luminance flicker can activate peripheral retinotopic representation (Study
III)
Originally study III aimed at localization of cortical regions that are more sensitive to
luminance flicker than to other visual stimuli. Previous results had been suggested that
human V6 is particularly sensitive to luminance (Portin et al., 1998; Dechent and Frahm,
2003). Retinotopic mapping and second control experiment were included to confirm that
possible luminance sensitivity arises from a separate area.
5.3.1. Methods
Two visual stimulus systems were designed for study III. First, we aimed to maximise the
contrast between the stimulus and the surround and to exclude all light reflections from the
stimulus surround. This is the desired setup in studying a response to luminance stimuli,
where one wants to exclude aberrant indirect stimulation of the retina. The subjects wore a
black matte mask which covered the peripheral parts of the visual field, and the interior of
the head coil was covered with the same cloth to diminish light reflections. In the second
setup, the aim was to minimise the contrast of light scattered inside the eye. The subjects
wore a white mask which was illuminated with bright light. Light was directed inside the
magnet bore with two bundles of optic fibers, one for each eye. The idea to illuminate
stimulus surround was adopted from the studies of cortical blindness (Barbur et al., 1994;
Weiskrantz et al., 1995). The parameters for the visual stimulus viewed through a 30-deg
aperture inside the mask was similar in both measurements and it consisted of luminance
flicker (black = 0.4 cd/m², white = 25 cd/m², flickering at 4 Hz), checkerboard pattern
reversal (reversing at 8 Hz, no change in the mean luminance), and rest (a fixation point with
a grey background 11 cd/m²) blocks in interleaved order. The data were analysed with SPM2
and BALC. Both pattern reversal and luminance flicker blocks were contrasted against rest
and the results were compared both in group and individual level and in 3D and on cortical
surface. Responses on cortical surface were localised in relation to retinotopic visual areas.
60
Figure 8. The results of one representative subject projected on his left medial occipital surface. The
responses to pattern reversal stimulus (pattern-rest, pFWE < 0.05) and the responses to luminance
flicker that extend beyond the pattern responses [(luminance-rest, pFWE < 0.05) excluding (pattern-
rest, p<0.05)] with both dark and illuminated stimulus surround. The borders between functional
areas have been defined according to the results of study II.
5.3.2. Results
Figure 8 visualises the results of one representative subject on his medial cortical surface.
Retinotopic areas V1-V3 responded strongly to the pattern reversal stimulus and luminance
flicker activated cortex outside the pattern-related responses only when the stimulus
surround is dark. This additional luminance response was located in the peripheral visual
field representations of the areas V1-V3. Figure 9 shows the signal changes related to all
stimulus conditions at the different eccentricities of V1. Responses to pattern reversal were
stronger in stimulus representations, but the responses to luminance flicker exceeded the
pattern responses in the periphery. Analysis of all data confirmed the results of the
representative subject. Both individual and group level results showed that the responses to
luminance flicker with dark stimulus surround reached further anteriorly than the responses
to pattern reversal. The mapping of retinotopic areas up to 50 degrees of eccentricity
confirmed that these additional luminance responses were located mainly in the peripheral
visual field representation of V1 and some activation was also detected in the peripheral
visual field representations of V2 and V3. When the illumination around the stimulus was
increased in the second experiment, these peripheral responses to luminance flicker
61
disappeared whereas the responses to pattern reversal stimulus behaved similarly in both
conditions.
Figure 9. The perceptual signal changes in left V1 of the same subject as presented in Figure 8. The
lines represent signal increases related patter-rest and luminance-rest contrasts during both dark and
illuminated stimulus periphery. I marked regions-of-interest on V1 surface and calculated the mean
signals within the ROIs. I delineated V1 according to the results of study II and placed the ROIs at
regular intervals.
5.3.3. Discussion
Against a previous proposal (Dechent and Frahm, 2003), our results showed that luminance
responses in PO sulcus originate from the peripheral visual field representations in visual
areas V1, V2, and V3 and not from a separate luminance sensitive functional area.
Moreover, we did not find any cortical region more sensitive to luminance flicker than to
pattern reversal stimuli. Thus a central luminance flicker stimulus cannot be used for
localisation of human V6 with fMRI. We suggest that the luminance responses in peripheral
representations result from intraocular scattering of light even though light reflection may
have a minor contribution to our responses. When the mean luminance difference between
the stimulus and the surround of the stimulus is large, scattered light has high contrast and it
62
can stimulate the retina outside the region which optically corresponds to the stimulus. The
intensity of stray light decreases with inverse square relationship with increasing angular
distance from the light source (Vos 2003). Because low contrast gives relatively high signal
in human V1 (Boynton et al., 1996), possibly enhanced by weighting of magnocellular
processing stream in the periphery, scattered luminance contrast could result in spread of the
response with linear amplitude decay as a function of distance detected in our study.
63
5.4. Peripheral visual field representation activates during saccades in darkness (Study
IV)
Previous studies have reported eye-movement related activation in human parieto-occipital
region (Bodis-Wollner et al., 1997; Dejardin et al., 1998). Study IV aimed at localisation of
the medial occipital responses during saccades in relation to retinotopic areas and retinotopic
positions. In addition, we aimed to explore the functional role of these responses.
5.4.1. Methods
Study IV comprised two experiments and two supplementary experiments that were all
carried out in complete darkness. In the first experiment eleven subjects made self paced
horizontal 10 degree saccades approximately twice per second. In the second measurement a
subgroup of six subjects made both horizontal and vertical saccades with small and large
amplitude. The second experiment was designed to examine whether the distribution of the
saccade-related responses in retinotopic areas reflected the distribution of the saccade target
in visual field. The supplementary experiments were designed to study whether the saccade-
related responses actually reflected covert shifts of attention or deviation of gaze. In the
supplementary measurements five subjects either made saccades or covertly shifted attention
in darkness and two subjects fixated on the left or on the right. Block design was used in all
measurements. The results of eleven subjects from the first measurement were analysed
individually and on group level to localize significant saccade-related responses in 3D brain.
The 3D analysis included the calculation of correlation coefficients between the signals from
the activated regions. In addition, all data from the subjects attending to two or more of the
experiments were analysed individually on cortical surfaces with BALC, and the responses
were localised in relation to retinotopic areas. I obtained retinotopic maps, which comprised
almost complete delineation of areas V1-V3 and localisation of V6, from study II. I marked
several ROIs on segmented cortex surfaces in V1 and V2 and calculated the mean signal
change during saccade and attention conditions at different eccentricities.
64
Figure 10. The responses of one representative subject projected on her right medial occipital
surface. On the right are the responses during different saccade conditions. Upper left corner shows
the retinotopic data from study II (experiment 2). Colours mark different eccentricities, and the area
borders have been defined according to the responses to stimuli in horizontal and vertical meridians.
Lower left corner shows the responses to peripheral upper visual field stimulation from study II
(experiment 1). The circle marks the proposed region of human V6. PO and CA indicate parieto-
occipital and calcarine sulci.
5.4.2 Results
3D analysis in group and individual level showed activation in the well known cortical and
subcortical network for saccades including responses in anterior calcarine sulcus and the
ventral part of parieto-occipital sulcus. Correlations between the signals in different regions
were highly significant (correlation coefficient more than 0.7). Surface analysis revealed that
the occipital saccade-related responses were located mainly in the peripheral visual field
representation of V1, V2, and V3. Figure 10 visualises the results of one representative
subject projected on her segmented cortical surface. All saccade conditions result in
significant signal in the periphery, but the signal-to-noise ratio is stronger for large amplitude
saccades. Calculation of BOLD signal change in different V1 locations confirmed the
65
peripheral weighting of the responses (Figure 11). The direction of the saccades, horizontal
vs. vertical, did not affect the magnitude or the distribution of the responses, whereas the
large amplitude saccades resulted in clearer peripheral weighting and the decrease of signal
in central representations. Saccades also activated the dorsal part of parieto-occipital sulcus.
The dorsal PO-responses overlapped with the putative location of human V6 described in
study II. Supplementary experiments 3 and 4 suggested that the responses during saccades in
peripheral representations did not reflect shift of spatial attention or deviation of gaze.
Figure 11. The average signals in representations of different eccentricities in V1. I marked the ROIs
at cortical surface of both hemispheres for all six subjects who attended both experiments 1 and 2. I
calculated the signals related to the effects of interest individually and then averaged the signals
between subjects. A) visualises the mean saccade-related signal increase in experiment 1 overlaid to
the visually evoked signals from experiment 2 of study II. B) shows the mean signal changes (±
S.E.M.) in experiment 2.
66
5.4.2. Discussion
Our results showed saccade-related responses in peripheral visual field representations in
retinotopic areas that are at low hierarchical level. Cortical regions in PO sulcus have been
shown to respond during eye movements (Dejardin et al., 1998; Law et al., 1998), and our
results extend the knowledge by localising the responses in the peripheral representations.
We suggest that the responses in early visual areas result from top-down signal because the
experiment conditions did not contain any visual bottom-up stimulus. The simultaneous
activation of dorsal stream areas and the lack of activation in ventral stream suggest the
dorsal stream origin of the top-down signal. Experiment 2 showed that the responses in
periphery did not reflect the retinotopic representation of saccade target, and supplementary
experiments 3 and 4 suggested that the peripheral responses reflected movement processing
and not spatial attention of deviation of gaze. However, the results from experiment 2
suggest that two simultaneous processes may modulate saccade-related BOLD responses.
The peripheral signal increase may be combined with another process that increases signal at
the eccentricity of saccade target.
Monkey studies provided the first direct link from higher order dorsal stream areas to low
level visual areas (Moore and Armstrong, 2003). In humans, TMS stimulation of right
frontal eye field and intraparietal sulcus have resulted in BOLD signal increases in
hierarchically low-level retinotopic areas (Ruff et al., 2006; Ruff et al., 2008; Ruff et al.,
2009). Stimulation of FEF caused signal increases in peripheral visual field representations
and decreases in central representations. Our data agrees with these findings; all saccade
conditions activated FEF region and peripheral V1, V2, and V3, and the large amplitude
saccades decreased signals in central V1, thus proposing that FEF could be a source of our
occipital responses. However, because in monkeys FEF does not directly project to V1
(Schall, 1997) and modulation of V1 response after stimulation of FEF is gated with a
bottom-up signal (Ekstrom et al., 2008) the activation may reach low-level retinotopic areas
via a network of parietal and extrastriate areas and subcortical structures.
In addition to responses in peripheral V1, V2 and V3, saccades also activated dorsal PO-
sulcus in the proposed location of human V6 (Pitzalis et al., 2006). In monkeys V6 is the
central node of dorso-medial stream whose function is to control movements on line
(Rizzolatti and Matelli, 2003). Simultaneous activation of peripheral visual field
representations and dorsal stream areas associate peripheral vision with dorsal stream
67
function. Dense connections between peripheral representations of low-level areas and
dorso-medial areas in monkey brain (Lewis and Van Essen, 2000; Gattass et al., 2005)
support the functional association in our human data. The peripheral signal increment is
supposedly related to motor processing reflecting corollary disharge signal of the motor plan
and command (Sommer and Wurtz, 2008) in accordance with the proposal of Sylvester and
Rees (2006). It may also reflect more non-specific resetting signal in line with results of Jack
and co-workers (2006).
68
5.5. Topography of attention in the primary visual cortex (Study V)
The aim of Study V was to examine the effect of attention to visual BOLD responses in V1.
Previously spatial attention has been shown to modulate V1 responses (Tootell et al., 1998).
We utilised good spatial accuracy of fMRI as we assumed that the spatial extent of the
BOLD response would provide information about the underlying mechanism of the response
modulation. Recent models and data show that the local spread of activation is most likely
limited to the extent of monosynaptic connections (Angelucci and Bullier, 2003), and thus
the large spread of responses indicates feedback from hierarchically higher cortical areas
where neurons have larger receptive fields.
5.5.1. Methods
We measured nineteen subjects. First we detected a grid of sixty functional ROIs in V1 for
each individual subject with multifocal fMRI. Four target regions in the middle of each
visual field quadrant were selected from the multifocal stimulus regions. During the event
related attention paradigm, the subjects covertly shifted attention to each of the target regions
while fixating at the centre and reported the letter at the target. The changes of fixation mark
guided the task. Eye movements were recorded simultaneously with the fMRI measurement
to confirm the spatial accuracy of the results and to reject epochs contaminated with
saccades.
Multifocal stimulus was black and white checkerboard pattern to obtain as strong response
as possible whereas the visual stimulus for attention task comprised a change in spatial noise
in four target regions and a letter inside the regions. We compared the topographies of
BOLD signal change when the target region was attended and unattended. After the
definition of spatial clusters, we quantified the responses as the percentage of signal change
within each cluster. The statistical significance of the signal change was tested with repeated
measures ANOVA. In addition, we used least square search to fit two 2-D Gaussian models
to responses that gave estimates for the offset from baseline, the amplitude, and the width of
the function. The first set of parameters explained the unattended data and the second set of
parameters described the portion of attended data that remained unexplained by the first set.
69
5.5.2. Results
Subjects showed good co-operation and performed the detection task reasonably well. They
detected approximately 75 % of the letters correctly with less than one second response time.
The eye-movement recordings did not show any attention effects. Neither performance of
the subjects nor the number of saccades was different in relation to visual field quadrant.
Figure 12 visualises the average BOLD signal changes in each ROI in attended and
unattended conditions. Spatial attention increased BOLD signal in the lower visual field. In
the upper visual field the difference between attended and unattended condition was not
significant. Attention increased signal on average 20 % in the target region in the lower
visual field whereas the increment was only 3 % in the upper visual field. Furthermore there
was a significant distance effect showing the strongest attention responses at the target and
the decrease of attention responses at the near surround of the target. Fit of Gaussian
function revealed that the attention-related signal increments spread to the target
surroundings with approximately 12.5 mm and 10.2 mm radius for the upper and the lower
visual field, respectively. The sensory responses spread only 3.9 mm and 4.2 mm
Figure 12. The mean signal changes in ROIs of right V1. The green spots mark fovea and red and
the white lines horizontal and vertical meridians, respectively. On the left are the corresponding
regions of the multifocal stimuli. The dark grey regions were targets in the attention experiment. In
the middle are the signal changes related to covert attention elsewhere and covert attention to the
region. The nodes of the grids represent centres of the ROIs. Within each grid the lower row
represents the central visual field and the periphery is represented in the upper rows. On the right are
the signal differences between attended and unattended conditions.
70
5.5.3. Discussion
We observed an increment of V1 BOLD signal in response to spatial attention. Moreover the
attentional modulation of the signal spread to target surrounds. The spread of the BOLD
activations would correspond to the local spread of neural response. The spread of
unattended visual response corresponded to 4 mm of cortex, whereas the responses to
attended stimulus extended more than 10 mm of cortex. The observed effect can not result
from the suppression of unattended region because the location of attention target did not
modulate it. It is unlikely that the response spread would result from point-spread of the
BOLD signal because a point spread in 4 tesla magnet is approximately 4 mm (Parkes et al.,
2005).
According to the recent models of visual cortex architecture in monkey brain, the extra
classical surround modulation depends on the feedforward-feedback connections (Angelucci
et al., 2002; Schwabe et al., 2006). In our results the attention-related response spread that
exceeded the sensory spread and apparently horizontal connections more than twofold is
most likely modulated via feedback connections. According to Angelucci and co-workers
(2003) the feedback signal in V1 from V2 and V3 spread approximately 3 mm and 7 mm
radius, respectively. Assuming that the extends of signal spreads are similar with humans,
our results suggest that the attention modulation observed in this study originates from a
functional area with bigger receptive fields, perhaps an area in frontal or parietal lobe
previously associated with the guidance of spatial attention (Corbetta et al., 1993).
71
5.6. Motion sensitivity of human V6: A magnetoencephalography study (Study VI)
Study VI was designed to examine human V6 with MEG. In addition, study VI aimed to
examine the visual areas that activate rapidly and are sensitive to motion stimulus. These
areas could respond to monkey dorsal stream areas. The excellent temporal resolution and
the reasonable spatial resolution of MEG enable differentiation of some functional areas
according to latency of activation and localisation of areas.
5.6.1. Methods
A visual stimulus was designed to match with the known sensitivity profile of monkey V6
(Galletti et al., 1996; Galletti et al., 1999b) and the organization of retinotopic areas in
human medial occipital cortex. The stimulus comprised 10-45 degrees of eccentricity in the
upper left visual quadrant. After the appearance it remained stable for 800 ms and then
drifted for another 800 ms. We measured and averaged approximately 200-300 epochs from
ten subjects. We performed the modelling of neuromagnetic signal sources with MNE
software at both individual and group level. We selected regions-of-interests showing
consistent activation across subjects and the location of V5 for further analysis. The
locations of V5 were defined according to previous fMRI V5 localiser experiments with the
same subjects or activation in the known position of human V5. The ROIs were selected
from the first 200 ms after the onset of static and moving stimulus, because, on the basis of
monkey and human studies (Galletti et al., 2001; Vanni et al., 2001), we expected V6
response at relatively short latency. The selected ROIs were relatively large to overcome the
source localisation ambiguity of the MEG. The time behaviours of the neuromagnetic signals
within the selected ROIs were examined and the latencies of the activations were compared.
5.6.2. Results
We localised six consistent activations and thus ROIs in both individual and group average
data. Figure 13 visualises the time-courses of selected activations. Four ROIs were located
on the medial surface over calcarine sulcus, parieto-occipital sulcus, precuneus, and the
region close to the posterior end of cingulate sulcus. On the lateral surface we located ROIs
72
in temporo-occipital region and intraparietal sulcus. All of these ROIs were motion sensitive
and the latencies of the activation onsets were significantly longer for the stimulus motion
onset than for the stimulus onset. The region in cingulate gyrus showed only activation for
the motion stimulus. The ROI in parieto-occipital sulcus showed activation for both static
and motion stimuli with peak of activation for the onsets followed by sustained activations
of 300-400 ms. Latencies of the activations after the stimulus onset in calcarine sulcus, PO
sulcus and precuneus were approximately 80 ms. Temporo-occipital region and intraparietal
sulcus were activated significantly later.
Figure 13. The amplitudes and the time courses of neuromagnetic responses in all six ROIs. The
dashed line represents the stimulus onset. The stimulus started to move at 800 ms.
5.6.3. Discussion
We show responses for both moving and static stimuli in the posterior bank of parieto-
occipital sulcus. This region comprises the previously defined location of human V6 (Pitzalis
et al., 2006) and most likely our PO responses correspond to human V6 perhaps with
contribution from V6A. The upper visual field stimulus enabled the differentiation of V6
responses from the neighbouring dorsal V2 and V3 containing lower visual field
73
representations. In line with previous monkey data (Galletti et al., 1996; Galletti et al.,
1999b), the peripheral stimulus strongly activated V6, and V6 also contains motion sensitive
neurons. A recent fMRI study confirmed the motion sensitivity of human V6 (Pitzalis et al.,
2010). The responses in V6 region appeared shortly after the stimulus onset in line with
direct magnocellular connections from V1 to V6 in monkey cortex (Galletti et al., 2001).
The response in V6 region was a part of a fast sequence of activations in medial cortex, and
it was followed by activations in precuneus and posterior part of cingulate sulcus. Cavanna
and Trimble (2006) reviewed the properties of precuneal cortex. It has connections with
visual cortex, FEF and SEF in frontal lobe and associative nuclei of thalamus. Posterior
precuneus is related to visually guided movements, attentive tracking, and shifting of
attention between targets whereas anterior precuneus activates during motor imaginery and
retrieval of episodic memory. Posterior cingulate cortex is related to consciousness and
vigilance, and it has high basic metabolic rate. The dorsal part of posterior cingulate cortex
has connections with premotor and parietal areas, and it functions in visuospatial orientation
topokinesia and navigation of the body (Vogt and Laureys, 2005; Vogt et al., 2006).
In monkeys, V6 is a central node of dorso-medial (also called dorso-dorsal) stream
controlling visually guided movements on line (Rizzolatti and Matelli, 2003). In our data the
fast activation sequence in medial cortex from V1 via V6 to precuneal and posterior
cingulate regions suggests that in humans as in monkeys V6 belongs to rapid dorso-medial
stream that conveys visual information towards premotor frontal cortex. In addition to
responses in the medial surface of the brain, we found temporo-occipital activation, most
likely corresponding to human V5, and activation in intraparietal sulcus. Our visual motion -
related IPS activation is in line with previous results showing motion sensitivity in several
human parietal areas (Sunaert et al., 1999). The latency of temporo-occipital activation was
delayed compared to PO-activation. The reason for the latency difference is unknown, but
possible explanations include different intra-areal population dynamics, contribution of other
functional areas to temporo-occipital activation, and differences in signal routing possibly
reflecting different speed of signal processing in dorso-medial and dorso-lateral streams
(Rizzolatti and Matelli, 2003).
74
6. General discussion
6.1. Controlling methodological confounds in MEG and fMRI
Instead of measuring the activity of a single neuron, neuroimaging methods detect
electromagnetic or hemodynamic responses which reflect the activation of a large group of
neurons. This results in uncertainties that depend on the chosen method. Study I compared
two different softwares based on different algorithms for estimation the neuromagnetic
inverse problem, equivalent current dipoles and minimum current estimate. Neither method
was able to differentiate close, simultaneous activations. Anatomical and/or physiological
constrains are needed to improve the localisation accuracy even though the association of
responses to visual areas remains ambiguous. Anatomical constrains have been implemented
in the most recent MNE modelling method used in study VI which utilises individual cortex
surface models for inverse calculations. These constraints, however, seem to be insufficient
for linking responses to specific functional areas without a priori knowledge.
Imaging technology impose restrictions to experimental settings because no ferromagnetic
stimulus equipment can be used either in MEG or fMRI measurements and the equipment
must be suited for the shape of the device and the position of the subject. In study II we built
a stimulus presentation system for wide visual field stimulation. The optical aid with + 10
dioptre lenses and Fressnel prisms enabled comfortable fixation at short distance and thus
enabled stimulus presentation also in peripheral visual field. This has been a challenge for
fMRI because of the physical limits of the device interior. Previously Pitzalis and co-workers
(2006) mapped wide visual field by placing the visual stimuli close to the subjects’ eyes.
However, fixation to such a close target is difficult for emmetropic subjects. Our stimulus
system provided relatively good results; even the peripheral visual stimulus provided a good
signal-to-noise ratio and we were able to delineate peripheral parts of areas V1-V3. Future
development should improve resolution and control of peripheral aberrations.
Studies III-V show some potential confounding factors in vision studies. In study III
intraocular scatter resulted in considerable spread of responses which can be misleadingly
interpreted as greater local sensitivity of neurons to given stimulus. In study IV we show
eye-movement related responses, which may confound visual studies with uncontrolled eye
movements, whereas in study V attention modulated V1 responses and showed that subjects
were prone to saccades during attention off the fixation. These results underline the
75
importance of experimental planning. The properties of the stimuli must be carefully
balanced and eye movements and attention controlled for.
6.2. Human visual area V6
Previous electrophysiological studies have delineated and characterised visual area V6 in the
macaque and both neuromagnetic and fMRI studies have aimed to localise its human
homologue. We explored the human visual area V6 in studies II, III, IV and VI. Figure 14
visualises the locations of proposed V6 activations in studies II, IV and VI. In study II, a
separate peripheral upper visual field representation was located in the posterior bank of
parieto-occipital sulcus, anterior and lateral to the peripheral visual field representations of
dorsal V2 and V3. This peripheral upper visual field representation is in the proximity of a
new retinotopic area with complete contralateral visual field representation defined by
Pitzalis and co-workers (2006). In line with their proposal, and on the basis of its location
and responsiveness to peripheral visual field signal, we suggest that this upper visual field
representation belongs to the human homologue of monkey V6. The upper visual field
stimulus enabled the differentiation of V6 from peripheral representations in neighbouring
retinotopic areas.
Figure 14. The locations of human V6 according to studies II, IV and VI. The circle represents the
ROI used in study VI. The spots are located at the maxima of the PO responses in studies II and IV.
PO and CA mark parieto-occipital sulcus and calcarine sulcus.
76
The peripheral visual field stimulation is essential for the localisation of V6 with fMRI when
checkerboard pattern reversal stimulus is used. In Study II, a central visual field pattern
stimulus did not evoke measurable responses in this region. Previous studies have used
central luminance flicker stimuli for V6 localisation (Dechent and Frahm, 2003; Stenbacka
et al., 2004). However, the results of Study III revealed that responses to luminance flicker
mainly arise from peripheral V1 and putative V6 activation results from indirect stimulation
of peripheral retina. Recently, Pitzalis and co-workers (2010) confirmed our results by
showing V6 activation during peripheral but not central luminance flicker. However, they
used central coherent motion stimulus to localise human V6.
In contrast to the proposed location of V6 in dorsal PO region, previous studies suggested
human V6 to be located in a more ventral part of PO sulcus (Dechent and Frahm, 2003;
Stiers et al., 2006). Studies II and III showed however that the more ventral part of PO sulcus
belongs to the peripheral V1. In the macaque, V6 feeds signals to area V6A, which functions
in controlling visually guided movements. Study II did not aim to locate a supposedly non-
retinotopic visuomotor human V6A with a visual retinotopic mapping stimulus. Previous
suggestions on the location of human V6A have varied from the ventral part of PO sulcus
(Dechent and Frahm, 2003) to superior parietal lobule (Simon et al., 2002) and parieto-
occipital junction (Prado et al., 2005; Filimon et al., 2009). Location of V6A at PO junction
is plausible assuming that the relative positions of neighbouring visual areas of the macaque
are conserved in the human brain. Furthermore, a lesion in human PO junction results in
similar misreaching symptoms as a lesion in monkey V6A (Battaglini et al., 2003; Karnath
and Perenin, 2005).
In study IV we found saccade-related responses in the posterior bank of the dorsal PO sulcus
at the proposed location of human V6. To my knowledge no saccade-related activity has
been found in macaque V6 even though responses are modulated by the eye position
(Galletti et al., 1996). The discrepancy between our results and the monkey studies may
result from methodological differences. BOLD signal is more sensitive to synaptic activity
than single cell recordings which measure action potentials (Logothetis and Wandell, 2004).
The discrepancy may also reflect differences between species. Alternatively, the saccade-
related responses may partially originate from human V6A, putatively located next to V6.
However, our results did show simultaneous saccade-related responses in peripheral V1-V3
and V6/V6A complex suggestive of connections between these regions and that these areas
77
belong to cortical network that is related to eye movements. In addition, the macaque V6
contains cells that are able to differentiate real stimulus motion from apparent motion
resulting from eye movements (Galletti and Fattori, 2003). These “real motion cells” need
information about eye movements and this may explain saccade-related activations detected
in study IV.
Study VI showed motion related activation in the human V6 region in line with monkey
single cell recordings showing orientation and direction sensitivity in V6 neurons (Galletti et
al., 1996). A recent fMRI study confirmed the sensitivity of human V6 to coherent motion
(Pitzalis et al., 2010). The V6 responses in study VI appeared at a short latency after
stimulus and stimulus motion onsets in line with previous studies (Tzelepi et al., 2001;
Vanni et al., 2001). Accordingly, monkey V6 receives direct and fast magnocellular input
from V1 (Galletti et al., 2001). After a short delay we detected activations also in precuneus
and PCC. Both precuneus and PCC have connections to motor areas in frontal cortex and
play a role in controlling visually guided movements. In monkeys, V6 is a central node of
the rapid dorso-medial stream which controls visually guided movements (Galletti et al.,
2003; Rizzolatti and Matelli, 2003). The fast activation sequence in medial cortex may
reflect dorso-medial stream in human brain that extends from V6 to parietal and frontal
lobes.
6.3. Top-down modulation of V1
Studies IV and V detected clear increases in the BOLD signal in V1 during motor and
cognitive tasks, and in both studies the BOLD increase resulted from top-down signal.
However, different processes may cause the BOLD response modulations in studies IV and
V. In the study IV neural activity increased diffusely in the peripheral representations,
whereas in study V visually evoked responses were locally enhanced and the response
increase may have reflected contextual modulation.
Functional and anatomical evidence has shown that contextual modulation in low-order
visual areas is mediated via feedback signal from intermediate areas (Bullier et al., 2001;
Angelucci and Bullier, 2003; Schwabe et al., 2006). In monkeys, spatial attention spreads
visually evoked neural activation (Ito and Gilbert, 1999) in relatively peripheral (six degrees
78
of eccentricity) visual field representations (Roberts et al., 2007). Our results showed that
spatial attention resulted in a larger spread of visually evoked BOLD responses than is
commonly observed after sensory stimulation (Parkes et al., 2005; Tolias et al., 2005; Pihlaja
et al., 2008). Our attended stimulus was located at approximately at the same eccentricity as
the stimulus in study of Roberts et al. (2007), and we detected a BOLD response spread that
was in accordance with the attention-related neural spread observed in their study. The width
of the BOLD response spread suggests a top-down origin for the signal increase that is
similar to the mechanism of attentional enhancement of contextual modulation found in
monkey brain (Ito and Gilbert, 1999).
In study IV, saccadic eye-movements in darkness increased BOLD responses in the
peripheral visual field representations. The lack of bottom-up signal and simultaneous
activation of frontal and parietal dorsal-stream areas suggests that the peripheral activation
originates from a top-down signal from the dorsal-stream areas. Distribution of interareal
connections in macaque brain (Gattass et al., 2005) and previous human TMS studies (Ruff
et al., 2008; Ruff et al., 2009) support the association between peripheral response and dorsal
stream activation. More specifically, the activation of human V6 suggests that the dorso-
medial stream played a role in our results. The diffuse peripheral signal increase in low level
retinotopic areas may thus result from a top-down signal originating from the dorso-medial
stream. This top-down signal is concentrated in the peripheral visual field representations
due to connection anisometry and functionally reflects activation of perception–action
network.
6.4. Peripheral visual field representation in human parieto-occipital sulcus
We examined peripheral vision in studies II, IV, and VI. Stimulation system for wide visual
field developed in study II enabled the mapping of medial occipital retinotopic areas up to 50
degrees of eccentricity. The results showed that areas V1-V3 extended to the anterior part of
calcarine sulcus and the posterior bank of PO sulcus and suggested that human V6 with
relatively large peripheral visual field representation is located in the posterior bank of PO
sulcus. In addition, our results showed that the magnification factor describing the extent of
the visual field representation is similar both in central and peripheral V1.
79
Results of studies IV and VI are in accordance with the association between peripheral
vision and dorsal stream. In study IV saccades in darkness activated both frontal and parietal
dorsal stream areas and peripheral visual field representations, suggesting a top-down signal
from higher order dorsal stream areas to periphery of V1 and V2. On the other hand,
activation of dorsal stream areas in occipital and parietal lobe after peripheral stimulation in
study VI, indicated feed-forward connections from peripheral retina to dorsal stream areas.
Previous knowledge from both human and monkey studies suggests that central and
peripheral vision serve partially different functions in the visual system. Study IV
demonstrates functional differences between central and peripheral vision by showing
peripheral eye-movement –related responses in low-order retinotopic visual areas. In
accordance with the study of Sylvester and Rees (2006), our results suggests that the
peripheral activation may reflect movement processing, more specifically corollary
discharge of a motor command (Sommer and Wurtz, 2008).
However, peripheral activation in study IV may also reflect more nonspecific top-down
processes. In monkey V1, enhanced activity and synchrony of neurons before stimulus
presentation facilitates stimulus detection (Supèr et al., 2003), whereas in humans, expecting
a stimulus increases BOLD signal in V1 (Kastner et al., 1999) and task changes can cause
BOLD signal increase in peripheral representations (Jack et al., 2006). Dorsal stream
activation during eye-movements could send a resetting signal to low-order visual areas
before the next trial. Thus the dorsal stream activation may prepare the visual cortex for a
change in the information flow.
Previous studies (Baizer et al., 1991; Falchier et al., 2002; Gattass et al., 2005; Roberts et al.,
2007) have suggested that the peripheral vision may be important for detecting sudden
changes in the environment which may lead to a redirecting of activation flow. According to
Bullier’s (2004b) proposal, the visual signal is first transmitted to the dorsal stream due to
the short latencies of magnocellular neurons. This is followed by feedback signals which
guide subsequent signal processing. Previous studies have provided evidence that the
feedback circuits may extend from the periphery to the center. Central visual field
representations contain information of the object presented in the peripheral visual field
(Williams et al., 2008) and the perception of self-movement due to peripheral motion
stimulus decreases visually evoked response in the central representations (Thilo et al.,
2003). A feedback signal would require a fast activation after the initial peripheral
80
stimulation. Consistent with this, a peripheral stimulus activates the parieto-occipital cortex
with a shorter latency than a central stimulus (Stephen et al., 2002) and stimulation of the
peripheral visual field in study VI resulted in a stream of activation at a short latency in the
dorso-medial stream areas in the human brain. Whether the peripheral vision and dorsal
stream have a role in global to local guidance of visual information processing remains to be
examined in the future studies.
81
7. Conclusions
This thesis investigated the processing of visual information in the human cortex. I measured
neuromagnetic signals and BOLD responses during visual stimulation as well as during
motor and cognitive tasks. Particularly, visual stimuli were designed to activate peripheral
visual field representations and cognitive and motor tasks were designed to activate dorsal
stream areas. The analysis of cortical responses concentrated on visual areas at low and
intermediate levels of the anatomical hierarchy.
Modern imaging methods provide enormous opportunities to neuroscience by enabling
studies of neural function in intact living human brain. This thesis examined some of the
problems that may arise in imaging studies of human visual processing. First I examined the
ambiguity of modelling neuromagnetic signal sources and showed, that even thought
nonsimultaneous sources can be located with good spatial and temporal accuracy a priori
information is needed to differentiate spatially close and temporally overlapping sources. In
addition, I attempted to overcome some of the physical limitations of stimulus design and
developed an optical system that enables stimulation of peripheral visual field in a narrow
magnet bore.
Peripheral vision is still relatively little studied and the second aim of my thesis was to study
peripheral vision in human brain. I mapped the peripheral visual field representation of low-
level retinotopic areas and showed functional differences between central and peripheral
vision. In addition, my results suggest that peripheral representations in low-level areas are
reciprocally connected with dorsal stream areas, especially within the dorso-medial stream.
In the monkey, the dorso-medial stream is involved in the processing of visuomotor actions.
My studies showed a rapid activation sequence and eye-movement related activation in the
medial and dorsal occipital and parietal lobes. This stream of areas could represent the
human dorso-medial stream suitable for a fast feed-forward-feedback analysis.
The third objective of my thesis was to study human V6. The results support previous
findings that human V6 is located anterior to peripheral lower visual field representation of
V2/V3 and is biased towards the peripheral visual field. My work shows that human V6 is
motion sensitive and that it is related to eye-movement processing. In the temporal domain it
is part of a fast sequence of activated areas, occupying the medial surface of occipital and
parietal lobes. This fast sequence may represent a dorso-medial stream in the human brain
82
that conveys information from the visual cortex to the parietal lobe, frontal eye fields and the
premotor cortex and controls visually guided movements.
Because animals are still widely used models for human brain function, the question of
interspecies differences in functional organisation of the visual cortex is fundamental. To
confirm interspecies homologue, the histology, relative position, functional profile,
retinotopy, and connections should be similar across species. My studies showed that this
putative human V6 has similar functional markers as its monkey homologue, such as a bias
towards the peripheral visual field, motion sensitivity and activation at short latency and
connections with the dorso-medial stream areas. These results, in line with previous function
and lesion studies, contribute to evidence that human homologue of monkey V6 is located in
the posterior bank of parieto-occipital sulcus.
The fourth aim of my thesis was to study the effect of top-down modulation in visual
processing. The results showed two examples of response modulation in hierarchically low-
level visual areas. Both of these modulations arose from top-down signals related to
cognitive tasks or motor behaviour. However, these modulations probably represent different
processes. One enhances the signal locally at the attended region and may increase the
stimulus saliency whereas the other is related to more non-specific dorsal stream activation
and may reflect motor processing or resetting signals that prepare visual cortex for change in
the environment.
83
84
8. Acknowledgements
This work was carried out in Brain Reserch Unit of Low Temperature Laboratory and
Advanced Magnetic Imaging Centre of Helsinki University of Technology (Aalto University
from 1.1.2010) and it was financially supported by the Academy of Finland, the Finnish
Gradute School of Neuroscience, Sigrid Juselius Foundation, Jenny and Antti Wihuri
Foundation, Finnish Medical Foundation and Oskar Öflund Foundation. I am most grateful
for all financial support and to the Director of Low Temperature Laboratory Professor
Mikko Paalanen and the Director of Brain Research Unit Professor Riitta Hari for providing
this excellent working environment.
I would like to thank my instructor Docent Simo Vanni for supervising and guiding my
journey in vision science and for all the hard work he has done. Thanks to past and present
members of the vision group especially to Dr. Linda Henriksson, Lauri Nurminen, and Jaana
Simola for friendship and to Dr. Juha Silvanto for his help with the language issues. I thank
Professor Riitta Hari for kind support and for teaching me to think and talk a little faster. I
would like thank the co-authors of my publications: Professor Claudio Galletti, Dr. Patrizia
Fattori, Professor Riitta Hari, Dr. Lauri Parkkonen, Jaana Simola, Veronika von Pföstl, Dr.
Kimmo Uutela, and Docent Simo Vanni for the opportunity to work with you in fruitful
collaborations. In addition, I wish to thank the preliminary examinators and the members of
the follow-up group Dr. Iiro Jääskeläinen, Docent Jyrki Mäkelä, and Professor Turgut
Tatlisumak for their effort and comments.
I want to express my deepest thanks to Marita Kattelus for friendly company and help with
the measurements and Dr. Antti Tarkiainen for his patience and help with the computer
matters. I thank the administrative forces of the Low Temperature Laboratory for helping me
with many practical issues, and Helge Kainulainen and Markku Korhonen for technical
support.
I spent many many years of my life working for my PhD and I want to thank people who
made these years so funny and memorable. Thank you Maarit Aro, Dr. Gina Caetano, Liisa
Helle, Jaana Hiltunen, Lotta Hirvenkari, Dr. Yevhen Hlushchuk, Annika Hultén, Dr. Jan
Kujala, Miiu Kujala, Hannu Laaksonen, Satu Lamminmäki, Mia Liljeström, Dr. Lauri
Parkkonen, Dr. Tiina Parviainen, Dr. Tommi Raij, Dr. Tuukka Raij, Pavan Ramkumar, Dr.
Hanna Renvall, Dr. Ville Renvall, Dr. Mika Seppä, Dr. Topi Tanskanen, Johanna Vartiainen,
85
Dr. Nuutti Vartiainen, and all others I forgot to mention. Special thanks to Sanna Malinen,
with whom I shared the office and many joys, sorrows, and jokes.
Thanks to my parents Anu and Nisse for support, my late grandmother Telma for her
enthusiasim and interest towards my work, and my dear old friends Mannis, Elina, Veera,
Katri, Tuure, Juri, Jone, Vappu, and Elina for great company. Finally I want to thank my
beloved family, my husband Jaska for his endless love and loyalty and our little Taru for
being so lovely and perfect.
Helsinki, April 2010
Linda Stenbacka
86
9. References
Albright TD, Stoner GR. Contextual influences on visual processing. Annu Rev Neurosci
2002, 25:339-379.
Amunts K, Malikovic A, Mohlberg H, Schormann T, Zilles K. Brodmann's areas 17 and 18
brought into stereotaxic space- where and how variable? NeuroImage 2000, 11:66-84.
Andersen RA, Buneo CA. Intentional maps in posterior parietal cortex. Annu Rev Neurosci
2002, 25:189-220.
Angelucci A, Bullier J. Reaching beyond the classical receptive field of V1 neurons:
horizontal or feedback axons? J Physiol Paris 2003, 97:141-154.
Angelucci A, Levitt JB, Walton EJS, Hupé JM, Bullier J, Lund JS. Circuits for local and
global signal integration in primary visual cortex. J Neurosci 2002, 22:8633-8646.
Arcaro MJ, McMains SA, Singer BD, Kastner S. Retinotopic organization of human ventral
visual cortex. J Neurosci 2009, 29:10638-10652.
Baizer JS, Ungerleider LG, Desimone R. Organization of visual inputs to the inferior
temporal and posterior parietal cortex. J Neurosci 1991, 11:168-190.
Barbur JL, Harlow AJ, Weiskrantz L. Spatial and temporal response properties of residual
vision in a case of hemianopia. Phil Trans R Soc Lond B 1994, 343:157-166.
Barone P, Batardiere A, Knoblauch K, Kennedy H. Laminar distribution of neurons in
extrastriate areas projecting to visual areas V1 and V4 correlates with hierarchical
rank and indicates the operation of a distance rule. J Neurosci 2000, 20:3263-3281.
Battaglini PP, Muzur A, Skrap M. Visuomotor deficits and fast recovery after area V6A
lesion in monkeys. Behavioural Brain Res 2003, 139:115-122.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful
approach to multiple testing. J R Stat Soc Ser B 1995, 57:289-300.
Blakemore C, Tobin EA. Lateral inhibition between orientation detectors in the cat's visual
cortex. Exp Brain Res 1972, 15:439-440.
Blanke O, Landis T, Mermoud C, Spinelli L, Safran AB. Direction-selective motion
blindness after unilateral posterior brain damage. Eur J Neurosci 2003, 18:709-722.
Bodis-Wollner I, Bucher SF, Seelos KC, Paulus W, Reiser M, Oertel WH. Functional MRI
mapping of occipital and frontal cortical activity during voluntary and imagined
saccades. Neurology 1997, 49:416-420.
Born RT, Bradley DC. Structure and function of visual area MT. Annu Rev Neurosci 2005,
28:157-189.
Boussaoud D, Ungerleider LG, Desimone R. Pathways for motion analysis: Cortical
connections of the medial superior temporal and fundus of the the superior temporal
visual areas in the macaque. J Comp Neurol 1990, 296:462-495.
Boynton GM, Hegdé J. Visual cortex: the continuing puzzle of area V2. Current Biology
2004, 14:523-524.
Boynton GM, Engel SA, Glover GH, Heeger DJ. Linear system analysis of functional
magentic resonance imaging in human V1. J Neurosci 1996, 16:4207-4221.
Brewer AA, Liu AK, Wade AR, Wandell BA. Visual field maps and stimulus selectivity in
human ventral occipital cortex. Nature Neurosci 2005, 8:1102-1109.
Bristow D, Frith C, Rees G. Two distinct neural effects of blinking on human visual
processing. NeuroImage 2005, 27:136-145.
Bullier J Neural basis of vision. In: Stevens' handbook of experimental psychology, 3
Edition (Pashler H, Yantis S, Medin D, Gallistel R, Wixted J, eds): Wiley. 2004a.
Bullier J Hierarchies of cortical areas. In: The primate visual system (Kaas JH, Collins CE,
eds): CRC Press. 2004b.
87
Bullier J, Nowak LG. Parallel versus serial processing: new vistas on the distributed
organization of the visual system. Curr Opin Neurobiology 1995, 5:497-503.
Bullier J, Hupé JM, James AC, Girard P The role of feedback connections in shaping the
responses of visual cortical neurons. In: Prog Brain Res (Casanova C, Ptito M, eds).
2001, 193-204.
Burr DC, Morrone MC, Ross J. Selective suppression of the magnocellular visual pathway
during saccadic eye movements. Nature 1994, 371:511-513.
Callaway EM. Feedforward, feedback and inhibitory connections in primate visual cortex.
Neural Networks 2004, 17:625-632.
Campbell FW, Cooper GF, Enroth-Cugell C. The spatial selectivity of the visual cell of the
cat. J Physiol 1969, 203:223-235.
Cappe C, Rouiller EM, Barone P. Multisensory antomical pathways. Hearing Research
2009, 258:28-36.
Castelo-Branco M, Mendes M, Silva MF, Januário C, Machado E, Pinto A, Figueiredo P,
Freire A. Specific retinotopically based magnocellular impairment in a patient with
medial visual dorsal stream damage. Neuropsychologia 2006, 44:238-253.
Cavanna AE, Trimble MR. The precuneus: a review of its functional anatomy and
behavioural correlates. Brain 2006, 129:564-583.
Colby CL, Goldberg ME. Space and attention in parietal cortex. Annu Rev Neurosci 1999,
22:319-349.
Colby CL, Gattass R, Olson CR, Gross CG. Topographical organization of cortical afferents
to extrastriate visual area PO in the macaque: a dual tracer study. J Comp Neurol
1988, 269:392-413.
Corbetta M. Frontoparietal cortical networks for directing attention and the eye to visual
locations: Identical, independent, or overlapping neural systems? Proc Natl Acad Sci
1998, 95:831-838.
Corbetta M, Miezin FM, Shulman GL, Petersen SE. A PET study of visuospatial attention. J
Neurosci 1993, 13:1202-1226.
Corbetta M, Akbudak E, Conturo TE, Snyder AZ, Ollinger JM, Drury HA, Raichle ME, Van
Essen DC, Shulman GL. A common network of functional areas for attention and eye
movements. Neuron 1998, 21:761-773.
Dacey DM. Physiology, morphology and spatial densities of identified ganglion cell types in
primate retina. Higher-order processing in the visual system 1994, 184:12-34.
Dacey DM. Parallel pathways for spectral coding in primate retina. Annu Rev Neurosci
2000, 23:743-775.
Dale AM, Sereno MI. Improved localization of cortical activity by combining EEG and
MEG with cortical surface reconstruction: A linear approach. J Cong Neurosci 1993,
5:162-176.
Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW, Lewine JD, Halgren E.
Dynamical parametric mapping: Combining fMRI and MEG for high-resolution
imaging of cortical activity. Neuron 2000, 26:55-67.
Daniel PM, Whitteridge D. The representation of the visual field on the cerebral cortex in
monkeys. J Physiol 1961, 159:203-221.
de Jong BM, van der Graaf FHCE, Paans AMJ. Brain activation related to the
representations of external space and body scheme in visuomotor control.
NeuroImage 2001, 14:1128-1135.
Dechent P, Frahm J. Characterization of the human visual V6 complex by functional
magnetic resonance imaging. Eur J Neurosci 2003, 17:2201-2211.
88
Dejardin S, Dubois S, Bodart JM, Schiltz C, Delinte A, Michel C, Roucoux A, Crommelinck
M. PET study of human voluntary saccadic eye movements in darkness: effect of task
repetition on the activation pattern. Eur J Neurosci 1998, 10:2328-2336.
Deutschländer A, Marx E, Stephan T, Riedel E, Wiesmann M, Dieterich M, Brandt T.
Asymmetric modulation of human visual cortex activity during 10 degree lateral gaze
(fMRI study). NeuroImage 2005, 28:4-13.
DeYoe EA, Carman GJ, Bandettini P, Glickman S, Wieser J, Cox R, Miller D, Neitz J.
Mapping striate and extrastriate visual areas in human cerebral cortex. PNAS 1996,
93:2382-2386.
Di Russo F, Martínez A, Sereno MI, Pitzalis S, Hillyard SA. Cortical sources of the early
evoked components of the visually evoked potential. Human Brain Mapp 2001,
15:95-111.
Dougherty RF, Koch VM, Brewer AA, Fischer B, Modersitzki J, Wandell BA. Visual field
representations and locations of visual areas V1/2/3 in human visual cortex. Journal
of Vision 2003, 3:586-598.
Duncan RO, Boynton GM. Cortical magnification within human primary visual cortex
correlates with acuity thresholds. Neuron 2003, 38:659-671.
Dupont P, Orban GA, De Bruyn B, Verbruggen A, Mortelmans L. Many areas in the human
brain respond to visual motion. J Neurophysiol 1994, 72:1420-1424.
Dupont P, De Bruyn B, Vandenberghe R, Rosier A-M, Marchal G, Mortelmans L, Orban
GA. The kinetic occipital region in human visual cortex. Cereb Cortex 1997, 7:283-
292.
Durand JB, Celebrini S, Trotter Y. Neural bases of stereopsis across visual field of the alert
monkey. Cereb Cortex 2007, 17:1260-1273.
Durand JB, Zhu S, Celebrini S, Trotter Y. Neurons in parafoveal areas V1 and V2 encode
vertical and horizontal disparities. J Neurophysiol 2002, 88:2874-2879.
Ekstrom LB, Roelfsema PR, Arsenault JT, Bonmassar G, Vanduffel W. Bottom-up
dependent gating of frontal signals in early visual cortex. Science 2008, 321:414-417.
Engel SA, Glover GH, Wandell BA. Retinotopic organization in human visual cortex and the
spatial precision of functional MRI. Cereb Cortex 1997, 7:181-192.
Engel SA, Rumelhart DE, Wandell BA, Lee AT, Glover G, H., Chichilnisky E-J, Shadlen
MN. fMRI of human visual cortex. Nature 1994, 396:525-525.
Falchier A, Clavagnier S, Barone P, Kennedy H. Anatomical evidence of multimodal
integration in primate striate cortex. J Neurosci 2002, 22:5749-5759.
Fattori P, Breveglieri R, Amoroso K, Galletti C. Evidence for both reaching and grasping
activity in the medial parieto-occipital cortex of the macaque. Eur J Neurosci 2004,
20:2457-2466.
Fattori P, Kutz DF, Breveglieri R, Marzocchi N, Galletti C. Spatial tuning of reaching
activity in the medial parieto-occipital cortex (area V6A) of macaque monkey. Eur J
Neurosci 2005, 22:956-972.
Faw B. Pre-frontal executive committee for perception, working memory, attention, long-
term memory, motor control, and thinking: A tutorial review. Consciouness and
cognition 2003, 12:83-139.
Felleman DJ, Van Essen DC. Distributed hierarchical processing in the primate cerebral
cortex. Cereb Cortex 1991, 1:1-47.
Ferster D, Miller KD. Neural Mechanisms of orientation selectivity in the visual cortex.
Annu Rev Neurosci 2000, 23:441-471.
Field GD, Chichilnisky EJ. Information processing in the primate retina: circuitry and
coding. Annu Rev Neurosci 2007, 30:1-30.
89
Filimon F, Nelson JD, Huang R-S, Sereno MI. Multiple parietal reach regions in humans:
cortical representations for visual and proprioceptive feedback during on-line
reaching. J Neurosci 2009, 29:2961-2971.
Fox PT, Miezin FM, Allman JM, Van Essen DC, Raichle ME. Retinotopic organization of
human visual cortex mapped with positron-emission tomography. J Neuroscience
1987, 7:913-922.
Frackowiak RSJ, Friston KJ, Frith CD, Dolan R, Price CJ, Zeki S, Ashburner J, Penny WD.
Human brain function: Academic Press, 2003.
Friston KJ, Holmes AP, Worsley KJ. How many subjects constitute a study? NeuroImage
1999, 10:1-5.
Galati G, Pappata S, Pantano P, Lenzi GL, Samson Y, Pizzamiglio L. Cortical control of
optokinetic nystagmus in humans: a positron emission tomography study. Exp Brain
Res 1999, 126:149-159.
Galletti C, Fattori P. Neuronal mechanisms for detection of motion in the field of view.
Neuropsychologia 2003, 41:1717-1727.
Galletti C, Battaglini PP, Fattori P. Functional properties of neurons in the anterior bank of
the parieto-occipital sulcus of the macaque monkey. Eur J Neurosci 1991, 3:452-461.
Galletti C, Battaglini PP, Fattori P. Eye position influence on the parieto-occipital area PO
(V6) of the macaque monkey. Eur J Neurosci 1995, 7:2486-2501.
Galletti C, Fattori P, Kutz DF, Battaglini PP. Arm movement-related neurons in the visual
area V6A of the macaque superior parietal lobule. Eur J Neurosci 1997, 9:410-413.
Galletti C, Fattori P, Kutz DF, Gamberini M. Brain location and visual topography of
cortical area V6A in the macaque monkey. Eur J Neurosci 1999a, 11:575-582.
Galletti C, Fattori P, Gamberini M, Kutz DF. The cortical visual area V6: brain location and
visual topography. Eur J Neurosci 1999b, 11:3922-3936.
Galletti C, Fattori P, Battaglini PP, Shipp S, Zeki S. Functional demarcation of a border
between areas V6 and V6A in the superior parietal gyrus of the macaque monkey.
Eur J Neurosci 1996, 8:30-52.
Galletti C, Kutz DF, Gamberini M, Breveglieri R, Fattori P. Role of the medial parieto-
occipital cortex in the control of reaching and grasping movements. Exp Brain Res
2003, 153:158-170.
Galletti C, Gamberini M, Kutz DF, Baldinotti I, Fattori P. The relationship between V6 and
PO in macaque extrastriate cortex. Eur J Neurosci 2005, 21:959-970.
Galletti C, Gamberini M, Kutz DF, Fattori P, Luppino G, Matelli M. The cortical
connections of area V6: an occipito-parietal network processing visual information.
Eur J Neurosci 2001, 13:1572-1588.
Garandini M, Demb JB, Mante V, Tolhurst DJ, Dan Y, Olshausen BA, Gallant JL, Rust NC.
Do we know what the early visual system does? J Neurosci 2005, 25:10577-10597.
Gattass R, Nascimento-Silva S, Soares JGM, Lima B, Jansen AK, Diogo ACM, Farias MF,
Marcondes M, Botelho EP, Mariani OS, Fiorani M. Cortical visual areas in monkeys:
location, topography, connections, columns, plasticity and cortical dynamics. Phil
Trans R Soc Lond 2005:709-731.
Gaymard B, Ploner CJ, Rivaud S, Vermersch AI, Pierrot-Deseilligny C. Cortical control of
saccades. Exp Brain Res 1998, 123:159-163.
Gegenfurtner KR, Kiper DC. Color Vision. Annu Rev Neurosci 2003, 26:181-206.
Geng JJ, Ruff CC, Driver J. Saccades to a remembered location elicits spatially specific
activation in the human retinotopic visual cortex. J Cong Neurosci 2008, 21:230-245.
Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps in functional
neuroimaging using the false discovery rate. NeuroImage 2002, 15:870-878.
90
Gilbert CD, Sigman M. Brain states: Top down influences in sensory processing. Neuron
2007, 54:677-696.
Gilbert CD, Ito M, Kapadia M, Westheimer G. Interactions between attention, context and
learning in primary visual cortex. Vision Res 2000, 40:1217-1226.
Gouras P. Trichromatic mechanisms in single cortical neurons. Science 1970, 168:489-492.
Grill-Spector K, Malach R. The human visual cortex. Annu Rev Neurosci 2004, 27:649-677.
Grunewald A, Skoumbourdis E. The integration of multiple stimulus features by V1
neurons. J Neurosci 2004, 24:9185-9194.
Grüsser O-J. Migraine phosphenes and the retino-cortical magnification factor. Vision Res
1995, 35:1125-1134.
Guillery RW, Sherman SM. Thalamic relay functions and their role in corticocortical
communication: Generalizations from the visual system. Neuron 2002, 33:163-175.
Hadjikhani N, Tootell RBH. Projection of rods and cones within human visual cortex.
Human Brain Mapp 2000, 9:55-63.
Hadjikhani N, Liu AK, Dale AM, Cavanagh P, Tootell RBH. Retinotopy and colour
sensitivity in human visual cortical area V8. Nature Neurosci 1998, 1:235-240.
Hagler DJ, Sereno MI. Spatial maps in frontal and prefrontal cortex. NeuroImage 2006,
29:567-577.
Hagler DJ, Halgren E, Martínez A, Huang M, Hillyard SA, Dale AM. Source estimates for
MEG/EEG visual evoked responses constrained by multiple, retinotopically mapped
stimulus locations. Human Brain Mapp 2009, 30:1290-1309.
Hari R Magnetoenchephalography as a tool of clinical neurophysiology. In:
Electroencephalography: Basic priciples, clinical applications, and related fields
(Niedermeyer E, Lopes Da Silva F, eds): Williams & Wilkins. 1999, 1107-1132.
Hasnain MK, Fox PT, Woldorff MG. Intersubject variability of functional areas in the
human visual cortex. Human Brain Mapping 1998, 6:301-315.
Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P. Distributed and
overlapping representations of faces and objects in ventral temporal cortex. Science
2001, 293:2425-2430.
He BJ, Snyder AZ, Vincent JL, Epstein A, Shulman GL, Corbetta M. Breakdown of
functional connectivity in frontoparietal networks undelies behavioral deficits in
spatial neglect. Neuron 2007, 53:905-918.
Hendry SH, Reid RC. The koniocellular pathway in primate vision. Annu Rev Neurosci
2000, 23:127-153.
Henriksson L, Nurminen L, Hyvärinen A, Vanni S. Spatial frequency tuning in human
retinotopic visual areas. Journal of Vision 2008, 8:1-13.
Hubel DH, Wiesel TN. Receptive fields of single neurons in the cat's striate cortex. J Physiol
1959, 148:574-591.
Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in
the cat's visual cortex. J Physiol 1962, 160:106-154.
Hubel DH, Wiesel TN. Shape and arrangement of columns in cat's striate cortex. J Physiol
1963, 165:559-568.
Hubel DH, Wiesel TN. Receptive fields and functional architecture of monkey striate cortex.
J Physiol 1968, 195:215-243.
Hubel DH, Wiesel TN. Uniformity of monkey striate cortex: A parallel relationship between
field scatter and magnification factor. J Comp Neurol 1974, 158:295-306.
Huettel SA, Song AW, McCarthy G. Functional magnetic resonance imaging: Sinauer
Associates, Inc, 2004.
91
Hupé JM, James AC, Girard P, Bullier J. Response modulations by the static texture
surround in area V1 of the macaque monkey do not depend on feedback connections
from V2. J Neurophysiol 2001a, 85:146-163.
Hupé JM, James AC, Payne BR, Lomber SG, Girard P, Bullier J. Cortical feedback
improves discrimination between figure and background by V1, V2 and V3 neurons.
Nature 1998, 394:784-787.
Hupé JM, James AC, Girard P, Lomber SG, Payne BR, Bullier J. Feedback connections act
on the early part of the responses in monkey visual cortex. J Neurophysiol 2001b,
85:134-145.
Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa O. Magnetoencephalography
theory, instrumentation, and applications to noninvasive studies of working human
brain. Reviews of Modern Physics 1993, 65:414-497.
Hämäläinen MS, Ilmoniemi RJ (1984) Interpreting measured magnetic fields in the brain;
Estimates of current distributions. In: Technical Report TKK-F-A559: Helsinki
University of Technology.
Hämäläinen MS, Ilmoniemi RJ. Interpreting magnetic fields of the brain: minimum norm
estimates. Med & Biol Eng & Comput 1994, 32:35-42.
Ijspeer JK, de Waard PWT, van den Berg TJTP, de Jong PTVM. The intraocular straylight
function in 129 healthy volunteers; dependence on angle, age and pigmentation.
Vision Res 1990, 30:699-707.
Ito M, Gilbert CD. Attention modulates contextual influences in the primary visual cortex of
alert monkeys. Neuron 1999, 22:593-604.
Jack AI, Shulman GL, Snyder AZ, McAvoy M, Corbetta M. Separate modulations of human
V1 associated with spatial attention and task structure. Neuron 2006, 51:135-147.
James A. The pattern-pulse multifocal visual evoked potential. Invest Ophtal Vis Sci 2003,
44:879-890.
Jousmäki V, Hämäläinen M, Hari R. Magnetic source imaging during a visually guided task.
NeuroReport 1996, 7:2961-2964.
Kandel ER, Schwartz JH, Jessel TM. Principles of Neural Science, Fourth edition Edition:
Appleton & Lange, 2000.
Kanwisher N, McDermott J, Chum MM. The fusiform face area: a module in human
extrastriate cortex specialized for face perception. J Neurosci 1997, 17:4302-4311.
Karnath H-O, Perenin M-T. Cortical control of visually guided reaching: evidence from
patients with optic ataxia. Cereb Cortex 2005, 15:1561-1569.
Kastner S, Ungerleider LG. Mechanisms of visual spatial attention in the human cortex.
Annu Rev Neurosci 2000, 23:315-341.
Kastner S, Nothdurft H, Pigarev IN. Neuronal correlates of pop-out in cat striate cortex.
Vision Res 1997, 37:371-376.
Kastner S, Pinsk MA, De Weerd P, Desimone R, Ungerleider LG. Increased activity in
human visual cortex during directed attention in the absense of visual stimulation.
Neuron 1999, 22:751-761.
Kastner S, De Weerd P, Pinsk MA, Elizondo MI, Desimone R, Ungerleider LG. Modulation
of sensory suppression: Implications for receptive field sizes in the human visual
cortex. J Neurophysiol 2001, 86:1398-1411.
Khan AZ, Pisella L, Vighetto A, Cotton F, Luauté J, Boisson D, Salemme R, Crawford JD,
Rossetti Y. Optic ataxia errors depend on remapped, not viewed, target location.
Nature Neurosci 2005, 8:418-420.
Khayat PS, Spekreijse H, Roelfsema PR. Correlates of transsaccadic integration in the
primary visual cortex of the monkey. Proc Natl Acad Sci 2004, 101:12712-12717.
92
Kleinschmidt A, Thilo KV, Büchel C, Gresty MA, Bronstein AM, Frackowiak RSJ. Neural
correlates of visual-motion perception as object- or self-motion. NeuroImage 2002,
16:873-882.
Kourtzi Z, Kanwisher N. Cortical regions invoved in perceiving object shape. J Neurosci
2000, 20:3310-3318.
Kuffler SW. Discharge patterns and functional organization of mammalian retina. J
Neurophysiol 1953, 16:37-68.
Lamme VAF. The neurophysiology of figure-ground segregation in primary visual cortex. J
Neurosci 1995, 15:1605-1615.
Larsson J, Heeger DJ. Two retinotopic visual areas in human lateral occipital cortex. J
Neurosci 2006, 26:13128-13142.
Law I, Svarer C, Rostrup E, Paulson OB. Parieto-occipital cortex activation during self-
generated eye movements in the dark. Brain 1998, 121:2189-2200.
Lee TS, Mumford D, Romero R, Lamme VAF. The role of the primary visual cortex in
higher level vision. Vision Res 1998, 38:2429-2454.
Lennie P, Movshon JA. Coding of color and form in the geniculostriate visual pathway. J
Opt Soc Am 2005, 22:2013-2033.
Lerner Y, Hendler T, Ben-Bashat D, Harel M, Malach R. A hierarchical axis of object
processing stages in the human visual cortex. Cereb Cortex 2001, 11:287-297.
Lewis JW, Van Essen DC. Corticocortical connections of visual, sensorimotor, and
multimodal processing areas in the perietal lobe of the macaque monkey. J Comp
Neurol 2000, 428:112-137.
Levy I, Hasson U, Avidan G, Hendler T, Malach R. Center-periphery organization of human
object areas. Nature Neurosci 2001, 4:533-539.
Li W, Piëch V, Gilbert CD. Perceptual learning and top-down influences in primary visual
cortex. Nature Neurosci 2004, 7:651-657.
Lin F-H, Belliveau JW, Dale AM, Hämäläinen MS. Distributed current estimates using
cortical orientation constraints. Human Brain Mapp 2006, 27:1-13.
Livingstone M, Hubel DH. Segregation of form, color, movement, and depth: anatomy,
physiology, and perception. Science 1988, 240:740-749.
Logothetis NK, Wandell BA. Interpreting the BOLD signal. Annu Rev Physiol 2004,
66:735-769.
Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neurophysiological
investigation of the basis of the fMRI signal. Nature 2001, 412:150-157.
Lueck CJ, Zeki S, Friston KJ, Deiber M-P, Cope P, Cunningham VJ, Lammertsma AA,
Kennard C, Frackowiak RSJ. The colour centre in the cerebral cortex of man. Nature
1989, 340:386-389.
Luppino G, Ben Hamed S, Gamberini M, Matelli M, Galletti C. Occipital (V6) and parietal
(V6A) areas in the anterior wall of the parieto-occipital sulcus of the macaque: a
cytoarchitectonic study. Eur J Neurosci 2005, 21:3056-3076.
Malach R, Reppas JB, Benson RR, Kwong KK, Jiang H, Kennedy H, Ledden PJ, Brady TJ,
Rosen BR, Tootell RBH. Object-related activity revealed by functional magnetic
resonance imaging in human occipital cortex. PNAS 1995, 92:8135-8139.
Marzocchi N, Breveglieri R, Galletti C, Fattori P. Reaching activity in parietal area V6A of
macaque: eye influence on arm activity or retinocentric coding of reaching
movements? Eur J Neurosci 2008, 27:775-789.
Matsuura K, Okabe Y. Selective minimum-norm solution of the biomagnetic inverse
problem. IEEE Trans Biomed Eng 1995, 42:608-615.
93
McDowell J, Dyckman K, Austin B, Clementz BA. Neurophysiology and neuroanatomy of
reflexive and volitional saccades: Evidence from studies of humans. Brain and
Cognition 2008, 68:255-270.
Mercier M, Schwartz S, Michel CM, Blanke O. Motion direction tuning in human visual
cortex. Eur J Neurosci 2009, 29:424-434.
Merriam EP, Genovese C, Colby CL. Spatial updating in human parietal cortex. Neuron
2003, 39:361-373.
Moore T, Armstrong K. Selective gating of visual signals by microstimulation of frontal
cortex. Nature 2003, 421:370-373.
Motter BC. Focal attention produces spatially selective processing in visual cortical areas
V1, V2, and V4 in the presence of competing stimuli. J Neurophysiol 1993, 70:909-
919.
Murakami S, Okada Y. Contributions of principal cortical neurons to
magnetoencephalographic and electroencephalographic signals. J Physiol 2006,
575:925-936.
Nakamura K, Colby CL. Updating of the visual representation in monkey striate and
extrastriate cortex during saccades. Proc Natl Acad Sci 2002, 99:4026-4031.
Ogawa S, Lee TM, Kay AR, Tank DW. Brain Magnetic resonance imaging with contrast
dependent blood oxygenation. PNAS 1990, 87:9868-9872.
Palmer SM, Rosa MGP. A distinct anatomical network of cortical areas for analysis of
motion in far peripheral vision. Eur J Neurosci 2006, 24:2389-2405.
Parkes LM, Schwarzbach JV, Bouts AA, Deckers RhR, Pullens P, Kerskens CM, Norris DG.
Quantifying the spatial resolution of the gradient echo and spin echo BOLD response
at 3 tesla. Magn Res Med 2005, 54:1465-1472.
Paus T, Jech R, Thompson CJ, Comeau R, Peters T, Evans AC. Transcranial magnetic
stimulation during positron emission tomography: a new method for studying
connectivity of the human cerebral cortex. J Neurosci 1997, 17:3178-3184.
Pihlaja M, Henriksson L, James AC, Vanni S. Quantitative multifocal fMRI shows active
suppression in human V1. Human Brain Mapp 2008, 29:1001-1014.
Pisella L, Binkofski F, Lasek K, Toni I, Rossetti Y. No double-dissociation between optic
ataxia and visual agnosia: Multiple sub-streams for multiple visuo-manual
integrations. Neuropsychologia 2006, 44:2734-2748.
Pisella L, Sergio L, Blangero A, Torchin H, Vighetto A, Rossetti Y. Optic ataxia and the
function of the dorsal stream: Contributions to perception and action.
Neuropsychologia 2009, 47:3033-3044.
Pitzalis S, Sereno MI, Committeri G, Fattori P, Galati G, Patria F, Galletti C. Human V6:
The medial motion area. Cereb Cortex 2010, 20:411-424.
Pitzalis S, Galletti C, Huang R-S, Patria F, Committeri G, Galati G, Fattori P, Sereno MI.
Wide-field retinotopy defines human cortical visual area V6. J Neurosci 2006,
26:7962-7973.
Portin K, Hari R. Human parieto-occipital visual cortex: lack of retinotopy and foveal
magnification. Proc R Soc Lond B Biol Sci 1999, 266:981-985.
Portin K, Salenius S, Salmelin R, Hari R. Activation of the human occipital and parietal
cortex by pattern and luminance stimuli: Neuromagnetic measurements. Cereb Cortex
1998, 8:253-260.
Prado J, Clavagnier S, Otzenberger H, Scheiber C, Kennedy H, Perenin M-T. Two cortical
systems for reaching in central and peripheral vision. Neuron 2005, 48:849-858.
Purpura K, Tranchina D, Kaplan E, Shapley RM. Light adaptation in the primate retina:
Analysis of changes in gain and dynamics of monkey retinal ganglion cells. Visual
Neuroscience 1990, 4:75-93.
94
Quinlan DJ, Culham JC. fMRI reveals a preference for near viewing in the human parieto-
occipital cortex. NeuroImage 2007, 36:167-187.
Rademacher J, Caviness VSJ, Steinmetz H, Galaburda AM. Topographical variation of the
human primary cortices: Implications for neuroimaging, brain mapping, and
neurobiology. Cereb Cortex 1993, 3:313-329.
Reynolds JH, Chelazzi L. Attentional modulation of visual processing. Annu Rev Neurosci
2004, 27:611-647.
Richer F, Martinez M, Cohen H, Saint-Hilaire J-M. Visual motion perception from
stimulation of the human medial parieto-occipital cortex. Exp Brain Res 1991,
87:649-652.
Rizzolatti G, Matelli M. Two different streams form the dorsal visual system: anatomy and
functions. Exp Brain Res 2003, 153:146-157.
Rizzolatti G, Riggio L, Dascola I, Umiltá C. Reorienting attention across the horizontal and
vertical meridians: evidence in favor of a premotor theory of attention.
Neuropsychologia 1987, 25:31-40.
Roberts MJ, Delicato LS, Herrero J, Gieselmann MA, Thiele A. Attention alters spatial
integration in macaque V1 in an eccentricity dependent manner. Nature Neurosci
2007, 10:1483-1491.
Rossi AF, Paradiso MA. Neural correlates of perceived brightness in the retina, lateral
geniculate nucleus, and striate cortex. J Neurosci 1999, 19:6145-6156.
Rossi AF, Rittenhouse CD, Paradiso MA. The representation of brightness in primary visual
cortex. Science 1996, 273:1104-1107.
Ruff CC, Blankenburg F, Bjoertomt O, Bestmann S, Weiskopf N, Driver J. Hemispheric
differences in frontal and parietal influences on the human occipital cortex: Direct
confirmation with concurrent TMS-fMRI. J Cong Neurosci 2009, 21:1146-1161.
Ruff CC, Blankenburg F, Bjoertomt O, Haynes J-D, Rees G, Josephs O, Deichmann R,
Driver J. Concurrent TMS-fMRI and psychophysics reveal frontal influences on
human retinotopic visual cortex. Current Biology 2006, 16:1479-1488.
Ruff CC, Bestmann S, Blankenburg F, Bjoertomt O, Josephs O, Weiskopf N, Deichmann R,
Driver J. Distinct causal influences of parietal versus frontal areas on human visual
cortex: Evidence from concurrent TMS-fMRI. Cereb Cortex 2008, 18:817-827.
Saalmann YB, Kastner S. Gain control in the visual thalamus during perception and
cognition. Curr Opin Neurobiology 2009, 19:1-7.
Saygin AP, Sereno MI. Retinotopy and attention in human occipital, temporal, parietal, and
frontal cortex. Cereb Cortex 2008, 18:2158-2168.
Schall JD Visuomotor areas of the frontal lobe. In: Cerebral cortex (Rockland K, Kaas JH,
Peters A, eds). New York and London: Plenum Press. 1997.
Schall JD, Morel A, King DJ, Bullier J. Topography of visual cortex connections with
frontal eye field in macaque: convergence and segregation of processing streams. J
Neurosci 1995, 15:4464-4487.
Scherg M Fundamentals of dipole source potential analysis. In: Auditory Evoked magnetic
fields and potentials (Gandori F, Hoke M, Romani GL, eds). Basel: Karger. 1990, 40-
70.
Schluppeck D, Glimcher P, Heeger DJ. Topographic organization for delayed saccades in
human posterior parietal cortex. J Neurophysiol 2005, 94:1372-1384.
Schmolesky MT, Wang Y, Hanes DP, Thompson KG, Leutgeb S, Schall JD, Leventhal AG.
Signal timing across macaque visual system. J Neurophysiol 1998, 79:3272-3278.
Schneider W, Noll DC, Cohen JD. Functional topographic mapping of the cortical ribbon in
human vision with conventional MRI scanners. Nature 1993, 365:150-153.
95
Schwabe L, Obermayer K, Angelucci A, Bressloff PC. The role of feedback in shaping the
extra-classical receptive field of cortical neurons: a recurrent network model. J
Neurosci 2006, 26:9117-9129.
Sereno MI, Huang R-S. A human parietal face area contains aligned head-centered visual
and tactile maps. Nature Neurosci 2006, 9:1337-1343.
Sereno MI, Pitzalis S, Martinez A. Mapping of contralateral space in retinotopic coordinates
by a parietal cortical area in humans. Science 2001, 294:1350-1354.
Sereno MI, Dale AM, Reppas JB, Kwong KK, Belliveau JW, Brady TJ, Rosen BR, Tootell
RBH. Borders of multiple visual areas in humans revealed by functional magnetic
resonance imaging. Science 1995, 268:889-893.
Sharma J, Dragoi V, Tenenbaum JB, Miller EK, Sur M. V1 neurons signal acquisition of an
internal representation of stimulus location. Science 2003, 300:1758-1763.
Shipp S. The functional logic of cortico-pulvinar connections. Phil Trans R Soc Lond Series
B 2002, 358:1605-1624.
Shipp S, Blanton M, Zeki S. A visuo-somatomotor pathway through superior parietal cortex
in the macaque monkey: cortical connections of areas V6 and V6A. Eur J Neurosci
1998, 10:3171-3193.
Shipp S, Watson JDG, Frackowiak RSJ, Zeki S. Retinotopic maps in human prestriate visual
cortex: The demarcation of areas V2 and V3. NeuroImage 1995, 2:125-132.
Silver MA, Ress D, Heeger DJ. Topographic maps of visual spatial attention in human
parietal cortex. J Neurophysiol 2005, 94:1358-1371.
Simon O, Mangin J-F, Cohen L, Le Bihan D, Dehaene S. Topographical layout of hand, eye,
calculation, and language-related areas in the human parietal lobe. Neuron 2002,
33:475-487.
Sincich LC, Horton JC. The circuitry of V1 and V2: Integration of color, form and motion.
Annu Rev Neurosci 2005, 28:303-326.
Sirotin YB, Das A. Anticipatory haemodynamic signals in sensory cortex not predicted by
local neuronal activity. Nature 2009, 457:475-480.
Sommer MA, Wurtz RH. Brain circuits for the internal monitoring of movements. Annu Rev
Neurosci 2008, 31:317-338.
Stenbacka L, Vanni S, Hari R. Activity of the luminance-sensitive area in the human parieto-
occipital sulcus during voluntary blinks and saccades. In: Human Brain Mapping
2004.
Stephen JM, Aine CJ, Christner RF, Ranken D, Huang M, Best E. Central versus peripheral
visual field stimulation results in timing differences in dorsal stream sources as
measured with MEG. Vision Res 2002, 42:3059-3074.
Stiers P, Peeters R, Lagae L, Van Hecke P, Sunaert S. Mapping multiple visual areas in the
human brain with a short fMRI sequence. NeuroImage 2006, 29:74-89.
Sunaert S, Van Hecke P, Marchal G, Orban GA. Motion-responsive regions of the human
brain. Exp Brain Res 1999, 127:355-370.
Supèr H. Figure-ground activity in V1 and guidance of saccadic eye movements. J Physiol
Paris 2006, 100:63-69.
Supèr H, Spekreijse H, Lamme VAF. A neural correlate of working memory in the monkey
primary visual cortex. Science 2001a, 293:120-124.
Supèr H, Spekreijse H, Lamme VAF. Two distinct modes of sensory processing observed in
monkey primary visual cortex (V1). Nature Neurosci 2001b, 4:304-310.
Supèr H, van der Togt C, Spekreijse H, Lamme VAF. Internal state of monkey primary
visual cortex (V1) predicts figure-ground perception. J Neurosci 2003, 23:3407-3414.
96
Supèr H, van der Togt C, Spekreijse H, Lamme VAF. Correspondence of presaccadic
activity in the monkey primary visual cortex with saccadic eye movements. Proc Natl
Acad Sci 2004, 101:3230-3235.
Sutter EE. Imaging visual function with the multifocal m-sequence technique. Vision Res
2001, 41:1241-1255.
Swisher JD, Halko MA, Merabet LB, McMains SA, Somers DC. Visual topography of
human intraparietal sulcus. J Neurosci 2007, 27:5326-5337.
Sylvester R, Rees G. Extraretinal saccadic signals in human LGN and early retinotopic
cortex. NeuroImage 2006, 30:214-219.
Sylvester R, Haynes J-D, Rees G. Saccades differentially modulate human LGN and V1
responses in the presence and absence of visual stimulation. Current Biology 2005,
15:37-41.
Tehovnik EJ, Scolum WM, Carvey CE, Schiller PH. Phosphene induction and the generation
of saccadic eye movements by striate cortex. J Neurophysiol 2005, 93:1-19.
Thilo KV, Kleinschmidt A, Gresty MA. Perception of self-motion from peripheral
optokinetic stimulation suppresses visual evoked responses to central stimuli. J
Neurophysiol 2003, 90:723-730.
Tikhonov A, Haarmeier T, Thier P, Braun C, Lutzenberger W. Neuromagnetic activity in
medial parieto-occipital cortex reflects the perception of visual motion during eye
movements. NeuroImage 2004, 21:593-600.
Tolias AS, Sultan F, Augath M, Oeltermann A, Tehovnik EJ, Schiller PH, Logothetis NK.
Mapping cortical activity elicited with electrical microstimulation using fMRI in the
macaque. Neuron 2005, 48:901-911.
Tootell RBH, Hadjikhani N, Hall EK, Marret S, Vanduffel W, Vaughan JT, Dale AM. The
retinotopy of visual spatial attention. Neuron 1998, 21:1409-1422.
Tootell RBH, Reppas JB, Kwong KK, Malach R, Born RT, Brady TJ, Rosen BR, Belliveau
JW. Functional analysis of human MT and related visual cortical areas using
magnetic resonance imaging. J Neurosci 1995, 15:3215-3230.
Tootell RBH, Mendola JD, Hadjikhani NK, Ledden PJ, Liu AK, Reppas JB, Sereno MI,
Dale AM. Functional analysis of V3A and related areas in human visual cortex. J
Neurosci 1997, 17:7060-7078.
Trotter Y, Celebrini S. Gaze direction controls response gain in primary visual-cortex
neurons. Nature 1999, 398:239-242.
Trotter Y, Celebrini S, Durand JB. Evidence for implication of primate area V1 in neural 3-
D spatial localization processing. J Physiol Paris 2004, 98:125-134.
Trotter Y, Celebrini S, Stricanne B, Thorpe S, Imbert M. Modulation of neural stereoscopic
processing in primate area v1 by the viewing distance. Science 1992, 257:1279-1281.
Tuomisto T, Hari R, Katila T, Poutanen T, Varpula T. Studies of auditory evoked magnetic
and alactric responses: Modality specificity and modelling. Nuovo Cimento 1983,
2D:471-483.
Tzelepi A, Ioannides AA, Poghosyan V. Early (N70m) neuromagnetic signal topography and
striate and extrastriate generators following pattern onset quadrant stimulation.
NeuroImage 2001, 13:702-718.
Ungerleider LG, Mishkin M Two cortical visual systems. In: The Analysis of Visual
Behaviour (Ingle D, Mansfield R, Goodale M, eds): MIT press. 1982.
Ungerleider LG, Desimone R. Projections to the the superior temporal sulcus from the
central and peripheral field representations of V1 and V2. J Comp Neurol 1986,
248:147-163.
Uutela K, Hämäläinen MS, Somersalo E. Visualization of magnetoencephalographic data
using minimum current estimates. NeuroImage 1999, 10:173-180.
97
Wade AR, Brewer AA, Rieger JW, Wandell BA. Functional measurements of human ventral
occipital cortex: retinotopy and colour. Phil Trans R Soc Lond 2002, 357:963-973.
Vallines I, Greenlee MW. Saccadic suppression of retinotopically localised blood oxygen
level -dependent responses in human primary visual area V1. J Neurosci 2006,
26:5965-5969.
Van Essen DC Organization of visual areas in macaque and human cerebral cortex. In:
Visual Neurosciences (Chalupa L, Werner J, eds): MIT press. 2004, 507-521.
Wandell BA. Foundations of vision. Sunderland, Massachusetts: Sinauer, 1995.
Wandell BA, Brewer AA, Dougherty RF. Visual field map clusters in human cortex. Philos
Trans R Soc Lond B Biol Sci 2005, 360:693-707.
Wandell BA, Dumoulin SO, Brewer AA. Visual field maps in human cortex. Neuron 2007,
56:366-383.
Wang Y, Celebrini S, Trotter Y, Barone P. Visuo-auditory interactions in the primary visual
cortex of the behaving monkey: Electrophysiological evidence. BMC Neuroscience
2008, 9:79.
Vanni S, Henriksson L, James AC. Multifocal fMRI mapping of visual cortical areas.
NeuroImage 2005, 27:95-105.
Vanni S, Tanskanen T, Seppä M, Uutela K, Hari R. Coinciding early activation of the human
primary visual cortex and anteromedial cuneus. Proc Natl Acad Sci 2001, 98:2776-
2780.
Vanni S, Warnking J, Dojat M, Delon-Martin C, Bullier J, Segebarth C. Sequence of pattern
onset responses in the human visual areas: an fMRI constrained VEP source analysis.
NeuroImage 2004, 21:801-817.
Warnking J, Dojat M, Guérin-Dugué A, Delon-Martin C, Olympieff S, Richard N,
Chéhikian A, Segebarth C. fMRI retinotopic mapping- Step by step. NeuroImage
2002, 17:1665-1683.
Watanabe T, Dale AM, Seiffert AE, Tootell RBH. Functional MRI reveals spatially specific
attentional modulation in human primary visual cortex. PNAS 1999, 96:1663-1668.
Watanabe T, Harner AM, Miyauchi S, Sasaki Y, Nielsen M, Palomo D, Mukai I. Task-
dependent influences of attention on the activation of human primary visual cortex.
Proc Natl Acad Sci 1998, 95:11489-11492.
Watson JDG, Myers R, Frackowiak RSJ, Hajnal JV, Woods RP, Mazziotta JC, Shipp S, Zeki
S. Area V5 of the human brain: evidence from a combined study using positron
emission tomography and magnetic resonance imaging. Cereb Cortex 1993, 3:79-94.
Weiskrantz L, Barbur JL, Sahraie A. Parameters affecting conscious versus unconscious
visual discrimination with damage to the visual cortex. Proc Natl Acad Sci 1995,
92:6122-6126.
Williams MA, Baker CI, Op de Beeck HP, Shim WM, Dang S, Triantafyllou C, Kanwisher
N. Feedback of visual object information to foveal retinotopic cortex. Nature
Neurosci 2008, 11:1439-1445.
Williamson SJ, Kaufman L. Biomagnetism. J Magn Magn Mat 1981, 22:129-201.
Virsu V, Rovamo J. Visual resolution, contrast sensitivity, and the cortical magnification.
Exp Brain Res 1979, 37:475-494.
Vogt BA, Laureys S. Posterior cingulate, precuneal and retrosplenial cortices: cytology and
components of the neural network correlates of consciousness. Prog Brain Res 2005,
150:205-217.
Vogt BA, Vogt L, Laureys S. Cytology and functionally correlated circuits of human
posterior cingulate areas. NeuroImage 2006, 29:452-466.
Vos JJ. On the cause of disability glare and its dependence on glare angle, age and ocular
pigmentation. Clin Exp Optom 2003, 86:363-370.
98
Xing J, Heeger DJ. Center-surround interactions in foveal and peripheral vision. Vision Res
2000, 40:3065-3072.
Xu X, Anderson TJ, Casagrande VA. How do functional maps in primary visual cortex vary
with eccentricity. J Comp Neurol 2007, 501:741-755.
Zeki S. The anatomy and physiology of area V6 of macaque monkey visual cortex. J Physiol
1986, 381:62P.
Zeki S, Watson JDG, Lueck CJ, Friston KJ, Kennard C, Frackowiak RSJ. A direct
demonstration of functional specialization in human visual cortex. J Neurosci 1991,
11:641-649.