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ORIGINAL ARTICLE
TBSS and probabilistic tractography reveal white matterconnections for attention to object features
Katja M. Mayer • Quoc C. Vuong
Received: 30 April 2013 / Accepted: 26 August 2013 / Published online: 5 September 2013
� Springer-Verlag Berlin Heidelberg 2013
Abstract Selective attention to features of interest facil-
itates object processing in a cluttered and dynamic envi-
ronment. Previous research found that distinct networks of
regions across cortex are activated depending on the
attended feature. These networks typically consist of pos-
terior feature-preferring regions and anterior regions
involved in attentional processes. In the current study, we
investigated the role of white matter connections between
the posterior and anterior regions within these networks for
attention to features of novel colored dynamic objects. We
asked participants to perform a 1-back feature-attention
task while we acquired both functional and diffusion-
weighted images. Using tract-based spatial statistics and
probabilistic tractography, we found that the right superior
longitudinal fasciculus (SLF) connected posterior and
anterior object-processing regions and that voxels within
the SLF correlated with response times on the task. Pos-
terior and anterior regions that were anatomically con-
nected also had increased functional connectivity relative
to posterior and anterior regions that were not connected.
Our results demonstrate that both functional and structural
information has to be taken into account to understand
selective attention and object perception.
Keywords Multi-featured objects � Feature
attention � Superior longitudinal fasciculus �Integrity–performance correlation � TBSS
Introduction
To achieve routine goals such as object recognition,
observers can actively attend to specific sensory informa-
tion from a cluttered environment for further neural pro-
cessing. For instance, the ability to select some features of
objects while filtering out others is crucial as not all fea-
tures are equally useful for the task at hand. The results
from single-cell recordings, human lesion studies and
neuroimaging converge on the view that large-scale net-
works of gray matter regions distributed throughout cortex
underpin attention and perception (Bressler and Menon
2010). It is well established that these regions, particularly
in posterior cortex, preferentially respond to different fea-
tures and that attention to a region’s preferred feature can
enhance that region’s activation (Corbetta et al. 1990).
What remains unknown is whether there are white matter
connections between regions that are involved in selective
attention to object features, and whether there are direct
connections between regions that are sensitive to object
features.
In healthy observers, functional magnetic resonance
imaging (fMRI) has greatly helped to localize posterior
regions that preferentially respond to basic features such as
shape, motion and color. A large posterior portion of the
lateral occipital cortex (LO) (Grill-Spector et al. 2001;
Kourtzi and Kanwisher 2000; Malach et al. 1995) and the
intraparietal sulcus (IPS) (Murray et al. 2003; Peuskens
et al. 2004) respond more to intact shapes than to scram-
bled shapes. Regions at the junction between the lateral
occipital sulcus and inferior temporal sulcus (MT) (Tootell
et al. 1995; Malach et al. 1995) and the IPS (Murray et al.
2003) respond more to dynamic stimuli than to static ones.
Lastly, regions along the collateral sulcus (CoS) on the
ventral posterior surface of the occipito-temporal cortex
K. M. Mayer � Q. C. Vuong
Institute of Neuroscience, Newcastle University,
Newcastle upon Tyne, UK
K. M. Mayer (&)
Max Planck Institute for Human Cognitive and Brain Sciences,
Stephanstr. 1a, 04103 Leipzig, Germany
e-mail: [email protected]
123
Brain Struct Funct (2014) 219:2159–2171
DOI 10.1007/s00429-013-0631-6
respond more to colored than to grayscale images (Cant
and Goodale 2007; Cavina-Pratesi et al. 2010; Hadjikhani
et al. 1998; Zeki 1980). There is evidence that shape and
motion are processed by common regions (Mayer and
Vuong 2013; Peuskens et al. 2004). Regions that process
color, in contrast, are mostly distinct from shape and
motion regions (Cant and Goodale 2007; Cavina-Pratesi
et al. 2010; Mayer and Vuong 2013; Paradis et al. 2008;
Peuskens et al. 2004). Furthermore, attention to a region’s
preferred feature enhances brain activation in many, if not
all, of these regions (Corbetta et al. 1990; Mayer and Vu-
ong 2013; Murray and Wojciulik 2004; Paradis et al. 2008;
Peuskens et al. 2004).
Although many imaging studies on object perception
focus on posterior regions, anterior regions in the frontal
lobe have also been implicated in shape (Schultz et al.
2008), motion (Zanto et al. 2010) and color (Zeki and
Marini 1998) processing. Moreover, the frontal cortex is
also involved in cognitive control (Miller 2000), mental
transformations (Amick et al. 2006), categorization
(Schendan and Kutas 2007), working memory (Cabeza
et al. 2003) and visual attention (Kanwisher and Wojciulik
2000). All of these processes are important for object
processing. Frontal regions may therefore provide ‘top-
down’ influences on processing object features (Kanwisher
and Wojciulik 2000). Therefore, fast communication
between posterior and anterior regions may be important
for selective attention and object processing. This view is
supported by studies investigating the functional connec-
tivity between brain regions (e.g., Friston et al. 1997;
Mayer and Vuong 2013; Nummenmaa et al. 2010; see
Horwitz 2003, for a review). Studies investigating brain
responses to complex cognitive tasks such as face (Num-
menmaa et al. 2010) or object processing (Mayer and
Vuong 2013; Schultz et al. 2008) revealed correlations in
the activation time series of posterior and anterior brain
regions suggesting that communications between distant
brain regions enabled observers to perform the tasks.
The functional interaction between distant object-pro-
cessing regions reported earlier (Mayer and Vuong 2013;
Nummenmaa et al. 2010; Schultz et al. 2008) may be
enabled by physical connections in the form of white
matter tracts. Importantly, the information-transmission
rate between regions depends to some extent on the
integrity of white matter connecting them (Jack et al.
1975). Therefore, white matter can play an important role
for general task performance. Consistent with this, the
inter-individual variability in the structural integrity of
white matter correlates with inter-individual variability in
accuracy and/or response times on sensory discrimination
(Boehr et al. 2007; Tuch et al. 2005), face-recognition
(Thomas et al. 2009) and memory tasks (Begre et al. 2007;
Sasson et al. 2010), for example. With respect to attention,
previous studies consistently found that damage to the
superior longitudinal fasciculus (SLF) played a crucial role
in the occurrence of spatial neglect (Shinoura et al. 2009)
or simultanagnosia (Chechlacz et al. 2012). The SLF is a
white matter tract with separate branches or subcompo-
nents that connects occipital, temporal, parietal and frontal
regions (Ffytche and Catani 2005; Makris et al. 2005;
Schmahmann et al. 2007; Thiebaut de Schotten et al.
2011); thus, it could be involved in connecting distant
regions that form networks for object processing (Peuskens
et al. 2004). No study to date, however, has investigated
whether the SLF is involved in non-spatial attention tasks
such as selective attention to different object features.
Here, we hypothesize that the SLF is involved in atten-
tional tasks (i.e., feature attention in object recognition)
other than spatial attention because of its anatomical
location that would allow for connecting gray matter
regions involved in processing object features (Peuskens
et al. 2004).
In our recent study (Mayer and Vuong 2013), we
showed that the functional connectivity between posterior
and anterior regions increased when ignored object features
changed from trial to trial compared to when these features
remained constant, irrespective of which features observers
attended. The goal of the present study was to determine
whether these regions were physically connected via the
SLF. For this goal, we used the same stimuli and a similar
feature-attention paradigm as in our previous study.
Observers in the current study discriminated novel colored
dynamic objects on the basis of their shape, non-rigid
motion, color, or all of these features simultaneously
(Corbetta et al. 1990; Mayer and Vuong 2013; Paradis et al.
2008; Peuskens et al. 2004). In this way, the stimuli and
discrimination task were constant across the different
attention conditions and only the observers’ attentional
state varied (Schultz and Lennert 2009). We used fMRI to
localize regions that showed feature-specific attentional
enhancements in activation. In the posterior cortical
regions, attention to shape or motion activated overlapping
regions bilaterally in lateral occipito-temporal (LO and
MT) and parietal cortex (IPS). Attention to color, by
comparison, activated medial brain regions which did not
overlap with the shape/motion regions. We then used dif-
fusion tensor imaging (DTI) to identify any white matter
that was involved in the feature-attention task by corre-
lating measurements of white matter integrity with
behavioral task performance using tract-based spatial sta-
tistics (TBSS; Smith et al. 2006). Furthermore, we recon-
structed white matter pathways between networks
identified with fMRI. We were able to reconstruct tracts
between posterior and anterior regions activated when
observers attended to shape, motion, and color. Crucially,
these tracts passed through the part of the SLF that was
2160 Brain Struct Funct (2014) 219:2159–2171
123
significantly correlated with task performance. We then
explored the relationship between functional connectivity
between regions connected by the reconstructed tracts. This
novel combination of TBSS and tract reconstruction pro-
vides direct evidence that communication between pos-
terior and anterior brain regions is important for selectively
attending to object features.
Materials and methods
Participants
Sixteen volunteers participated (8 males, 8 females; mean
age = 24 years, SD = 4 years; 15 reported that they used
their right hand for writing). Behavioral data for two vol-
unteers were lost, so these were excluded from all further
analyses. All participants had normal or corrected to normal
vision and were naive to the purpose of the study. They were
informed about the safety precautions for MRI experiments
prior to giving informed consent. The study was conducted in
accordance with the Declaration of Helsinki and approved by
the Newcastle University ethics committee.
Material and apparatus
The stimuli consisted of 64 novel objects which were pro-
duced from the factorial combination of four distinct three-
dimensional volumetric shapes (e.g., cylinder), four distinct
colors (e.g., red) and four distinct non-rigid motions (e.g.,
bending) (Mayer and Vuong 2012). Each object subtended
approximately 7.7� (height) 9 3.8� (width) of visual angle.
Examples of these objects can be found at: http://www.staff.
ncl.ac.uk/q.c.vuong/MayerVuong.html.
We used a canon XEED LCD projector (1,280
pixel 9 1,024 pixel) to backproject the visual stimuli onto a
projection screen at the foot-end of the scanner. Participants
viewed the projection through an angled mirror attached to the
head coil approximately 10 cm above their eyes. The exper-
iment was run on a Windows PC using the Psychophysics
toolbox version 3 (http://www.psychtoolbox.org; Brainard
1997; Pelli 1997) to control the experiment, stimulus pre-
sentation and record responses. Participants responded via a
MR-compatible response box (LumiTouchTM) using the
index and middle finger of their dominant hand.
Experimental design and procedure
The experiment used a within-subjects design with two
trial types (same and different) and four attention condi-
tions (attend-shape, attend-motion, attend-color and attend-
all features). We used a continuous 1-back task, in which
participants judged whether the attended feature of the
current object (on Trial N) matched the attended feature of
the preceding object (on Trial N - 1) by responding
‘same’ or ‘different’ (Schultz and Lennert 2009).
The attention conditions were run in separate blocks with
an equal number of same and different trials randomly
interleaved. There were four same trials and four different
trials on each block. Each functional run consisted of 12
experimental blocks. These blocks were divided into three
sets. Each attention condition was presented once in each set.
We ensured that each attention condition was preceded by a
different attention condition across the three sets in a given
run. There were also 13 fixation blocks presented before each
block and after the final block. The fixation blocks consisted
of a white fixation cross rendered against a black back-
ground. Participants maintained fixation on the cross during
these blocks. Each experimental block lasted 31 s and each
fixation block lasted 12 s. Participants were tested on three
functional runs, each approximately 9 min in length.
At the beginning of each experimental block, the word
‘color’, ‘shape’, ‘motion’ or ‘all’ was shown to indicate the
attended feature for that block. The word was presented at
the center of the screen for 2 s in white letters against a
black background. Participants then saw a sequence of nine
objects, each shown for 2.5 s. No response was made to the
first object. Participants could respond at any time after the
onset of an object. They were asked to respond as quickly
and as accurately as possible. If they did not respond while
the object was present, the trial was counted as an error trial
and the program proceeded to the next trial. The assignment
of finger to a ‘same’ or ‘different’ response was counter-
balanced across participants. Correct responses were indi-
cated by a green ‘v’, while errors and misses were indicated
by a red ‘x’ which appeared on the screen for 0.5 s. There
was a 0.5 s blank screen after the feedback screen. On same
trials, all three features matched on two consecutive objects
irrespective of the attended feature. In 25 % of the different
trials, all three features were different between consecutive
objects. On the remaining 75 % of the different trials, the
attended feature and one other feature differed between
consecutive objects. The non-attended feature that was
different was randomly determined on each trial.
Participants practiced the continuous 1-back task outside
the scanner to familiarize themselves with the stimuli,
block sequence and response mapping. They practiced with
at least one block of each attention condition. They also
received a few practice trials while they were in the
scanner to ensure that they could see the stimuli and to
become familiar with the response box used.
Image acquisition
All images were acquired with a 3 T Philips Intera Achieva
scanner at the Newcastle Magnetic Resonance Centre
Brain Struct Funct (2014) 219:2159–2171 2161
123
(NMRC). The signal was received with an 8-channel head
coil. For the high-resolution anatomical scan, T1-weighted
images were acquired [150 sagittal slices; resolution: 208
voxels 9 208 voxels; field of view (FOV): 240 mm 9
240 mm; and thickness: 1.2 mm]. For the functional scans,
284 T2*-weighted echo planar images (EPIs) were
acquired in each run. We use sensitivity encoding (SENSE)
with factor = 2 to increase the signal-to-noise ratio of the
functional images. Each image consisted of 29 axial slices
(TR = 2 s; flip angle = 90�; TE = 40 ms; resolution: 64
voxels 9 64 voxels; FOV = 192 mm 9 192 mm; and
thickness: 3 mm with a 1 mm gap in between slices).
Before each functional run, ‘dummy’ scans were per-
formed to allow for equilibration of the T1 signal.
The diffusion-weighted images were acquired in 64
isotropic directions on the unit sphere. Each image con-
sisted of 59 axial slices acquired during one TR
(TR = 6.1 s; flip angle = 90�; TE = 70 ms; resolution:
120 voxels 9 124 voxels; FOV: 270 mm 9 270 mm;
thickness: 2.11 mm; and diffusion weighting: b = 1,000
s/mm2). The DTI scan also included one non-diffusion-
weighted image (b = 0) acquired before the 64 diffusion-
weighted images. The duration of the DTI scan was
approximately 7.5 min.
fMRI data preprocessing
The functional data were preprocessed and analyzed using
SPM8 (Wellcome Department of Imaging Neuroscience,
http://www.fil.ion.ucl.ac.uk/spm/). Functional images were
realigned to the first image from the first run, resliced with
a 3 9 3 9 3 mm3 resolution, smoothed with a 6 mm
full-width half-maximum Gaussian kernel, and normalized
to the Montreal Neurological Institute (MNI) EPI
T2*-weighted template. Low-frequency drifts in the pre-
processed data were removed using a temporal high-pass
filter with a cutoff of 128 s. Serial correlations were esti-
mated using a first-order autoregressive model and used to
adjust the degrees of freedom appropriately.
fMRI data analysis
We first estimated beta weights for the eight experimental
conditions (2 trial types 9 4 attended features) using the
general linear model (GLM) framework. The durations of
each condition (2.5 s), fixation condition (12 s) and task
instruction (2 s) were separately modeled as boxcar func-
tions for each run and convolved with a canonical hemo-
dynamic response function (HRF; modeled in SPM8 as the
difference of two gamma functions). These regressors were
entered into a design matrix to model the experimentally
induced effects. In addition, the design matrix included the
six movement parameters (yaw, pitch, roll and three
translation terms) and a constant term for each run. Thus,
there were 51 regressors in the design matrix.
In a first-level single-subject analysis, we created con-
trast images by subtracting the beta weights for the
experimental conditions of interest. For the results reported
here, we collapsed across same and different trials. To
localize shape-preferring regions, we used the contrast S
(attend-shape) [ C (attend-color) ? M (attend-motion); to
localize motion-preferring regions, we used the contrast
M [ C ? S; and to localize color-preferring regions, we
used the contrast C [ M ? S. For the attend-all condition,
we used the contrast A (attend-all) [ C ? M ? S. In a
second-level group analysis, statistical tests were per-
formed on the participants’ contrast images by testing each
contrast of interest against zero in a 1-sample t test at each
voxel. No further smoothing of the data was applied at this
level. We report only the results from the group analysis.
Unless otherwise stated, for the fMRI analyses, we used
p \ 0.05 corrected for multiple comparisons across the
whole brain at the cluster level with an initial threshold of
p = 0.001 and a cluster size threshold of k = 10 voxels for
all statistical tests. Labeling of activated clusters was done
with the WFU Pickatlas (http://fmri.wfubmc.edu/software/
PickAtlas; Maldjian et al. 2003).
DTI data preprocessing
The DTI data were preprocessed and analyzed using FSL’s
functional diffusion toolbox (FDT; Behrens et al. 2003;
Smith et al. 2004; Woolrich et al. 2009; http://fmrib.ox.ac.
uk/fsl/). The diffusion-weighted images were corrected for
eddy current distortions using in-house routines that rotated
the B-matrix (Leemans and Jones 2009). They were then
corrected for head motion using linear registration (FLIRT;
Jenkinson et al. 2002). We used the robust brain extraction
tool (BET; Smith 2002) to extract the brain for each partic-
ipant’s T1 and DTI images. Following brain extraction, we
coregistered each participant’s T1 and DTI data to MNI
space using FLIRT. The transformation matrices were used
to coregister the functionally localized regions from the
group analysis to each participant’s diffusion space. Fol-
lowing previous work (Begre et al. 2007; Boehr et al. 2007;
Thomas et al. 2008; 2009; Tuch et al. 2005), we used the
fractional anisotropy (FA) as our measure of structural
integrity.
Tract-based spatial statistics
We used tract-based spatial statistics (TBSS; Smith et al.
2006) to identify voxels whose FA value was correlated
with response times (RTs) in our feature-attention task. All
participants’ FA images were first aligned to the
FMRIB58_FA image using a non-linear registration
2162 Brain Struct Funct (2014) 219:2159–2171
123
procedure (FNIRT; Andersson et al. 2007a, b; Rueckert
et al. 1999) and resampled to a 1 9 1 9 1 mm3 resolution.
Secondly, a mean FA image was created from the aligned
FA images. This mean FA image was then thinned on the
basis of the FA values using the default FA threshold of
0.2. The resulting binary ‘skeleton’ image represented the
white matter tracts that are common to all participants in
the sample. Each participant’s aligned FA image was then
projected onto this skeleton image. The aligned single-
subject skeletonized FA images were subsequently sub-
mitted to a multiple regression analysis using SPM8. For
this analysis, we included each participant’s mean correct
RT for each attention condition (attend-shape, -motion, -
color, or -all) as a covariate, along with a constant term.
We then carried out a voxel-wise multiple regression in
SPM8 to determine the beta weights for each attention
condition which reflected the magnitude (and direction) of
correlations between FA values and RTs.
Probabilistic tractography
To investigate whether the functionally localized regions
are directly connected via SLF fibers, we used probabilistic
fiber tracking (Behrens et al. 2007). We used functionally
localized regions as seeds (Kim and Kim 2005). To ensure
that these connections were directly involved in our task,
we further constrained our reconstruction by requiring
fibers to pass through the ‘waypoint’ region identified by
the TBSS. If a seed region was in both hemispheres we
only used it as a seed region for the hemisphere in which
the peak voxel of the cluster was located. Voxels with
x = 0 in MNI space and voxels belonging to the other
hemisphere with respect to the peak voxel were removed
from the seed region. Thus, every seed region used for
probabilistic tractography could uniquely be allocated to
one hemisphere. Fiber tracking was then carried out in
diffusion space. The diffusion tensors were estimated at
each voxel in each participant’s diffusion space using
DTIFIT. For fiber tracking, a probability distribution of
diffusion directions at each voxel was first estimated using
Bayesian sampling techniques (BEDPOSTX). The tracking
algorithm then repeatedly sampled the distribution at each
voxel to produce fibers or ‘streamlines’ connecting voxels
from a source seed region to voxels in a target seed region
using PROBTRACKX. Streamlines were kept only if they
passed through source, target and waypoint voxels. Each
seed in the pair was used as both source and target seed
regions. The default parameters for both BEDPOSTX and
PROBTRACKX were used. At each voxel, the streamlines
were counted to reconstruct the structural pathway between
two functionally localized seed regions. For each partici-
pant and tract, we further applied a threshold that removed
all voxels which had fewer streamlines than 10 % of the
maximum number of streamlines across all voxels in the
reconstructed tract.
Results
Behavioral results
Overall, participants (N = 14) responded accurately and
quickly on the continuous 1-back task, as reflected by the
mean proportion correct (mean ± standard error: across all
conditions: 0.95 ± 0.01; attend-shape: 0.92 ± 0.01;
attend-motion: 0.95 ± 0.01; attend-color: 0.97 ± 0.01;
attend-all: 0.97 ± 0.01) and the mean RTs from correct
trials (across all conditions: 980 ± 60 ms; attend-shape:
1,036 ± 53 ms; attend-motion: 1,122 ± 48 ms; attend-
color: 782 ± 43 ms; attend-all: 989 ± 49 ms). The
behavioral results were based on 14 of the 16 participants,
as two data sets were lost due to technical fault.
Functional networks for selective attention
For the 14 participants with complete data sets, we used
whole-brain analyses to localize regions that preferentially
responded when participants attended to shape, motion,
color or all features simultaneously. There were no regions
that showed larger activation in the attend-all condition
compared to the other conditions (A [ C ? S ? M).
We next compared functional activation for attention to
each individual feature relative to the remaining two fea-
tures. Figure 1 and Table 1 present the results of these
analyses. Consistent with previous work (Mayer and Vu-
ong 2013; Peuskens et al. 2004), we found that attention to
shape (S [ C ? M) and attention to motion (M [ C ? S)
activated similar regions in each hemisphere. These
included regions in lateral occipito-temporal cortex (LO/MT),
regions along the IPS and regions along the IFG.
By comparison, the attend-color condition (C [ S ? M)
activated middle frontal gyrus (MFG) in the left hemi-
sphere and the transverse temporal gyrus (TTG) in the right
hemisphere. This condition also activated medial brain
regions along the cingulate gyrus, which has been impli-
cated in task difficulty (Leech et al. 2011) and during
retrieval of color information of previously encountered
objects (Chao and Martin 1999). Attention to color did not
activate the expected color-preferring V4/V8 regions found
in previous studies (Cant and Goodale 2007; Hadjikhani
et al. 1998; Paradis et al. 2008; Peuskens et al. 2004; Zeki
1980). However, when we lowered the initial threshold to
p = 0.01, we found a cluster on the ventral surface of the
right occipital lobe expanding along the lingual gyrus
(x = 12, y = -64, z = -8; Z = 3.51, k = 109, p = 0.01,
Brain Struct Funct (2014) 219:2159–2171 2163
123
FDR-corrected at the cluster level; this peak was located in
the anterior cerebellum).
Tract-based spatial statistics
Based on the role of the SLF in attentional processes
reported previously (Anderson et al. 2011; Chechlacz et al.
2012; Shinoura et al. 2009) and its spatial proximity to
posterior and anterior regions within networks that process
objects (Peuskens et al. 2004), we restricted our multiple
regression analyses to the SLF in each hemisphere. For our
SLF mask, we used the probabilistic map from the John
Hopkins University white matter atlas (Wakana et al. 2004)
implemented in FSL. To remove voxels that have a low
probability of being part of the SLF, we used a 50 %
threshold.
Our previous results indicated that functional connec-
tions between posterior and anterior regions did not depend
on the specific feature attended (Mayer and Vuong 2013).
We therefore conducted a 1-sample t test on the estimated
beta weights across all attention conditions to investigate if
there are white matter voxels whose FA value is correlated
Fig. 1 a Results of the fMRI whole-brain analyses for attend-shape,
attend-motion and attend-color. Coordinates are in MNI space. IFG
inferior frontal gyrus, MFG middle frontal gyrus, MeFG medial
frontal gyrus, IPL inferior parietal lobule, SPL superior parietal
lobule, TTG transverse temporal gyrus, STG superior temporal gyrus,
IOG inferior occipital gyrus, MOG middle occipital gyrus, CG
cingulate gyrus, C color, M motion, S shape. Red regions show larger
BOLD responses in the attend-color condition with respect to attend-
shape and attend-motion. Blue regions show larger BOLD responses
in the attend-motion condition with respect to attend-shape and
attend-color. Green regions show larger BOLD responses in the
attend-shape condition with respect to attend-motion and attend-color.
b Medial brain regions that showed larger BOLD responses in the
attend-color condition with respect to attend-shape and attend-motion.
c Result of the TBSS analysis. The FA values of the voxels in the blue
cluster are significantly correlated with response times on the 1-back
feature-attention task (p \ 0.05, FWE-corrected for multiple com-
parisons at the voxel level with small-volume correction using a
superior longitudinal fasciculus mask). This cluster was used as a
waypoint for probabilistic tractography
2164 Brain Struct Funct (2014) 219:2159–2171
123
with task performance. For this analysis, we assigned -1 to
all the RT covariates (attend-color, attend-motion, attend-
shape, attend-all) which tested whether or not the beta
weights were significantly less than 0. We assigned a
negative value as we expected that shorter RTs were
associated with higher FA values (e.g., Sasson et al. 2010).
For the TBSS analyses, we used p \ 0.05, FWE-corrected
at the voxel-level and small-volume corrected with the SLF
mask. The initial statistical threshold was p \ 0.001 and
the initial minimum cluster was k = 10 voxels. We found a
significant cluster in the right SLF (MNI coordinates:
x = 40 y = -11 z = 29; k = 11 voxels, Z = 3.80,
p = 0.034). To also determine whether any specific
attended features were correlated with task performance,
we tested for the individual contribution of each attention
condition. We assigned -1 to the attend-feature covariate
(e.g., attend-color) and 0 to the remaining ones (attend-
motion, attend-shape, attend-all). No cluster reached sig-
nificance for any of the attention conditions indicating that
the significant SLF cluster reflects a general task-perfor-
mance effect.
Probabilistic tractography
Our TBSS analyses identified one cluster only in right SLF
in which FA values and RTs were correlated across par-
ticipants. This cluster was used as a waypoint for the
tracking procedure. The cluster was located in the right
hemisphere. Therefore, we only reconstructed tracts in this
hemisphere. Since we were interested in direct connections
between posterior and anterior regions, we conducted
probabilistic tractography only between occipito-temporal
and frontal regions and between parietal and frontal regions
identified in the fMRI. There were a total of 28 posterior-
anterior (P–A) pairs from our 11 seeds (Table 1). Of these
seed pairs, two tracts were reconstructed for the attend-
color and the attend-shape regions, respectively, and six
tracts were reconstructed for attend-motion regions, as
shown in Table 2. To maintain the same significance
thresholds for all seed regions identified by fMRI, we did
not use the cluster in the lingual gyrus identified in the
attend-color condition as a seed.
Example tracts of one representative participant are
shown in Fig. 2. For a given tract reconstructed between a
pair of seed regions, we next normalized each partici-
pant’s tract to the MNI standard brain and combined them
together across participants. In these combined tract
images, the intensity at each voxel represents the number
of participants whose individual tract reconstruction
included that voxel. Thus, for each of the 10 reconstructed
tracts, we can extract the maximum number of participants
that had overlapping connections between a pair of seed
regions. Table 2 shows the number of participants in
which we could reconstruct a tract between the seed pairs,
the mean FA value in the reconstructed tract across those
participants, and the maximum number of participants
with overlapping tracts. Figure 3 shows the overlap of
normalized example tracts across participants. Overall, the
combination of TBSS and probabilistic tractography pro-
vides a strong demonstration that the right SLF (or parts
thereof) is important for selective attention to object
features.
Table 1 fMRI results
MNI
Region x y z k Z
Attend-shape [ attend-color/motion
IFGb 48 38 10 112 4.47
IPLb 39 -61 40 287 4.76
SPL -30 -67 46 90 3.87
MOGa,b 45 -67 -14 40 3.51
MOG -45 -85 -2 51 4.09
MOG -48 -64 -11 32 3.94
Attend-motion [ attend-color/shape
dIFGb 51 8 31 228 4.87
vIFGb 33 23 -8 58 4.62
IFG -45 8 25 32 3.79
IPLb 36 -34 40 121 4.00
IPL -39 -46 55 26 3.82
SPLb 30 -67 46 32 3.42
IOGb 45 -85 -8 535 5.01
MOG -51 -76 -2 292 4.61
Cerebellum -15 -76 -29 176 4.71
Attend-color [ attend-shape/motion
MeFGa,b 0 50 -11 241 4.92
MeFG -6 62 10 36 3.45
MFG -30 41 25 50 4.11
MFG -27 29 40 45 4.00
Precuneus -6 -67 16 273 4.56
STG -57 -7 -2 32 3.81
TTGb 57 -16 10 187 4.63
CGb 15 2 43 226 4.55
MNI coordinates, cluster size (k) and Z scores for the peak voxel of
clusters localized by the different attention conditions
IFG inferior frontal gyrus, MeFG medial frontal gyrus, MFG middle
frontal gyrus, IPL inferior parietal lobule, SPL superior parietal lob-
ule, TTG transverse temporal gyrus, IOG inferior occipital gyrus,
MOG middle occipital gyrus, CG cingulate gyrus, d dorsal, v ventral
p \ 0.05, FWE-corrected for multiple comparisons at the cluster level
for all reported clustersa Peak voxels that are not allocated to an anatomical landmark in
WFU pickatlas. The label of the anatomical landmark of the nearest
allocated voxel of the cluster is reportedb Clusters that were used as seeds for probabilistic tractography
Brain Struct Funct (2014) 219:2159–2171 2165
123
Relationship between functional and structural
connections
Recent studies have suggested that there is a relationship
between functional activation/connectivity and structural
integrity (e.g., Fox and Raichle 2007; Hagmann et al. 2008;
Thiebaut de Schotten et al. 2012). For example, Thiebaut
de Schotten et al. (2012) showed that activation in func-
tionally localized regions correlated with FA values. We
did not find correlations between the level of activation and
the FA of the SLF. Rather, we found stronger correlations
in the overall activation and functional connectivity
between regions connected by the SLF than for regions
with no reconstructed connection. In our fMRI analysis, we
identified several feature-preferring regions in posterior
and anterior parts of the right hemisphere. We were able to
reconstruct tracts between some P-A seed pairs but not
others. The reconstructed tracts were part of the SLF. We
could therefore compare the functional activation and
functional connectivity (i.e., temporal correlations) of P–A
seed pairs from reconstructed tracts (Table 2) with those
from P–A seed pairs that did not result in any reconstruc-
tion. There were 10 P–A seed pairs that resulted in
reconstructed tracts and 18 P–A seed pairs that resulted in
no tracts. For functional activation, we extracted the mean
beta weight computed in the GLM analysis from each seed
in a pair for each participant; that is, we computed the
mean beta weights averaged across all voxels in the seed.
We then computed the Pearson correlation between the two
mean beta weights. Figure 4a shows a histogram of these
correlations for the 28 P–A pairs. We compared the abso-
lute value of the correlations to compare their magnitude
irrespective of their sign. A 2-sample t test revealed that the
mean correlation was larger for P–A pairs from recon-
structed tracts (gray bars) than the mean correlation from
other P–A pairs (white bars) [t(26) = 6.88, p \ 0.001;
r = 0.64 vs. r = 0.23, respectively].
We next looked at the functional connectivity between
the P–A seed pairs. Following previous work (Schultz et al.
2008; Haynes et al. 2005), we extracted the mean residual
time series from each seed for each P–A pair and each
participant. That is, we computed the mean residual time
series averaged across all voxels in the seed. The residual
time series factor out any correlations between the two
seeds induced by the experimental paradigm. We then
computed the Pearson correlation of the residuals of each
seed pair, and averaged this correlation across participants.
Figure 4b shows a histogram of these correlations for the
28 P–A pairs. Consistent with the correlation of beta
weights, a 2-sample t test revealed that the mean correla-
tion was larger for P–A pairs from reconstructed tracts
(gray bars) than the mean correlation from other P–A pairs
(white bars) [t(26) = 2.08, p = 0.047; r = 0.39 vs.
r = 0.26, respectively]. Table 2 also shows the specific
Pearson correlation for beta weights and residual time
series for the 10 P–A pairs with reconstructed tracts that
were part of the SLF. Overall, the P–A seed pairs that are
structurally connected tend to have stronger functional
interactions between them in terms of the mean activation
and temporal correlation. Although exploratory, these
results are consistent with the literature (Fox and Raichle
2007; Hagmann et al. 2008; Thiebaut de Schotten et al.
2012).
Discussion
Selective attention plays an important role for many tasks;
for example, the ability to select features, such as shape,
motion or color is important for rapid object processing in a
dynamic and cluttered environment. Thus, it is important to
understand the underlying neural mechanisms of attention
and perception, particularly for processing object features
Table 2 Results of the probabilistic tractography
Anterior seed Posterior seed NN FA N Beta Residual
Attend-shape [ attend-motion/color
IFG IPL 14 0.41 13 0.73 0.63
IFG MOG 13 0.44 8 0.73 0.26
Attend-motion [ attend-shape/color
dIFG SPL 14 0.35 13 0.74 0.60
dIFG IPL 14 0.43 14 0.69 0.55
dIFG IOG 14 0.39 14 0.81 0.46
vIFG SPL 12 0.42 9 0.55 0.30
vIFG IPL 14 0.40 13 0.72 0.30
vIFG IOG 14 0.44 12 0.63 0.26
Attend-color [ attend-shape/motion
MeFGa TTG 8 0.39 5 0.50 0.25
MeFGa CG 3 0.42 3 0.27 0.34
p \ 0.05, FWE-corrected for multiple comparisons at the cluster level
for all reported seeds
NN numbers of participants for whom direct connections between
posterior and anterior seed regions identified by the fMRI experiment
via the SLF-waypoint could be reconstructed, FA fractional anisot-
ropy averaged across participants, N number of participants with
overlapping tracts, Beta Pearson correlation coefficient between two
seeds’ mean beta weights across participants, Residual Pearson cor-
relation coefficient between two seeds’ mean residual time series,
averaged across participants, IFG inferior frontal gyrus, MeFG medial
frontal gyrus, MFG middle frontal gyrus, IPL inferior parietal lobule,
SPL superior parietal lobule, TTG transverse temporal gyrus, IOG
inferior occipital gyrus, MOG middle occipital gyrus, CG cingulate
gyrusa The peak voxel of the MeFG cluster could not uniquely be allocated
to a hemisphere. As the cluster predominately expanded to the right
hemisphere it was allocated to the right hemisphere. Voxels with MNI
x-coordinates of B0 were removed from the seed region
2166 Brain Struct Funct (2014) 219:2159–2171
123
of interest. Consistent with previous work (Corbetta et al.
1990; Mayer and Vuong 2013; Paradis et al. 2008; Peus-
kens et al. 2004), we found co-activation of different
occipital, temporal, parietal and frontal regions when
observers attended to different features. Furthermore, we
combined fMRI with TBSS and probabilistic tract recon-
struction to provide direct evidence for the involvement of
the SLF in attention to object features.
Previous studies found that attention to features is
reflected at the neural level by an increase in cortical
activation in regions that preferentially process the atten-
ded feature (Kanwisher and Wojciulik 2000; Murray and
Wojciulik 2004). Our whole-brain analyses localized dif-
ferent networks of regions distributed throughout gray
matter that showed enhanced brain activation when
observers attended to shape, motion or color. Other than
participants’ attentional state (induced by the task
instruction), the stimulus and discrimination task were the
same across all attention conditions. For the attend-shape
and attend-motion conditions, the posterior regions corre-
spond to those that have consistently been found to pref-
erentially respond to shape and motion (Corbetta et al.
1990; Mayer and Vuong 2013; Paradis et al. 2008; Peus-
kens et al. 2004; Schultz et al. 2008). We also found
bilateral inferior frontal regions that showed attentional
enhancements in activation when participants attended to
shape or motion and left-lateralized middle frontal regions
when they attended to color. Consistent with Peuskens
et al. (2004), we found that attention to shape and motion
lead to large overlapping regions. Importantly, regions
within these networks for shape and motion processing
were anatomically connected to each other. These con-
nections may enhance communication between regions
during our feature-attention task, leading to the correlation
between structural integrity (i.e., FA values) and perfor-
mance (i.e., response times) in the TBSS analyses.
Our results therefore have implications for understand-
ing the organization of selective attention and object per-
ception. With respect to attentional processes, previous
work on the role of white matter in spatial attention
(Anderson et al. 2011; Chechlacz et al. 2012; Shinoura
et al. 2009; Thiebaut de Schotten et al. 2011) identified the
SLF as a fiber tract that facilitates attentional processes.
Using TBSS, we were able to show that the FA values of
voxels within the SLF were correlated with RTs when
participants performed a feature-attention task. Our results
therefore extend previous findings by showing that the SLF
is not only relevant for spatial attention (such as spatial
neglect and simultanagnosia) but also for feature attention
in object processing. Furthermore, we were able to recon-
struct tracts between parietal and frontal regions that were
activated for all observers while they performed our
Fig. 2 Results of the
probabilistic tractography for
one representative participant.
IFG inferior frontal gyrus,
MeFG medial frontal gyrus,
TTG transverse temporal gyrus,
MOG middle occipital gyrus,
C color, M motion, S shape.
a An example tract
reconstructed between regions
that showed larger BOLD
responses in the attend-motion
condition with respect to attend-
shape and attend-color. b An
example tract reconstructed
between regions that show
larger BOLD responses in the
attend-color condition with
respect to attend-shape and
attend-motion. c An example
tract reconstructed between
regions that show larger BOLD
responses in the attend-shape
condition with respect to attend-
motion and attend-color. This
figure was created with
Fibernavigator (Schurade et al.
2010)
Brain Struct Funct (2014) 219:2159–2171 2167
123
feature-attention task. Consistent with previous studies
(Anderson et al. 2011), our results support the hypothesis
that white matter connections between parietal and frontal
regions are important for attentional processes.
In our study, we used a relatively large mask (Wakana
et al. 2004) to define the SLF. Recent literature, however,
indicates that the SLF can be partitioned into subcompo-
nents (Makris et al. 2005; Schmahmann et al. 2007;
Thiebaut de Schotten et al. 2011). Based on previous
studies investigating the role of the SLF for attention
(Chechlacz et al. 2012; Shinoura et al. 2009), we did not
have a specific hypothesis on which subcomponent of the
SLF would be critical for our feature-attention task. A
rough comparison of the coordinates of our waypoint to the
results of Makris et al. (2005) suggests that our waypoint
may be located in SLF-II or SLF-III. These two subcom-
ponents of the SLF connect parietal regions to frontal
regions (Makris et al. 2005).
The structural connections between posterior and ante-
rior feature-preferring regions reconstructed in our study
were complemented by the correlations of functional
activation and functional connectivity between seed
regions connected by the SLF (Hagmann et al. 2008;
Schultz et al. 2008; Thiebaut de Schotten et al. 2012). We
found stronger correlations between regions for which
there were reconstructed tracts in comparison to regions for
which there were no reconstructed tracts. Although
exploratory, such correlations further support the concept
of a network consisting of posterior and anterior feature-
preferring regions that enables efficient processing of
multi-featured objects (Bressler and Menon 2010). Future
research is needed to further investigate the relationship
between structural integrity and functional activation and
connectivity.
While we were able to reconstruct direct white matter
connections via SLF between posterior and anterior regions
(especially between posterior parietal and inferior frontal)
that were activated in the attend-shape/motion conditions
for nearly all our participants, we only reconstructed such
connections in a small number of participants for regions
activated in the attend-color condition. This may be due to
the nature of the seed regions localized by this condition.
At the functional level, the attend-color condition only
weakly activated previously reported color-preferring
Fig. 3 Overlap of the results of the probabilistic tractography of
N = 14 participants. The heatmap shows the number of reconstructed
tracts in each voxel. The coordinates are in MNI space. a The seed
regions (Table 1) for the tractography were defined based on the
fMRI results for the contrast motion [ shape ? color. b The seed
regions (Table 1) for the tractography were defined based on the
fMRI results of the shape [ motion ? color contrast. IOG inferior
occipital gyrus, dIFG dorsal inferior frontal gyrus, IPL inferior
parietal lobule, IFG inferior frontal gyrus
-0.5 -0.25 0 0.25 0.5 0.750
1
2
3
4
Pearson correlation of beta weights
freq
uenc
y
P-A pairs of non-reconstructed tracts
P-A pairs of reconstructed tracts
0 0.2 0.4 0.60
1
2
3
4
freq
uenc
yPearson correlation of residual time series
a
b
Fig. 4 a Histogram of the Pearson correlations between the beta
weights of the seed pairs with reconstructed tracts (gray bars; see
Table 2) and seed pairs with no tract reconstructions (white bars).
b Histogram of the correlations between the residual time series of the
seed pairs with tract reconstruction (gray bars; see Table 2) and seed
pairs with no tract reconstructions. P–A posterior–anterior
2168 Brain Struct Funct (2014) 219:2159–2171
123
regions (Cavina-Pratesi et al. 2010; Hadjikhani et al. 1998;
Zeki 1980) at the ventral surface of the occipito-temporal
cortex (i.e., the right lingual gyrus in our study). Instead,
activation in medial brain regions was found. These regions
are consistent with those previously reported to be involved
in cognitive control (MacDonald et al. 2000), suggesting
that the cognitive processes might differ for attention to
motion/shape compared to attention to color in our exper-
iment. Consistent with this assumption, Peuskens et al.
(2004) stated that different strategies can be used to attend
to different object features. While the best strategy for
succeeding at the attend-shape/motion tasks is to attend to
the edges of the objects, the attend-color task can also be
performed by attending to a surface patch at the center of
an object. The weak activation in established color-pre-
ferring regions alongside with the novel activation pattern
in medial cortical ones may be due to the specific objects
and task used in our study.
In addition to the findings reported above, we also found
evidence for hemispheric asymmetry in our study. TBSS
indicated that FA was only correlated with performance in
the right SLF. This finding is consistent with other DTI
studies that used different tasks and found lateralization in
the correlations of white matter integrity and task perfor-
mance (Boehr et al. 2007; Thiebaut de Schotten et al. 2011;
Thomas et al. 2008; Tuch et al. 2005). With respect to
object processing, previous work suggests that there is little
lateralization in functional activation (Grill-Spector et al.
2001). There is evidence, however, for right hemispheric
lateralization of attentional processes in humans (Anderson
et al. 2011). Moreover, Thiebaut de Schotten et al. (2011)
found an anatomical basis for right hemisphere dominance
in visuospatial attention. They showed that the amount of
anatomical lateralization in terms of volume correlated
with behavioral performance. The SLF-II and SLF-III
subcomponents but not the SLF-I subcomponent showed a
right anatomical lateralization, consistent with our tract
reconstruction. Further research will be necessary to
investigate how hemispheric lateralization of white matter
tracts affects feature attention and object perception.
The regions activated in our fMRI experiment and the
structural connections between them suggest that perfor-
mance on our feature-attention task was predominantly
carried out by cortical regions and the communication
between them. We did find an increase in activation in the
left cerebellum in comparison to the other conditions when
observers attended to motion. However, there was no evi-
dence for differential activation that depended on the
attention conditions in other subcortical structures such as
the thalamus.
It is becoming increasingly important to consider large-
scale networks for understanding higher cognitive func-
tions (Bressler and Menon 2010), such as attention and
object perception. These networks include both gray
matter regions and white matter pathways between
regions. Our results suggest that such networks involve
functional co-activations of distant cortical regions and
communication via specific white matter tracts between
these regions.
Acknowledgments We would like to thank Anya Hurlbert, Gabi
Jordan and Cristiana Cavina-Pratesi for their comments on earlier
versions of this manuscript. We would also like to thank Michael
Firbank for data processing advice and scripts and the radiographers
from the NMRC for their help with the data acquisition.
Conflict of interest The authors state no conflict of interest.
References
Amick MM, Schendan HE, Ganis G, Cronin-Golomb A (2006)
Frontostriatal circuits are necessary for visuomotor transforma-
tion: mental rotation in Parkinson’s disease. Neuropsychologia
44(3):339–349. doi:10.1016/j.neuropsychologia.2005.06.002
Anderson EJ, Jones DK, O’Gorman RL, Leemans A, Catani M,
Husain M (2011) Cortical network for gaze control in humans
revealed using multimodal MRI. Cereb Cortex. doi:10.1093/
cercor/bhr110
Andersson JLR, Jenkinson M, Smith S (2007a) Non-linear optimi-
sation. http://www.fmrib.ox.ac.uk/analysis/techrep
Andersson JLR, Jenkinson M, Smith S (2007b) Non-linear registra-
tion aka Spatial normalisation. http://www.fmrib.ox.ac.uk/
analysis/techrep
Begre S, Frommer A, von Kaenel R, Kiefer C, Federspiel A (2007)
Relation of white matter anisotropy to visual memory in 17
healthy subjects. Brain Res 1168:60–66. doi:10.1016/j.brainres.
2007.06.096
Behrens TEJ, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes
RG, Clare S, Matthews PM, Brady JM, Smith SM (2003)
Characterization and propagation of uncertainty in diffusion-
weighted MR imaging. Magn Reson Med 50(5):1077–1088.
doi:10.1002/mrm.10609
Behrens TEJ, Berg HJ, Jbabdi S, Rushworth MFS, Woolrich MW
(2007) Probabilistic diffusion tractography with multiple fibre
orientations: what can we gain? Neuroimage 34(1):144–155.
doi:10.1016/j.neuroimage.2006.09.018
Boehr S, Guellmar D, Knab R, Reichenbach JR, Witte OW, Haueisen
J (2007) Fractional anisotropy correlates with auditory simple
reaction time performance. Brain Res 1186:194–202. doi:10.
1016/j.brainres.2007.10.013
Brainard DH (1997) The psychophysics toolbox. Spat Vis
10(4):433–436. doi:10.1163/156856897x00357
Bressler SL, Menon V (2010) Large-scale brain networks in
cognition: emerging methods and principles. Trends Cogn Sci
14(6):277–290. doi:10.1016/j.tics.2010.04.004
Cabeza R, Dolcos F, Prince SE, Rice HJ, Weissman DH, Nyberg L
(2003) Attention-related activity during episodic memory
retrieval: a cross-function fMRI study. Neuropsychologia
41(3):390–399. doi:10.1016/s0028-3932(02)00170-7
Cant JS, Goodale MA (2007) Attention to form or surface properties
modulates different regions of human occipitotemporal cortex.
Cereb Cortex 17(3):713–731. doi:10.1093/cercor/bhk022
Cavina-Pratesi C, Kentridge RW, Heywood CA, Milner AD (2010)
Separate channels for processing form, texture, and color:
Brain Struct Funct (2014) 219:2159–2171 2169
123
evidence from fMRI adaptation and visual object agnosia. Cereb
Cortex 20(10):2319–2332. doi:10.1093/cercor/bhp298
Chao LL, Martin A (1999) Cortical regions associated with perceiv-
ing, naming, and knowing about colors. J Cogn Neurosci
11(1):25–35. doi:10.1162/089892999563229
Chechlacz M, Rotshtein P, Hansen PC, Riddoch JM, Deb S,
Humphreys GW (2012) The neural underpinnings of simultan-
agnosia: disconnecting the visuospatial attention network.
J Cogn Neurosci 24(3):718–735
Corbetta M, Miezin FM, Dobmeyer S, Shulman GL, Petersen SE
(1990) Attentional modulation of neural processing of shape,
color, and velocity in humans. Sci 248(4962):1556–1559. doi:10.
1126/science.2360050
Ffytche DH, Catani M (2005) Beyond localization: from hodology to
function. Philos Trans R Soc B-Biol Sci 360(1456):767–779.
doi:10.1098/rstb.2005.1621
Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain
activity observed with functional magnetic resonance imaging.
Nat Rev Neurosci 8(9):700–711. doi:10.1038/nrn2201
Friston KJ, Buechel C, Fink GR, Morris J, Rolls E, Dolan RJ (1997)
Psychophysiological and modulatory interactions in neuroimag-
ing. Neuroimage 6(3):218–229
Grill-Spector K, Kourtzi Z, Kanwisher N (2001) The lateral occipital
complex and its role in object recognition. Vis Res 41(10–11):
1409–1422. doi:10.1016/s0042-6989(01)00073-6
Hadjikhani N, Liu AK, Dale AM, Cavanagh P, Tootell RBH (1998)
Retinotopy and color sensitivity in human visual cortical area
V8. Nat Neurosci 1(3):235–241. doi:10.1038/681
Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen
VJ, Sporns O (2008) Mapping the structural core of human
cerebral cortex. PLoS Biol 6(7):1479–1493. doi:10.1371/journal.
pbio.0060159
Haynes JD, Driver J, Rees G (2005) Visibility reflects dynamic
changes of effective connectivity between V1 and fusiform
cortex. Neuron 46(5):811–821. doi:10.1016/j.neuron.2005.05.
012
Horwitz B (2003) The elusive concept of brain connectivity.
Neuroimage 19(2):466–470. doi:10.1016/s1053-8119(03)
00112-5
Jack JJB, Noble D, Tsien RW (1975) Electric current flow in
excitable cells. Clarendon Press, Oxford
Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved
optimization for the robust and accurate linear registration and
motion correction of brain images. Neuroimage 17(2):825–841.
doi:10.1006/nimg.2002.1132
Kanwisher N, Wojciulik E (2000) Visual attention: insights from
brain imaging. Nat Rev Neurosci 1(2):91–100. doi:10.1038/
35039043
Kim D-S, Kim M (2005) Combining functional and diffusion tensor
MRI. Ann N Y Acad Sci 1064(1):1–15. doi:10.1196/annals.
1340.005
Kourtzi Z, Kanwisher N (2000) Cortical regions involved in
perceiving object shape. J Neurosci 20(9):3310–3318
Leech R, Kamourieh S, Beckmann CF, Sharp DJ (2011) Fractionating
the default mode network: distinct contributions of the ventral
and dorsal posterior cingulate cortex to cognitive control.
J Neurosci 31(9):3217–3224. doi:10.1523/jneurosci.5626-10.
2011
Leemans A, Jones DK (2009) The B-Matrix must be rotated when
correcting for subject motion in DTI data. Magn Reson Med
61(6):1336–1349. doi:10.1002/mrm.21890
MacDonald AW, Cohen JD, Stenger VA, Carter CS (2000) Disso-
ciating the role of the dorsolateral prefrontal and anterior
cingulate cortex in cognitive control. Sci 288(5472):1835–1838
Makris N, Kennedy DN, McInerney S, Sorensen AG, Wang R,
Caviness VS, Pandya DN (2005) Segmentation of
subcomponents within the superior longitudinal fascicle in
humans: a quantitative, in vivo, DT-MRI study. Cereb Cortex
15(6):854–869. doi:10.1093/cercor/bhh186
Malach R, Reppas JB, Benson RR, Kwong KK, Jiang H, Kennedy
WA, Ledden PJ, Brady TJ, Rosen BR, Tootell RBH (1995)
Object-related activity revealed by functional magnetic-reso-
nance-imaging in human occipital cortex. Proc Natl Acad Sci
USA 92(18):8135–8139. doi:10.1073/pnas.92.18.8135
Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH (2003) An
automated method for neuroanatomic and cytoarchitectonic
atlas-based interrogation of fMRI data sets. Neuroimage
19(3):1233–1239. doi:10.1016/S1053-8119(03)00169-1
Mayer KM, Vuong QC (2012) The influence of unattended features
on object processing depends on task demand. Vis Res 56:20–27.
doi:10.1016/j.visres.2012.01.013
Mayer KM, Vuong QC (2013) Automatic processing of unattended
object features by functional connectivity. Front Hum Neurosci
7. doi:10.3389/fnhum.2013.00193
Miller EK (2000) The prefrontal cortex and cognitive control. Nat
Rev Neurosci 1(1):59–65. doi:10.1038/35036228
Murray SO, Wojciulik E (2004) Attention increases neural selectivity
in the human lateral occipital complex. Nat Neurosci 7(1):70–74.
doi:10.1038/nn1161
Murray SO, Olshausen BA, Woods DL (2003) Processing shape,
motion and three-dimensional shape-from-motion in the human
cortex. Cereb Cortex 13(5):508–516. doi:10.1093/cercor/13.5.
508Nummenmaa L, Passamonti L, Rowe J, Engell AD, Calder AJ (2010)
Connectivity analysis reveals a cortical network for eye gaze
perception. Cereb Cortex 20(8):1780–1787. doi:10.1093/cercor/
bhp244
Paradis AL, Droulez J, Cornilleau-Peres V, Poline JB (2008)
Processing 3D form and 3D motion: respective contributions
of attention-based and stimulus-driven activity. Neuroimage
43(4):736–747. doi:10.1016/j.neuroimage.2008.08.027
Pelli DG (1997) The VideoToolbox software for visual psychophys-
ics: transforming numbers into movies. Spat Vis 10(4):437–442.
doi:10.1163/156856897x00366
Peuskens H, Claeys KG, Todd JT, Norman JF, Van Hecke P, Orban
GA (2004) Attention to 3-D shape, 3-D motion, and texture in
3-D structure from motion displays. J Cogn Neurosci
16(4):665–682. doi:10.1162/089892904323057371
Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ
(1999) Nonrigid registration using free-form deformations:
application to breast MR images. IEEE Trans Med Imaging
18(8):712–721. doi:10.1109/42.796284
Sasson E, Doniger GM, Pasternak O, Assaf Y (2010) Structural
correlates of memory performance with diffusion tensor imag-
ing. Neuroimage 50(3):1231–1242. doi:10.1016/j.neuroimage.
2009.12.079
Schendan HE, Kutas M (2007) Neurophysiological evidence for the
time course of activation of global shape, part, and local contour
representations during visual object categorization and memory.
J Cogn Neurosci 19(5):734–749. doi:10.1162/jocn.2007.19.5.
734
Schmahmann JD, Pandya DN, Wang R, Dai G, D’Arceuil HE, de
Crespigny AJ, Wedeen VJ (2007) Association fibre pathways of
the brain: parallel observations from diffusion spectrum imaging
and autoradiography. Brain 130:630–653. doi:10.1093/brain/
awl359
Schultz J, Lennert T (2009) BOLD signal in intraparietal sulcus
covaries with magnitude of implicitly driven attention shifts.
Neuroimage 45(4):1314–1328. doi:10.1016/j.neuroimage.2009.
01.012
Schultz J, Chuang L, Vuong QC (2008) A dynamic object-processing
network: metric shape discrimination of dynamic objects by
2170 Brain Struct Funct (2014) 219:2159–2171
123
activation of occipitotemporal, parietal, and frontal cortices.
Cereb Cortex 18(6):1302–1313. doi:10.1093/cercor/bhm162
Schurade R, Hlawitschka M, Hamann B, Scheuermann G, Knosche
TR, Anwander A (2010) Visualizing white matter fiber tracts
with optimally fitted curved dissection surfaces. In: Vcbm’10,
pp 41–48
Shinoura N, Suzuki Y, Yamada R, Tabei Y, Saito K, Yagi K (2009)
Damage to the right superior longitudinal fasciculus in the
inferior parietal lobe plays a role in spatial neglect. Neuropsych-
ologia 47(12):2600–2603. doi:10.1016/j.neuropsychologia.2009.
05.010
Smith SM (2002) Fast robust automated brain extraction. Hum Brain
Mapp 17(3):143–155. doi:10.1002/hbm.10062
Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ,
Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney
DE, Niazy RK, Saunders J, Vickers J, Zhang YY, De Stefano N,
Brady JM, Matthews PM (2004) Advances in functional and
structural MR image analysis and implementation as FSL.
Neuroimage 23:S208–S219. doi:10.1016/j.neuroimage.2004.07.
051
Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE,
Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews
PM, Behrens TEJ (2006) Tract-based spatial statistics: voxelwise
analysis of multi-subject diffusion data. Neuroimage
31(4):1487–1505. doi:10.1016/j.neuroimage.2006.02.024
Thiebaut de Schotten M, Dell’Acqua F, Forkel SJ, Simmons A,
Vergani F, Murphy DGM, Catani M (2011) A lateralized brain
network for visuospatial attention. Nat Neurosci 14(10):
1245–1246. doi:10.1038/nn.2905
Thiebaut de Schotten M, Cohen L, Amemiya E, Braga LW, Dehaene
S (2012) Learning to read improves the structure of the arcuate
fasciculus. Cereb Cortex. doi:10.1093/cercor/bhs383
Thomas C, Moya L, Avidan G, Humphreys K, Jung KJ, Peterson MA,
Behrmann M (2008) Reduction in white matter connectivity,
revealed by diffusion tensor imaging, may account for age-
related changes in face perception. J Cogn Neurosci
20(2):268–284. doi:10.1162/jocn.2008.20025
Thomas C, Avidan G, Humphreys K, Jung K-j, Gao F, Behrmann M
(2009) Reduced structural connectivity in ventral visual cortex in
congenital prosopagnosia. Nat Neurosci 12(1):29–31. doi:10.
1038/nn.2224
Tootell RBH, Reppas JB, Dale AM, Look RB, Sereno MI, Malach R,
Brady TJ, Rosen BR (1995) Visual-motion aftereffect in human
cortical area MT revealed by functional magnetic-resonance-
imaging. Nat 375(6527):139–141. doi:10.1038/375139a0
Tuch DS, Salat DH, Wisco JJ, Zaleta AK, Hevelone ND, Rosas HD
(2005) Choice reaction time performance correlates with diffu-
sion anisotropy in white matter pathways supporting visuospatial
attention. Proc Natl Acad Sci USA 102(34):12212–12217.
doi:10.1073/pnas.0407259102
Wakana S, Jiang HY, Nagae-Poetscher LM, van Zijl PCM, Mori S
(2004) Fiber tract-based atlas of human white matter anatomy.
Radiol 230(1):77–87. doi:10.1148/radiol.2301021640
Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S,
Behrens T, Beckmann C, Jenkinson M, Smith SM (2009)
Bayesian analysis of neuroimaging data in FSL. Neuroimage
45(1):S173–S186. doi:10.1016/j.neuroimage.2008.10.055
Zanto TP, Rubens MT, Bollinger J, Gazzaley A (2010) Top-down
modulation of visual feature processing: the role of the inferior
frontal junction. Neuroimage 53(2):736–745. doi:10.1016/j.
neuroimage.2010.06.012
Zeki S (1980) The representation of colors in the cerebral-cortex. Nat
284(5755):412–418. doi:10.1038/284412a0
Zeki S, Marini L (1998) Three cortical stages of colour processing in
the human brain. Brain 121:1669–1685. doi:10.1093/brain/121.
9.1669
Brain Struct Funct (2014) 219:2159–2171 2171
123