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: kmayer@cbs.mpg.de

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

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(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

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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.

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