Feature-based attention modulates direction-selective hemodynamic activity within human MT.

Abstract

Attending to the spatial location or to nonspatial features of a stimulus modulates neural activity in cortical areas that process its perceptual attributes. The feature-based attentional selection of the direction of a moving stimulus is associated with increased firing of individual neurons tuned to the direction of the movement in area V5/MT, while responses of neurons tuned to opposite directions are suppressed. However, it is not known how these multiplicatively scaled responses of individual neurons tuned to different motion-directions are integrated at the population level, in order to facilitate the processing of stimuli that match the perceptual goals. Using functional magnetic resonance imaging (fMRI) the present study revealed that attending to the movement direction of a dot field enhances the response in a number of areas including the human MT region (hMT) as a function of the coherence of the stimulus. Attending the opposite direction, however, lead to a suppressed response in hMT that was inversely correlated with stimulus-coherence. These findings demonstrate that the multiplicative scaling of single-neuron responses by feature-based attention results in an enhanced direction-selective population response within those cortical modules that processes the physical attributes of the attended stimuli. Our results provide strong support for the validity of the "feature similarity gain model" on the integrated population response as quantified by parametric fMRI in humans.

Full text

Page 1

r Human Brain Mapping 00:000–000 (2011) r
Feature-Based Attention Modulates
Direction-Selective Hemodynamic Activity
Within Human MT
Christian Michael Stoppel,1* Carsten Nicolas Boehler,2Hendrik Strumpf,1
Hans-Jochen Heinze,1,2Toemme Noesselt,1Jens-Max Hopf,1,2
and Mircea Ariel Schoenfeld1,2,3
1Department of Neurology and Centre for Advanced Imaging, Otto-von-Guericke-University,
Leipziger Str. 44, 39120 Magdeburg, Germany
2Leibniz-Institute for Neurobiology, Brennecke Str. 6, 39118 Magdeburg, Germany
3Kliniken Schmieder, Zum Tafelholz 8, 78476 Allensbach, Germany
rr
Abstract: Attending to the spatial location or to nonspatial features of a stimulus modulates neural ac-
tivity in cortical areas that process its perceptual attributes. The feature-based attentional selection of
the direction of a moving stimulus is associated with increased firing of individual neurons tuned to
the direction of the movement in area V5/MT, while responses of neurons tuned to opposite direction-
sare suppressed. However, it is not known how these multiplicatively scaled responses of individual
neurons tuned to different motion-directions are integrated at the population level, in order to facilitate
the processing of stimuli that match the perceptual goals. Using functional magnetic resonance
imaging (fMRI) the present study revealed that attending to the movement direction of a dot field
enhances the response in a number of areas including the human MT region (hMT) as a function of
the coherence of the stimulus. Attending the opposite direction, however, lead to a suppressed
response in hMT that was inversely correlated with stimulus-coherence. These findings demonstrate
that the multiplicative scaling of single-neuron responses by feature-based attention results in an
enhanced direction-selective population response within those cortical modules that processes the
physical attributes of the attended stimuli. Our results provide strong support for the validity of the
‘‘feature similarity gain model’’ on the integrated population response as quantified by parametric
fMRI in humans. Hum Brain Mapp 00:000–000, 2011.
V
C 2011 Wiley-Liss, Inc.
Keywords: feature-based attention; motion; coherence; fMRI; human MT
rr
INTRODUCTION
The detection of a moving object, indicative of the
appearance of another living and perhaps dangerous
being, is of vital importance to survival. Psychophysical
performance in motion-detection tasks has been directly
linked to the responses of direction-selective neurons
within the visual area V5/MT [Britten et al., 1992, 1996;
Newsome et al., 1989]. These neurophysiological investiga-
tions in primates revealed a nearly linear correlation
between the motion-coherence of a stimulus and the firing
Contract grant sponsor: Deutsche Forschungsgemeinschaft (DFG);
Contract grant numbers: Scho 1217/1-1, SFB 779-A1.
*Correspondence to: Christian Stoppel, Department of Neurology,
Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120
Magdeburg, Germany. E-mail: christian.stoppel@med.ovgu.de
Received for publication 2 June 2010; Accepted 7 September 2010
DOI: 10.1002/hbm.21180
Published online in Wiley Online Library (wileyonlinelibrary.com).
V
C 2011 Wiley-Liss, Inc.

Page 2

rate of individual V5/MT neurons tuned to that direction.
A similar relationship has also been observed at the popu-
lation-level in human studies using functional magnetic
resonance imaging (fMRI) and magnetoencephalography
(MEG), [Handel et al., 2007; Nakamura et al., 2003; Rees
et al., 2000; Siegel et al., 2007].
In addition to the physical characteristics of the stimuli,
the detection performance can also be markedly affected
by attention, resulting in a concurrent modulation of the
firing-rate of direction-selective neurons within area V5/
MT [Cook and Maunsell, 2002a,b, 2004]. These attentional
modulations have been shown to modify the response pro-
file of direction-selective neurons within V5/MT in a mul-
tiplicativemanner:neurons
closely matches the attended motion-direction increase
their firing rate, while the firing of neurons tuned to oppo-
site directions is suppressed [Martinez-Trujillo and Treue,
2004; Treue and Martinez Trujillo, 1999]. These findings
gave rise to the ‘‘feature-similarity gain model’’ that postu-
lates that an individual neuron’s response depends on the
feature-similarity between a current behaviorally relevant
target and the feature-preference of that neuron. This mul-
tiplicative attentional modulation at the individual-neuron
level has been proposed to result in an improved selectiv-
ity for the attended feature at the population level.
Similar feature-based attentional modulations have also
been demonstrated using fMRI in humans for moving
stimuli presented within or outside the focus of spatial
attention [O’Craven et al., 1997; Saenz et al., 2002] and
even in absence of direct visual stimulation [Chawla et al.,
1999]. However, direction-selective attentional modulations
throughout multiple stages of the human visual cortex
thus far have only been demonstrated using pattern classi-
fication methods for fMRI data analysis [Kamitani and
Tong, 2006; Serences and Boynton, 2007]. In these studies,
direction-selective information could be decoded from
multiple areas across the visual hierarchy, which, how-
ever, does not necessarily imply the existence of direction-
selective neuronal populations within all of these regions
[Serences and Boynton, 2007]. The response profile of a
given voxel within these regions also could reflect feedfor-
ward and/or feedback activations instead of true direc-
tion-selective population-activity [Sillito et al., 2006].
In the present study direction-selective attention and
stimulus-coherence were simultaneously manipulated to
investigate direction-selective population activity in the
human brain using fMRI. To this end, we parametrically
manipulated the coherence of dots within an aperture
while attention was either directed to the main direction
of the dots (left or right) or to the opposite direction. This
experimental setup permitted to test predictions from the
feature similarity-gain model [Martinez-Trujillo and Treue,
2004] at the population level in human motion-responsive
areas. The highest responses were expected when the
properties of the presented stimulus perfectly match the
attended feature, e.g., at 100% coherence in the attended
direction. When the opposite direction is attended, the
whosefeature-preference
responses were expected to correlate inversely with stimu-
lus coherence, i.e., to decrease with rising coherence.
MATERIALS AND METHODS
Subjects
Twelve healthy right-handed subjects (nine females), all
with normal or corrected-to-normal vision, participated as
paid volunteers in the study [mean age: 25.0 ? 0.8 (SEM)
years]. All participants gave informed consent, were paid
for participation and the local ethics committee of the Uni-
versity of Magdeburg approved the study.
Stimuli and Experimental Design
Stimuli were presented against a dark background (45
cd m?2) within a square region (8?? 8?) that was pre-
sented above a central fixation cross (4?to the lower edge
of the square) and centered on the vertical meridian.
Within this square, one hundred stationary white dots
(brightness 200 cd m?2) were present continuously during
the inter-trial intervals (see Fig. 1). The mean contrast of
the stimulus within the squared aperture was 30.4 cd m?2
(the stimulus contrast was quantified as the SD of local
luminance values [Moulden et al., 1990], and computed as
previously described for comparable stimulus-configura-
tions [Martinez-Trujillo and Treue, 2002]). In each trial a
certain proportion (100, 85, and 70%) of the dots moved
coherently in the same direction (either to the left or to the
Figure 1.
Schematic illustration of the experimental design. At the begin-
ning of each block an arrow indicated which direction of motion
had to be attended (left- or rightward motion). The dots
remained stationary during the inter-stimulus interval and at the
beginning of each trial moved either left- or rightwards for
300 ms. There were three alternative coherence-levels for both
motion-directions (100, 85, and 70%). On some trials, the dots
moved with a higher velocity, and subjects responded to those
in the attended direction as targets independent of the motion-
coherence of the dots.
rStoppel et al. r
r 2 r

Page 3

right) for 300 ms and thus was perceived as a transparent
surface. The motion velocity of the transparent surface
could either be slow (4?s?1) or fast (6?s?1) predefined on
a pseudo-random basis. All remaining dots were ran-
domly displaced with the same motion speed as the trans-
parent surface. The inter-trial interval varied randomly
between 1 and 7 s following a gamma function to allow
trial separation in an event-related analysis [Hinrichs
et al., 2000]. Subjects received six scanning runs of 8 min,
which consisted of 10 blocks of 20 trials, resulting in 212–
233 trials per condition. Before each block, a central cue (a
white arrow pointing to the left or right) replacing the fix-
ation cross for 2 s indicated which direction of motion the
subjects had to attend. Subjects were required to make a
speeded button-press response after detecting a fast move-
ment of the transparent surface into the attended direction.
Such fast movements (targets) occurred in 20% of the cases
while in 80% of the trials the movements were slow
(standards). Thus we were able to compare the neuronal
modulations elicited by moving transparent surfaces of
variable coherence (100, 85, and 70%) while their motion
direction was attended or opposite to the attended direction.
fMRI Data Acquisition
fMRI data acquisition was performed on a 3-Tesla MR
scanner (Siemens Magnetom Trio, Erlangen, Germany)
using an eight-channel head coil. An LCD projector back-
projected the stimuli onto a screen positioned behind the
head coil, while subjects viewed the stimuli via a mirror
attached to the coil reflecting the images displayed on the
screen. Thirty slices (thickness ¼ 4 mm, in plane resolution
64 ? 64 mm2, FoV 224 ? 224 mm2, no gap, resulting voxel
size ¼ 3.5 ? 3.5 ? 4 mm3, AC-PC oriented) were acquired
with a T2*-weighted echo planar imaging (EPI) gradient
sequence (TR ¼ 2,000 ms, TE ¼ 30 ms, flip angle ¼ 80?) in
an odd-even interleaved sequence. Each scanning session
consisted of 205 vol. In a structural session, sagittal whole-
head T1-weighted images were collected (48 slices, thick-
ness ¼ 4 mm, 64 ? 64 matrix, FoV 224 ? 224 mm2, gap ¼
0.8 mm, spatial resolution ¼ 0.9 ? 0.9 ? 4 mm3, TE ¼ 4.9
ms, TR ¼ 15,000 ms).
fMRI Data Analysis
Data analysis was performed using SPM5 software
(Wellcome Department of Cognitive Neurology, University
College London, UK) and MATLAB 7.4 (The Mathwork).
Following correction for differences in slice acquisition
time, EPI volumes were realigned to the first volume and
spatially normalized to an EPI template in standard MNI
space with sub-sampling to a resultant voxel size of 2 ? 2
? 2 mm3. The normalized images were spatially smoothed
using an 8-mm full-width at half-maximum isotropic
Gaussian kernel. Statistical analysis of the data was per-
formed employing the standard hemodynamic-response
function implemented in SPM5 in an event-related design
for each subject that additionally included the movement
parameters derived from the realignment procedure as
covariates. Contrasts of parameter estimates comparing trials
of different motion coherence levels vs. baseline were calcu-
lated for both attention conditions and the corresponding
contrast images were subsequently entered into a random
effects analysis. Stereotactic coordinates for voxels with maxi-
mal F-values within activation clusters are reported in the
MNI standard space (significance threshold at a whole-brain
corrected false discovery rate (FDR) of P < 0.01 with a mini-
mum cluster extent of k ¼ 20 contiguous voxels). For visual-
ization of the data, activation maps were superimposed on a
semitransparent surface-based representation of the MNI ca-
nonical brain using the SPM surfrend toolbox (http://
spmsurfrend.sourceforge.net)
www.neurolens.org/NeuroLens/Home.html).
For direct comparison of the magnitude of hemody-
namic modulations induced by the different conditions a
region of interest (ROI) analysis was performed using the
MarsBar toolbox in SPM5 [Brett et al., 2002]. The ROIs
were functionally defined based on the local activation
maxima given by the overall effects of interest F-contrast
of a second-level 2 ? 3 factorial ANOVA including all six
condition of interest (2 attention ? 3 motion coherence lev-
els; see Table I for the activation-maxima of the main effect
and Table II for coordinates of the ROIs), treating inter-
subject variability as a random effect to account for inter-
individual variance. For all ROIs [anterior cingulated cor-
tex (ACC), fundus of the intraparietal sulcus (fIPS), human
analogue of the middle temporal area (hMT), lateral parie-
tal cortex, superior frontal gyrus (SFG), superior parietal
lobe (SPL), thalamus, and V3a] mean beta values were
extracted from the individual subjects’ data. These data
were subjected to a repeated-measures analysis of variance
(repeated-measures ANOVA) with the factors region,
hemisphere (left vs. right), attention condition (direction
attended vs. anti-direction attended), and motion coher-
ence (100, 85, and 70%). The significance threshold was set
to P < 0.05 following Greenhouse-Geisser correction for
nonsphericity if necessary. Because no significant main
effect or interactions were observed for the factor hemi-
sphere, data were collapsed over both hemispheres for
analysis of each individual ROI. The data for each ROI
were separately subjected to repeated measures ANOVAs
with the factors attention condition and motion coherence.
and NeuroLens (http://
RESULTS
Behavioral Results
Mean reaction times (mean ? standard error of the
mean (SEM): 701 ? 46 ms) and the percentage of correct
responses (mean ? SEM: 73.1% ? 6.3%) were separately
submitted to a repeated-measures ANOVA with the factor
motion coherence (100, 85, and 70% coherence). These
analyses revealed a significant main effect of motion co-
herence on the hit rate (F(2,22) ¼ 8.7, P < 0.005) but not
rDirection-Selective Attentional Modulations r
r 3 r

Page 4

on the reaction times (F(2,22) ¼ 1.6, P > 0.2), consistent
with a speed-accuracy trade-off under increased percep-
tual demands (low coherence-levels). The main effect of
motion coherence on the percentage of correct responses
resulted from a significantly higher hit rate on full coherent
stimuli in comparison to 70% coherent motion (P < 0.01)
and an almost significantly higher hit rate on 85% coherent
stimuli in comparison to 70% coherent motion (P ¼ 0.07).
TABLE II. MNI-coordinates of the regions of interest (ROIs)
MNI coordinates (left hemisphere)MNI coordinates (right hemisphere)
xyzxyz
ACC
fIPS
hMT
Lateral parietal
SFG
SPL
Thalamus
V3a
?10? 4
?24 ? 4
?43 ? 5
?46 ? 4
?14 ? 4
?22 ? 4
?10 ? 4
?20 ? 4
40 ? 4
?70 ? 6
?73 ? 5
?66 ? 4
36 ? 4
?38 ? 4
?15 ? 7
?82 ? 4
32 ? 4
39 ? 5
21 ? 5
52 ? 4
52 ? 4
70 ? 4
3 ? 5
32 ? 4
4 ? 4
27 ? 5
45 ? 5
46 ? 4
16 ? 4
22 ? 4
15 ? 3
12 ? 4
38 ? 4
?72 ? 6
?71 ? 7
?62 ? 4
22 ? 4
?40 ? 4
?15 ? 3
?92 ? 4
38 ? 4
38 ? 6
15 ? 5
42 ? 4
60 ? 4
72 ? 4
2 ? 4
24 ? 4
Abbreviations: ACC, anterior cingulated cortex; fIPS, fundus of the intraparietal sulcus; SFG, superior frontal gyrus; SPL, superior parie-
tal lobe; hMT, human analogue of the middle temporal area.
TABLE I. Peak activation foci to motion-stimuli in the group random-effects analysis
Anatomical structurecluster-size (voxels) FDR-corrected P-value HemisphereMaximum F-value
MNI coordinates
(x,y,z)
ACC143
274
213
231
96
185
148
173
476
498
36
215
512
514
229
283
178
102
265
156
71
229
311
143
397
240
158
439
417
<0.01
<0.01
<0.001
<0.001
<0.005
<0.005
<0.001
<0.001
<0.001
<0.001
<0.005
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.01
<0.001
<0.005
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
L
R
L
R
L
R
L
R
L
R
L
R
L
R
L
L
R
L
R
L
R
L
R
L
R
L
R
L
R
15.13
18.10
32.20
36.66
22.86
22.89
38.60
27.12
92.21
95.33
24.64
32.14
261.33
437.43
38.24
28.55
29.78
17.45
34.60
24.43
26.56
35.61
56.35
38.75
84.34
32.93
39.86
73.46
62.74
?10 40
38
32
38
2
4
30
30
58
54
?4
?2
42
42
22
18
10
54
40
52
60
44
52
26
28
70
72
4
Cuneus
?10
16
?42
42
?48
36
?44
34
?24
26
?42
42
?48
?38
52
?14
16
?10
?78
?76
10 Dorsolateral PFC
4
FEF
?4
?4
?70
?76
?70
?72
?74
?68
40
?66
?70
36
22
12
14
?30
?26
?38
?40
?14
?14
?90
?90
FG
fIPS
hMT
Inferior frontal gyrus
Lateral parietal cortex
SFG
SMA
8
SMG
?54
50
?22
22
?10
16
?10
10
SPL
Thalamus2
6
V3a30
26
FDR-corrected cluster P-value <0.01; extent threshold k ¼ 25; distance for main submaxima > 16 mm.
Abbreviations: ACC, anterior cingulate cortex; FEF, frontal eye field; FG, fusiform gyrus; fIPS, fundus of the intraparietal sulcus; PFC,
prefrontal cortex; SFG, superior frontal gyrus; SMA, supplementary motor area; SMG, supramarginal gyrus; SPL, superior parietal lobe;
hMT, human analogue of the middle temporal area.
rStoppel et al. r
r 4 r

Page 5

fMRI Results
The effects of interest contrast from the 2 ? 3 factorial
ANOVA group analysis identified clusters of significant
attention and/orcoherency-dependent
fronto-parietal [anterior cingulated cortex (ACC), frontal
eye field (FEF), lateral parietal cortex, superior parietal
lobe (SPL), superior frontal gyrus (SFG), and supplemen-
tary motor area (SMA)], extrastriate visual [fusiform gyrus
(FG), human analogue of the middle temporal area (hMT),
fundus of the intraparietal sulcus (fIPS), and V3a] and tha-
lamic regions (see Fig. 2 and Table I for MNI coordinates
and F-values). To directly assess the influence of feature-
based attention on the magnitude of neural modulations
induced by stimuli of varying signal-to-noise characteris-
tics (different coherence-levels), a region of interest (ROI)
analysis was performed within fronto-parietal and extras-
triate regions identified by the group analysis (see Table II
for the corresponding MNI coordinates of the ROIs).
activations in
Region of Interest Analyses
The ROI-data were analyzed by a repeated measures
ANOVA with the factors region (ACC, fIPS, lateral parietal
cortex, SFG, SPL, thalamus, V3a, hMT), hemisphere (left
vs. right), coherence (100, 85, and 70% coherence) and
attention condition (direction attended vs. anti-direction
attended). This analysis showed significant main effects
for the factors region (F(7,77) ¼ 66.9, P < 0.001) and atten-
tion (F(1,11) ¼ 6.1, P < 0.05), as well as a significant three-
way (region ? attention ? coherence) interaction between
factors (F(6,66) ¼ 4.9, P < 0.005). Because neither a signifi-
cant main effect, nor a significant interaction could be
observed for the factor hemisphere, data were collapsed
over hemispheres before further analysis. For direct com-
parison of attention- and coherence-dependent effects the
data for each ROI were separately subjected to repeated
measures ANOVA with the factors attention condition and
motion coherence.
These analyses revealed remarkable differences in the
activation pattern between lower-tier regions of the visual
cortex (fIPS, thalamus, V3a, and hMT; see Fig. 2) and
higher-tier attentional control structures (ACC, lateral pari-
etal cortex, SPL, and SFG; see Fig. 3). The intraparietal and
thalamic ROIs showed a nearly linear relationship between
the magnitude of the hemodynamic response and the co-
herence of the moving transparent surface. This was
reflected by a significant main effect for the factor motion
coherence (F(2,22) ¼ 7.2, P < 0.005 for the fIPS and F(2,22)
¼ 10.1, P < 0.001 for the thalamus) in absence of a main
effect of attention or an interaction between both factors.
For V3a the analysis of the ROI-data showed no significant
main effects or interactions for the factors attention and
motion coherence. In contrast, hMT showed a significant
main effect for the factor attention (F(1,11) ¼ 28.9, P <
0.001) and a significant attention x motion coherence inter-
action (F(2,22) ¼ 5.5, P < 0.05), which was due to an oppo-
site near-linear coherence-dependency for the attended
and unattended motion direction, respectively. When the
direction of the moving transparent surface was attended,
the hemodynamic modulation in hMT showed a positive
linear relationship with motion coherence, while it was
inversely correlated with the coherence of the stimuli
when their motion direction had to be ignored (see Fig.
2B).
Analyses of the ROI-data from frontal and parietal atten-
tional control regions revealed an entirely different pat-
tern: the SPL showed main effects of attention (F(1,11) ¼
16.3, P < 0.005) and motion coherence (F(2,22) ¼ 16.5, P <
0.001) but no attention x motion coherence interaction. The
attentional main effect was due to higher modulations to
unattended than attended stimulus motion, whereas the
main effect of motion coherence was reflected by an
inverse linear dependency of the modulation magnitude
on the coherence of the stimuli irrespective of attention. In
contrast, the ACC, the SFG and the lateral parietal ROIs
showed no main effects for the factors attention and
motion coherence but a significant interaction between
both factors (F(2,22) ¼ 24.1, P < 0.001 for the ACC; F(2,22)
¼ 9.3 P < 0.001 for the SFG and F(2,22) ¼ 4.5, P < 0.05 for
the lateral parietal cortex). The hemodynamic modulations
within these regions were opposed to the pattern observed
for area hMT: when the direction was attended, the high-
est modulations occurred for the least coherent stimuli,
while for stimuli moving opposed to the attended direc-
tion the magnitude of the modulation showed a positive
linear relationship with stimulus-coherence (see Fig. 3B).
The pattern observed in fronto-parietal areas is consistent
with previous reports demonstrating higher activation
magnitudes within attentional control regions [Culham
et al., 2001; Jovicich et al., 2001; Lavie, 2005] under condi-
tions of increased perceptual demands (e.g., low coher-
ence-levels).
DISCUSSION
In the present study we manipulated the direction and
the coherence of moving dots within a squared aperture
under two different attention conditions, in which either
the direction or the opposite direction of the stimulus was
attended. This approach permitted us to investigate activ-
ity changes in motion responsive regions as a function of
attention and motion coherence under identical physical
stimulus properties. We found the hemodynamic activity
within area hMT to be strongly and specifically modulated
by the attended direction of motion and the coherence of
the stimulus. Similar to the behavioral performance, the
activity in hMT was positively correlated with the stimu-
lus coherence when its direction was attended. Impor-
tantly, when the opposite direction was attended we
observed a relative suppression of the response, with a
reversed relationship in which the hMT activation was
inversely correlated with stimulus coherence. Strikingly,
rDirection-Selective Attentional Modulations r
r 5 r

Page 6

Figure 2.
(A) Group random-effects analysis showing regions more active
during (non-target) motion-trials than during presentation of sta-
tionary dots. The activation map displays activations from the
effects of interest F-contrast from the 2 ? 3 ANOVA group
analysis. The threshold for significance was set at a (corrected)
family-wise error level of P < 0.05. (B) Attentional modulation
of neural activations to visual motion coherence within extras-
triate and thalamic regions. The mean beta values to each level
of motion-coherence are depicted for both attention conditions.
Activity profiles are averaged over all subjects (n ¼ 12) and
both hemispheres for each ROI. Note that in contrast to all
other regions, hMT displays an inverse linear relationship
between motion-coherence and the magnitude of the signal esti-
mate for attended and unattended conditions. Abbreviations:
fIPS, fundus of the intraparietal sulcus; hMT, human analogue of
the middle temporal area.
Figure 3.
(A) Group random-effects analysis showing regions more active
during presentation of attended incoherent (70% coherence)
than attended coherent (100% coherence) motion-trials. The
threshold for significance was set at a P < 0.001 (uncorrected).
(B) Attentional modulation of neural activations to visual motion
coherence within frontal and parietal attentional control struc-
tures. The mean beta values to each level of motion-coherence
are depicted for both attention conditions. Activity profiles are
averaged over all subjects (n ¼ 12) and both hemispheres for
each ROI. Abbreviations: ACC, anterior cingulated cortex; SFG,
superior frontal gyrus; SPL, superior parietal lobe.

Page 7

out of all activated areas, hMT was the only to exhibit
such a specific pattern of responses.
In agreement with neurophysiological investigations in
primates, previous fMRI studies in humans have also dem-
onstrated feature-based attentional modulations in hMT.
These modulations were observed when a moving transpar-
ent surface was attended as opposed to an overlapping sta-
tionary stimulus [O’Craven et al., 1997], or even in absence
of direct visual stimulation [Chawla et al., 1999]. Further-
more, feature-based attention can spread to moving stimuli
outside the focus of spatial attention if they match the
attended feature [Saenz et al., 2002]. These neuroimaging
studies have repeatedly demonstrated attention-related
changes of activity in area hMT; nevertheless none of the
studies specifically investigated attentional modulations as a
function of individual changes within a single feature dimen-
sion. To date, with the exception of two studies that
employed classifiers [Kamitani and Tong, 2006; Serences and
Boynton, 2007], fMRI studies have failed to show direction
selectivity, a hallmark of MT neurons in neurophysiological
measurements. The most plausible explanation is that the
native responses of hMT neurons to different motion-direc-
tions are too small in view of the spatial and temporal resolu-
tion of the employed methods. The two recent fMRI studies
that used pattern-classification algorithms could show that
attention influences direction-selective activity within multi-
ple stages of the visual cortex [Kamitani and Tong, 2006;
Serences and Boynton, 2007]. The interpretation of these
results, however, requires some caution because the neural
processes underlying classification accuracy are not entirely
understood [Bartels et al., 2008]. It has to be kept in mind
that although direction-selective information could be
decoded from multiple stages throughout the visual hierar-
chy, the results do not necessarily imply the existence of
direction-selective neuronal populations within all of these
visual areas [Serences and Boynton, 2007]. The response pro-
file across a population within a given voxel could also
reflect feedback activity from higher order visual areas [Sil-
lito et al., 2006] instead of true direction-selective population-
responses within a particular region.
The results of the present study are consistent with cur-
rent theories on feature-based attention like the ‘‘feature-
similarity gain’’ [Martinez-Trujillo and Treue, 2004] model.
This model posits that attention modulates an individual
neuron’s response according to the similarity between a
currently attended feature and the feature-preference of
that neuron. This multiplicative attentional modulation has
in fact been demonstrated using single-cell recordings in
non-human primates [Martinez-Trujillo and Treue, 2004;
Treue and Martinez Trujillo, 1999] and may as recently
shown vary in dependence of the contrast of the presented
stimuli [Khayat et al., 2010a]. It is important to note that
such feature-based attentional modulations in the motion-
domain have recently been also demonstrated using
recordings of LFP power in the c-band [Khayat et al.,
2010b], suggestive of an improved selectivity for the
attended feature also at the population-level. Our results
strongly support this notion by demonstrating that feature-
basedattentionenhances direction-selective
within cortical area hMT as assessed by fMRI. Notably,
responses to stimuli in the attended direction are enhanced
while those to stimuli in the opposite direction are sup-
pressed (see Fig. 2). In this way the difference between a
stimulus in the attended and a stimulus in the opposite
direction is much higher than the native hMT responses to
such stimuli when no direction is specifically attended. As
a consequence of the multiplicative attentional modulation
the direction selectivity in hMT becomes observable not
only with pattern classifiers but also with classical analysis
approaches. Moreover our results emphasize the notion
that the integration of these direction-selective responses
(multiplicatively scaled by feature-based attention) occurs
in cortical area hMT in dependence of the signal-to-noise
characteristics of the presented stimuli, thereby enhancing
their neural representations according to the current per-
ceptual goal of the observer.
In contrast to hMT, the area V3a displayed a robust
response to the stimulation that did not vary much as a
function of attention or stimulus coherence. This at first
glance surprising result is most likely due to the task of
attending a specific motion direction and to the less preva-
lent direction sensitivity of this area [Galletti et al., 1990;
Gaska et al., 1988; Vanduffel et al., 2001; Zeki, 1978]. Most of
the studies that found strong attentional modulations in
area V3a have either employed spatial attention [Tootell
et al., 1997] or directed attention to global motion [Buchel
et al., 1998; Chawla et al., 1999]. An earlier study did also
report coherence-dependent modulations in V3a [Rees et al.,
2000]. However, the coherence-dependency reported there
followed a nonlinear U-shaped function, which for high co-
herence-levels was only determined by virtue of two meas-
uring points at 100 and 50% coherence. The stimulus
material employed in the current study only encompassed a
range of 70–100% motion coherence. Therefore, it is most
likely that the previously observed second-order coherence-
dependency is more pronounced at lower coherence levels.
The previous studies that used full coherent stimuli are well
in line with the current results. Directing feature-based
attention to a specific motion direction under full coherence
conditions elicits either small [Stoppel et al., 2007] or no
modulations at all in area V3a [Schoenfeld et al., 2003, 2007].
We also observed coherence-dependent activations of
the fIPS and the thalamus that did not vary as a function
of attention. This does not necessarily mean that these
areas are not at all under the influence of feature-based
attention. It is rather plausible that attention-related modu-
lations were relatively small, and therefore not detected by
our analysis. Importantly, the activity in these regions var-
ied as a function of stimulus coherence with higher values
for higher coherence levels, indexing the involvement of
these areas in the qualitative processing of motion. In con-
trast to the activation pattern observed in hMT, hemody-
namic activity in the fIPS and thalamus was positively
correlated with motion-coherence for movements both into
responses
rDirection-Selective Attentional Modulations r
r 7 r

Page 8

the attended and the unattended direction. Previous fMRI
studies have shown that moving stimuli robustly activate
the IPS [Liu et al., 2003; Shulman et al., 1999], and a recent
single-unit study demonstrated that the majority of IPS
neurons display direction-selective tuning [Fanini and
Assad, 2009]. Visual thalamic nuclei also encode direction-
selective information [Casanova et al., 2001; Kastner et al.,
2006] and integrate the global motion-direction of trans-
parent surfaces composed of incoherently moving ele-
ments[Dumbrava etal.,
direction-selectivity both regions are modulated by spatial
attention [Corbetta and Shulman, 2002; Kastner et al.,
2006; Stoner et al., 2005; Van Essen, 2005], but only sparse
evidence suggests an involvement of these regions in fea-
ture-based attentional selection [Liu et al., 2003; Schen-
kluhn et al., 2008; Vanduffel et al., 2000]. Our present data
are in line with previous results, by showing that hemody-
namic activity within the fIPS and the thalamus depend
on the coherence of the motion signal, without being
strongly modulated when attention is directed to an indi-
vidual aspect of the feature motion such as its direction.
Numerous studies have revealed a positive linear corre-
lation between motion coherence and the magnitude of
evoked neural signals within extrastriate visual areas as
assessed by single-cell electrophysiology in monkeys [Brit-
ten et al., 1992; Newsome et al., 1989; Shadlen et al., 1996]
as well as MEG [Aspell et al., 2005; Handel et al., 2007,
2008; Siegel et al., 2007] and fMRI [Rees et al., 2000] in
humans. The present data are fully consistent with these
findings, by showing a linear relationship between the
strength of the visual motion signal and hemodynamic
modulations within the fIPS, hMT and—beyond previous
reports—in bilateral thalamic regions. Importantly, the
modulations of fIPS and the thalamus activity as a func-
tion of coherence were independent of the attended direc-
tion of motion. In contrast, the activity in area hMT was
highly dependent on the attended direction of motion.
While activity to attended stimuli was positively correlated
with motion coherence, the inverse pattern (a negative cor-
relation) was observed for stimuli moving into the unat-
tended direction. This is a perfect match with the
predictions from the feature-similarity gain model, sug-
gesting neural population-responses to reflect changes in
the signal-to-noise characteristics of the stimuli [Martinez-
Trujillo and Treue, 2004]. In addition to the modulations
within extrastriate visual cortex, activations were also
observed within attentional control structures including
the anterior cingulate, superior frontal, superior parietal
and the lateral parietal cortex (see Fig. 3B). These regions
exhibited an activation pattern different from the one
observed within area hMT: hemodynamic activations cor-
relatednegativelywithmotion-coherency
motion-direction of the stimulus was attended (all fronto-
parietal regions depicted in Fig. 3B) and the ACC also
showed a positive correlation with motion-coherence
when the stimuli moved into the opposite direction (see
Fig. 3B). From a signal detection point of view stimuli of
2001].Beyondthisrough
when the
lower coherence contain noise resulting in a higher ambi-
guity therefore requiring more attention to identify the
prominent direction of movement. Within this framework,
the patterns observed in superior frontal, superior parietal
and lateral parietal cortex fit well to earlier observations that
activity within attentional control structures varies as a func-
tion of the attentional requirements of the task [Culham et al.,
2001; Jovicich et al., 2001; Lavie, 2005]. Thereby endogenous
signals about the subjects’ current goals (e.g., the attended
motion-direction) are complemented with information about
the current stimulus contingencies to provide optimal top-
down signals to bias the processing of appropriate stimulus
features and locations in early visual regions [Corbetta et al.,
2008]. Thus, increased processing resources (e.g., through
modulation of superior frontal, superior parietal, and lateral
parietal activity) are recruited when stimulus-ambiguity is
high. However, the pattern of hemodynamic activity within
the ACC only is compatible with this notion if the motion-
direction of the stimulus was attended, while it displayed an
inverse relationship when the stimulus moved into the oppo-
site direction (see Fig. 3B). It has to be kept in mind that the
appearance of an opposite movement when attending a cer-
tain movement direction represents a perceptual conflict. The
observed pattern of activation within the ACC is consistent
with its role in the detection of perceptual conflicts [Weiss-
man et al., 2003, 2005; Zimmer et al., 2010] and provides evi-
dence for the interplay between higher-tier regions and
perceptual lower-tier regions during top-down attention.
In conclusion, the present results demonstrate that fea-
ture-basedattentionmodulates
within hMT in a direction-selective manner. These atten-
tional modulations and the corresponding behavioral per-
formance were positively correlated with the coherence of
the motion signal, whereas activity within the fIPS and the
thalamus occurred irrespective of feature-based attention.
In contrast, attentional control regions displayed an activa-
tion pattern opposed to the one observed within hMT,
matching the predictions drawn from a signal-detection
theory perspective. These results provide strong support
for models of feature-based attention [Martinez-Trujillo
and Treue, 2004], suggesting that attention improves be-
havioral performance by modulation of direction-selective
population-activity within cortical area hMT.
hemodynamicactivity
ACKNOWLEDGMENT
The authors thank Dr. Michael Scholz for technical
advice.
REFERENCES
Aspell JE, Tanskanen T, Hurlbert AC (2005): Neuromagnetic
correlates of visual motion coherence. Eur J Neurosci 22:
2937–2945.
Bartels A, Logothetis NK, Moutoussis K (2008): fMRI and its inter-
pretations: An illustration on directional selectivity in area V5/
MT. Trends Neurosci 31:444–453.
rStoppel et al. r
r 8 r

Page 9

Brett M, Anton J-L, Valabregue R, Poline J-B (2002): Region of in-
terest analysis using an SPM toolbox. 8th International Confer-
ence on Functional Mapping of the Human Brain. Sendai,
Japan.
Britten KH, Shadlen MN, Newsome WT, Movshon JA (1992): The
analysis of visual motion: A comparison of neuronal and psy-
chophysical performance. J Neurosci 12:4745–4765.
Britten KH, Newsome WT, Shadlen MN, Celebrini S, Movshon JA
(1996): A relationship between behavioral choice and the visual
responses of neurons in macaque MT. Vis Neurosci 13:87–100.
Buchel C, Josephs O, Rees G, Turner R, Frith CD, Friston KJ
(1998): The functional anatomy of attention to visual motion. A
functional MRI study. Brain 121 (Part 7):1281–1294.
Casanova C, Merabet L, Desautels A, Minville K (2001): Higher-
order motion processing in the pulvinar. Prog Brain Res
134:71–82.
Chawla D, Rees G, Friston KJ (1999): The physiological basis of
attentional modulation in extrastriate visual areas. Nat Neuro-
sci 2:671–676.
Cook EP, Maunsell JH (2002a): Attentional modulation of behav-
ioral performance and neuronal responses in middle temporal
and ventral intraparietal areas of macaque monkey. J Neurosci
22:1994–2004.
Cook EP, Maunsell JH (2002b): Dynamics of neuronal responses
in macaque MT and VIP during motion detection. Nat Neuro-
sci 5:985–994.
Cook EP, Maunsell JH (2004): Attentional modulation of motion
integration of individual neurons in the middle temporal vis-
ual area. J Neurosci 24:7964–7977.
Corbetta M, Shulman GL (2002): Control of goal-directed and
stimulus-driven attention in the brain. Nat Rev Neurosci
3:201–215.
Corbetta M, Patel G, Shulman GL (2008): The reorienting system
of the human brain: From environment to theory of mind.
Neuron 58:306–324.
Culham JC, Cavanagh P,Kanwisher NG (2001): Attention
response functions: Characterizing brain areas using fMRI acti-
vation during parametric variations of attentional load. Neu-
ron 32:737–745.
Dumbrava D, Faubert J, Casanova C (2001): Global motion inte-
gration in the cat’s lateral posterior-pulvinar complex. Eur J
Neurosci 13:2218–2226.
Fanini A, Assad JA (2009): Direction selectivity of neurons in the
macaque lateral intraparietal area. J Neurophysiol 101:289–305.
Galletti C, Battaglini PP, Fattori P (1990): ‘‘Real-motion’’ cells in
area V3A of macaque visual cortex. Exp Brain Res 82:67–76.
Gaska JP, Jacobson LD, Pollen DA (1988): Spatial and temporal
frequency selectivity of neurons in visual cortical area V3A of
the macaque monkey. Vis Res 28:1179–1191.
Handel B, Lutzenberger W, Thier P, Haarmeier T (2007): Opposite
dependencies on visual motion coherence in human area MT
and early visual cortex. Cereb Cortex 17:1542–1549.
Handel B, Lutzenberger W, Thier P, Haarmeier T (2008): Selective
attention increases the dependency of cortical responses on
visual motion coherence in man. Cereb Cortex 18:2902–2908.
Hinrichs H, Scholz M, Tempelmann C, Woldorff MG, Dale AM,
Heinze HJ (2000):Deconvolution
responses in fast-rate experimental designs: Tracking ampli-
tude variations. J Cogn Neurosci 12 (Suppl 2):76–89.
Jovicich J, Peters RJ, Koch C, Braun J, Chang L, Ernst T (2001):
Brain areas specific for attentional load in a motion-tracking
task. J Cogn Neurosci 13:1048–1058.
ofevent-relatedfMRI
Kamitani Y, Tong F (2006): Decoding seen and attended motion
directions from activity in the human visual cortex. Curr Biol
16:1096–1102.
Kastner S, Schneider KA, Wunderlich K (2006): Beyond a relay
nucleus: Neuroimaging views on the human LGN. Prog Brain
Res 155:125–143.
Khayat PS, Niebergall R, Martinez-Trujillo JC (2010a): Attention
differentially modulates similar neuronal responses evoked by
varying contrast and direction stimuli in area MT. J Neurosci
30:2188–2197.
Khayat PS, Niebergall R, Martinez-Trujillo JC (2010b): Frequency-
dependent attentional modulation of local field potential sig-
nals in macaque area MT. J Neurosci 30:7037–7048.
Lavie N (2005): Distracted and confused?: Selective attention
under load. Trends Cogn Sci 9:75–82.
Liu T, Slotnick SD, Serences JT, Yantis S (2003): Cortical mechanisms
of feature-based attentional control. Cereb Cortex 13:1334–1343.
Martinez-Trujillo J,TreueS
strength in cortical area MT depends on stimulus contrast.
Neuron 35:365–370.
Martinez-Trujillo JC, Treue S (2004): Feature-based attention
increases the selectivity of population responses in primate vis-
ual cortex. Curr Biol 14:744–751.
Moulden B, Kingdom F, Gatley LF (1990): The standard deviation
of luminance as a metric for contrast in random-dot images.
Perception 19:79–101.
Nakamura H, Kashii S, Nagamine T, Matsui Y, Hashimoto T,
Honda Y, Shibasaki H (2003): Human V5 demonstrated by
magnetoencephalography using random dot kinematograms of
different coherence levels. Neurosci Res 46:423–433.
Newsome WT, Britten KH, Movshon JA (1989): Neuronal corre-
lates of a perceptual decision. Nature 341:52–54.
O’Craven KM, Rosen BR, Kwong KK, Treisman A, Savoy RL
(1997): Voluntary attention modulates fMRI activity in human
MT-MST. Neuron 18:591–598.
Rees G, Friston KJ, Koch C (2000): A direct quantitative relation-
ship between the functional properties of human and macaque
V5. Nat Neurosci 3:716–723.
Saenz M, Buracas GT, Boynton GM (2002): Global effects of fea-
ture-based attention in human visual cortex. Nat Neurosci
5:631–632.
Schenkluhn B, Ruff CC, Heinen K, Chambers CD (2008): Parietal
stimulation decouples spatial and feature-based attention. J
Neurosci 28:11106–11110.
Schoenfeld MA, Tempelmann C, Martinez A, Hopf JM, Sattler C,
Heinze HJ, Hillyard SA (2003): Dynamics of feature binding
during object-selective attention. Proc Natl Acad Sci USA
100:11806–11811.
Schoenfeld MA, Hopf JM, Martinez A, Mai HM, Sattler C, Gasde
A, Heinze HJ, Hillyard SA (2007): Spatio-temporal analysis of
feature-based attention. Cereb Cortex 17:2468–2477.
Serences JT, Boynton GM (2007): Feature-based attentional modu-
lations in the absence of direct visual stimulation. Neuron
55:301–312.
Shadlen MN, Britten KH, Newsome WT, Movshon JA (1996): A
computational analysis of the relationship between neuronal
and behavioral responses to visual motion. J Neurosci 16:1486–
1510.
Shulman GL, Ollinger JM, Akbudak E, Conturo TE, Snyder AZ,
Petersen SE, Corbetta M (1999): Areas involved in encoding
and applying directional expectations to moving objects. J
Neurosci 19:9480–9496.
(2002):Attentionalmodulation
rDirection-Selective Attentional Modulations r
r 9 r

Page 10

Siegel M, Donner TH, Oostenveld R, Fries P, Engel AK (2007):
High-frequency activity in human visual cortex is modulated
by visual motion strength. Cereb Cortex 17:732–741.
Sillito AM, Cudeiro J, Jones HE (2006): Always returning: Feed-
back and sensory processing in visual cortex and thalamus.
Trends Neurosci 29:307–316.
Stoner GR, Mitchell JF, Fallah M, Reynolds JH (2005): Interacting
competitive selection in attention and binocular rivalry. Prog
Brain Res 149:227–234.
Stoppel CM, Boehler CN, Sabelhaus C, Heinze HJ, Hopf JM, Schoen-
feld MA (2007): Neural mechanisms of spatial- and feature-
based attention: A quantitative analysis. Brain Res 1181:51–60.
Tootell RB, Mendola JD, Hadjikhani NK, Ledden PJ, Liu AK, Reppas
JB, Sereno MI, Dale AM (1997): Functional analysis of V3A and
related areas in human visual cortex. J Neurosci 17:7060–7078.
Treue S, Martinez Trujillo JC (1999): Feature-based attention influ-
ences motion processing gain in macaque visual cortex. Nature
399:575–579.
Van Essen DC (2005): Corticocortical and thalamocortical informa-
tion flow in the primate visual system. Prog Brain Res
149:173–185.
Vanduffel W, Tootell RB, Orban GA (2000): Attention-dependent
suppression of metabolic activity in the early stages of the
macaque visual system. Cereb Cortex 10:109–126.
Vanduffel W, Fize D, Mandeville JB, Nelissen K, Van Hecke P,
Rosen BR, Tootell RB, Orban GA (2001): Visual motion proc-
essing investigated using contrast agent-enhanced fMRI in
awake behaving monkeys. Neuron 32:565–577.
Weissman DH, Giesbrecht B, Song AW, Mangun GR, Woldorff
MG (2003): Conflict monitoring in the human anterior cingu-
late cortex during selective attention to global and local object
features. Neuroimage 19:1361–1368.
Weissman DH, Gopalakrishnan A, Hazlett CJ, Woldorff MG
(2005): Dorsal anterior cingulate cortex resolves conflict from
distracting stimuli by boosting attention toward relevant
events. Cereb Cortex 15:229–237.
Zeki SM (1978): The third visual complex of rhesus monkey pres-
triate cortex. J Physiol 277:245–272.
Zimmer U, RobertsKC, Harshbarger
(2010): Multisensory conflict modulates the spread of vis-
ual attention across a multisensory object. Neuroimage
52:606–616.
TB,Woldorff MG
rStoppel et al. r
r 10 r