1 Functional parcellation of visual cortex in humans and monkeys Reza Rajimehr 1 *, Simon Kornblith 2,3 , Doris Y. Tsao 4 , Robert Desimone 1 1 McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 2 Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 3 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 4 Department of Computation & Neural Systems, California Institute of Technology (Caltech), Pasadena, CA * Corresponding author: Reza Rajimehr McGovern Institute for Brain Research Massachusetts Institute of Technology (MIT) 43 Vassar St., Building 46, Room 5127 Cambridge, MA 02139 Phone: 617-324-5530 Fax: 617-324-6875 Email: [email protected], [email protected]Running title: Functional parcellation of visual cortex Number of main figures: 8 Number of supplementary figures: 18 Number of tables: 1 Number of equations: 1
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1
Functional parcellation of visual cortex in humans and monkeys
Reza Rajimehr1*, Simon Kornblith2,3, Doris Y. Tsao4, Robert Desimone1
1 McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA
2 Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge,
MA
3 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge,
MA
4 Department of Computation & Neural Systems, California Institute of Technology (Caltech), Pasadena,
hemodynamic response function (in humans) or a gamma hemodynamic response function (in
monkeys). The parameters related to subject’s head motion were also included in the GLM, so that they
were regressed out. The average beta weights were calculated for each stimulus condition. Vertex-wise
statistical tests were conducted for a contrast of two conditions, and thresholded statistical maps were
obtained.
For clustering, we used an agglomerative hierarchical clustering algorithm. The squared Euclidean
distance was used as a distance metric, and the Ward’s method was used for linkage. The similarity
between two hierarchical clusterings (clustering similarity – CS) was determined based on Fowlkes–
Mallows index (Fowlkes and Mallows, 1983):
k
j
k
i
ji
k
i
k
j
ji
k
i
k
j
ji
k
nmnm
nm
CS
1
2
1
,
1
2
1
,
1 1
2
,
where n is the number of objects in the two clusterings, k is the number of clusters, and mi,j is the
number of objects in common between the ith cluster in one clustering and the jth cluster in the other
clustering. CS can be calculated for every k, and it ranges between 0 and 1. A higher value for CS means
that the two clusterings are systematically related to each other. The mathematical procedure for
calculating the analytic chance, where the two clusterings are assumed to be completely unrelated, has
been described in Fowlkes and Mallows, 1983.
To find the preferred and non-preferred movie frames of a cluster, we first obtained the mean time-
course across vertices of the cluster. The mean time-course was z-normalized, and its significant peaks
were detected. The resulting time-points were sorted based on the magnitude of response. We then
reconstructed short (2-second) movie segments composed of the frames that led to the highest and
lowest responses, assuming a standard hemodynamic lag of 5 seconds for BOLD response (Menon et al.,
1995; Boynton et al., 1996) and 4 seconds for MION response (Vanduffel et al., 2001). In the figures, the
middle frames from the selected movie segments are shown. These preferred (or non-preferred) images
25
are ordered consecutively first from left to right in a row then from top to bottom – where the top-left
image elicited the highest (or lowest) response.
Acknowledgements:
We would like to thank Nicole Schweers for help in collecting monkey data, Christina Triantafyllou for
setting up the pulse sequences in the human scanning, Doug Greve, Sheeba Arnold, Shahin Nasr,
Maryam Vaziri Pashkam, Elias Issa, Michael Arcaro, Jonathan Polimeni, Alexander Kell, Elahe’ Yargholi,
Donna Dierker, Kaitlin Bohon, Christoph Witzel, Karthik Srinivasan for help in data analysis and providing
scripts/datasets, and Roger Tootell, Nancy Kanwisher, Jim DiCarlo, Bevil Conway for helpful comments.
This research was supported by McGovern Institute for Brain Research, Martinos Imaging Center at MIT,
and NIH grant R01 EY019702 to D.T. The authors declare no competing financial interests.
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Figure Captions:
Figure 1. Structural data, functional data, and PCA analysis
A, Structural data were used to reconstruct the cortical surface in each hemisphere and make flattened
patches. The red-green overlay shows major sulci-gyri of cortex, respectively. Sulcal abbreviations: CaS,
Supplementary Figure 17. Comparison between human and monkey parcellation maps
A, The correlation matrix demonstrating the functional correspondence between clusters in map25 (X-
axis) and clusters in map25m (Y-axis). For each cluster, the mean time-course of response was computed
by averaging time-courses of all vertices in that cluster. Then, the temporal correlation was calculated
for all pairs of human-monkey clusters. The black circles indicate pairs with high correlation values. In
the colorbar, positive correlations are yellow -> red, and negative correlations are light blue -> dark blue.
B, The significance matrix at the threshold of Bonferroni-corrected p-value (p < 0.05/625; p < 0.00008).
The significant pairs are shown black. C, Example clusters in human map25 and their corresponding
clusters in monkey map25m. The clusters are displayed on the flat patches. Intra-species correlation
matrices (CMs) are shown in the rightmost panels (human: top panel, monkey: bottom panel).
Supplementary Figure 18. Data-driven framework for functional parcellation
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geniculate nucleus of macaque. The Journal of physiology 357, 241-265. 2. Engel, S.A., Glover, G.H., and Wandell, B.A. (1997). Retinotopic organization in human visual
cortex and the spatial precision of functional MRI. Cerebral cortex 7, 181-192. 3. Hasler, D., and Susstrunk, S.E. (2003). Measuring colorfulness in natural images. Proc. SPIE 5007,
Human Vision and Electronic Imaging VIII, 87-95. 4. Julian, J.B., Fedorenko, E., Webster, J., and Kanwisher, N. (2012). An algorithmic method for
functionally defining regions of interest in the ventral visual pathway. NeuroImage 60, 2357-2364.
5. Murphey, D.K., Yoshor, D., and Beauchamp, M.S. (2008). Perception matches selectivity in the human anterior color center. Current biology : CB 18, 216-220.
6. Weiner, K.S., and Grill-Spector, K. (2013). Neural representations of faces and limbs neighbor in human high-level visual cortex: evidence for a new organization principle. Psychological research 77, 74-97.
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V2dV3d
V1V2v
V3v
hV4
V7V3A
LO1-2
0 10 20 30 40 50 60 70 80 90
−0.5
−0.4
−0.3
−0.2
−0.1
0
0.1
0.2
Motion energy
Nor
mal
ized
resp
onse
r = −0.85 , p < 0.01
FFAPPAcFFA
cPPA
LT*
D E
A B CClusters in early visual cortex
n = 53
n = 40n = 15
phPIT/phTEO
Eccentricity map
0o - 15o
Category-selective clusters
XFC
ITS
STS
MTG
cpSTS
cOPA
Figure 4
A
B C
Static stimuli vs. Dynamic stimuli
STSLTMT
10 - 8
10 - 4
10 - 8
10 - 4
10 - 5
10 - 310 - 5
10 - 3
−1 8−1
8
PSC for static stimuli
PSC
for d
ynam
ic s
timul
i
MT
−1 1−1
1
PSC for static stimuli
PSC
for d
ynam
ic s
timul
i
LTD E
S1 S2 S3
Figure 5
CaS
CaS
HS
OTS
IOS
LuS
IPS
POS
STS
LS
CiS
CeS AS PS
A
C
B
n = 2 n = 3 n = 4 n = 5
n = 6
n = 10
n = 7 n = 8 n = 9
n = 11 n = 12 n = 25
posterior anterior
dorsal
ventral
PCA map
map25m
...
occipito-temporalcortex
Figure 6
Cluster A Cluster B−0.3
−0.2
−0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Nor
mal
ized
resp
onse
FacesNon−Faces
P < 10−28 P = 0.95
A B
C
PITd
PITv
CITd
CITv
AITd
AITv
STP
Cluster B
Cluster A
STS
PMTS ML
AL
MF
AF
0 0.2 0.4 0.6 0.8 1−0.3
−0.2
−0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Proportion of face frames
Nor
mal
ized
resp
onse
in c
lust
er A
r = 0.85 ; p < 0.0005D E
Felleman and Van Essen, 1991
VOT
IT cortex
Face patches(based on a probabilistic map of 3 monkeys)
STS
Conjunction map of place vs. face, place vs. object, and place vs. scrambled place contrastsF G
mPPA mPPA
MPP
LPPLPP
OTS
Figure 7
Outdoor scenes vs. Indoor scenesA Outdoor+inanimate scenes vs. Indoor+inanimate scenesB
10 -2
10 -2
10 -5
10 -5
10 -3
10 -3
10 -5
10 -5
10 -3
10 -3
10 -5
10 -5
Outdoor+animate scenes vs. Indoor+animate scenesCD