Individual functional atlasing of the human brain with multitask fMRI data: leveraging the IBC dataset

Online seminar
@ALuisaPinho
Individual functional atlasing of the human brain with
multitask fMRI data: leveraging the IBC dataset
Ana Luı́sa Pinho, Ph.D.
Parietal Team
Inria Saclay – Île-de-France
NeuroSpin, CEA-Saclay
France
25th of March, 2021

Background and motivations (1/2)
In cognitive neuroscience:
Brain systems
⇐⇒
Mental functions
2/25

Brain systems
⇐⇒
Mental functions
Experiments typically shall:
I tackle one psychological domain
2/25

Brain systems
⇐⇒
Mental functions
I be specific enough to accurately isolate brain processes
2/25

Brain systems
⇐⇒
Mental functions
I be specific enough to accurately isolate brain processes
⇓
Very hard to achieve!
Lack of generality.

Brain systems
⇐⇒
Mental functions
Task-fMRI experiments allow to:
I link brain systems to behavior
I map neural activity at mm-scale
2/25

Data-pooling analysis
I Meta-analysis:
pooling data derivatives
I Mega-analysis:
pooling raw data
Requisites for cognitive mapping
Minimize variability of Successful interpretation of
spatial location combined results
same processing no loss of info from sparse
routines peak-coord. representation
same experimental consistency of
settings cognitive annotations
low inter-subject variability sufficient multi-task data
3/25

I Meta-analysis:
I Mega-analysis:
pooling raw data
settings ( ) cognitive annotations
3/25

I Meta-analysis:
I Mega-analysis:
pooling raw data
Large-scale repositories:
I OpenNeuro
I NeuroVault
I EBRAINS
3/25

I Meta-analysis:
I Mega-analysis:
pooling raw data
I OpenNeuro
I NeuroVault
I EBRAINS
Individual analysis:
I Fedorenko, E. et al. (2011)
I Haxby, J. et al. (2011)
I Hanke, M. et al. (2014)
3/25

I Meta-analysis:
I Mega-analysis:
pooling raw data
settings ( )( ) cognitive annotations
I OpenNeuro
I NeuroVault
I EBRAINS
Large-scale datasets:
I HCP
I studyforrest
I CONNECT/Archi
3/25

I Meta-analysis:
I Mega-analysis:
pooling raw data
settings ( )( ) cognitive annotations
I OpenNeuro
I NeuroVault
I EBRAINS
Large-scale datasets:
I HCP
I studyforrest
I CONNECT/Archi
IBC dataset: a facility that
meets the requisites all
together
3/25

The IBC dataset
I High spatial-resolution fMRI data (1.5mm)
4/25

The IBC dataset
I TR = 2s
4/25

The IBC dataset
I TR = 2s
I Task-wise dataset:
I Many tasks
4/25

The IBC dataset
I TR = 2s
I Many tasks
I Fixed cohort - 12 healthy adults
4/25

The IBC dataset
I TR = 2s
I Many tasks
I Fixed environment
NeuroSpin platform, CEA-Saclay, France
Siemens 3T Magnetom Prismafit
64-channel coil
4/25

The IBC dataset
I TR = 2s
I Many tasks
I Fixed environment
I Inclusion of other MRI modalities
64-channel coil
4/25

The IBC dataset
I TR = 2s
I Many tasks
I Fixed environment
I Inclusion of other MRI modalities
I Not a longitudinal study!
64-channel coil
4/25

Tasks
First release:
I ARCHI battery
Pinel, P. et al. (2007)
I Standard
I Spatial
I Social
I Emotional
I HCP battery
Barch, D. M. et al. (2013)
I Emotion
I Gambling
I Motor
I Language
I Relational
I Social
I WM
I RSVP Language task
Humphries, C. et al. (2006)
Second release:
I Mental Time Travel battery
Gauthier, B., & van Wassenhove, V. (2016a,b)
I Preference battery
Lebreton, M. et al. (2015)
I ToM + Pain Matrices battery
Dodell-Feder, D. et al. (2010)
Jacoby, N. et al. (2015)
Richardson, H. et al. (2018)
I Visual Short-Term Memory +
Enumeration tasks
Knops, A. et al. (2014)
I Self-Reference Effect task
Genon, S. et al. (2014)
I “Bang!” task
Campbell, K. L. et al. (2015)
Third release:
I Clips task
Nishimoto, S. et al. (2011)
I Retinotopy task
Sereno, M. et al. (1995)
I “Raiders” task
Haxby, J. V. et al. (2011)
Fourth release
I Lyon battery
Hamamé, C. M. et al. (2012) / Ossandón, T. et al. (2012)
Saignavongs, M. et al. (2017) / Vidal, J. R. et al. (2010)
Perrone-Bertolotti, M. et al. (2012)
I Realistic Sounds task
Santoro, R. et al. (2017)
I Stanford battery
Ward, G. and Allport, A. (1997)
Shallice, T. (1992) / Stroop, J. R. (1935)
Bissett, P. G. and Logan, G. D. (2011)
Eriksen, B. A. and Eriksen, C. W. (1974) 5/25

Tasks
First release:
I ARCHI battery
I Standard
I Spatial
I Social
I Emotional
I HCP battery
I Emotion
I Gambling
I Motor
I Language
I Relational
I Social
I WM
I RSVP Language task
Humphries, C. et al. (2006)
Second release:
I Mental Time Travel battery
Gauthier, B., & van Wassenhove, V. (2016a,b)
I Preference battery
Lebreton, M. et al. (2015)
I ToM + Pain Matrices battery
Dodell-Feder, D. et al. (2010)
Jacoby, N. et al. (2015)
Richardson, H. et al. (2018)
I Visual Short-Term Memory +
Enumeration tasks
Knops, A. et al. (2014)
I Self-Reference Effect task
Genon, S. et al. (2014)
I “Bang!” task
Campbell, K. L. et al. (2015)
Third release:
I Clips task
Nishimoto, S. et al. (2011)
I Retinotopy task
Sereno, M. et al. (1995)
I “Raiders” task
Haxby, J. V. et al. (2011)
Fourth release
I Lyon battery
Hamamé, C. M. et al. (2012) / Ossandón, T. et al. (2012)
Saignavongs, M. et al. (2017) / Vidal, J. R. et al. (2010)
Perrone-Bertolotti, M. et al. (2012)
I Realistic Sounds task
Santoro, R. et al. (2017)
I Stanford battery
Ward, G. and Allport, A. (1997)
Shallice, T. (1992) / Stroop, J. R. (1935)
Bissett, P. G. and Logan, G. D. (2011)
Eriksen, B. A. and Eriksen, C. W. (1974)
1st rel. + 2nd rel. + Retinotopy
All contrasts: 216
Elementary contrasts: 120
Cognitive concepts: 113
5/25

Behavioral Protocols
Software Tools:
6/25

Behavioral Protocols
Software Tools:
I More public repositories of behavioral protocols to
reproduce experiments!
I Normatives to describe the experimental paradigms!
6/25

Accessibility
Data organization
I BIDS Specification
I Documentation: https://project.inria.fr/IBC/data/
https://project.inria.fr/IBC/data/
Raw MRI data
I ds002685 Link
I Individual brain Charting (IBC, release 2)
Link
Data derivatives
I Collection id = 6618 Link
Github Repositories
I Behavioral Protocols: hbp-brain-charting/public protocols
hbp-brain-charting/public protocols
I Analysis Pipeline: hbp-brain-charting/public analysis code
hbp-brain-charting/public analysis code
8/25

Accessibility
Data organization
I BIDS Specification
I Documentation: https://project.inria.fr/IBC/data/
Raw MRI data
I ds002685 Link
I Individual brain Charting (IBC, release 2)
Link
Data derivatives
I Collection id = 6618 Link
Github Repositories
I Behavioral Protocols: hbp-brain-charting/public protocols
I Analysis Pipeline: hbp-brain-charting/public analysis code
Pinho, A.L. et al. SciData(2018)
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/sdata.2018.105
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
doi.org/10.1038/s41597-020-00670-4
8/25

IBC reproduces ARCHI and HCP
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tale vs. mental addition
mental motion vs. random motion
punishment vs. reward
left foot vs. any motion
left hand vs. any motion
right foot vs. any motion
right hand vs. any motion
tongue vs. any motion
face image vs. shape outline
relational processing vs. visual matching
2-back vs. 0-back
body image vs. any image
face image vs. any image
place image vs. any image
tool image vs. any image
horizontal checkerboard vs. vertical checkerboard
mental subtraction vs. sentence
read sentence vs. listen to sentence
read sentence vs. checkerboard
left hand vs. right hand
saccade vs. fixation
guess which hand vs. hand palm or back
object grasping vs. mimic orientation
false-belief story vs. mechanistic story
false-belief tale vs. mechanistic tale
face trusty vs. face gender
expression intention vs. expression gender
HCP contrasts ARCHI contrasts
IBC
contrasts
1.00
0.75
0.50
0.25
0.00
0.25
0.50
0.75
1.00 ARCHI batteries:
HCP batteries:
n = 13
Pinho, A.L. et al. Hum Brain Mapp(2021)
10/25

Effect of subject and task on brain activity
Per-voxel one-way ANOVA qFDR < 0.05
x=10
L R
z=10 -28
-14
0
14
28
L R
y=-50
Subject effect
x=10
L R
z=10 -37
-19
0
19
37
L R
y=-50
Condition effect
x=-6
L R
z=3 -12
-5.9
0
5.9
12
L R
y=45
Phase encoding effect
Pinho, A.L. et al. SciData(2018) Pinho, A.L. et al. SciData(2020)
11/25

Effect of subject and task on brain activity
Per-voxel one-way ANOVA qFDR < 0.05
x=10
L R
z=10 -28
-14
0
14
28
L R
y=-50
Subject effect
x=10
L R
z=10 -37
-19
0
19
37
L R
y=-50
Condition effect
x=-6
L R
z=3 -12
-5.9
0
5.9
12
L R
y=45
Phase encoding effect
Pinho, A.L. et al. SciData(2018) Pinho, A.L. et al. SciData(2020)
I IBC data is suitable for cognitive mapping
and individual-brain modeling!
11/25

Activation similarity fits task similarity
n = 11
Similarity between
activation maps
of elementary contrasts
Similarity between
cognitive description
Pinho, A.L. et al. SciData(2020) 12/25

Activation similarity fits task similarity
n = 11
Similarity between
activation maps
Similarity between
cognitive description
Spearman correlation
First Release: 0.21 (p ≤ 10−17)
Second Release: 0.21 (p ≤ 10−13)
First+Second Releases: 0.23 (p ≤ 10−72)
12/25

Brain coverage
Group-level F-map pFWE < 0.05
x=27
x=12
x=-13
x=-28
x=-43 -80
-40
0
40
80
Comprehensive brain coverage of functional activity!
13/25

Individual functional atlasing

Variability of Functional Signatures
Pinho, A.L. et al. Hum Brain Mapp(2021) n = 13
Individual z-maps
15/25

Variability of Functional Signatures
0.00 0.25 0.50
read sentence vs. listen to sentence
read sentence vs. checkerboard
left hand vs. right hand
horizontal checkerboard vs. vertical checkerboard
mental subtraction vs. sentence
saccade vs. fixation
guess which hand vs. hand palm or back
object grasping vs. mimic orientation
false-belief story vs. mechanistic story
false-belief tale vs. mechanistic tale
expression intention vs. expression gender
face trusty vs. face gender
face image vs. shape outline
punishment vs. reward
0.00 0.25 0.50
tongue vs. any motion
right foot vs. any motion
left foot vs. any motion
right hand vs. any motion
left hand vs. any motion
tale vs. mental addition
relational processing vs. visual matching
tool image vs. any image
place image vs. any image
face image vs. any image
body image vs. any image
2-back vs. 0-back
read pseudowords vs. consonant strings
read words vs. consonant strings
read words vs. read pseudowords
read sentence vs. read jabberwocky
read sentence vs. read words
inter-subject correlation
intra-subject correlation
Intra- and inter- subject correlation of brain maps
15/25

Study 1
Dictionary of cognitive components

Decomposition of 51 contrasts
with dictionary learning
Individual topographies of
20 components (n = 13)
Each component gets the name
of the active condition from the
contrast with the highest value in
the dictionary.
Multi-subject, sparse dictionary learning:
min(Us )s=1...n,V∈C
n
X
s=1

kXs
− Us
Vk2
+ λkUs
k1

,
with Xs
p×c , Us
p×k and Vk×c
I Functional correspondence: dictionary
of functional profiles (V) common to
all subjects
I Sparsity: `1−norm penalty and
Us ≥ 0 , ∀s ∈ [n]
17/25

Components are consistently mapped across subjects.
17/25

0.25 0.30 0.35 0.40 0.45 0.50 0.55
Intra-subject
correlation
Inter-subject
correlation
Correlations of the dictionary components on split-half data
Variability of topographies linked to individual differences.
17/25

Study 2
Reconstruction of functional contrasts

Leave-p-out CV (p=3 subjects)
experiment to learn the shared
representations from contrasts of
eleven tasks. (n = 13)
Predict all contrasts from the
remaining task
19/25

Leave-p-out CV (p=3 subjects)
experiment to learn the shared
representations from contrasts of
eleven tasks. (n = 13)
Predict all contrasts from the
remaining task
Train a Ridge-regression model with individual
contrast maps i of tasks −j to predict task j on
individual contrast-maps s 6= i:
b
ws,λ,j
= argminw∈Rc−1
X
i6=s
kXi
j − Xi
−j wk2
+ λkwk2
Prediction output for one contrast of task j in
subject s:
b
Xs
j = Xs
−j b
ws,λ,j
.
Cross-validated R-squared for task j at location i:
R2
i (j) = 1 − means∈[n]
kb
Xs
i,j − Xs
i,j k2
kXs
i,j k2
19/25

n = 13
max R2
Most of the brain regions
are covered by the
predicted functional
signatures.
19/25

n = 13
Ridge-Regression model
for the scrambled case:
b
ws,λ,j
= argminw∈Rc−1
X
i,k 6= s
kXi
j −Xk
−j wk2
+λkwk2
Cross-validated R-squared:
R2
i (j) = 1 − means∈[n]
kb
Xs
i,j − Xs0
i,j k2
kXs0
i,j k2
Permutations of subjects
decrease the proportion of
well-predicted voxels in all
tasks, showing that
topographies are driven by
subject-specific variability.
19/25

Study 3
Example: Functional mapping of the language network

Ex: Functional mapping of the language network
Goal: Cognitive profile of ROIs based on IBC language-related contrasts
Select ROIs / Select IBC contrasts
Individualize ROIs using dual-regression
and the left-out contrasts
R(s) = R pinv X(s)

X(s)
Voxelwise z-scores average for each
ROI at every selected contrast
21/25

Ex: Functional mapping of the language network
Linear SVC (upper triangle)
Dummy Classifier (lower triangle)
LOGOCV scheme
Prediction within pairs of ROIs
13 groups = 13 participants
21/25

Concluding remarks
Functional atlasing using a large dataset in the task dimension
I Investigation of common functional profiles between tasks
I Common
functional profiles
Shared
behavioral responses
Mental
functions
22/25

Concluding remarks
Functional atlasing using a large dataset in the task dimension
I Investigation of common functional profiles between tasks
I Common
functional profiles
Shared
behavioral responses
Mental
functions
Individual brain modeling using data with higher spatial resolution
I generalize across subjects
I elicit variability between subjects
22/25

Future outcomes
I Article on the IBC-dataset third-release
I Fifth and sixth releases out this year
23/25

Thanks!
Bertrand Thirion
The IBC volunteers!

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