Deep behavioral phenotyping in functional MRI for cognitive mapping of the human brain

@ALuisaPinho@fediscience.org
@ALuisaPinho Seminar at the Cognitive Science Lab
Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Deep behavioral phenotyping in functional MRI for
cognitive mapping of the human brain
Ana Luı́sa Pinho, Ph.D.
BrainsCAN Postdoctoral Fellow
Western University, London Ontario, Canada
This work was developed in the Parietal Team at NeuroSpin/Inria-Saclay, Paris, France.
22nd of February, 2023

Overview of the Individual Brain Charting (IBC) dataset
Data-quality assessment of IBC
Individual Functional Atlasing leveraging IBC First-Release
Encoding analysis of naturalistic stimuli from IBC Third-Release using
the Fast Shared Response Model (FastSRM)
Future perspectives: mapping cognitive-specific mechanisms
2/35

Overview of the IBC dataset

Background and motivations (1/2)
In cognitive neuroscience:
Brain systems
⇐⇒
Mental functions
4/ 35

Brain systems
⇐⇒
Mental functions
Experiments typically shall:
tackle one psychological domain
4/ 35

Brain systems
⇐⇒
Mental functions
be specific enough to accurately isolate brain processes
4/ 35

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

Brain systems
⇐⇒
Mental functions
Task-fMRI experiments allow to:
link brain systems to behavior
map neural activity at mm-scale
4/ 35

Data-pooling analysis
Meta-analysis:
pooling data derivatives
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
5/ 35

Meta-analysis:
Mega-analysis:
pooling raw data
settings ( ) cognitive annotations
5/ 35

Meta-analysis:
Mega-analysis:
pooling raw data
Large-scale repositories:
OpenNeuro
NeuroVault
EBRAINS
5/ 35

Meta-analysis:
Mega-analysis:
pooling raw data
OpenNeuro
NeuroVault
EBRAINS
Individual analysis:
Fedorenko, E. et al. (2011)
Haxby, J. et al. (2011)
Hanke, M. et al. (2014)
5/ 35

Meta-analysis:
Mega-analysis:
pooling raw data
settings ( )( ) cognitive annotations
OpenNeuro
NeuroVault
EBRAINS
Large-scale datasets:
HCP
studyforrest
CONNECT/Archi
5/ 35

Meta-analysis:
Mega-analysis:
pooling raw data
settings ( )( ) cognitive annotations
OpenNeuro
NeuroVault
EBRAINS
Large-scale datasets:
HCP
studyforrest
CONNECT/Archi
The IBC dataset meets all
together the requisites for
cognitive mapping.
5/ 35

The IBC dataset
High spatial-resolution fMRI data (1.5mm)
6/ 35

The IBC dataset
TR = 2s
6/ 35

The IBC dataset
TR = 2s
Task-wise dataset:
Many tasks
6/ 35

The IBC dataset
TR = 2s
Task-wise dataset:
Many tasks
Fixed cohort - 12 healthy adults
6/ 35

The IBC dataset
TR = 2s
Task-wise dataset:
Many tasks
Fixed environment
NeuroSpin platform, CEA-Saclay, France
Siemens 3T Magnetom Prismafit
64-channel coil
6/ 35

The IBC dataset
TR = 2s
Task-wise dataset:
Many tasks
Fixed environment
Inclusion of other MRI modalities
64-channel coil
6/ 35

The IBC dataset
TR = 2s
Task-wise dataset:
Many tasks
Fixed environment
Inclusion of other MRI modalities
Not a longitudinal study!
64-channel coil
6/ 35

Analysis pipeline
7/ 35

Data-quality assessment of
the IBC First-Release

Tasks of the First Release
▶ ARCHI tasks
Standard
Spatial
Social
Emotional
▶ HCP tasks
Emotion
Gambling
Motor
Language
Relational
Social
Working Memory
▶ RSVP Language
9/ 35

▶ ARCHI tasks
Standard
Spatial
Social
Emotional
▶ HCP tasks
Emotion
Gambling
Motor
Language
Relational
Social
Working Memory
▶ RSVP Language
▶ Sensory processing:
Retinotopy
Tonotopy
Somatotopy
▶ High-cognitive order:
Calculation
Language
Social cognition
Theory-of-mind
9/ 35

▶ ARCHI tasks
Standard
Spatial
Social
Emotional
▶ HCP tasks
Emotion
Gambling
Motor
Language
Relational
Social
Working Memory
▶ RSVP Language
▶ Sensory processing:
Retinotopy
Tonotopy
Somatotopy
▶ High-cognitive order:
Calculation
Language
Social cognition
Theory-of-mind
All contrasts: 119
Elementary contrasts: 59
9/ 35

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:
Pinel, P. et al. (2007)
HCP batteries:
Barch, D. M. et al. (2013)
n = 13
Pinho, A.L. et al. Hum Brain Mapp(2021) 10/ 35

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
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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)
11/ 35

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
IBC data is suitable for cognitive mapping and
individual-brain modeling!
11/ 35

Activation similarity fits task similarity
Similarity between
activation maps
of elementary contrasts
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archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
Similarity between
cognitive description
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archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
12/ 35

Similarity between
activation maps of
elementary contrasts
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archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
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Similarity between
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archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
Second release:
Mental Time Travel battery
Gauthier, B., & van Wassenhove, V. (2016a,b)
Preference battery
Lebreton, M. et al. (2015)
ToM + Pain Matrices battery
Dodell-Feder, D. et al. (2010)
Jacoby, N. et al. (2015)
Richardson, H. et al. (2018)
Visual Short-Term Memory + Enumeration
tasks
Knops, A. et al. (2014)
Self-Reference Effect task
Genon, S. et al. (2014)
“Bang!” task
Campbell, K. L. et al. (2015)
First + Second releases:
All contrasts: 279
13/ 35

Similarity between
activation maps of
elementary contrasts
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archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
Similarity between
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archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
Second release:
Mental Time Travel battery
Gauthier, B., & van Wassenhove, V. (2016a,b)
Preference battery
Lebreton, M. et al. (2015)
ToM + Pain Matrices battery
Dodell-Feder, D. et al. (2010)
Jacoby, N. et al. (2015)
Richardson, H. et al. (2018)
Visual Short-Term Memory + Enumeration
tasks
Knops, A. et al. (2014)
Self-Reference Effect task
Genon, S. et al. (2014)
“Bang!” task
Campbell, K. L. et al. (2015)
First + Second releases:
All contrasts: 279
Spearman correlation
First Release: 0.21 (p ≤ 10−17)
Second Release: 0.21 (p ≤ 10−13)
First+Second Releases: 0.23 (p ≤ 10−72)
13/ 35

Individual Functional Atlasing

Variability of Functional Signatures
Pinho, A.L. et al. Hum Brain Mapp(2021) n = 13
Individual z-maps
15/ 35

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/ 35

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

∥Xs
− Us
V∥2
+ λ∥Us
∥1

,
with Xs
p×c , Us
p×k and Vk×c
Functional correspondence: dictionary
of functional profiles (V) common to
all subjects
Sparsity: ℓ1−norm penalty and
Us ≥ 0 , ∀s ∈ [n]
17/ 35

Components are consistently mapped across subjects.
17/ 35

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/ 35

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
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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 ̸= i:
b
ws,λ,j
= argminw∈Rc−1
X
i̸=s
∥Xi
j − Xi
−j w∥2
+ λ∥w∥2
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]
∥b
Xs
i,j − Xs
i,j ∥2
∥Xs
i,j ∥2
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Pinho, A.L. et al. Hum Brain Mapp(2021)
n = 13
max R2
Most of the brain regions
are covered by the
predicted functional
signatures.
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n = 13
Pinho, A.L. et al. Hum Brain Mapp(2021)
Ridge-Regression model
for the scrambled case:
b
ws,λ,j
= argminw∈Rc−1
X
i,k ̸= s
∥Xi
j −Xk
−j w∥2
+λ∥w∥2
Cross-validated R-squared:
R2
i (j) = 1 − means∈[n]
∥b
Xs
i,j − Xs′
i,j ∥2
∥Xs′
i,j ∥2
Permutations of subjects
decrease the proportion of
well-predicted voxels in all
tasks, showing that
topographies are driven by
subject-specific variability.
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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 Pinho, A.L. et al. Hum Brain Mapp(2021)
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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
Pinho, A.L. et al. Hum Brain Mapp(2021) 21/ 35

Conclusions
Functional atlasing using a large dataset in the task dimension
Investigation of common functional profiles between tasks
Common
functional profiles
Shared
behavioral responses
Mental
functions
22/ 35

Conclusions
Functional atlasing using a large dataset in the task dimension
Investigation of common functional profiles between tasks
Common
functional profiles
Shared
behavioral responses
Mental
functions
Individual brain modeling using data with higher spatial resolution
generalize across subjects
elicit variability between subjects
22/ 35

FastSRM encoding analysis of naturalistic
stimuli

Naturalistic Tasks from the third release
Clips task: 4 fMRI Sessions / 21 Runs
Nishimoto, S. et al. (2011)
Raiders task: 2 fMRI Sessions: 13 Runs
Haxby, J. V. et al. (2011)
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Analyzing naturalistic-stimuli fMRI data with FastSRM
Shared Response Model by Chen et al. (2015)
Fast Shared Response Model (FastSRM) by Richard et al. (2019):
https://hugorichard.github.io/FastSRM/
25/ 35

Why FastSRM?
25/ 35

Why FastSRM?
Standard GLM applied to naturalistic stimuli leads to high-dimensional
controlled-design models.
25/ 35

Why FastSRM?
Unsupervised data-driven approach where the design matrix and the spatial maps
are learnt jointly is more wieldy.
25/ 35

Why FastSRM?
Unsupervised data-driven approach where the design matrix and the spatial maps
are learnt jointly is more wieldy.
High-dimensional data (many voxels) require a decomposition method with
scalability.
25/ 35

Qualitative description of FastSRM
Richard et al. (2019)
26/ 35

Formal description of SRM
For all subjects and time frames, SRM can be formally defined as follows:
X = SW + E (1)
X ∈ RG×nv → concatenation of G brain images with v vertices for n=12 subjects
S ∈ RG×k → shared response: concatenation of the weights across time frames
W ∈ Rk×nv → concatenation of the k spatial components with v vertices for the n subjects
E → the additive noise
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Formal description of SRM
For all subjects and time frames, SRM can be formally defined as follows:
X = SW + E (1)
To estimate W and S, consider the following two-step SRM minimization problem:
∀s ∈ {1, ..., n}, argmin{Ws : Ws W T
s =Ik }
n
X
j=1
∥Xj − SWj∥2
= UsVs (2)
argminS
n
X
s=1
∥Xs − SWs∥2
=
1
n
n
X
s=1
XsWT
s , (3)
27/ 35

FastSRM algorithm
Reduce data: Dimensions of the input data are reduced using Principal Component
Analysis (PCA) on X, using c components, with c ≪ v, in order to estimate the
reduced data b
X, such that b
X ∈ RG×nc.
Apply SRM algorithm: b
X is applied on the two-step algorithm using alternate
minimization, in order to find both the shared response b
S and the spatial
components c
W in the reduced space.
Recover Spatial Components: The spatial components of each subject are recovered by
orthonormal regression using the shared response in reduced space b
S and the data
X.
UsDsVs = SVD(ST
Xs)
28/ 35

Co-Smoothing, Double K-Fold CV for FastSRM for each task
1: b
X = PCA(X)
2: b
W,b
S = SRM(b
X)
3: UDV
(−R/2)
−4s
= SVD b
SX
(−R/2)
−4s

, where W
(−R/2)
−4s = UV
(−R/2)
−4s
4: S
(−R/2)
−4s =
Pn
z=1,z̸∈[4s]
X
(−R/2)
−4s
W
(−R/2)T
−4s
n−4
5: S
(R/2)
−4s =
Pn
z=1,z̸∈[4s]
X
(R/2)
−4s
W
(−R/2)T
−4s
n−4
6: UDV
(−R/2)
4s
= SVD S
(−R/2)
−4s X
(−R/2)
4s

, where W
(−R/2)
4s = UV
(−R/2)
4s
7: e
X
(R/2)
4s = S
(R/2)
−4s W
(−R/2)
4s
8: ρ
(s,r)
e
XX
=
X
e
X
(r)
s − µ
e
X

X
(r)
s − µX

qX
e
X
(r)
s − µ
e
X
2
X
X
(r)
s − µX
2
,
where µ
e
X
and µX represent the means of e
X
(r)
s and X
(r)
s , respectively.
CV scheme applied for each
task with K = 3 for 12
subjects and K = 2 for R runs
Co-Smoothing described in Wu, A. et al.(2018) NeurIPS
29/ 35

Significance of group-level correlation of original vs.
reconstructed data from FastSRM
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Group-level activation between Raiders and Clips
q ⩽ 0.05
Top 10 regions of Glasser atlas w/ areas displaying
≥ 5% of significant voxels in both hemispheres
1 Auditory Association Cortex
2 Temporo-Parieto-Occipital Junction
3 Posterior Cingulate Cortex
4 Superior Parietal Cortex
5 Inferior Parietal Cortex
6 Early Auditory Cortex
7 Dorsal Stream Visual Cortex
8 Lateral Temporal Cortex
9 MT+Complex and Neighboring Visual Areas
10 Primary Visual Cortex (V1)
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Conclusions
IBC Third-Release reflect responses to behavior.
FastSRM is a computational low-cost approach to analyze task-fMRI time-series.
Useful to analyse high-dimensional paradigms, such as naturalistic stimuli
32/ 35

Thanks!
Bertrand Thirion
The IBC volunteers!
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Concluding Remarks
Deep Behavioral Phenotyping in functional neuroimaging to study
cognitive-specific mechanisms
Remove idiosyncrasies of single tasks from the study
Map commonalities of different but similar tasks to assess consistency (ex: map
across different sensory modalities)
Isolate executive and perceptual mechanisms from the same high-order cognitive
function
Improve connectivity models
34/ 35

Thank you for your attention.

Deep behavioral phenotyping in functional MRI for cognitive mapping of the human brain

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