- The document describes a method called "dynamic parcellation by aggregation of clusters" (dypac) to generate individual brain parcellations from large fMRI datasets. - The method was applied to cneuromod datasets totaling over 30 hours of fMRI data from movies and TV shows. - Results show the individual parcellations have high reproducibility across similar contexts like different seasons of a TV show, and more moderate reproducibility across different stimuli like movies and tasks. Individual parcellations also better predict brain activity than group parcellations.

1. Individual brain parcellations
in CNeuroMod 2020
Pierre Bellec
Département de Psychologie
pierre.bellec@criugm.qc.ca

2. Main objective
Build high quality individual
brain parcellations, which
generalize to a variety of
videos in CNeuroMod 2020.
“One parcellation to rule them
all, and in the embedding
space bind them.”
Background public domain photo by Erik Stein. Other
movie images are under copyright, and their inclusion
falls under “fair use” (hopefully).

3. Contributions
Pierre Bellec -
code, data analysis,
conceptual design
Amal Boukhdhir -
code, data analysis,
conceptual design
François Paugam -
code, data analysis,
conceptual design
Hanad Shamarke -
code, data analysis
Valentina Borghesani -
data analysis
Yu Zhang -
conceptual design
Max Mignotte -
conceptual design

4. Fondation Courtois
The CNeuroMod Team
The Subjects
& the Scanning Team
UNIQUE
Acknowledgments

5. Background
&
Objectives

6. Brain parcellation to compare ANNs with the brain
Works trying to quantitatively compare the activity of artificial neural networks (ANNs) with the brain often compare
specific ANN layers with specific brain parcels. Figure from Schrimpf et al. Biorxiv 2020 reused under CC-BY license.

7. Functional
connectivity
Slow spontaneous fluctuations and
seed-based connectivity map from the
posterior cingulate cortex identifies the
default-mode network. The method can
be extended using many different
seeds.
Method introduced by Biswal and colleagues
(1995). Application to the default-mode network
by Greicius and colleagues (2003). Figure
generated with nilearn.
Seed voxel in the
posterior cingulate (PCC)

8. Functional
parcellation
Data-driven cluster analysis automatically
detects brain parcels with homogeneous
connectivity patterns.
Biologically meaningful parcels can be generated
at various resolution (number of parcels).
Hard parcellations (top rows) are binary, non
overlapping. Soft parcellations (bottom) are
weighted and potentially overlapping, making
dynamic parcel reconfiguration possible.
Hard parcellations from Yeo, Krienen and colleagues (2011)
with figures generated using nilearn. Soft parcellations from
Dadi et al., Neuroimage (2020) under CC BY-NC-ND
license.
Difumo

9. Reproducibility
With very large amount of data, individual
parcellations can be generated with high
reproducibility. Here using ~7 hours of
resting-state fMRI per subject.
Figure from Xu et al. (2020) Journal of
Neurophysiology, re-used from a preprint on Biorxiv
under CC-BY-NC-ND license.

10. Reproducibility
Reproducibility estimated through split-half. Although connectivity maps converge within ~30 mns (left), binary
parcels are much slower to converge and reaches a lower asymptote (right). Resting-state data from the Midnight Brain
Scan (N=10). Figure 2 From Kraus et al. (2020) NeuroImage, re-used from a preprint on Biorxiv under CC-BY-NC-ND.

11. Homogeneity
Homogeneity of hard group brain parcels can closely be predicted from parcel size alone, and different algorithms
have only marginal impact.
Figure from Urchs et al. MNI open research (2019) under CC-BY license.

12. Homogeneity
Homogeneity can be generalized to soft parcellations by examining the R2 of compressing a brain image in the
parcellation space. Soft Difumo parcels substantially improve over hard parcels.
Figure from Dadi et al., Neuroimage (2020) under CC BY-NC-ND license.

13. Generalization
Salehi and colleagues noticed systematic
differences in parcellation reproducibility
across different fMRI task data (at the
individual level).
Figure from Salehi et al. (2020) Neuroimage, reused
from Biorxiv under CC-BY.
Midnight Brain Scan
Yale sample

14. 1. Extend the individual soft parcellation method from Boukhdhir et al. (2020)
to the full brain.
We hypothesized that this method can scale to very large fMRI datasets.
2. Assess the reproducibility of individual parcels across cognitive contexts.
We hypothesized that dynamic parcels are largely context-independent.
3. Assess the homogeneity of individual parcels across cognitive contexts.
We hypothesized that dynamic parcels are homogeneous across contexts.
Specific objectives & Hypotheses

15. Methods

16. Data: cneuromod-2020 release
● Movie10 (12h)
● Bourne Supremacy, Wolf of Wall
Street, Hidden Figures (x2),
● Life (x2)
●
● Friends s1 & s2 (18h)
● HCP test-retest (9h)
○ 15 repetitions of HCP
8 domains: gambling,
motor, working memory,
social, language, relational,
emotion, rest.
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Movie images are under copyright, and their inclusion falls under “fair use” (hopefully).

17. Data: preprocessing
1. fMRI data was preprocessed using the 2020 LTS release of the fmriprep pipeline
(Esteban et al., 2018). See the cneuromod docs for details.
2. fMRI data was resampled in the MNI 2009 asymmetric template (Fonov et al.,
2009) at 2 mm isotropic and smoothed at 8 mm isotropic (for parcellation
generation) and 5 mm isotropic (for assessing homogeneity of parcels).
3. fMRI time series were denoised using the Params36 strategy implemented in
load_confounds, including slow time drifts, second-order and derivatives
expansions of motion parameters, white matter and CSF averages, as well as
global signal (Ciric et al., 2017).

18. Dypac algorithm
Second level: a k-means clustering
procedure aggregates one-hot
encoders and generates a set of
state stability maps.
n_state=1024
First level: run k-Means on sliding
windows of fMRI time series.
Parcels are represented with
one-hot encoders.
n_cluster=256
Number of windows / run n_replication=100

19. Dypac algorithm
… in numbers
First-level cluster analysis transforms a series
of ~ 50 brain volumes into a hard parcellation
of functionally connected brain regions,
which is represented as a sparse matrix.
Top image taken from the nilearn documentation
(under BSD license). https://nilearn.github.io

20. Dypac algorithm … in numbers (Ced)

21. Dypac algorithm … in numbers (Ced bis)

22. Dypac scalability
● Memory footprint is reasonable and does not depend on the
number of clusters, thanks to sparse boolean arrays.
● Running k-means on a 100k x 5M sparse array is feasible (a
few hours using 32 cores), thanks to scikit-learn, with support
for sparse arrays and multi-core processing.
● Implementation of full-brain “dynamic parcellation by
aggregation of clusters” (dypac) is available on github.

23. Results

24. Parcel reproducibility, friends s01 vs s02
Reproducibility is measured by maximizing spatial correlation of stability maps inside the grey matter between test and
retest between friends-s01 to friends-s02. Left: parcels from the same subject are matched. Right: parcels from different
subjects are matched.
excellent reproducibility

25. Parcel reproducibility, friends s01 vs s02
Parcels are matched from friends-s01 to friends-s02 by maximizing spatial correlation (sub-01).
high reproducibility low reproducibility
friends-s01 friends-s02 friends-s01 friends-s02

26. R2 friends-s02: subject- vs group- atlas
Individual dypac parcels are generated
from friends-s01, and R2 is estimated on
friends-s02.
The R2 of a number of group parcellation
with varying number of parcels is
presented, for reference.

27. R2 friends-s02: intra- vs inter-subject
Individual dypac parcels are generated
from friends-s01, and R2 is estimated on
friends-s02.
The R2 is compared when a subject is
embedded with its own parcellation, vs a
parcellation from another subject.

28. Parcel reproducibility, friends-s01 vs movie10
Left: parcels from the same subject are matched between friends-s01 and friends-s02. Right: parcels from the same
subject are matched between friends-s01 and movie10.
Moderate
reproducibility

29. Parcels matching, friends-s01 vs movie10
Parcels are matched from friends-s01 to movie10 by maximizing spatial correlation (sub-01).
high reproducibility low reproducibility
friends-s01 movie10 friends-s01 movie10

30. Parcel reproducibility, friends-s01 vs hcptrt
Left: parcels from the same subject are matched between friends-s01 and friends-s02. Right: parcels from the same
subject are matched between friends-s01 and hcptrt.
Good
reproducibility

31. Parcels matching, friends-s01 vs hcptrt
Parcels are matched from friends-s01 to hcptrt by maximizing spatial correlation (sub-01).
high reproducibility low reproducibility
friends-s01 hcptrt friends-s01 hcptrt

32. R2: friends vs movie10
Individual dypac parcels are
generated from friends-s01,
and R2 is estimated on
friends-s01, friends-s02, all
the movies from movie10,
and all the tasks in hcptrt.

33. R2 movie10 & hcptrt: subject- vs group- atlas
Individual dypac parcels are generated from friends-s01, and R2 is estimated on friends-s02 (left) and movie10
(middle) and hcptrt (right), along with R2 of a number of group parcellations.
friends-s02 movie10 hcptrt

34. Individual dypac parcels are generated from friends-s01, and R2 is estimated on friends-s02 (left), movie10 (middle)
and hcptrt (right). The R2 is compared when a subject is embedded with its own parcellation, vs a parcellation from
another subject.
R2 movie10 & hcptrt: intra- vs inter-subject
friends-s02 movie10 hcptrt

35. Conclusions
&
next steps

36. 1. Extend the dynamic parcellation method to the full brain.
The proposed method (dypac) scales to very large individual fMRI datasets.
2. Assess the reproducibility of dynamic parcels across cognitive contexts.
○ Reproducibility is good to excellent with very long time series (~10h) and
similar types of stimuli (two different seasons of friends).
○ Some departures in parcellation were observed on movies from different
genres (moderate) or in the HCP tasks (small).
3. Assess the homogeneity of dynamic parcels across cognitive contexts.
○ Individual dypac parcels have markedly higher homogeneity (R2) than group
parcels or parcels generated on other subjects (except in hcptrt).
○ R2 was extremely stable across friends seasons, a slight decrease was
observed on movie10, with a marked decrease in some hcptrt tasks.
Conclusions

37. 1. Investigate other cneuromod datasets
This is ongoing work for years to come.
2. Compare with other algorithms.
○ Dynamic parcels are soft, overlapping parcels. Convergence with ICA and
sparse matrix factorization should be investigated.
○ The API for model evaluation, dypac parcels and documentation will be
released for further assessment by the community.
3. Establish group vs individual parcellation best practices
○ Individual parcels embed data better than group atlases and generalize
adequately across cognitive contexts.
○ As a group atlas, Difumo performs very well and may be suitable for
situations where comparison of embeddings across subjects is required.
○ The importance of individual vs group parcellations for brain-augmented
learning remains an open question.
Next steps