# Reconstructing Functional Connectomes and Predicting Behavior Using Contrastive Learning

Victoria Shevchenko, Carlo Alberto Barbano, Malo Renaudin, Robert Scholz, R. Austin Benn, Wei Wei, Carla Pallavicini, Ulysse Klatzmann, Francesco Alberti, Pierre Louis Bazin, et al.

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## ► To cite this version:

Victoria Shevchenko, Carlo Alberto Barbano, Malo Renaudin, Robert Scholz, R. Austin Benn, et al. Reconstructing Functional Connectomes and Predicting Behavior Using Contrastive Learning. OHBM 2025 - Organization for Human Brain Mapping, Jun 2025, Brisbane (AU), Australia. OHBM 2025 Annual Meeting Abstract Book, 2025. hal-05231771

HAL Id: hal-05231771

https://hal.science/hal-05231771v1

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# Reconstructing Functional Connectomes and Predicting Behavior Using Contrastive Learning

Victoria Shevchenko*<fnref n="1" />,²,³,⁴, Carlo Alberto Barbano³,⁴,⁵, Malo Renaudin³,⁴, Robert Scholz<fnref n="1" />,²,⁶,⁷, R. Austin Benn<fnref n="1" />,², Wei Wei<fnref n="1" />,², Carla Pallavicini<fnref n="1" />, Ulysse Klatzmann<fnref n="1" />,², Francesco Alberti<fnref n="1" />,², Pierre-Louis Bazin⁸, Theodore D. Satterthwaite⁹, Daniel S. Margulies<fnref n="1" />,², Demian Wassermann³,⁴

*victoria.shevchenko@inria.fr

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@victoris93.bsky.social

## Introduction

Predicting cognitive ability and mental health from functional connectivity (FC) remains a major challenge<fnref n="1" />. Contrastive learning (CL)² and joint embeddings can improve prediction performance by filtering mutual information³ between brain data and behavioral measures in a shared latent space. This work explores this avenue using HCP and ABCD data.

## Conclusions

* CL offers state-of-the-art prediction of cognition
* An embedding which is sufficiently dispersed and is governed by local spatial principles yields optimal performance
* Higher-order networks underpin cognitive ability
* Sensory networks are implicated in mental health
* Visual regions underpin both cognition and mental health in children

## Methods: Custom Contrastive Model for FC

$$ \text{Layer initialization: } \bar{C} = U \Lambda U^T \rightarrow W = [u_1, ..., u_n]^3, u \in U, n = 100 \qquad \text{D'Souza et al.}^4 $$

### A. FC Matrix Autoencoder (MatAE)

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Enc 1

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Enc 2

<figure><img src="image_7.png" /><figcaption>Reduced matrix</figcaption></figure>

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C: FC matrix

Ĉ: reconstructed FC matrix

### B. Reduced matrix encoder

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### D. Linear model (e.g., Ridge)

### Step-wise training for optimal loss convergence

**A: [Training step 1] Matrix autoencoder (MatAE) embeds the full connectivity matrix into a vector and reconstructs it using fully connected layers.**

**Loss:**

$$ \mathcal{L}_{\text{Recon}} = \|C - \hat{C}\|_F^2 $$

yᵢ, yᵣₙₐp: behavior

i: sample for which the loss is computed

n, p: unique samples different from i

K: radial basis function kernel

Z: FC embedding

**C: [Training step 3] Reduced matrix decoder** is trained to reconstruct the reduced matrix from the embedding.

**Loss:**

$$ \mathcal{L}_{\text{Recon}} = \|R - \hat{R}\|_F^2 $$

R: reduced FC matrix

R̂: reconstructed reduced FC matrix

**D: [Training step 4] Linear estimator** fitted on the embedding to predict the behavioral target of interest (e.g., fluid intelligence).

## Results: Cognition Regulated by Higher-Order Regions

**1. Hypothesized embedding topology.** Given a neighborhood H, its centroid must always be more similar to its closest neighbor than to its farthest neighbor. Every neighborhood H was defined using the KDTree algorithm.

If $sim(\text{centroid}, \text{closest}) > sim(\text{centroid}, \text{farthest})$

$$ \begin{cases} sim(b_{\text{centroid}}, b_{\text{closest}}) > sim(b_{\text{centroid}}, b_{\text{farthest}}), \\ sim(FC_{\text{centroid}}, FC_{\text{closest}}) > sim(FC_{\text{centroid}}, FC_{\text{farthest}}) \end{cases} $$

b: behavioral measure
FC: functional connectivity

**3. FC reconstruction performance.** Correlation and MAPE mapped onto the surface of the cortex in **HCP** and **ABCD**. The color scale was thresholded to only show the top 20% of regions.

<figure><img src="image_18.png" /><figcaption>ABCD</figcaption></figure>

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MAPE: mean absolute percentage error

**2. Results of the embedding analysis.** Hypothesis in panel 1 is confirmed for coordinates, behavior and FC in **train** embeddings. In **test** embeddings, it is confirmed for behavior and FC only. Results displayed for HCP.

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**4. Prediction performance of Ridge regression fitted on the embedding space.** Correlation (green) and MAPE (purple) ± 1 standard deviation between true and predicted scores for cognition (blue) and psychopathology (black) measures from the NIH Toolbox in the **test** set. Results displayed for HCP.

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DCCS: dimensional change card sort
NIHTB: National Institute of Health Toolbox

## References:

1. Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).

2. van den Oord, A., Li, Y. & Vinyals, O. Representation learning with Contrastive Predictive Coding. arXiv [cs.LG] (2018).

3. Radford, A. et al. Learning Transferable Visual Models From Natural Language Supervision. arXiv [cs.CV] (2021).

4. D'Souza, N. S., Nebel, M. B., Crocetti, D., Robinson, J., Mostofsky, S., & Venkataraman, A. (2021). A matrix autoencoder framework to align the functional and structural connectivity manifolds as guided by behavioral phenotypes. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (pp. 625–636). Springer International Publishing.

5. Dufumier, B. et al. Contrastive learning with continuous proxy meta-data for 3D MRI classification. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 58–68 (Springer International Publishing, Cham, 2021).

6. Barbano, C. A., Dufumier, B., Duchesnay, E., Grangetto, M. & Gori, P. Contrastive Learning for Regression in Multi-Site Brain Age Prediction. in 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) 1–4 (IEEE, 2023).

## Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 866533) awarded to D.S.M. This study was supported by the NIHR Oxford Health Biomedical Research Centre (NIHR203316). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The Centre for Integrative Neuroimaging was supported by core funding from the Wellcome Trust (203139/Z/16/Z and 203139/A/16/Z). This project was also supported by the contrat doctoral spécifique normalien (CDSN) awarded to V.S. by the Ministry of Higher Education and Research of France. The computational aspects of this research were supported by the Wellcome Trust Core Award Grant Number 203141/Z/16/Z and the NIHR Oxford BRC. This work was also granted access to the HPC resources of DRIS under the allocation AD011014848 made by GENCI.

OHBM 2025

<footnote n="1">¹Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France; ²Oxford University Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford, United Kingdom; ³MIND Team, Inria Saclay, Université Paris-Saclay, Palaiseau, France; ⁴Neurospin, CEA, Gif-Sur-Yvette, France; ⁵University of Turin, Turin, Italy; ⁶Max Planck School of Cognition, Leipzig, Germany; ⁷Wilhelm Wundt Institute for Psychology, Leipzig University, Leipzig, Germany; ⁸Full brain picture Analytics, Leiden, The Netherlands; ⁹University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA</footnote>