Self-Supervised Learning for Functional Connectivity Networks

Paper Title:

A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data

Published on:

4 December 2023

Primary Category:

Machine Learning

Paper Authors:

Jungwon Choi,

Seongho Keum,

EungGu Yun,

Byung-Hoon Kim,

Juho Lee

Bullets

Key Details

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Proposes ST-MAE, a self-supervised model for dynamic functional connectivity graphs

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Pre-trains on 40K+ UK Biobank fMRI samples to learn useful representations

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Outperforms baselines in downstream tasks like classification and regression

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Particularly effective for psychiatric diagnosis with limited labeled data

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Validated extensively on diverse fMRI datasets like ABCD, HCP, ABIDE

Explore the topics in this paper

fmri

functional connectivity

psychiatric diagnosis

self-supervised learning

spatio-temporal modeling

AI generated summary

Self-Supervised Learning for Functional Connectivity Networks

This paper proposes a self-supervised learning framework tailored for functional connectivity networks from fMRI data. It introduces Spatio-Temporal Masked Autoencoder (ST-MAE) to effectively capture both spatial graph structure and temporal dynamics. Pre-trained on a large UK Biobank fMRI dataset, it demonstrates superior performance over baselines in downstream tasks including gender/age prediction and psychiatric diagnosis.