Generalizable Representation Learning for fMRI-based Neurological Disorder Identification

TL;DR

This paper introduces a novel meta-learning and self-supervised learning approach for fMRI data that enhances the generalization of neurological disorder classification models, especially in scenarios with scarce and heterogeneous clinical data.

Contribution

It proposes a new representation learning strategy combining meta-learning with self-supervised learning to improve generalization across diverse clinical datasets.

Findings

Outperforms existing methods on multiple clinical datasets

Enhances model robustness with limited training data

Demonstrates effective generalization to unseen clinical tasks

Abstract

Despite the impressive advances achieved using deep learning for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in tasks such as identifying neurological disorders. For functional Magnetic Resonance Imaging (fMRI), while data may be abundantly available from healthy controls, clinical data is often scarce, especially for rare diseases, limiting the ability of models to identify clinically-relevant features. We overcome this limitation by introducing a novel representation learning strategy integrating meta-learning with self-supervised learning to improve the generalization from normal to clinical features. This approach enables generalization to challenging clinical tasks featuring scarce training data. We achieve this by leveraging self-supervised learning on the control dataset to focus on inherent features that are not limited to a particular supervised task and incorporating meta-learning to improve the generalization across domains. To explore the generalizability of the learned representations to unseen clinical applications, we apply the model to four distinct clinical datasets featuring scarce and heterogeneous data for neurological disorder classification. Results demonstrate the superiority of our representation learning strategy on diverse clinically-relevant tasks. Code is publicly available at https://github.com/wenhui0206/MeTSK/tree/main