Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning

TL;DR

This paper introduces a meta-learning approach for brain connectome analysis that improves data efficiency by transferring knowledge from large datasets to small ones, incorporating brain-network-specific design strategies.

Contribution

It proposes a novel meta-training framework tailored for brain connectomes, enhancing performance on limited data and revealing dataset and disease similarities.

Findings

Meta-learning outperforms traditional pre-training strategies.

The approach achieves higher and more stable performance.

It uncovers insights into dataset and disease relationships.

Abstract

Brain networks characterize complex connectivities among brain regions as graph structures, which provide a powerful means to study brain connectomes. In recent years, graph neural networks have emerged as a prevalent paradigm of learning with structured data. However, most brain network datasets are limited in sample sizes due to the relatively high cost of data acquisition, which hinders the deep learning models from sufficient training. Inspired by meta-learning that learns new concepts fast with limited training examples, this paper studies data-efficient training strategies for analyzing brain connectomes in a cross-dataset setting. Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets. In addition, we also explore two brain-network-oriented designs, including atlas transformation and adaptive task reweighing. Compared to other pre-training strategies, our meta-learning-based approach achieves higher and stabler performance, which demonstrates the effectiveness of our proposed solutions. The framework is also able to derive new insights regarding the similarities among datasets and diseases in a data-driven fashion.