Towards Zero-Shot Task-Generalizable Learning on fMRI

TL;DR

This paper introduces TA-GAT, a supervised task-aware network that enhances the generalizability of models across diverse task-based fMRI data by jointly learning task-specific and general-purpose brain representations.

Contribution

The paper presents a novel task-aware neural network architecture that effectively integrates task-specific information into a general-purpose encoder for fMRI analysis.

Findings

Improved cross-task generalization in fMRI analysis.

Flexible plug-and-play architecture for various neural networks.

Enhanced capture of task-dependent brain activity patterns.

Abstract

Functional MRI measuring BOLD signal is an increasingly important imaging modality in studying brain functions and neurological disorders. It can be acquired in either a resting-state or a task-based paradigm. Compared to resting-state fMRI, task-based fMRI is acquired while the subject is performing a specific task designed to enhance study-related brain activities. Consequently, it generally has more informative task-dependent signals. However, due to the variety of task designs, it is much more difficult than in resting state to aggregate task-based fMRI acquired in different tasks to train a generalizable model. To resolve this complication, we propose a supervised task-aware network TA-GAT that jointly learns a general-purpose encoder and task-specific contextual information. The encoder-generated embedding and the learned contextual information are then combined as input to multiple modules for performing downstream tasks. We believe that the proposed task-aware architecture can plug-and-play in any neural network architecture to incorporate the prior knowledge of fMRI tasks into capturing functional brain patterns.