Transfer learning to decode brain states reflecting the relationship between cognitive tasks

TL;DR

This paper introduces a transfer learning framework that decodes brain states to reveal relationships between cognitive tasks, aligning with neural overlaps and aiding in task decoding with limited fMRI data.

Contribution

It links transfer learning with neuroscience to reflect cognitive task relationships and guides source task selection for neural decoding.

Findings

Transfer learning creates a cognitive taskonomy consistent with neurosynth.

Transfer learning improves task decoding when source and target activate similar brain regions.

The framework provides insights into relationships among multiple cognitive tasks.

Abstract

Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance improvement by transfer learning. In neuroscience, the relationship between cognitive tasks is usually represented by similarity of activated brain regions or neural representation. However, no study has linked transfer learning and neuroscience to reveal the relationship between cognitive tasks. In this study, we propose a transfer learning framework to reflect the relationship between cognitive tasks, and compare the task relations reflected by transfer learning and by the overlaps of brain regions (e.g., neurosynth). Our results of transfer learning create cognitive taskonomy to reflect the relationship between cognitive tasks which is well in line with the task relations derived from neurosynth. Transfer learning performs better in task decoding with fMRI data if the source and target cognitive tasks activate similar brain regions. Our study uncovers the relationship of multiple cognitive tasks and provides guidance for source task selection in transfer learning for neural decoding based on small-sample data.