Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction

TL;DR

This paper uses masked autoencoders to reconstruct fMRI data, revealing cognitive task relationships and improving neural decoding by capturing brain dynamics and task similarities.

Contribution

It introduces a transfer learning framework with MAE for fMRI reconstruction and uncovers a cognitive taskonomy based on task similarities.

Findings

MAE effectively captures brain dynamics and interactions.

The derived taskonomy reveals subtask correlations and task similarities.

Reconstruction improves neural decoding performance.

Abstract

Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal completion and denoising, neural signal reconstruction is less studied. Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fMRI) data, and utilize a transfer learning framework to obtain the cognitive taskonomy, a matrix to quantify the similarity between cognitive tasks. Our experimental results demonstrate that the MAE model effectively captures the temporal dynamics patterns and interactions within the brain regions, enabling robust cross-subject fMRI signal reconstruction. The cognitive taskonomy derived from the transfer learning framework reveals the relationships among cognitive tasks, highlighting subtask correlations within motor tasks and similarities between emotion, social, and gambling tasks. Our study suggests that the fMRI reconstruction with MAE model can uncover the latent representation and the obtained taskonomy offers guidance for selecting source tasks in neural decoding tasks for improving the decoding performance on target tasks.