CvFormer: Cross-view transFormers with Pre-training for fMRI Analysis of Human Brain

TL;DR

CvFormer is a novel transformer-based method that effectively integrates region of interest and connectivity information in fMRI data for improved brain analysis, using a two-stage training strategy and efficient cross-view modules.

Contribution

This work introduces CvFormer, a cross-view transformer model with a dual-stage training process that captures complementary fMRI features more effectively than existing methods.

Findings

Outperforms existing methods on ABIDE and ADNI datasets

Demonstrates effective cross-view information integration

Validates robustness with a two-stage training strategy

Abstract

In recent years, functional magnetic resonance imaging (fMRI) has been widely utilized to diagnose neurological disease, by exploiting the region of interest (RoI) nodes as well as their connectivities in human brain. However, most of existing works only rely on either RoIs or connectivities, neglecting the potential for complementary information between them. To address this issue, we study how to discover the rich cross-view information in fMRI data of human brain. This paper presents a novel method for cross-view analysis of fMRI data of the human brain, called Cross-view transFormers (CvFormer). CvFormer employs RoI and connectivity encoder modules to generate two separate views of the human brain, represented as RoI and sub-connectivity tokens. Then, basic transformer modules can be used to process the RoI and sub-connectivity tokens, and cross-view modules integrate the complement information across two views. Furthermore, CvFormer uses a global token for each branch as a query to exchange information with other branches in cross-view modules, which only requires linear time for both computational and memory complexity instead of quadratic time. To enhance the robustness of the proposed CvFormer, we propose a two-stage strategy to train its parameters. To be specific, RoI and connectivity views can be firstly utilized as self-supervised information to pre-train the CvFormer by combining it with contrastive learning and then fused to finetune the CvFormer using label information. Experiment results on two public ABIDE and ADNI datasets can show clear improvements by the proposed CvFormer, which can validate its effectiveness and superiority.