BrainTAP: Brain Disorder Prediction with Adaptive Distill and Selective Prior Integration

TL;DR

BrainTAP is a novel transformer-based framework that adaptively fuses multimodal brain connectivity data and expert priors to improve prediction of neurodevelopmental disorders.

Contribution

It introduces Adaptive Mutual Distill and Selective Prior Fusion, enabling effective, adaptive integration of heterogeneous neuroimaging data and expert knowledge.

Findings

Outperforms state-of-the-art baselines on ABCD dataset

Effectively preserves modality-specific signals and cross-modal synergies

Demonstrates superior accuracy in predicting attention-related disorders

Abstract

Predicting clinical outcomes from brain networks in large-scale neuroimaging cohorts such as the Adolescent Brain Cognitive Development (ABCD) study requires effectively integrating functional connectivity (FC) and structural connectivity (SC) while incorporating expert neurobiological knowledge. However, existing multimodal fusion approaches are shallow or over-homogenize the inherently heterogeneous characteristics of FC and SC, while expert-defined anatomical priors are underutilized with static integration. To address these limitations, we propose Brain Transformer with Adaptive Mutual-Distill and Selective Prior Fusion (BrainTAP). We introduce Adaptive Mutual Distill (AMD), which enables layer-wise information exchange between modalities through learnable distill-intact ratios, preserving modality-specific signals while capturing cross-modal synergies. We further develop Selective Prior Fusion (SPF), which integrates expert-defined anatomical priors in an adaptive way. Evaluated on the ABCD dataset for predicting attention-related disorders, BrainTAP achieves superior performance over state-of-the-art baselines, demonstrating its effectiveness for brain disorder prediction.