Ada-FCN: Adaptive Frequency-Coupled Network for fMRI-Based Brain Disorder Classification

TL;DR

This paper introduces Ada-FCN, a novel adaptive frequency-coupled network that learns task-specific frequency bands and interactions in fMRI data, significantly improving brain disorder classification accuracy.

Contribution

The work presents a new framework combining adaptive frequency decomposition and frequency-coupled connectivity learning for personalized brain disorder diagnosis.

Findings

Outperforms existing methods on ADNI and ABIDE datasets.

Effectively captures intra- and cross-band interactions.

Enhances diagnostic sensitivity and specificity.

Abstract

Resting-state fMRI has become a valuable tool for classifying brain disorders and constructing brain functional connectivity networks by tracking BOLD signals across brain regions. However, existing mod els largely neglect the multi-frequency nature of neuronal oscillations, treating BOLD signals as monolithic time series. This overlooks the cru cial fact that neurological disorders often manifest as disruptions within specific frequency bands, limiting diagnostic sensitivity and specificity. While some methods have attempted to incorporate frequency informa tion, they often rely on predefined frequency bands, which may not be optimal for capturing individual variability or disease-specific alterations. To address this, we propose a novel framework featuring Adaptive Cas cade Decomposition to learn task-relevant frequency sub-bands for each brain region and Frequency-Coupled Connectivity Learning to capture both intra- and nuanced cross-band interactions in a unified functional network. This unified network informs a novel message-passing mecha nism within our Unified-GCN, generating refined node representations for diagnostic prediction. Experimental results on the ADNI and ABIDE datasets demonstrate superior performance over existing methods. The code is available at https://github.com/XXYY20221234/Ada-FCN.