Encoding and Decoding of Brain Dynamic Functional Connectivity for ADHD Diagnosis

Deepank Girish, Yi Hao Chan, Sukrit Gupta, Jing Xia, Jagath C. Rajapakse

Abstract:

Recent studies have demonstrated strong associations between the changes in dynamic functional connectivity (FC) and both behavioral and cognitive functions.
The sliding window technique is the most widely used
method for evaluating dynamic FC; however, it faces two
key challenges: distributional shifts across windows and
high dimensionality, as FC is computed across windows
of the entire time series. To address these issues, we
propose BRAINMAP (Bi-level Representation using Attention for INterpretability with Mamba-Aided Prediction) to
model the dynamic FC of the brain. BRAINMAP employs
the Optimal Transport technique to correct distributional
shifts across sliding windows and leverages Graph Neural
Networks (GNNs) in conjunction with a hybrid approach
that integrates an attention mechanism and the Mamba
block to effectively capture spatiotemporal features for
functional MR images. Finally, we introduce a novel Top-K
sliding window feature selection algorithm to induce sparsity in dynamic FC. We conducted an extensive evaluation
of our model for diagnosing Attention Deficit Hyperactivity
Disorder (ADHD) using three resting-state fMRI datasets:
ADHD-200, UCLA, and CNI-TLC, which comprise a total
of 447 subjects with ADHD and 845 typically developing
controls. Our architecture outperformed existing state-ofthe-art dynamic FC models in ADHD detection, achieving
improvements ranging from 3% to 12% across the three
datasets. We demonstrate that our proposed model produces robust biomarkers, most notably the connection between the dorsal attention network and the visual network.
Using an association study, we further establish the clinical
relevance of the identified biomarkers.