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SELF-CLUSTERING GRAPH TRANSFORMER APPROACH TO MODEL RESTING STATE FUNCTIONAL BRAIN ACTIVITY.

Bishal Thapaliya^1,2, Esra Akbas¹, Ram Sapkota^1,2

¹Department of Computer Science, Georgia State University, Atlanta, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|April 3, 2026

Summary

A new Self Clustering Graph Transformer (SCGT) method improves brain subnetwork analysis using resting-state fMRI data. SCGT enhances predictions for cognitive scores and gender classification by capturing brain functional connectivity more effectively.

Keywords:

Brain Networks Cognitive Score Prediction Functional Connectivity Gender Classification Graph Transformers

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Area of Science:

Neuroscience
Machine Learning
Brain Imaging

Background:

Resting-state functional magnetic resonance imaging (rs-fMRI) is crucial for understanding brain organization and cognitive processes.
Graph transformers traditionally use uniform node updates, which may not optimally capture complex brain subnetworks.

Purpose of the Study:

Introduce a novel attention mechanism, Self Clustering Graph Transformer (SCGT), for graph transformers.
Address limitations of uniform node updates in graph transformers for brain subnetwork analysis.

Main Methods:

Developed SCGT, a novel attention mechanism for graphs with subnetworks.
Utilized static functional connectivity (FC) correlation features as input.
Applied SCGT to the Adolescent Brain Cognitive Development (ABCD) dataset (7,957 participants).

Main Results:

SCGT effectively captures and interprets brain subnetwork structures through cluster-specific node updates.
SCGT outperformed vanilla graph transformers and other recent models in predicting total cognitive score and gender.
Demonstrated SCGT's efficacy on a large-scale dataset.

Conclusions:

SCGT offers a promising advancement for modeling brain functional connectivity.
The method provides enhanced interpretability of underlying subnetwork structures.
SCGT represents a valuable tool for neuroscience research and brain-related predictions.