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Resting-state functional magnetic resonance imaging (rsfMRI) has become a widely used approach for detecting subtle differences in functional brain fluctuations in various studies of the healthy and disordered brain. Such studies are often based on temporal functional connectivity (i.e., the correlation between time courses derived from regions or networks within the fMRI data). While being successful for a number of tasks, temporal connectivity does not fully leverage the available spatial information. In this research study, we present a new perspective on spatial functional connectivity, which involves learning patterns of spatial coupling among brain networks by utilizing recent advances in deep learning as well as the contrastive learning framework. We show that we can learn domain-specific mappings of brain networks that can, in turn, be used to characterize differences between schizophrenia patients and control. Furthermore, we show that the coupling of intradomain networks in the controls is stronger than in patients suffering from the disorder. We also evaluate the coupling among networks of different domains and find various patterns of stronger or weaker coupling among certain domains, which provide additional insights about the brain.
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This content will become publicly available on April 11, 2026
BrainMAP: Learning Multiple Activation Pathways in Brain Networks
Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and human behavior. To enhance analysis and comprehension of brain activity, Graph Neural Networks (GNNs) have been widely applied to the analysis of functional connectivities (FC) derived from fMRI data, due to their ability to capture the synergistic interactions among brain regions. However, in the human brain, performing complex tasks typically involves the activation of certain pathways, which could be represented as paths across graphs. As such, conventional GNNs struggle to learn from these pathways due to the long-range dependencies of multiple pathways. To address these challenges, we introduce a novel framework BrainMAP to learn multiple pathways in brain networks. BrainMAP leverages sequential models to identify long-range correlations among sequentialized brain regions and incorporates an aggregation module based on Mixture of Experts (MoE) to learn from multiple pathways. Our comprehensive experiments highlight BrainMAP's superior performance. Furthermore, our framework enables explanatory analyses of crucial brain regions involved in tasks.
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- PAR ID:
- 10589584
- Publisher / Repository:
- AAAI
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 39
- Issue:
- 13
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 14432 to 14440
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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