mlsanityData to 7 Oct 2026

BrainMAP: Learning Multiple Activation Pathways in Brain Networks

arXiv 2412.17404 ↗First posted 23 Dec 2024cs.AIcited by 2

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Abstract (arXiv)

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 Activation 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. Our code is provided at https://github.com/LzyFischer/Graph-Mamba.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 54 references that are in the map. First posted on arXiv 23 Dec 2024.

Builds on

28 of 54 references.

References · 28

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16 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Modelscited by 22
  2. 2025Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomescited by 0

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  1. 2025BrainCSD: A Hierarchical Consistency-Driven MoE Foundation Model for Unified Connectome Synthesis and Multitask Brain Trait Prediction
  2. 2026Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis
  3. 2026NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning
  4. 2023BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Model
  5. 2026Behavior Score Prediction in Resting-State Functional MRI by Deep State Space Modeling ↗
  6. 2025Geometry-Guided Generative Representation for Functional Brain Graphs
  7. 2022Improving the Diagnosis of Psychiatric Disorders with Self-Supervised Graph State Space Models
  8. 2018Mixture of Regression Experts in fMRI Encoding