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Learning the Hierarchical Organization in Brain Network for Brain Disorder Diagnosis

Jingfeng Tang, Peng Cao, Guangqi Wen, Jinzhu Yang, Xiaoli Liu, Osmar R. Zaiane

TL;DR

The Brain Hierarchical Organization Learning (BrainHO) is proposed to learn inherently hierarchical brain network dependencies based on their intrinsic features rather than predefined sub-network labels, to ensure diverse, complementary, and stable organizations.

Abstract

Brain network analysis based on functional Magnetic Resonance Imaging (fMRI) is pivotal for diagnosing brain disorders. Existing approaches typically rely on predefined functional sub-networks to construct sub-network associations. However, we identified many cross-network interaction patterns with high Pearson correlations that this strict, prior-based organization fails to capture. To overcome this limitation, we propose the Brain Hierarchical Organization Learning (BrainHO) to learn inherently hierarchical brain network dependencies based on their intrinsic features rather than predefined sub-network labels. Specifically, we design a hierarchical attention mechanism that allows the model to aggregate nodes into a hierarchical organization, effectively capturing intricate connectivity patterns at the subgraph level. To ensure diverse, complementary, and stable organizations, we incorporate an orthogonality constraint loss, alongside a hierarchical consistency constraint strategy, to refine node-level features using high-level graph semantics. Extensive experiments on the publicly available ABIDE and REST-meta-MDD datasets demonstrate that BrainHO not only achieves state-of-the-art classification performance but also uncovers interpretable, clinically significant biomarkers by precisely localizing disease-related sub-networks.

Learning the Hierarchical Organization in Brain Network for Brain Disorder Diagnosis

TL;DR

The Brain Hierarchical Organization Learning (BrainHO) is proposed to learn inherently hierarchical brain network dependencies based on their intrinsic features rather than predefined sub-network labels, to ensure diverse, complementary, and stable organizations.

Abstract

Brain network analysis based on functional Magnetic Resonance Imaging (fMRI) is pivotal for diagnosing brain disorders. Existing approaches typically rely on predefined functional sub-networks to construct sub-network associations. However, we identified many cross-network interaction patterns with high Pearson correlations that this strict, prior-based organization fails to capture. To overcome this limitation, we propose the Brain Hierarchical Organization Learning (BrainHO) to learn inherently hierarchical brain network dependencies based on their intrinsic features rather than predefined sub-network labels. Specifically, we design a hierarchical attention mechanism that allows the model to aggregate nodes into a hierarchical organization, effectively capturing intricate connectivity patterns at the subgraph level. To ensure diverse, complementary, and stable organizations, we incorporate an orthogonality constraint loss, alongside a hierarchical consistency constraint strategy, to refine node-level features using high-level graph semantics. Extensive experiments on the publicly available ABIDE and REST-meta-MDD datasets demonstrate that BrainHO not only achieves state-of-the-art classification performance but also uncovers interpretable, clinically significant biomarkers by precisely localizing disease-related sub-networks.
Paper Structure (16 sections, 6 equations, 3 figures, 1 table)

This paper contains 16 sections, 6 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Method
  3. overview
  4. Hierarchical Attention Mechanism
  5. Node to Node:
  6. Node to subgraph:
  7. Subgraph to graph:
  8. Subgraph Orthogonality
  9. Hierarchical Consistency
  10. Experiments
  11. Dataset and experimental setup
  12. Results and Discussion
  13. Comparison with state-of-the-art Methods:
  14. Ablation study:
  15. Interpretability of the learned sub-networks:
  16. ...and 1 more sections

Figures (3)

  • Figure 1: (a) Average Pearson correlation between brain regions on the ABIDE dataset, revealing strong interactions of brain regions across predefined sub-networks. (b) Learned sub-networks on the ABIDE and REST-meta-MDD datasets. Derived from attention weights, these visualizations demonstrate that BrainHO uncovers disease-related sub-networks that span predefined sub-networks(identified via subgraph to graph attention weights) as well as functionally consistent sub-networks(determined by the proportion of functional sub-networks).
  • Figure 2: Framework of the proposed BrainHO. The model constructs the brain network in a bottom-up attention learning manner while enforcing top-down hierarchical consistency. The Hierarchical Attention module comprises three distinct stages: node-to-node, node-to-subgraph, and subgraph-to-graph.
  • Figure 3: The interpretibility of the sub-networks identified by our model.