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LUMINA: Laplacian-Unifying Mechanism for Interpretable Neurodevelopmental Analysis via Quad-Stream GCN

Minkyung Cha, Jooyoung Bae, Jaewon Jung, Ping Shu Ho, Ka Chun Cheung, Namjoon Kim

Abstract

Functional Magnetic Resonance Imaging(fMRI) has now become a classic way for measuring brain activity, and recent trend is shifting toward utilizing fMRI brain data for AI-driven diagnosis. Given that the brain functions as not a discrete but interconnected whole, Graph-based architectures represented by Graph Convolutional Network(GCN) has emerged as a dominant framework for such task, since they are capable of treating ROIs as dynamically interconnected nodes and extracting relational architecture between them. Ironically, however, it is the very nature of GCN's architecture that acts as an obstacle to its performance. The mathematical foundation of GCN, effective for capturing global regularities, acts as a tradeoff; by smoothing features across the connected nodes repeatedly, traditional GCN tend to blur out the contrastive dynamics that might be crucial in identifying certain neurological disorders. In order to break through this structural bottleneck, we propose LUMINA, a Laplacian-Unifying Mechanism for Interpretable Neurodevelopmental Analysis. Our model is a Quad-Stream GCN that employs a bipolar RELU activation and a dual-spectrum graph Laplacian filtering mechanism, thereby capturing heterogeneous dynamics that were often blurred out in conventional GCN. By doing so, we can preserve the diverse range and characteristics of neural connections in each fMRI data. Through 5-fold cross validation on the ADHD200(N=144) and ABIDE(N=579) dataset, LUMINA demonstrates stable diagnostic performance in two of the most critical neurodevelopmental disorder in childhood, ADHD and ASD, outperforming existing models with an accuracy of 84.66% and 88.41% each.

LUMINA: Laplacian-Unifying Mechanism for Interpretable Neurodevelopmental Analysis via Quad-Stream GCN

Abstract

Functional Magnetic Resonance Imaging(fMRI) has now become a classic way for measuring brain activity, and recent trend is shifting toward utilizing fMRI brain data for AI-driven diagnosis. Given that the brain functions as not a discrete but interconnected whole, Graph-based architectures represented by Graph Convolutional Network(GCN) has emerged as a dominant framework for such task, since they are capable of treating ROIs as dynamically interconnected nodes and extracting relational architecture between them. Ironically, however, it is the very nature of GCN's architecture that acts as an obstacle to its performance. The mathematical foundation of GCN, effective for capturing global regularities, acts as a tradeoff; by smoothing features across the connected nodes repeatedly, traditional GCN tend to blur out the contrastive dynamics that might be crucial in identifying certain neurological disorders. In order to break through this structural bottleneck, we propose LUMINA, a Laplacian-Unifying Mechanism for Interpretable Neurodevelopmental Analysis. Our model is a Quad-Stream GCN that employs a bipolar RELU activation and a dual-spectrum graph Laplacian filtering mechanism, thereby capturing heterogeneous dynamics that were often blurred out in conventional GCN. By doing so, we can preserve the diverse range and characteristics of neural connections in each fMRI data. Through 5-fold cross validation on the ADHD200(N=144) and ABIDE(N=579) dataset, LUMINA demonstrates stable diagnostic performance in two of the most critical neurodevelopmental disorder in childhood, ADHD and ASD, outperforming existing models with an accuracy of 84.66% and 88.41% each.
Paper Structure (17 sections, 9 equations, 2 figures, 3 tables)

This paper contains 17 sections, 9 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. fMRI as a Biomarker for Neurodevelopmental Disorder
  3. Graph Neural Networks
  4. Methodology
  5. overview
  6. Breakdown of LUMINA framework
  7. Laplacian Filters and Quad-stream generation
  8. NeuroGraphBlock and Final Classification
  9. Experiments and Results
  10. Experimental Setup
  11. Datasets.
  12. Baseline Models for Comparison.
  13. Implementation Details.
  14. Performance Evaluation
  15. Ablation Study
  16. ...and 2 more sections

Figures (2)

  • Figure 1: Overview of the LUMINA framework. Unlike conventional GCN(Top), our model(Bottom) incorporates a quad-stream input to capture diverse connectivity patterns. After processing by the Multi-Scale Regional Mixer and multiple GCN streams, they are passed through attention mechanism for final classification.
  • Figure 2: Post-hoc analysis of ADHD and ASD via the LUMINA framework. Top 1% ($\approx 61$) connections with highest IGs are shown as lines, with top 5 highlighted in distinct colors. The rest are visualized on a colorscale from blue ($-1$) to red ($+1$).