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Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging

Jiwon Youn, Dong Woo Kang, Hyun Kook Lim, Mansu Kim

TL;DR

A novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs), designed to improve interpretability and predictive accuracy in neuroimaging for early AD diagnosis, is introduced.

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory and cognitive decline, affecting millions worldwide. Diagnosing AD is challenging due to its heterogeneous nature and variable progression. This study introduces a novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs), designed to improve interpretability and predictive accuracy in neuroimaging for early AD diagnosis. By clustering brain regions based on functional connectivity and node embedding, this layer improves the GNN's capability to capture complex brain network characteristics. We analyzed neuroimaging data from 383 participants, including both cognitively normal and preclinical AD individuals, using T1-weighted MRI, resting-state fMRI, and FBB-PET to construct brain graphs. Our results show that GNNs with the BA readout layer significantly outperform traditional models in predicting the Preclinical Alzheimer's Cognitive Composite (PACC) score, demonstrating higher robustness and stability. The adaptive BA readout layer also offers enhanced interpretability by highlighting task-specific brain regions critical to cognitive functions impacted by AD. These findings suggest that our approach provides a valuable tool for the early diagnosis and analysis of Alzheimer's disease.

Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging

TL;DR

A novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs), designed to improve interpretability and predictive accuracy in neuroimaging for early AD diagnosis, is introduced.

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory and cognitive decline, affecting millions worldwide. Diagnosing AD is challenging due to its heterogeneous nature and variable progression. This study introduces a novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs), designed to improve interpretability and predictive accuracy in neuroimaging for early AD diagnosis. By clustering brain regions based on functional connectivity and node embedding, this layer improves the GNN's capability to capture complex brain network characteristics. We analyzed neuroimaging data from 383 participants, including both cognitively normal and preclinical AD individuals, using T1-weighted MRI, resting-state fMRI, and FBB-PET to construct brain graphs. Our results show that GNNs with the BA readout layer significantly outperform traditional models in predicting the Preclinical Alzheimer's Cognitive Composite (PACC) score, demonstrating higher robustness and stability. The adaptive BA readout layer also offers enhanced interpretability by highlighting task-specific brain regions critical to cognitive functions impacted by AD. These findings suggest that our approach provides a valuable tool for the early diagnosis and analysis of Alzheimer's disease.

Paper Structure

This paper contains 24 sections, 7 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Aggregation Layer
  4. Graph Convolutional Networks(GCN).
  5. GraphSAGE.
  6. Graph Attention Networks(GAT).
  7. Readout Layer
  8. Mean Pooling Readout Layer
  9. Add Readout Layer
  10. Methods
  11. Data Description
  12. Data Preprocessing
  13. Brain-aware Graph Neural Network
  14. Motivation
  15. Brain-aware Readout Layer (BA readout layer)
  16. ...and 9 more sections

Figures (4)

  • Figure 1: Overview of data processing and analysis pipeline for neuroimaging and graph neural network application. (A-1) fMRI data processing involves acquiring fMRI time-series, followed by brain parcellation and extraction of time courses. (A-2) For FBB-PET data, The T1-weighted images are processed by partial volume correction to produce PET SUVR maps. (B-1) Functional connectivity is calculated from fMRI data, resulting in connectivity matrices that are further refined through thresholding. (B-2) PET SUVR maps are obtained from processed FBB-PET images. (C) The processed data are then represented as graphs, which are input into a GNN. (D) The GNN structure processes these graphs to yield graph-level representations and predicts clinical scores such as PACC.
  • Figure 2: Visualization of brain function clusters derived from GAT
  • Figure 3: Visualization of brain function clusters derived from GraphSAGE
  • Figure 4: Visualization of brain function clusters derived from GCN