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

arXiv: https://arxiv.org/abs/2410.14683 · HTML: https://mlsanity.com/paper/2410.14683/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)

## Built on by (most cited first, 2 of 2)

- [Explainable Graph Neural Networks: Understanding Brain Connectivity and Biomarkers in Dementia](https://mlsanity.com/paper/2509.18568/) (2025)
- [MVMGNN;Multi-View Masked Graph Neural Network for Alzheimer's Disease Diagnosis using Structural MRI](https://mlsanity.com/paper/2607.09788/) (2026)

## Abstract (arXiv)

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.