Explainable Graph Neural Networks: Understanding Brain Connectivity and Biomarkers in Dementia

• URL: http://arxiv.org/abs/2509.18568v1
• Date: Tue, 23 Sep 2025 02:52:00 GMT
• Title: Explainable Graph Neural Networks: Understanding Brain Connectivity and Biomarkers in Dementia
• Authors: Niharika Tewari, Nguyen Linh Dan Le, Mujie Liu, Jing Ren, Ziqi Xu, Tabinda Sarwar, Veeky Baths, Feng Xia,
• Abstract summary: This review aims to guide future work toward trustworthy, clinically meaningful, and scalable use of XGNNs in dementia research.<n>This paper presents the first comprehensive review dedicated to XGNNs in dementia research.<n>We examine their applications across Alzheimer's disease, Parkinson's disease, mild cognitive impairment, and multi-disease diagnosis.
• Score: 8.97624586025435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dementia is a progressive neurodegenerative disorder with multiple etiologies, including Alzheimer's disease, Parkinson's disease, frontotemporal dementia, and vascular dementia. Its clinical and biological heterogeneity makes diagnosis and subtype differentiation highly challenging. Graph Neural Networks (GNNs) have recently shown strong potential in modeling brain connectivity, but their limited robustness, data scarcity, and lack of interpretability constrain clinical adoption. Explainable Graph Neural Networks (XGNNs) have emerged to address these barriers by combining graph-based learning with interpretability, enabling the identification of disease-relevant biomarkers, analysis of brain network disruptions, and provision of transparent insights for clinicians. This paper presents the first comprehensive review dedicated to XGNNs in dementia research. We examine their applications across Alzheimer's disease, Parkinson's disease, mild cognitive impairment, and multi-disease diagnosis. A taxonomy of explainability methods tailored for dementia-related tasks is introduced, alongside comparisons of existing models in clinical scenarios. We also highlight challenges such as limited generalizability, underexplored domains, and the integration of Large Language Models (LLMs) for early detection. By outlining both progress and open problems, this review aims to guide future work toward trustworthy, clinically meaningful, and scalable use of XGNNs in dementia research.

Related papers

• R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression [63.97617759805451]
  Early detection of Alzheimer's disease requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility.<n>We introduce R-GenIMA, an interpretable multimodal large language model that couples a novel ROI-wise vision transformer with genetic prompting.<n>R-GenIMA achieves state-of-the-art performance in four-way classification across normal cognition, subjective memory concerns, mild cognitive impairment, and AD.
  arXiv  Detail & Related papers  (2025-12-22T02:54:10Z)
• Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective [89.99666725996975]
  The brain age gap prediction (BAGP) models estimate the difference between a person's predicted brain age from data and their chronological age.<n>This tutorial article provides an overview of BAGP and introduces a principled framework for this application based on recent advancements in graph signal processing (GSP)<n>VNNs offer strong theoretical grounding and operational interpretability, enabling robust estimation of brain age gap predictions.
  arXiv  Detail & Related papers  (2025-10-14T17:44:45Z)
• Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks [56.75602443936853]
  One in five adolescents currently live with a diagnosed mental or behavioral health condition, such as anxiety, depression, or conduct disorder.<n>While prior works use graph neural network (GNN) approaches for disorder prediction, they remain black-boxes, limiting their reliability and clinical translation.<n>In this work, we propose a concept-based diagnosis framework that that encodes interpretable functional connectivity concepts.<n>Our design ensures predictions through clinically meaningful connectivity patterns, enabling both interpretability and strong predictive performance.
  arXiv  Detail & Related papers  (2025-10-02T19:38:46Z)
• Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification [11.038002513199299]
  Alzheimer's Disease is a progressive neurological disorder that is one of the most common forms of dementia.<n>Recent research has utilized electroencephalography (EEG) data to identify biomarkers that distinguish Alzheimer's Disease patients from healthy individuals.
  arXiv  Detail & Related papers  (2025-04-02T03:29:12Z)
• GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks [0.6282459656801734]
  We propose a novel single-modal framework that integrates Kolmogorov-Arnold Networks (KAN) into Graph Convolutional Networks (GCNs) to enhance both diagnostic accuracy and interpretability.<n>GCN-KAN outperforms traditional GCNs by 4-8% in classification accuracy while providing interpretable insights into key brain regions associated with Alzheimer's Disease (AD)<n>This approach offers a robust and explainable tool for early AD diagnosis.
  arXiv  Detail & Related papers  (2025-04-01T16:31:00Z)
• Deep Learning for Early Alzheimer Disease Detection with MRI Scans [1.9806397201363817]
  Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients.<n>This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis.<n>We perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency.
  arXiv  Detail & Related papers  (2025-01-17T07:30:16Z)
• Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks [94.06526659234756]
  Black-box machine learning approaches to brain age gap prediction have limited practical utility.<n>We apply the VNN-based approach to study brain age gap using cortical thickness features for various prevalent neurodegenerative conditions.<n>Our results reveal distinct anatomic patterns for brain age gap in Alzheimer's disease, frontotemporal dementia, and atypical Parkinsonian disorders.
  arXiv  Detail & Related papers  (2025-01-02T19:37:09Z)
• Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging [1.074960192271861]
  This study introduces a novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs) By clustering brain regions based on functional connectivity and node embedding, this layer improves the GNN's capability to capture complex brain network characteristics. Our results show that GNNs with the BA readout layer significantly outperform traditional models in predicting the Preclinical Alzheimer's Cognitive Composite (PACC) score.
  arXiv  Detail & Related papers  (2024-10-03T05:04:45Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring.<n>As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs.<n>The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features [119.45320143101381]
  Graph convolutional networks (GCN) leverage topology-driven graph convolutional operations to combine information across the graph for inference tasks. We have studied GCNs with covariance matrices as graphs in the form of coVariance neural networks (VNNs) VNNs inherit the scale-free data processing architecture from GCNs and here, we show that VNNs exhibit transferability of performance over datasets whose covariance matrices converge to a limit object.
  arXiv  Detail & Related papers  (2023-05-02T22:15:54Z)
• Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation [150.52617238140868]
  We propose low-resource medical dialogue generation to transfer the diagnostic experience from source diseases to target ones. We also develop a Graph-Evolving Meta-Learning framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease.
  arXiv  Detail & Related papers  (2020-12-22T13:20:23Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)
• Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders [10.12649945620901]
  We propose Multi-resolution Edge Network (MENET) to detect disease-specific connectomic benchmarks. MENET accurately predicts diagnostic labels and identifies brain connectivities highly associated with neurological disorders.
  arXiv  Detail & Related papers  (2019-12-03T03:46:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.