arXiv Machine Learning August 18, 2026 By An Phan, Yufei Jin, Xingquan Zhu

M-LINKX: Multiview Graph Learning for Brain Cognitive Disease Detection

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arXiv:2608. 14847v1 Announce Type: new Abstract: Electroencephalogram (EEG) is a non-invasive and relatively low-cost procedure that measures brain electricity for the detection of cognitive diseases.

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arXiv Machine Learning

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Interpretable Information-Decomposed Brain Graph Learning for fMRI-based Disease Diagnosis

arXiv:2608.20380v1 Announce Type: cross Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) has enabled non-invasive mapping of functional brain interactions for computer-aided di...

By Dengyi Zhao, Zhiheng Zhou, Zihan Wang, Guiying Yan, Xingqin Qi

arXiv Machine Learning

Jul 15

Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks

arXiv:2510. 11917v2 Announce Type: replace Abstract: Dementia disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit overlapping electrophysiological signatures in EEG that challenge accurate diagnosis.

By Jun-En Ding, Anna Zilverstand, Shihao Yang, Albert Chih-Chieh Yang, Feng Liu

arXiv Machine Learning

Jul 14

MVMGNN;Multi-View Masked Graph Neural Network for Alzheimer's Disease Diagnosis using Structural MRI

arXiv:2607. 09788v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a common neurodegenerative disorder, and early diagnosis is of great significance for delaying disease progression and enabling timely intervention.

By Ni Yao, Zhenxu Wang, Danyang Sun, Chuang Han, Yanting Li, Jiaofen Nan, Fubao Zhu, Chen Zhao, Weihua Zhou

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arXiv AI

Jul 3

SA-HGNN: Sample-Adaptive Hyperbolic Graph Neural Network for EEG-Based Depression Recognition

arXiv:2607. 02063v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have been widely used to capture spatial functional connectivity patterns to improve electroencephalography (EEG)-based depression recognition performance.

By Yang Li, Pan Hu, Yan Zhang, Wenfan Yang, Tao Wu, Lianbo Guo

arXiv Machine Learning

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Explainable Graph-theoretical Machine Learning with Application to Alzheimer's Disease Prediction

The paper introduces Explainable Graph-theoretical Machine Learning (XGML) to build individual metabolic brain graphs from FDG-PET data and identify subgraphs predictive of multivariate Alzheimer’s disease outcomes. Using ADNI data, the best model—kernel density estimation with Hellinger distance and random forest—achieved a Pearson correlation of 0.595 across eight cognitive scores, with the highest performance on ADAS13, ADAS11, and ADASQ4. Key edges were found to be jointly but differentially predictive, indicating potential network biomarkers for cognitive decline, though external validation on OASIS3 showed weaker performance likely due to cohort differences.

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arXiv AI

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MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classification

arXiv:2607. 28681v1 Announce Type: cross Abstract: Functional magnetic resonance imaging (fMRI) is a widely used technique for studying the brain.

By Yang Zhang, Xiao Zhou, Jonathan Warrell, Avram Holmes, Xuan Zhang, Mark Gerstein

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