Predicting Alzheimer's disease progression using rs-fMRI and a history-aware graph neural network

• URL: http://arxiv.org/abs/2604.06469v1
• Date: Tue, 07 Apr 2026 21:13:50 GMT
• Title: Predicting Alzheimer's disease progression using rs-fMRI and a history-aware graph neural network
• Abstract summary: Alzheimer's disease (AD) affects more than seven million people in the United States alone.<n>We propose a graph neural network (GNN)-based model for predicting whether a subject will transition to a more severe stage of cognitive impairment at their next clinical visit.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Alzheimer's disease (AD) is a neurodegenerative disorder that affects more than seven million people in the United States alone. AD currently has no cure, but there are ways to potentially slow its progression if caught early enough. In this study, we propose a graph neural network (GNN)-based model for predicting whether a subject will transition to a more severe stage of cognitive impairment at their next clinical visit. We consider three stages of cognitive impairment in order of severity: cognitively normal (CN), mild cognitive impairment (MCI), and AD. We use functional connectivity graphs derived from resting-state functional magnetic resonance imaging (rs-fMRI) scans of 303 subjects, each with a different number of visits. Our GNN-based model incorporates a recurrent neural network (RNN) block, enabling it to process data from the subject's entire visit history. It can also work with irregular time gaps between visits by incorporating visit distance information into our input features. Our model demonstrates robust predictive performance, even with missing visits in the subjects' visit histories. It achieves an accuracy of 82.9%, with an especially impressive accuracy of 68.8% on CN to MCI conversions - a task that poses a substantial challenge in the field. Our results highlight the effectiveness of rs-fMRI in predicting the onset of MCI or AD and, in conjunction with other modalities, could offer a viable method for enabling timely interventions to slow the progression of cognitive impairment.

Related papers

• 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)
• Uncovering Alzheimer's Disease Progression via SDE-based Spatio-Temporal Graph Deep Learning on Longitudinal Brain Networks [18.54013631358448]
  We develop a neural network framework to predict future Alzheimer's disease (AD) progression.<n>Our framework effectively learns sparse regional and connective importance probabilities.<n>Our findings highlight the potential of-temporal graph-based learning for early, individualized prediction of AD progression.
  arXiv  Detail & Related papers  (2025-09-26T01:02:34Z)
• Naturalistic Language-related Movie-Watching fMRI Task for Detecting Neurocognitive Decline and Disorder [60.84344168388442]
  Language-related functional magnetic resonance imaging (fMRI) may be a promising approach for detecting cognitive decline and early NCD.<n>We examined the effectiveness of this task among 97 non-demented Chinese older adults from Hong Kong.<n>The study demonstrated the potential of the naturalistic language-related fMRI task for early detection of aging-related cognitive decline and NCD.
  arXiv  Detail & Related papers  (2025-06-10T16:58:47Z)
• Predicting Human Brain States with Transformer [45.25907962341717]
  We show that a self-attention-based model can accurately predict the brain states up to 5.04s with the previous 21.6s.<n>These promising initial results demonstrate the possibility of developing gen-erative models for fMRI data.
  arXiv  Detail & Related papers  (2024-12-11T00:18:39Z)
• Leveraging Bi-Focal Perspectives and Granular Feature Integration for Accurate Reliable Early Alzheimer's Detection [0.0]
  We propose a novel Granular Feature Integration method to combine information extraction at different scales along with an efficient information flow.<n>We also propose a Bi-Focal Perspective mechanism to highlight the subtle neurofibrillary tangles and amyloid plaques in the MRI scans.<n>Our model achieved an F1-Score of 99.31%, precision of 99.24%, and recall of 99.51%.
  arXiv  Detail & Related papers  (2024-07-15T17:22:16Z)
• Multimodal Neuroimaging Attention-Based architecture for Cognitive Decline Prediction [1.4548651568912518]
  Early detection of Alzheimer's Disease is imperative to ensure early treatment and improve patient outcomes. There is very small literature in predicting the conversion to AD and MCI from normal cognitive condition. We propose a novel multimodal neuroimaging attention-based CNN architecture, MNA-net, to predict whether cognitively normal (CN) individuals will develop MCI or AD within a period of 10 years.
  arXiv  Detail & Related papers  (2023-12-21T00:56:51Z)
• Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression [46.394865849252696]
  Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. In a recent paper it was shown that disease progression can be predicted effectively using performance outcome measures and demographic data. We benchmark four continuous time models using a publicly available multiple sclerosis dataset. We find that the best continuous model is often able to outperform the best benchmarked discrete time model.
  arXiv  Detail & Related papers  (2023-02-15T18:45:32Z)
• Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs [0.0]
  Alzheimer's disease (AD) is a neuro-degenerative disease that can cause dementia and result severe reduction in brain function inhibiting simple tasks. We propose a deep Convolutional Neural Network (CNN) architecture consisting of 7,866,819 parameters. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three class accuracy.
  arXiv  Detail & Related papers  (2022-10-22T02:25:07Z)
• Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction [53.122045119395594]
  Unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results. We propose deep learning for UAD in 3D brain MRI considering additional age information. Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction.
  arXiv  Detail & Related papers  (2022-01-31T09:39:52Z)
• Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay [67.50637511633212]
  A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents that adapt is that neural systems struggle to retain previously-acquired knowledge when learning from new samples. This problem is known as catastrophic forgetting (interference) and remains an unsolved problem in the domain of machine learning to this day.
  arXiv  Detail & Related papers  (2021-12-09T07:11:14Z)
• Overcoming the Domain Gap in Neural Action Representations [60.47807856873544]
  3D pose data can now be reliably extracted from multi-view video sequences without manual intervention. We propose to use it to guide the encoding of neural action representations together with a set of neural and behavioral augmentations. To reduce the domain gap, during training, we swap neural and behavioral data across animals that seem to be performing similar actions.
  arXiv  Detail & Related papers  (2021-12-02T12:45:46Z)
• Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data [8.609787905151563]
  Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory. In this paper, we have proposed a smart and accurate way of diagnosing AD based on a two-dimensional deep convolutional neural network (2D-DCNN) using imbalanced three-dimensional MRI dataset. The model classifies MRI into three categories: AD, mild cognitive impairment, and normal control: and has achieved 99.89% classification accuracy with imbalanced classes.
  arXiv  Detail & Related papers  (2021-01-08T06:51:08Z)
• Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity [39.57255380551913]
  We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect Alzheimer's dementia (AD) and its severity. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression.
  arXiv  Detail & Related papers  (2020-08-30T21:47:26Z)
• Longitudinal Pooling & Consistency Regularization to Model Disease Progression from MRIs [11.979581631288832]
  We propose to combine features across visits by coupling feature extraction with a novel longitudinal pooling layer. We evaluate the proposed method on the longitudinal structural MRIs from three datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI), a dataset composed of 274 normal controls and 329 patients with Alcohol Use Disorder (AUD) In all three experiments our method is superior to other widely used approaches for longitudinal classification thus making a unique contribution towards more accurate tracking of the impact of conditions on the brain.
  arXiv  Detail & Related papers  (2020-03-31T05:28:51Z)

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.