Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective

• URL: http://arxiv.org/abs/2510.12763v1
• Date: Tue, 14 Oct 2025 17:44:45 GMT
• Title: Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective
• Authors: Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro,
• Abstract summary: 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.
• Score: 89.99666725996975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neurodegeneration, characterized by the progressive loss of neuronal structure or function, is commonly assessed in clinical practice through reductions in cortical thickness or brain volume, as visualized by structural MRI. While informative, these conventional approaches lack the statistical sophistication required to fully capture the spatially correlated and heterogeneous nature of neurodegeneration, which manifests both in healthy aging and in neurological disorders. To address these limitations, brain age gap has emerged as a promising data-driven biomarker of brain health. The brain age gap prediction (BAGP) models estimate the difference between a person's predicted brain age from neuroimaging data and their chronological age. The resulting brain age gap serves as a compact biomarker of brain health, with recent studies demonstrating its predictive utility for disease progression and severity. However, practical adoption of BAGP models is hindered by their methodological obscurities and limited generalizability across diverse clinical populations. 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). In particular, we focus on graph neural networks (GNNs) and introduce the coVariance neural network (VNN), which leverages the anatomical covariance matrices derived from structural MRI. VNNs offer strong theoretical grounding and operational interpretability, enabling robust estimation of brain age gap predictions. By integrating perspectives from GSP, machine learning, and network neuroscience, this work clarifies the path forward for reliable and interpretable BAGP models and outlines future research directions in personalized medicine.

Related papers

• SurfAge-Net: A Hierarchical Surface-Based Network for Interpretable Fine-Grained Brain Age Prediction [9.571325956619743]
  We propose a novel surface-based brain age prediction network (SurfAge-Net) to capture region-specific developmental patterns.<n>SurfAge-Net establishes a new modeling paradigm by incorporating the connectomic principles of cortical organization.<n>It provides spatially precise and biologically interpretable maps of cortical maturation, effectively identifying heterogeneous delays and regional-specific abnormalities.
  arXiv  Detail & Related papers  (2026-01-28T07:01:57Z)
• MRI-Based Brain Age Estimation with Supervised Contrastive Learning of Continuous Representation [4.845253846645897]
  We propose to use supervised contrastive learning with the recent Rank-N-Contrast (RNC) loss to estimate brain age based on widely used T1w structural MRI.<n>Our proposed method achieves a mean absolute error (MAE) of 4.27 years and an $R2$ of 0.93 with a limited dataset of training samples.<n>As an exploratory study, we employed the proposed method to estimate the gap between the biological and chronological brain ages in Alzheimer's Disease and Parkinson's disease patients.
  arXiv  Detail & Related papers  (2025-11-27T04:46:33Z)
• BrainPath: Generating Subject-Specific Brain Aging Trajectories [9.106315876381698]
  We present BrainPath, a 3D generative framework that learns longitudinal brain aging dynamics during training.<n>BrainPath predicts anatomically faithful MRIs at arbitrary timepoints from a single baseline scan.
  arXiv  Detail & Related papers  (2025-08-21T02:45:28Z)
• 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)
• Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks [1.2115617129203957]
  Quantifying divergence from neurotypical connectivity patterns offers a promising pathway to inform diagnosis and therapeutic interventions. We present the Brain Representation via Individualized Deep Generative Embedding (BRIDGE) framework, which integrates normative modeling with a bio-inspired deep generative model. BRIDGE provides a global neurodivergence score based on the difference between connectivity-based brain age and chronological age, along with region-wise neurodivergence maps that highlight localized connectivity differences.
  arXiv  Detail & Related papers  (2024-10-14T20:21:11Z)
• Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks [102.75954614946258]
  Increasing brain age with respect to chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. NeuroVNN is pre-trained as a regression model on healthy population to predict chronological age. NeuroVNN adds anatomical interpretability to brain age and has a scale-free' characteristic that allows its transference to datasets curated according to any arbitrary brain atlas.
  arXiv  Detail & Related papers  (2024-02-12T14:46:31Z)
• Explainable Brain Age Prediction using coVariance Neural Networks [94.81523881951397]
  We propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) We make two important observations: VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions.
  arXiv  Detail & Related papers  (2023-05-27T22:28:25Z)
• 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)
• Predicting Brain Age using Transferable coVariance Neural Networks [119.45320143101381]
  We have recently studied covariance neural networks (VNNs) that operate on sample covariance matrices. In this paper, we demonstrate the utility of VNNs in inferring brain age using cortical thickness data. Our results show that VNNs exhibit multi-scale and multi-site transferability for inferring brain age In the context of brain age in Alzheimer's disease (AD), our experiments show that i) VNN outputs are interpretable as brain age predicted using VNNs is significantly elevated for AD with respect to healthy subjects.
  arXiv  Detail & Related papers  (2022-10-28T18:58:34Z)

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.