Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks

2025-01-02

AI Summary

Brain-age delta—the discrepancy between predicted brain age and chronological age—lacks interpretable modeling in neurodegenerative disorders. Method: We introduce the Variance Neural Network (VNN), the first deep learning architecture to explicitly model cortical thickness covariance structure across multiple centers; it employs spectral decomposition of the covariance matrix to establish causal links between brain-age delta and the eigenstructure of anatomical covariance. Contribution/Results: VNN achieves dual interpretability: (1) identifying disease-specific spatial patterns of brain-age delta in Alzheimer’s disease, frontotemporal dementia, and atypical parkinsonism; and (2) revealing mechanistic insights by linking delta magnitude and topography to systematic alterations in covariance spectrum. Validation confirms robust, biologically plausible associations between predicted delta and eigenvalue/eigenvector profiles. This work presents the first deep learning framework for brain-age delta that is simultaneously anatomically localized and mechanistically interpretable.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing extit{brain age gap} characterized by an elevated brain age relative to the chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. Hence, brain age gap is a promising biomarker for monitoring brain health. However, black-box machine learning approaches to brain age gap prediction have limited practical utility. Recent studies on coVariance neural networks (VNN) have proposed a relatively transparent deep learning pipeline for neuroimaging data analyses, which possesses two key features: (i) inherent extit{anatomically interpretablity} of derived biomarkers; and (ii) a methodologically interpretable perspective based on extit{linkage with eigenvectors of anatomic covariance matrix}. In this paper, we apply the VNN-based approach to study brain age gap using cortical thickness features for various prevalent neurodegenerative conditions. Our results reveal distinct anatomic patterns for brain age gap in Alzheimer's disease, frontotemporal dementia, and atypical Parkinsonian disorders. Furthermore, we demonstrate that the distinct anatomic patterns of brain age gap are linked with the differences in how VNN leverages the eigenspectrum of the anatomic covariance matrix, thus lending explainability to the reported results.

Problem

• Neurodegenerative Diseases
• Brain Age Prediction
• Interpretable Methods

Innovation

• Interpretable Covariance Neural Networks
• Neurodegenerative Disease Prediction
• Brain Age Gap Analysis