{"doi":"10.1109/msp.2025.3596731","title":"Disentangling Neurodegeneration With Brain Age Gap Prediction Models: A graph signal processing perspective","abstract":"Neurodegenerative disorders exhibit correlated patterns of cortical atrophy across the brain, where the degree of atrophy deviates from what is expected in a typically healthy individual. Brain age is a data-driven estimate of biological age derived from neuroimaging datasets. An increasing brain age gap—defined as a higher predicted brain age relative to chronological age—can indicate greater vulnerability to neurodegeneration and cognitive decline. As such, the brain age gap is a promising biomarker for monitoring brain health. However, the practical adoption of brain age gap metrics derived from prevalent machine learning (ML) approaches is limited by various methodological obscurities that stem from opaque decision-making processes and insufficient handling of statistical phenomena inherent to neurodegeneration. This article introduces key mathematical principles for brain age gap prediction from the perspective of graph signal processing (GSP), aiming to address the core challenges hindering the practical use of the brain age gap as a biomarker. In this context, we survey a principled deep learning framework based on coVariance Neural Networks (VNNs) to infer the brain age gap from anatomical features extracted from structural neuroimaging. VNNs operate on the covariance matrix as a graph, and their theoretical foundations are inspired by recent advances in GSP. We demonstrate that brain age gap derived from a VNN-based ML pipeline exhibit stability, transferability across multi-scale datasets, and improved interpretability; all key properties that enhance the reproducibility and transparency required for principled brain age gap prediction in clinical applications.","journal":"IEEE Signal Processing Magazine","year":2025,"id":548794,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9538,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":998132,"name":"Gonzalo Mateos","orcid":"0000-0002-9847-6298","position":1,"is_corresponding":false},{"id":998133,"name":"Alejandro Ribeiro","orcid":"0000-0003-4230-9906","position":2,"is_corresponding":false},{"id":998131,"name":"Saurabh Sihag","orcid":"0000-0001-9209-7943","position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":null,"created_at":"2026-07-19T02:54:03.053965Z","pmid":"41281222","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}