{"doi":"10.48550/arxiv.2408.00527","title":"Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps","abstract":"In the field of neuroimaging, accurate brain age prediction is pivotal for uncovering the complexities of brain aging and pinpointing early indicators of neurodegenerative conditions. Recent advancements in self-supervised learning, particularly in contrastive learning, have demonstrated greater robustness when dealing with complex datasets. However, current approaches often fall short in generalizing across non-uniformly distributed data, prevalent in medical imaging scenarios. To bridge this gap, we introduce a novel contrastive loss that adapts dynamically during the training process, focusing on the localized neighborhoods of samples. Moreover, we expand beyond traditional structural features by incorporating brain stiffness - a mechanical property previously underexplored yet promising due to its sensitivity to age-related changes. This work presents the first application of self-supervised learning to brain mechanical properties, using compiled stiffness maps from various clinical studies to predict brain age. Our approach, featuring dynamic localized loss, consistently outperforms existing state-of-the-art methods, demonstrating superior performance and paving the way for new directions in brain aging research.","journal":"arXiv (Cornell University)","year":2024,"id":501796,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9477,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":289290,"name":"Lucy V. Hiscox","orcid":"0000-0001-6296-7442","position":1,"is_corresponding":false},{"id":289302,"name":"Curtis L. Johnson","orcid":"0000-0002-7760-131X","position":2,"is_corresponding":false},{"id":638656,"name":"Carola‐Bibiane Schönlieb","orcid":"0000-0003-0099-6306","position":3,"is_corresponding":false},{"id":323386,"name":"Gabriele S. Kaminski Schierle","orcid":"0000-0002-1843-2202","position":4,"is_corresponding":false},{"id":1351071,"name":"Angelica I. Avilés-Rivero","orcid":"0000-0002-8878-0325","position":5,"is_corresponding":false},{"id":1351070,"name":"Jakob Träuble","orcid":"0009-0007-2619-9395","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:10:15.999370Z","pmid":null,"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":[]}