{"doi":"10.1093/brain/awae388","title":"Deep learning reveals pathology-confirmed neuroimaging signatures in Alzheimer’s, vascular and Lewy body dementias","abstract":"Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical setting, with histopathology remaining the definitive modality for dementia-type diagnosis. To address this clinical challenge, we introduce a neuropathology-based, data-driven, multi-label deep-learning framework to identify and quantify in vivo biomarkers for Alzheimer's disease (AD), vascular dementia (VD) and Lewy body dementia (LBD) using antemortem T1-weighted MRI scans of 423 demented and 361 control participants from National Alzheimer's Coordinating Center and Alzheimer's Disease Neuroimaging Initiative datasets. Based on the best-performing deep-learning model, explainable heat maps were extracted to visualize disease patterns, and the novel Deep Signature of Pathology Atrophy REcognition (DeepSPARE) indices were developed, where a higher DeepSPARE score indicates more brain alterations associated with that specific pathology. A substantial discrepancy in clinical and neuropathological diagnosis was observed in the demented patients: 71% had more than one pathology, but 67% were diagnosed clinically as AD only. Based on these neuropathological diagnoses and leveraging cross-validation principles, the deep-learning model achieved the best performance, with a balanced accuracy of 0.844, 0.839 and 0.623 for AD, VD and LBD, respectively, and was used to generate the explainable deep-learning heat maps and DeepSPARE indices. The explainable deep-learning heat maps revealed distinct neuroimaging brain alteration patterns for each pathology: (i) the AD heat map highlighted bilateral hippocampal regions; (ii) the VD heat map emphasized white matter regions; and (iii) the LBD heat map exposed occipital alterations. The DeepSPARE indices were validated by examining their associations with cognitive testing and neuropathological and neuroimaging measures using linear mixed-effects models. The DeepSPARE-AD index was associated with Mini-Mental State Examination, the Trail Making Test B, memory, hippocampal volume, Braak stages, Consortium to Establish a Registry for Alzheimer's Disease (CERAD) scores and Thal phases [false-discovery rate (FDR)-adjusted P < 0.05]. The DeepSPARE-VD index was associated with white matter hyperintensity volume and cerebral amyloid angiopathy (FDR-adjusted P < 0.001), and the DeepSPARE-LBD index was associated with Lewy body stages (FDR-adjusted P < 0.05). The findings were replicated in an out-of-sample Alzheimer's Disease Neuroimaging Initiative dataset by testing associations with cognitive, imaging, plasma and CSF measures. CSF and plasma tau phosphorylated at threonine-181 (pTau181) were significantly associated with DeepSPARE-AD in the AD and mild cognitive impairment amyloid-β positive (AD/MCIΑβ+) group (FDR-adjusted P < 0.001), and CSF α-synuclein was associated solely with DeepSPARE-LBD (FDR-adjusted P = 0.036). Overall, these findings demonstrate the advantages of our innovative deep-learning framework in detecting antemortem neuroimaging signatures linked to different pathologies. The newly deep-learning-derived DeepSPARE indices are precise, pathology-sensitive and single-valued non-invasive neuroimaging metrics, bridging the traditional widely available in vivo T1 imaging with histopathology.","journal":"Brain","year":2024,"id":424416,"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":22,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9546,"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":921615,"name":"Nicolas Honnorat","orcid":"0000-0003-2210-9341","position":1,"is_corresponding":false},{"id":244574,"name":"Jon B. Toledo","orcid":"0000-0003-4366-9268","position":2,"is_corresponding":false},{"id":1051348,"name":"Karl Li","orcid":null,"position":3,"is_corresponding":false},{"id":1050774,"name":"Sokratis Charisis","orcid":"0000-0001-6578-393X","position":4,"is_corresponding":false},{"id":252969,"name":"Tanweer Rashid","orcid":null,"position":5,"is_corresponding":false},{"id":1051349,"name":"Anoop Benet Nirmala","orcid":null,"position":6,"is_corresponding":false},{"id":1221396,"name":"Sachintha Ransara Brandigampala","orcid":null,"position":7,"is_corresponding":false},{"id":1221397,"name":"Mariam Mojtabai","orcid":null,"position":8,"is_corresponding":false},{"id":17639,"name":"Sudha Seshadri","orcid":"0000-0001-6135-2622","position":9,"is_corresponding":false},{"id":230029,"name":"Mohamad Habes","orcid":"0000-0001-9447-5805","position":10,"is_corresponding":false},{"id":1025755,"name":"Di Wang","orcid":"0000-0003-0460-1463","position":0,"is_corresponding":true}],"reference_count":117,"raw_metadata":null,"created_at":"2026-07-19T01:58:07.191194Z","pmid":"39657969","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":[]}