{"id":"W4399912647","doi":"10.1088/1741-2552/ae087d","title":"An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer’s disease","year":2025,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Fondazione Cassa di Risparmio di Verona Vicenza Belluno e Ancona; Eisai; Ministero dell’Istruzione, dell’Università e della Ricerca; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; BioClinica; Eli Lilly and Company; Bristol-Myers Squibb; Foundation for the National Institutes of Health; U.S. Department of Defense; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Interpretability; Artificial intelligence; Neuroimaging; Dementia; Grey matter; Computer science; Generative model; Cognition; Default mode network; Neuroscience; Resting state fMRI; Alzheimer's disease; Disease; Machine learning; Psychology; Cognitive psychology; Generative grammar; Medicine; White matter; Pathology; Magnetic resonance imaging","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009348436,0.0008838628,0.0005858143,0.0008519134,0.0002439031,0.0008187076,0.001235968,0.0009810789,0.001622816],"category_scores_gemma":[0.001958717,0.0003291932,0.001117704,0.0005020514,0.000641325,0.0005746048,0.0009544257,0.00121775,0.0004116554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008396607,"about_ca_system_score_gemma":0.0006239417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006504747,"about_ca_topic_score_gemma":0.007262932,"domain_scores_codex":[0.9996907,0.0001141367,0.00001051303,0.0001005116,0.00004239476,0.0000418021],"domain_scores_gemma":[0.9995065,0.0002918166,0.00005312815,0.00004360253,0.00007242281,0.00003248275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000194727,0.0001155417,0.003691593,0.00008973271,0.0001846429,0.0003330641,0.0002052882,0.7884613,0.007812052,0.01505417,0.002377925,0.1814799],"study_design_scores_gemma":[0.000003377676,0.00001377225,0.0002419469,0.000004468988,0.00001176748,0.00002393354,0.00000614582,0.9931584,0.0004147169,0.005854333,0.0002629882,0.00000422334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02848072,0.0006032785,0.9680704,0.000617583,0.00003514916,0.00003807282,0.0002910856,0.0007010471,0.001162608],"genre_scores_gemma":[0.8155865,0.0005167032,0.1768275,0.0006674296,0.0001702734,0.0001555856,0.001169819,0.0001439301,0.004762253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006504747,"threshold_uncertainty_score":0.01293379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01122829743688899,"score_gpt":0.2757049507213792,"score_spread":0.2644766532844903,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}