{"id":"W4408111203","doi":"10.1101/2025.02.27.25322897","title":"Leveraging multimodal neuroimaging and GWAS for identifying modality-level causal pathways to Alzheimer’s disease","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Addiction and Mental Health; Public Health Ontario; University of Toronto","funders":"National Heart, Lung, and Blood Institute; Medical Research Council; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Hjartavernd; Connaught Fund; Simon Fraser University; Krembil Foundation; University of Toronto; Erasmus Medisch Centrum; Bundesministerium für Bildung und Forschung; Institut National de la Santé et de la Recherche Médicale; Université de Lille; Canadian Institutes of Health Research; Centre hospitalier régional universitaire de Lille; Centre for Addiction and Mental Health Foundation; Wellcome Trust; Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease; National Institute on Aging; Alzheimer's Association","keywords":"Imaging genetics; Biobank; Neuroimaging; Genome-wide association study; Modality (human–computer interaction); Mendelian randomization; Causality (physics); Genetic association; Genomics; Psychology; Computer science; Data science; Computational biology; Neuroscience; Biology; Genetic variants; Bioinformatics; Artificial intelligence; Genetics; Genome; Gene; Single-nucleotide polymorphism","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006490424,0.0008143833,0.000984474,0.002689842,0.0004235624,0.001340105,0.0008030885,0.00073361,0.006004307],"category_scores_gemma":[0.02798155,0.000437932,0.001786279,0.002500551,0.0005955084,0.0007689107,0.001556045,0.00101418,0.0006847722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002678909,"about_ca_system_score_gemma":0.0009459102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004688783,"about_ca_topic_score_gemma":0.005261674,"domain_scores_codex":[0.9978383,0.001313764,0.000138153,0.0004148427,0.0001836636,0.0001113202],"domain_scores_gemma":[0.9905191,0.006974943,0.0007172757,0.001260542,0.0003053665,0.0002227444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001914884,0.0002693903,0.434602,0.0009487485,0.005995378,0.003396109,0.0007686754,0.03812237,0.01346739,0.03638282,0.02663519,0.437497],"study_design_scores_gemma":[0.0005985024,0.0005461625,0.21129,0.0003001644,0.003284548,0.003112283,0.0003560077,0.4498716,0.009548598,0.2953399,0.02553434,0.0002179468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1635334,0.00228093,0.8159478,0.003299413,0.0002023695,0.0001715716,0.007835253,0.004193687,0.002535427],"genre_scores_gemma":[0.7543449,0.001039468,0.2367632,0.000658372,0.0004067799,0.0002307351,0.004273112,0.0005173454,0.001766023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006490424,"threshold_uncertainty_score":0.03432506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2798626862278928,"score_gpt":0.4197805164507116,"score_spread":0.1399178302228188,"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."}}