{"id":"W2980408648","doi":"10.1101/19008631","title":"NeuroMark: a fully automated ICA method to identify effective fMRI markers of brain disorders","year":2019,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre; Lawson Health Research Institute","funders":"National Institute on Aging; National Institutes of Health; H. Lundbeck A/S; Servier; National Natural Science Foundation of China; Eisai; Genentech; IXICO; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Science Foundation","keywords":"Neuroimaging; Schizophrenia (object-oriented programming); Independent component analysis; Functional magnetic resonance imaging; Brain activity and meditation; Computer science; Bipolar disorder; Autism; Autism spectrum disorder; Psychology; Artificial intelligence; Neuroscience; Cognition; Psychiatry; Electroencephalography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002002023,0.0006485924,0.001027237,0.0006146224,0.0001782762,0.00009095525,0.001053529,0.0003062869,0.00008401296],"category_scores_gemma":[0.03892041,0.0006466645,0.0004704045,0.0009203342,0.0002326528,0.0001355517,0.002502758,0.0009073543,0.0002602219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001653827,"about_ca_system_score_gemma":0.0001665757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002064462,"about_ca_topic_score_gemma":0.00009331736,"domain_scores_codex":[0.9932044,0.00252641,0.0006234336,0.002066934,0.0009634199,0.0006153713],"domain_scores_gemma":[0.9734833,0.02446245,0.0004867293,0.001234166,0.0001629603,0.0001703984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001976558,0.000665137,0.03448609,0.002280317,0.0006182906,0.00009724248,0.001636744,0.02215654,0.828422,0.0008372981,0.09338635,0.01343744],"study_design_scores_gemma":[0.001250624,0.00104499,0.8700126,0.0005295179,0.0001746772,0.00002744837,0.0001187543,0.01953251,0.08761541,0.003730461,0.01456499,0.001398056],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9512561,0.00008073152,0.01369661,0.02012572,0.004230573,0.003997724,0.0002379843,0.00101435,0.005360198],"genre_scores_gemma":[0.9924476,0.00002284056,0.002083495,0.003852251,0.00009840433,0.0005508404,0.000008533288,0.0001209067,0.0008151146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8355265,"threshold_uncertainty_score":0.9995984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02483698434242245,"score_gpt":0.3368775214976273,"score_spread":0.3120405371552049,"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."}}