{"id":"W3048396787","doi":"10.1016/j.nicl.2020.102375","title":"NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders","year":2020,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":463,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre; Lawson Health Research Institute","funders":"Johnson and Johnson Pharmaceutical Research and Development; National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; Genentech; National Natural Science Foundation of China; GE Healthcare; Janssen Alzheimer Immunotherapy Research And Development; National Institute of General Medical Sciences; Northern California Institute for Research and Education; University of Southern California; National Institute on Aging; Fujirebio Europe; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; National Institutes of Health; H. Lundbeck A/S; Canadian Institutes of Health Research; National Science Foundation","keywords":"Independent component analysis; Neuroimaging; Pipeline (software); Neuroscience; Psychology; Brain mapping; Computer science; Artificial intelligence","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":[0.001145718,0.000274312,0.0005234372,0.0001122197,0.0001698018,0.00005747571,0.0003943577,0.00008880925,0.00005536908],"category_scores_gemma":[0.1222382,0.0002777709,0.0001643443,0.0007761293,0.0005505545,0.0002912589,0.0004155623,0.0004613113,0.00005120662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001106234,"about_ca_system_score_gemma":0.00009623357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002084354,"about_ca_topic_score_gemma":0.00002345407,"domain_scores_codex":[0.9949813,0.001299208,0.0007858737,0.002107037,0.000474534,0.0003520112],"domain_scores_gemma":[0.9851413,0.01333983,0.0002291158,0.0007821971,0.0001050706,0.0004024592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.008702701,0.001833834,0.02609015,0.0001747738,0.00004580904,0.000330544,0.0005024262,0.001207947,0.3906429,0.0002479563,0.5521767,0.01804434],"study_design_scores_gemma":[0.003676733,0.009559969,0.6967873,0.0000439391,0.00008356068,0.00002167094,0.0001464593,0.2409261,0.01780487,0.0002508358,0.02988688,0.0008116994],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8229291,0.00002416579,0.001517958,0.1721803,0.000840586,0.0007658399,0.00009513271,0.0007368995,0.0009100277],"genre_scores_gemma":[0.9322131,0.0000204819,0.001032452,0.06635728,0.0002190246,0.0000194172,0.000003628209,0.00005301263,0.00008157904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6706971,"threshold_uncertainty_score":0.9999675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1257707909536788,"score_gpt":0.4012757250295192,"score_spread":0.2755049340758404,"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."}}