{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002035582,0.002715923,0.001208815,0.003418416,0.0007451288,0.001257465,0.001468025,0.001148815,0.005293209],"category_scores_gemma":[0.004684956,0.0007906755,0.002274901,0.001844954,0.0005173062,0.001092257,0.001776947,0.001526256,0.004217344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006691766,"about_ca_system_score_gemma":0.002205214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008761946,"about_ca_topic_score_gemma":0.01603653,"domain_scores_codex":[0.9994656,0.0001068775,0.00004038598,0.0002225224,0.00009816382,0.00006636227],"domain_scores_gemma":[0.9990408,0.0004439274,0.0001150681,0.0001473643,0.0002028559,0.00005009149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008096182,0.0003405143,0.008687546,0.0007262643,0.001025846,0.0006174539,0.0005518153,0.02861181,0.09439531,0.003966246,0.04535582,0.8149118],"study_design_scores_gemma":[0.0001897149,0.0002922029,0.02116684,0.00006955104,0.000383033,0.0006556113,0.000242178,0.8846275,0.04296718,0.02782624,0.02138129,0.0001986888],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01231868,0.000479228,0.9518843,0.0002535493,0.00006810859,0.0003267847,0.002615193,0.03117268,0.0008814432],"genre_scores_gemma":[0.0814527,0.0004740589,0.900852,0.0002371555,0.0001040456,0.001229139,0.01103715,0.001863058,0.00275072],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008761946,"threshold_uncertainty_score":0.01770759,"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."}}