{"id":"W3157614183","doi":"10.1002/hbm.25448","title":"Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease","year":2021,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 European Research Council; National Institute of Mental Health; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Takeda Pharmaceutical Company; IXICO; H. Lundbeck A/S; Servier; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Eisai; University of Southern California; Purdue University; Roche; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Drug Discovery Foundation; AbbVie; Alzheimer's Association; National Institute on Alcohol Abuse and Alcoholism; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics","keywords":"Connectome; Cognition; Neuroscience; Resting state fMRI; Generalizability theory; Functional connectivity; Psychology; Identifiability; Machine learning; Computer science; Developmental psychology","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":[0.003409722,0.000790879,0.0008349666,0.0008163564,0.0004199723,0.001052197,0.0008829756,0.0008664172,0.0007176959],"category_scores_gemma":[0.01343198,0.0004883931,0.000820862,0.0005998548,0.0009983592,0.001373515,0.00149338,0.001426021,0.0001327476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008800522,"about_ca_system_score_gemma":0.001163599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005442985,"about_ca_topic_score_gemma":0.005460688,"domain_scores_codex":[0.9993544,0.0003232734,0.00003195761,0.0001571932,0.00007797837,0.00005512656],"domain_scores_gemma":[0.9933572,0.00535656,0.000596398,0.0004050267,0.0001793463,0.0001053549],"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":[0.0000664737,0.00005022773,0.00583602,0.00002892694,0.00008084739,0.00006856785,0.00004908442,0.9725849,0.0009570365,0.004925911,0.0003482757,0.01500383],"study_design_scores_gemma":[0.000005580808,0.00001845603,0.001014464,0.000006398123,0.00001232444,0.00001734663,0.000005681049,0.9884574,0.0002392207,0.01012148,0.0000944858,0.000007162217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2570837,0.0004077199,0.7392398,0.001067362,0.00002162669,0.00004515332,0.0002544911,0.0004271002,0.001453097],"genre_scores_gemma":[0.9599526,0.0002438505,0.0383304,0.0001368818,0.00003639685,0.00006674517,0.0004120261,0.00005549715,0.0007657387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005442985,"threshold_uncertainty_score":0.01803261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07598904256295445,"score_gpt":0.2804950844992605,"score_spread":0.204506041936306,"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."}}