{"id":"W2922358010","doi":"10.1002/brb3.1255","title":"Assessment of dynamic functional connectivity in resting‐state fMRI using the sliding window technique","year":2019,"lang":"en","type":"article","venue":"Brain and Behavior","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Dynamic functional connectivity; Sliding window protocol; Correlation; Functional magnetic resonance imaging; Pearson product-moment correlation coefficient; Metric (unit); Covariance; Pattern recognition (psychology); Partial correlation; Resting state fMRI; False discovery rate; Computer science; Multivariate statistics; Functional connectivity; Spearman's rank correlation coefficient; Mathematics; Statistics; Artificial intelligence; Window (computing); Psychology; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001097986,0.0004563692,0.0003303441,0.0009467233,0.0001719635,0.000426971,0.000330444,0.000358624,0.001135736],"category_scores_gemma":[0.003469721,0.0001384887,0.0003408825,0.0006811348,0.0004041579,0.0005804631,0.0002288176,0.0002525912,0.0001562725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000180136,"about_ca_system_score_gemma":0.0003354937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001000849,"about_ca_topic_score_gemma":0.001430629,"domain_scores_codex":[0.9997213,0.00009396,0.00002149616,0.00007238852,0.0000755566,0.00001532447],"domain_scores_gemma":[0.9992501,0.0004357696,0.0001144616,0.00007872548,0.00009464787,0.00002621928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001007422,0.0002475961,0.0366936,0.001173406,0.0008069654,0.0007948055,0.0006546355,0.04592736,0.4929846,0.006507737,0.001275564,0.4119263],"study_design_scores_gemma":[0.00007073276,0.002450743,0.256149,0.0002100701,0.0005872338,0.003279972,0.0002385056,0.5229259,0.1947562,0.01500674,0.004104977,0.0002198069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5848939,0.001894818,0.4109067,0.00008404296,0.00003493456,0.0001491022,0.0004008903,0.000426328,0.001209269],"genre_scores_gemma":[0.8894204,0.0006072603,0.1092823,0.00001266553,0.00002249529,0.0001376262,0.0002517006,0.00003610318,0.0002294019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001135736,"threshold_uncertainty_score":0.005806804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04910734202987023,"score_gpt":0.3242569765822917,"score_spread":0.2751496345524215,"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."}}