{"id":"W4220966970","doi":"10.3390/e24030390","title":"Functional Connectivity Methods and Their Applications in fMRI Data","year":2022,"lang":"en","type":"review","venue":"Entropy","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Human Connectome Project; Computer science; Neuroimaging; Connectome; Resting state fMRI; Functional connectivity; Neuroscience; Human brain; Functional neuroimaging; Artificial intelligence; Focus (optics); Field (mathematics); Machine learning; Data science; Psychology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001043555,0.000370813,0.0009686072,0.0002497409,0.0004234306,0.00005377472,0.0006162762,0.00009854933,0.0004668435],"category_scores_gemma":[0.008210519,0.000296575,0.000136721,0.0007471542,0.0001766828,0.0001961215,0.001650386,0.0007103495,0.00003401193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002163477,"about_ca_system_score_gemma":0.0001957636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002093065,"about_ca_topic_score_gemma":0.00001826896,"domain_scores_codex":[0.9963073,0.001449585,0.0003489856,0.001389216,0.0002309277,0.0002739737],"domain_scores_gemma":[0.9663114,0.03236529,0.0002161608,0.001034725,0.00001187315,0.00006054775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008137996,0.0001614295,0.00001829032,0.001031548,0.00005420808,0.000004702004,0.00004807147,0.000002674824,0.00004874426,0.02167776,0.005041228,0.9719032],"study_design_scores_gemma":[0.0001276695,0.00002321955,0.00003681204,0.00009921149,0.00006418073,0.00004880281,0.00004667263,0.00007751735,0.00001876761,0.001933066,0.9972647,0.0002593528],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000003649503,0.9863549,0.009931126,0.0004026539,0.0005503951,0.001107144,0.0007439613,0.00009294137,0.0008131655],"genre_scores_gemma":[0.00001917535,0.9972274,0.0006441344,0.0003288805,0.0002260494,0.001038827,0.0002220842,0.00004403864,0.0002494244],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9922235,"threshold_uncertainty_score":0.9999486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2362083248272686,"score_gpt":0.4119511168013659,"score_spread":0.1757427919740973,"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."}}