{"id":"W4410298844","doi":"10.1162/jocn_a_02345","title":"Personalized Neural State Segmentation: Validating the Greedy State Boundary Search Algorithm for Individual-level Functional Magnetic Resonance Imaging Data","year":2025,"lang":"en","type":"article","venue":"Journal of Cognitive Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Functional magnetic resonance imaging; Segmentation; Artificial intelligence; Pattern recognition (psychology); Computer science; Psychology; Boundary (topology); Normative model of decision-making; Machine learning; Normative; Mathematics; Neuroscience","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","sts"],"consensus_categories":[],"category_scores_codex":[0.002350148,0.0002671177,0.0002982128,0.0003243639,0.001623006,0.0006170804,0.001156188,0.00002143974,0.00002243033],"category_scores_gemma":[0.0170217,0.0002064712,0.0001304139,0.001182027,0.001467169,0.001620323,0.0007415355,0.000586684,0.000003825524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000875588,"about_ca_system_score_gemma":0.000740314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008407137,"about_ca_topic_score_gemma":0.000005597782,"domain_scores_codex":[0.9958743,0.0006202632,0.0006308848,0.0008123431,0.00152099,0.0005412271],"domain_scores_gemma":[0.9850624,0.01324254,0.0004365285,0.0003071931,0.0008495695,0.0001017235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008298417,0.0003284866,0.005962769,0.000052347,0.00002701029,0.00017735,0.001681247,0.0004141425,0.1369255,0.0001678105,0.008453249,0.8449802],"study_design_scores_gemma":[0.01442581,0.002520726,0.5009208,0.0009779132,0.0004608924,0.002390198,0.006168459,0.2771762,0.1531787,0.007754449,0.03256725,0.00145861],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3726435,0.004856378,0.5780991,0.02677856,0.008692542,0.002603472,0.005647039,0.0001141889,0.0005652448],"genre_scores_gemma":[0.9505614,0.0003985635,0.004595615,0.03971954,0.0005463119,0.0001019578,0.00002812647,0.00006394015,0.003984532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8435216,"threshold_uncertainty_score":0.9996768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1548623174150973,"score_gpt":0.3566925809275665,"score_spread":0.2018302635124692,"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."}}