{"id":"W2911363141","doi":"10.3389/fnagi.2019.00008","title":"Evaluation of Functional Decline in Alzheimer’s Dementia Using 3D Deep Learning and Group ICA for rs-fMRI Measurements","year":2019,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Douglas Mental Health University Institute","funders":"Ministry of Science and ICT, South Korea","keywords":"Dementia; Clinical Dementia Rating; Functional connectivity; Psychology; Resting state fMRI; Audiology; Artificial intelligence; Medicine; Neuroscience; Internal medicine; Disease; Computer science","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":[0.001070944,0.0007635699,0.0004448947,0.001376397,0.0002048371,0.000511913,0.0003332473,0.0004704505,0.0006053827],"category_scores_gemma":[0.001744509,0.0001572661,0.0004549406,0.0003671446,0.0003074396,0.0003935351,0.0003401526,0.0002738767,0.0002144405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002982322,"about_ca_system_score_gemma":0.000329439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0017538,"about_ca_topic_score_gemma":0.003764187,"domain_scores_codex":[0.9997231,0.00009961692,0.00002123345,0.00006748908,0.00006510349,0.00002345225],"domain_scores_gemma":[0.9996451,0.0001000604,0.00008251543,0.00004900949,0.0000908036,0.00003248118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002578035,0.0006569419,0.3633172,0.000526518,0.001023211,0.0005599828,0.0004950805,0.03819339,0.1510594,0.001199702,0.002179804,0.4382108],"study_design_scores_gemma":[0.00009640789,0.001086795,0.5327096,0.00005780163,0.0003627164,0.001587828,0.0002027961,0.4180106,0.04174531,0.002778552,0.001258025,0.0001035513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9239883,0.0006584878,0.07295306,0.00008884246,0.00002425207,0.0001291989,0.0006158996,0.0004952202,0.001046693],"genre_scores_gemma":[0.9720161,0.0001474464,0.02707396,0.000028718,0.00002209049,0.0001005451,0.000364587,0.00001799799,0.0002285195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0017538,"threshold_uncertainty_score":0.005663753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1430336907744502,"score_gpt":0.327438957027496,"score_spread":0.1844052662530459,"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."}}