{"id":"W4290793146","doi":"10.1016/j.neuroimage.2022.119553","title":"Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability","year":2022,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Institute of Mental Health; McDonnell Center for Systems Neuroscience; National Institutes of Health; NIH Blueprint for Neuroscience Research; Canada Research Chairs; Canada First Research Excellence Fund; Canada Foundation for Innovation; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Tractography; Connectome; Human Connectome Project; Diffusion MRI; Neuroscience; Thalamus; Connectomics; Reliability (semiconductor); Computer science; Psychology; Artificial intelligence; Magnetic resonance imaging; Functional connectivity; Medicine; Physics; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005605338,0.0007540664,0.0005406301,0.001565316,0.0003972642,0.001302759,0.0006253765,0.0008847906,0.0005394274],"category_scores_gemma":[0.01629842,0.0004436828,0.0003183277,0.0007700411,0.001119509,0.00102763,0.0009272441,0.0005806996,0.0002952621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002270719,"about_ca_system_score_gemma":0.0005005326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002217334,"about_ca_topic_score_gemma":0.006112665,"domain_scores_codex":[0.9978143,0.001115821,0.0001646396,0.0005078731,0.000340713,0.00005670812],"domain_scores_gemma":[0.9922092,0.004193377,0.0009757783,0.001384436,0.001076113,0.000161098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001863742,0.0002576224,0.231231,0.001188702,0.00136067,0.0008812201,0.00285896,0.02427213,0.5182811,0.003129952,0.001190521,0.2134844],"study_design_scores_gemma":[0.0001461693,0.001552372,0.5873418,0.0002648855,0.0007250614,0.007337736,0.001133781,0.2400004,0.1379947,0.01496792,0.008267094,0.0002679658],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6394126,0.001621851,0.3557965,0.0002665054,0.00004747756,0.0001964918,0.0006021324,0.0004567499,0.001599654],"genre_scores_gemma":[0.9032652,0.0007920474,0.09466144,0.00007379381,0.00003553625,0.0001147078,0.0004941756,0.0001599916,0.000403045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005605338,"threshold_uncertainty_score":0.02964425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1161828445213634,"score_gpt":0.3562926542491656,"score_spread":0.2401098097278022,"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."}}