{"id":"W3010641024","doi":"10.1038/s42003-020-0794-7","title":"BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets","year":2020,"lang":"en","type":"article","venue":"Communications Biology","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":681,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Centre Azrieli de recherche sur l'autisme, Institut et Hôpital Neurologiques de Montréal; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Savoy Foundation; National Institutes of Health; McDonnell Center for Systems Neuroscience; Hospital for Sick Children; NIH Blueprint for Neuroscience Research; Canada Research Chairs","keywords":"Toolbox; Connectomics; Python (programming language); Computer science; Neuroimaging; Visualization; Artificial intelligence; Neuroscience; Machine learning; Functional connectivity; Connectome; Biology","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"],"consensus_categories":[],"category_scores_codex":[0.0001915557,0.00006654783,0.0001857367,0.0001034865,0.0002172003,0.00001187707,0.0005860492,0.00002388967,0.000002844382],"category_scores_gemma":[0.008848201,0.00005351692,0.00004654024,0.000759268,0.0005623112,0.00005149029,0.0004742312,0.0001089455,9.812198e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001119095,"about_ca_system_score_gemma":0.00001404918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000448535,"about_ca_topic_score_gemma":0.000412884,"domain_scores_codex":[0.9992384,0.0002118415,0.000166478,0.0002381267,0.00003138698,0.0001137529],"domain_scores_gemma":[0.9850568,0.01412934,0.00008306625,0.0006824607,0.00002515744,0.00002314587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003323618,0.0004515346,0.435582,0.00007355057,0.0006736394,0.000001724485,0.006565012,0.001112995,0.3464976,0.1828766,0.006610438,0.01922267],"study_design_scores_gemma":[0.001978409,0.0002909113,0.2569966,0.00001357954,0.0005974561,0.000007893564,0.001289374,0.5685091,0.007329081,0.003754626,0.1588339,0.0003990317],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7535115,0.001683031,0.004464863,0.2355774,0.0001346548,0.0009376979,0.003311636,0.00005903225,0.0003202411],"genre_scores_gemma":[0.9933783,0.0003801069,0.0004517856,0.005633505,0.000006296106,0.0000580605,0.00008388956,0.000004705395,0.000003383075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5673961,"threshold_uncertainty_score":0.9995007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1297192152391053,"score_gpt":0.3592362413183255,"score_spread":0.2295170260792203,"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."}}