{"id":"W2896337276","doi":"10.1109/jbhi.2018.2875456","title":"Dynamic Graph Theoretical Analysis of Functional Connectivity in Parkinson's Disease: The Importance of Fiedler Value","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pacific Centre for Reproductive Medicine; University of British Columbia","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Dynamic functional connectivity; Graph theory; Connectomics; Power graph analysis; Modularity (biology); Functional magnetic resonance imaging; Graph; Computer science; Connectome; Functional connectivity; Artificial intelligence; Mathematics; Neuroscience; Psychology; Theoretical computer science; Biology; Combinatorics","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":[0.001184265,0.0003761614,0.0004911852,0.002705579,0.0003518919,0.0008341594,0.0004310692,0.0005466294,0.0006577098],"category_scores_gemma":[0.01009831,0.000165863,0.0004268574,0.001268797,0.001110536,0.001888809,0.0003982634,0.000633997,0.0001062148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006947542,"about_ca_system_score_gemma":0.0003733193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003492517,"about_ca_topic_score_gemma":0.002532854,"domain_scores_codex":[0.9996423,0.0001242108,0.00002162607,0.0001003329,0.00008131235,0.00003034927],"domain_scores_gemma":[0.9963061,0.002708563,0.0004202797,0.0002175875,0.0002475933,0.00009981908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003062235,0.0001305032,0.08218403,0.0003597475,0.0003632545,0.001097601,0.001103341,0.4972467,0.02064727,0.1509895,0.002592528,0.2429794],"study_design_scores_gemma":[0.000009313551,0.00009126598,0.04939698,0.00004471319,0.00004291987,0.0005094652,0.0001379491,0.8161386,0.001683245,0.13026,0.00160618,0.00007929122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5170943,0.00239777,0.4754713,0.0007922378,0.00004985967,0.0000696194,0.0004429963,0.0002393121,0.00344258],"genre_scores_gemma":[0.9642794,0.0007416552,0.03404271,0.00004759555,0.00004226277,0.00004634956,0.000285527,0.0000323157,0.0004821713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003492517,"threshold_uncertainty_score":0.006944358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03348454030728518,"score_gpt":0.312632211544642,"score_spread":0.2791476712373568,"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."}}