{"id":"W2037354398","doi":"10.1016/j.nicl.2015.01.007","title":"Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods","year":2015,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; National Institute on Aging; DoD Alzheimer's Disease Neuroimaging Initiative; Canadian Institutes of Health Research; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; Northern California Institute for Research and Education; BioClinica; Alzheimer's Disease Neuroimaging Initiative; Biogen; U.S. Department of Defense","keywords":"Clustering coefficient; Dementia; Graph; Correlation; Graph theory; Connectivity; Psychology; Disease; Mathematics; Cluster analysis; Medicine; Combinatorics; Statistics; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.004293243,0.0002215183,0.0007775544,0.0001250709,0.0002041639,0.00002414078,0.0006320655,0.0000676994,0.00003095965],"category_scores_gemma":[0.0529804,0.0001290693,0.0007988951,0.00194776,0.003861895,0.0001678464,0.0003336481,0.0003936336,0.000001125708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001174094,"about_ca_system_score_gemma":0.000101348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005597707,"about_ca_topic_score_gemma":0.0001005822,"domain_scores_codex":[0.9943216,0.003003997,0.001026841,0.0006951811,0.0006733148,0.0002790857],"domain_scores_gemma":[0.9671836,0.03026975,0.0009835839,0.001192127,0.0002547435,0.0001162233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00123972,0.000405922,0.9460961,0.00003501886,0.001063347,0.00001579616,0.0009243569,0.002051572,0.008342685,0.0369559,0.0005298819,0.002339699],"study_design_scores_gemma":[0.0004729885,0.0002952131,0.935725,0.000007258867,0.001324256,0.00000405555,0.0002049849,0.004505714,0.01525849,0.04201674,0.00005669679,0.0001286428],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914424,0.0004272,0.001629747,0.004942043,0.0005638622,0.0004102949,0.0002146812,0.00003237354,0.0003373813],"genre_scores_gemma":[0.9986748,0.00006406187,0.0002247014,0.0009186201,0.00006949653,0.00001621185,0.000003422233,0.00001764208,0.00001108816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04868716,"threshold_uncertainty_score":0.998849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1283346274009246,"score_gpt":0.4441665703765584,"score_spread":0.3158319429756339,"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."}}