{"id":"W2981493641","doi":"10.1109/access.2019.2949577","title":"MCADNNet: Recognizing Stages of Cognitive Impairment Through Efficient Convolutional fMRI and MRI Neural Network Topology Models","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; University Health Network; Centre for Addiction and Mental Health; University of Toronto; McMaster University","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Servier; Eisai; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; BioClinica; Eli Lilly and Company; Bristol-Myers Squibb; Northern California Institute for Research and Education; Alzheimer's Drug Discovery Foundation; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Computer science; Convolutional neural network; Cognition; Network topology; Cognitive impairment; Artificial neural network; Artificial intelligence; Topology (electrical circuits); Neuroscience; Computer network; Psychology; Mathematics","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":[0.000503113,0.0008820204,0.0004611695,0.0006621194,0.0002898727,0.000658981,0.001431089,0.0007277341,0.001155129],"category_scores_gemma":[0.00128467,0.0005006219,0.0005744735,0.0003348591,0.0003081459,0.0009070971,0.0006001373,0.000812194,0.0003320948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00151636,"about_ca_system_score_gemma":0.001358132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01290066,"about_ca_topic_score_gemma":0.02201414,"domain_scores_codex":[0.9998586,0.00001950099,0.000009811512,0.00005105278,0.00003751955,0.00002345165],"domain_scores_gemma":[0.9997584,0.00008525165,0.00003485513,0.00002560109,0.00007335617,0.00002251975],"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":[0.0001905033,0.000104939,0.0037107,0.000102016,0.00008244353,0.0002252142,0.00006690483,0.7486367,0.01565128,0.004908391,0.004382869,0.2219381],"study_design_scores_gemma":[0.000005534906,0.00001896958,0.000289925,0.000003593703,0.000006339982,0.00002691619,0.000003692771,0.994944,0.003090559,0.001023717,0.0005818194,0.00000494633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1373902,0.0006619669,0.8518298,0.0004550396,0.0001499334,0.0002450257,0.0009113711,0.004605148,0.003751535],"genre_scores_gemma":[0.64094,0.0003492953,0.3495604,0.0002743881,0.00004763619,0.0004222119,0.002375976,0.0002494552,0.005780613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01290066,"threshold_uncertainty_score":0.0256511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06509699839379122,"score_gpt":0.3165660777629353,"score_spread":0.251469079369144,"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."}}