2023
Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders
Zhu T, Wang W, Chen Y, Kranzler H, Li C, Bi J. Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2023, 9: 326-336. PMID: 37696489, PMCID: PMC10976073, DOI: 10.1016/j.bpsc.2023.08.010.Peer-Reviewed Original ResearchNicotine use disorderHealthy controlsFunctional connectivity featuresUse disordersMagnetic resonance imagingNUD subjectsVisual cortexResonance imagingClinical metricsFunctional connectivityNoninvasive toolNeural phenotypesSample of individualsMulti-task learningTransdiagnostic approachUK BiobankReplication setGenetic profileMarkersReplication sampleHighest areaDisordersDepressionAUDBody of literature
2019
An information network flow approach for measuring functional connectivity and predicting behavior
Kumar S, Yoo K, Rosenberg MD, Scheinost D, Constable RT, Zhang S, Li C, Chun MM. An information network flow approach for measuring functional connectivity and predicting behavior. Brain And Behavior 2019, 9: e01346. PMID: 31286688, PMCID: PMC6710195, DOI: 10.1002/brb3.1346.Peer-Reviewed Original ResearchConceptsFunctional brain connectivityFunctional magnetic resonance imagingFMRI time coursesIndividual differencesTask performanceMeasures of attentionSustained attention taskAttention task performanceResting-state fMRI dataSample of individualsAttention taskFMRI dataFunctional connectivityFC patternsBrain connectivityPearson correlationInformation theory statisticsInformation flowMachine-learning modelsMeasuresMagnetic resonance imagingAttentionNetwork flow approachTime courseDifferent datasets