2022
Cognitive Challenges Are Better in Distinguishing Binge From Nonbinge Drinkers: An Exploratory Deep‐Learning Study of fMRI Data of Multiple Behavioral Tasks and Resting State
Li G, Zhang Z, Chen Y, Wang W, Bi J, Tang X, Li C. Cognitive Challenges Are Better in Distinguishing Binge From Nonbinge Drinkers: An Exploratory Deep‐Learning Study of fMRI Data of Multiple Behavioral Tasks and Resting State. Journal Of Magnetic Resonance Imaging 2022, 57: 856-868. PMID: 35808911, DOI: 10.1002/jmri.28336.Peer-Reviewed Original ResearchConceptsNonbinge drinkersBinge drinkingResting-state functional magnetic resonance imaging (fMRI) dataSignificant group main effectBehavioral tasksSupplementary motor areaGradient-echo echo-planar sequenceMultiple behavioral tasksGroup main effectNodal metricsRight cuneusEcho-planar sequenceMagnetic resonance imaging dataMotor areaRegion of interestFunctional connectivityBingeDrinkersTechnical efficacyNeural responsesFunctional magnetic resonance imaging (fMRI) dataNumber of ROIsP-valueStage 2Human Connectome Project
2021
Perceived stress, self-efficacy, and the cerebral morphometric markers in binge-drinking young adults
Li G, Le TM, Wang W, Zhornitsky S, Chen Y, Chaudhary S, Zhu T, Zhang S, Bi J, Tang X, Li CR. Perceived stress, self-efficacy, and the cerebral morphometric markers in binge-drinking young adults. NeuroImage Clinical 2021, 32: 102866. PMID: 34749288, PMCID: PMC8569726, DOI: 10.1016/j.nicl.2021.102866.Peer-Reviewed Original ResearchConceptsGray matter volumePerceived stressNeural markersRegional cortical thicknessCortical structuresAlcohol use behaviorsNon-binge drinkersHuman Connectome ProjectChronic alcohol exposure altersCortical thicknessVoxel-based morphometryEmotional distressConnectome ProjectDrinking menPath analysisCortical regionsDrinking womenBinge drinkingMatter volumeYoung adultsUse behaviorsStress regulationPCC thicknessBingeAlcohol exposure altersIdentification of Binge Drinkers via Convolutional Neural Network and Support Vector Machine
Li G, Du S, Niu J, Zhang Z, Gao T, Tang X, Wang W, Li C. Identification of Binge Drinkers via Convolutional Neural Network and Support Vector Machine. 2021, 00: 715-720. DOI: 10.1109/icma52036.2021.9512720.Peer-Reviewed Original ResearchConvolutional neural networkNeural networkGray matter volumeSVM modelSupport vector machineNon-binge drinkersVector machine modelDeep learningVector machinePsychosocial measuresTraining samplesBinge drinkingYoung adult binge drinkingMachine modelAdult binge drinkingHuman Connectome ProjectNetworkPsychosocial markersClassificationConnectome ProjectBinge drinkersCortical thicknessMatter volumeBingeVolumetric differences