2020
Predicting alcohol dependence from multi‐site brain structural measures
Hahn S, Mackey S, Cousijn J, Foxe JJ, Heinz A, Hester R, Hutchinson K, Kiefer F, Korucuoglu O, Lett T, Li C, London E, Lorenzetti V, Maartje L, Momenan R, Orr C, Paulus M, Schmaal L, Sinha R, Sjoerds Z, Stein DJ, Stein E, van Holst R, Veltman D, Walter H, Wiers RW, Yucel M, Thompson PM, Conrod P, Allgaier N, Garavan H. Predicting alcohol dependence from multi‐site brain structural measures. Human Brain Mapping 2020, 43: 555-565. PMID: 33064342, PMCID: PMC8675424, DOI: 10.1002/hbm.25248.Peer-Reviewed Original ResearchConceptsAlcohol dependenceRight lateral orbitofrontal cortexLeft superior frontal gyrusCurrent alcohol dependenceTransverse temporal gyrusENIGMA Addiction Working GroupInclusion of casesSuperior frontal gyrusStructural magnetic resonanceCortical surface areaRight transverse temporal gyrusLateral orbitofrontal cortexBrain structural measuresCortical thicknessPutamen volumePotential biomarkers
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