2021
Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups
Clausen AN, Fercho KA, Monsour M, Disner S, Salminen L, Haswell CC, Rubright EC, Watts AA, Buckley MN, Maron-Katz A, Sierk A, Manthey A, Suarez-Jimenez B, Olatunji BO, Averill CL, Hofmann D, Veltman DJ, Olson EA, Li G, Forster GL, Walter H, Fitzgerald J, Théberge J, Simons JS, Bomyea JA, Frijling JL, Krystal JH, Baker JT, Phan KL, Ressler K, Han LKM, Nawijn L, Lebois LAM, Schmaal L, Densmore M, Shenton ME, van Zuiden M, Stein M, Fani N, Simons RM, Neufeld RWJ, Lanius R, van Rooij S, Koch SBJ, Bonomo S, Jovanovic T, deRoon-Cassini T, Ely TD, Magnotta VA, He X, Abdallah CG, Etkin A, Schmahl C, Larson C, Rosso IM, Blackford JU, Stevens JS, Daniels JK, Herzog J, Kaufman ML, Olff M, Davidson RJ, Sponheim SR, Mueller SC, Straube T, Zhu X, Neria Y, Baugh LA, Cole JH, Thompson PM, Morey RA. Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups. Brain And Behavior 2021, 12: e2413. PMID: 34907666, PMCID: PMC8785613, DOI: 10.1002/brb3.2413.Peer-Reviewed Original ResearchConceptsPosttraumatic stress disorderEffects of PTSDBrain-PADBrain ageMale controlsOld maleStress disorderAge-related brain changesStructural magnetic resonance imagingBrain structural magnetic resonance imagingOlder age groupsMagnetic resonance imagingChronological ageSubset of controlsContext of PTSDControl subjectsBrain changesBrain agingFuture longitudinal researchTreatment approachesLinear mixed effects modelsAdult subjectsResonance imagingAge groupsPTSD assessment
2018
Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality
Zhang X, Hu Y, Aouizerat BE, Peng G, Marconi VC, Corley MJ, Hulgan T, Bryant KJ, Zhao H, Krystal JH, Justice AC, Xu K. Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality. Clinical Epigenetics 2018, 10: 155. PMID: 30545403, PMCID: PMC6293604, DOI: 10.1186/s13148-018-0591-z.Peer-Reviewed Original ResearchConceptsWhite blood cellsSmoking-associated DNA methylationHIV prognosisInfection-related clinical outcomesBlood cellsSmoking-associated CpGsHIV-positive individualsImmune-related outcomesEpigenome-wide significant CpGsClinical outcomesTobacco smokingVeteran populationSurvival rateDNA methylation indexMortalityFrailtyHIVMethylation indexPrognosisMethylation signaturesDNA methylationOutcomesCell cycleCpGSignificant CpGs
2016
Cross-trial prediction of treatment outcome in depression: a machine learning approach
Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, Cannon TD, Krystal JH, Corlett PR. Cross-trial prediction of treatment outcome in depression: a machine learning approach. The Lancet Psychiatry 2016, 3: 243-250. PMID: 26803397, DOI: 10.1016/s2215-0366(15)00471-x.Peer-Reviewed Original ResearchConceptsTreatment outcomesTreatment groupsEscitalopram treatment groupSpecific antidepressantsPatient-reported dataSequenced Treatment AlternativesClinical trial dataIndependent clinical trialsClinical remissionSymptomatic remissionClinical trialsTreatment efficacyPatientsProspective identificationTreatment alternativesTrial dataDepressionRemissionAntidepressantsOutcomesGroupLevel 1CitalopramCohortClinicians