2024
A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data
Ajith M, M. Aycock D, B. Tone E, Liu J, B. Misiura M, Ellis R, M. Plis S, Z. King T, M. Dotson V, Calhoun V. A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data. Aperture Neuro 2024, 4 DOI: 10.52294/001c.118576.Peer-Reviewed Original ResearchStatic functional network connectivityBrain health indexBrain healthResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingPsychological assessment measuresAssessment dataFunctional network connectivityMental health disordersBrain systemsEvaluating brain healthNeuroimaging dataRs-fMRINeural patternsPhysical well-beingCognitive declineAssessment measuresHealth disordersVariational autoencoderNeuroimagingHealthy brainBrainMagnetic resonance imagingTesting phaseWell-beingA deep learning approach for mental health quality prediction using functional network connectivity and assessment data
Ajith M, Aycock D, Tone E, Liu J, Misiura M, Ellis R, Plis S, King T, Dotson V, Calhoun V. A deep learning approach for mental health quality prediction using functional network connectivity and assessment data. Brain Imaging And Behavior 2024, 18: 630-645. PMID: 38340285, DOI: 10.1007/s11682-024-00857-y.Peer-Reviewed Original ResearchStatic functional network connectivityMental health qualityFunctional network connectivityMental health categoriesRs-fMRIMental healthPatterns of abnormal connectivityHealth categoriesHealth qualityDevelopment of personalized interventionsManagement of mental healthResting-state fMRIMeasure mental healthUK Biobank datasetNeural patternsBrain healthVisual domainAbnormal connectionPersonalized interventionsBiobank datasetTreatment responseHealthNetwork connectivityBehavioral aspectsAssessment data
2023
Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience
Schumann G, Andreassen O, Banaschewski T, Calhoun V, Clinton N, Desrivieres S, Brandlistuen R, Feng J, Hese S, Hitchen E, Hoffmann P, Jia T, Jirsa V, Marquand A, Nees F, Nöthen M, Novarino G, Polemiti E, Ralser M, Rapp M, Schepanski K, Schikowski T, Slater M, Sommer P, Stahl B, Thompson P, Twardziok S, van der Meer D, Walter H, Westlye L, Heinz A, Lett T, Vaidya N, Serin E, Neidhart M, Jentsch M, Eils R, Taron U, Schütz T, Banks J, Meyer-Lindenberg A, Tost H, Holz N, Schwarz E, Stringaris A, Christmann N, Jansone K, Siehl S, Ask H, Fernández-Cabello S, Kjelkenes R, Tschorn M, Böttger S, Bernas A, Marr L, Feixas Viapiana G, Eiroa-Orosa F, Gallego J, Pastor A, Forstner A, Claus I, Miller A, Heilmann-Heimbach S, Boye M, Wilbertz J, Schmitt K, Petkoski S, Pitel S, Otten L, Athanasiadis A, Pearmund C, Spanlang B, Alvarez E, Sanchez M, Giner A, Renner P, Gong Y, Dai Y, Xia Y, Chang X, Liu J, Young A, Ogoh G. Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience. JAMA Psychiatry 2023, 80: 1066-1074. PMID: 37610741, DOI: 10.1001/jamapsychiatry.2023.2996.Peer-Reviewed Original ResearchConceptsMental illnessMechanisms of mental illnessSymptoms of depressionEvidence-based interventionsBrain mechanismsPopulation neuroscienceSocioeconomic inequalitiesEnvironmental adversityMental healthSubstance misuseBrain healthPsychosocial effectsDigital healthCohort dataDeep phenotyping dataObjective biomarkersHealthIllnessBrainDevelopment of objective biomarkersImprove outcomesPopulation levelCOVID-19 pandemicPollution measurementsResearch strategy