2024
Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries
García-Marín L, Campos A, Diaz-Torres S, Rabinowitz J, Ceja Z, Mitchell B, Grasby K, Thorp J, Agartz I, Alhusaini S, Ames D, Amouyel P, Andreassen O, Arfanakis K, Arias-Vasquez A, Armstrong N, Athanasiu L, Bastin M, Beiser A, Bennett D, Bis J, Boks M, Boomsma D, Brodaty H, Brouwer R, Buitelaar J, Burkhardt R, Cahn W, Calhoun V, Carmichael O, Chakravarty M, Chen Q, Ching C, Cichon S, Crespo-Facorro B, Crivello F, Dale A, Smith G, de Geus E, De Jager P, de Zubicaray G, Debette S, DeCarli C, Depondt C, Desrivières S, Djurovic S, Ehrlich S, Erk S, Espeseth T, Fernández G, Filippi I, Fisher S, Fleischman D, Fletcher E, Fornage M, Forstner A, Francks C, Franke B, Ge T, Goldman A, Grabe H, Green R, Grimm O, Groenewold N, Gruber O, Gudnason V, Håberg A, Haukvik U, Heinz A, Hibar D, Hilal S, Himali J, Ho B, Hoehn D, Hoekstra P, Hofer E, Hoffmann W, Holmes A, Homuth G, Hosten N, Ikram M, Ipser J, Jack Jr C, Jahanshad N, Jönsson E, Kahn R, Kanai R, Klein M, Knol M, Launer L, Lawrie S, Hellard S, Lee P, Lemaître H, Li S, Liewald D, Lin H, Longstreth W, Lopez O, Luciano M, Maillard P, Marquand A, Martin N, Martinot J, Mather K, Mattay V, McMahon K, Mecocci P, Melle I, Meyer-Lindenberg A, Mirza-Schreiber N, Milaneschi Y, Mosley T, Mühleisen T, Müller-Myhsok B, Maniega S, Nauck M, Nho K, Niessen W, Nöthen M, Nyquist P, Oosterlaan J, Pandolfo M, Paus T, Pausova Z, Penninx B, Pike G, Psaty B, Pütz B, Reppermund S, Rietschel M, Risacher S, Romanczuk-Seiferth N, Romero-Garcia R, Roshchupkin G, Rotter J, Sachdev P, Sämann P, Saremi A, Sargurupremraj M, Saykin A, Schmaal L, Schmidt H, Schmidt R, Schofield P, Scholz M, Schumann G, Schwarz E, Shen L, Shin J, Sisodiya S, Smith A, Smoller J, Soininen H, Steen V, Stein D, Stein J, Thomopoulos S, Toga A, Tordesillas-Gutiérrez D, Trollor J, Valdes-Hernandez M, van ′t Ent D, van Bokhoven H, van der Meer D, van der Wee N, Vázquez-Bourgon J, Veltman D, Vernooij M, Villringer A, Vinke L, Völzke H, Walter H, Wardlaw J, Weinberger D, Weiner M, Wen W, Westlye L, Westman E, White T, Witte A, Wolf C, Yang J, Zwiers M, Ikram M, Seshadri S, Thompson P, Satizabal C, Medland S, Rentería M. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries. Nature Genetics 2024, 56: 2333-2344. PMID: 39433889, DOI: 10.1038/s41588-024-01951-z.Peer-Reviewed Original ResearchSubcortical brain volumesBrain volumePolygenic scoresEffects of brain volumeAttention-deficit/hyperactivity disorderIndividuals of diverse ancestryComorbid neuropsychiatric disordersSubcortical brain structuresGenome-wide association study meta-analysesBrain substratesParticipants of European ancestryAttention-deficit/hyperactivityGene expression patternsNeuropsychiatric disordersDifferentiation time pointsBrain structuresGenomic analysisDiverse ancestryBrain developmentStudy meta-analysesGenetic variantsNeural cell typesPhenotypic varianceRisk genesAging-related processesDistribution of Connectivity Strengths Across Functional Regions has Higher Entropy in Schizophrenia Patients than in Controls
Maksymchuk N, Miller R, Calhoun V. Distribution of Connectivity Strengths Across Functional Regions has Higher Entropy in Schizophrenia Patients than in Controls. 2024, 00: 37-40. DOI: 10.1109/ssiai59505.2024.10508663.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingGroup independent component analysisSchizophrenia patientsCognitive controlResting-state functional magnetic resonance imagingIntrinsic connectivity networksHealthy controlsGender-matched healthy controlsSZ patientsNeuropsychiatric disordersBrain areasBrain networksSchizophreniaDisrupted integrityBrain domainsConnection strengthIndependent component analysisConnectivity networksMagnetic resonance imagingSomatomotorDistribution of connection strengthsResonance imagingCross-sectional dataPatientsDiagnostic tests
2023
Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics
Ellis C, Miller R, Calhoun V. Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics. Neuroimage Reports 2023, 3: 100186. DOI: 10.1016/j.ynirp.2023.100186.Peer-Reviewed Original ResearchEffect of schizophreniaDynamic functional network connectivityBrain network dynamicsNeuropsychiatric disordersBrain activityFunctional magnetic resonance imagingInteractions of brain regionsFunctional network connectivityNetwork dynamicsBrain regionsSchizophreniaClustering algorithmEffect of SZHealthy controlsLearning classificationBrainMagnetic resonance imagingDeep learning modelsDeep learning classificationDisordersNetwork interactionsMachine learning classificationResonance imagingClustersNovel measuresNeuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *
Ellis C, Miller R, Calhoun V. Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083012, DOI: 10.1109/embc40787.2023.10340837.Peer-Reviewed Original ResearchConceptsDynamic functional network connectivityResting-state functional magnetic resonanceFunctional magnetic resonanceNeuropsychiatric disordersFunctional network connectivityCharacterization of schizophreniaCognitive controlDeep learning classifierContext of schizophreniaAuditory networkBrain activityBrain networksVisual networkSubcortical networksCerebellar network