2024
Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm
Jiang Y, Luo C, Wang J, Palaniyappan L, Chang X, Xiang S, Zhang J, Duan M, Huang H, Gaser C, Nemoto K, Miura K, Hashimoto R, Westlye L, Richard G, Fernandez-Cabello S, Parker N, Andreassen O, Kircher T, Nenadić I, Stein F, Thomas-Odenthal F, Teutenberg L, Usemann P, Dannlowski U, Hahn T, Grotegerd D, Meinert S, Lencer R, Tang Y, Zhang T, Li C, Yue W, Zhang Y, Yu X, Zhou E, Lin C, Tsai S, Rodrigue A, Glahn D, Pearlson G, Blangero J, Karuk A, Pomarol-Clotet E, Salvador R, Fuentes-Claramonte P, Garcia-León M, Spalletta G, Piras F, Vecchio D, Banaj N, Cheng J, Liu Z, Yang J, Gonul A, Uslu O, Burhanoglu B, Uyar Demir A, Rootes-Murdy K, Calhoun V, Sim K, Green M, Quidé Y, Chung Y, Kim W, Sponheim S, Demro C, Ramsay I, Iasevoli F, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Park M, Kirschner M, Georgiadis F, Kaiser S, Van Rheenen T, Rossell S, Hughes M, Woods W, Carruthers S, Sumner P, Ringin E, Spaniel F, Skoch A, Tomecek D, Homan P, Homan S, Omlor W, Cecere G, Nguyen D, Preda A, Thomopoulos S, Jahanshad N, Cui L, Yao D, Thompson P, Turner J, van Erp T, Cheng W, Feng J. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm. Nature Communications 2024, 15: 5996. PMID: 39013848, PMCID: PMC11252381, DOI: 10.1038/s41467-024-50267-3.Peer-Reviewed Original ResearchConceptsGray matter changesDisorder constructsEnlarged striatumPsychiatric conditionsMental disordersSubcortical regionsSchizophreniaBiological foundationsMatter changesBrain imagingStriatumDisordersBiological factorsIndividualsSubtypesHealthy subjectsCross-sectional brain imagingHippocampusTemporal trajectoriesInternational cohortSubgroup 2Subgroup 1Subgroups
2023
Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification
Rokham H, Falakshahi H, Fu Z, Pearlson G, Calhoun V. Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification. Human Brain Mapping 2023, 44: 3180-3195. PMID: 36919656, PMCID: PMC10171526, DOI: 10.1002/hbm.26273.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceBrainDeep LearningHumansMagnetic Resonance ImagingPsychotic DisordersReproducibility of ResultsConceptsDynamic functional network connectivityFunctional network connectivityDSM-IVFMRI-based measuresResting-state fMRI dataBiomarker-based approachPsychosis disordersClinical courseBipolar-Schizophrenia NetworkClinical evaluationSymptomatic measuresHealthy controlsPsychotic illnessHealthy individualsNeurological observationsMental disordersReliability of diagnosisStatistical group differencesMental healthNeuroimaging techniquesStatistical ManualDiagnostic problemsGroup differencesIntermediate phenotypesDisorders
2000
White matter hyperintensity volume in late‐onset and early‐onset schizophrenia
Rivkin P, Kraut M, Barta P, Anthony J, Arria A, Pearlson G. White matter hyperintensity volume in late‐onset and early‐onset schizophrenia. International Journal Of Geriatric Psychiatry 2000, 15: 1085-1089. PMID: 11180463, DOI: 10.1002/1099-1166(200012)15:12<1085::aid-gps250>3.0.co;2-x.Peer-Reviewed Original ResearchMeSH KeywordsAge of OnsetAgedCerebral CortexFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedReproducibility of ResultsSchizophreniaConceptsWhite matter hyperintensitiesMagnetic resonance imagingWhite matter hyperintensity volumeLate-onset psychosisEarly-onset schizophreniaEarly-onset schizophrenicsNeuro-imaging studiesNeuro-degenerative processesControl subjectsCase ascertainmentOnset psychosisHyperintensity volumeMatter hyperintensitiesOnset schizophreniaWMH volumeAnatomic correlatesResonance imagingStudy designSchizophreniaSignificant differencesWMH measurementsPresent studySchizophrenicsContinuous measureLatent vulnerabilityA weighted least‐squares algorithm for estimation and visualization of relative latencies in event‐related functional MRI
Calhoun V, Adalı T, Kraut M, Pearlson G. A weighted least‐squares algorithm for estimation and visualization of relative latencies in event‐related functional MRI. Magnetic Resonance In Medicine 2000, 44: 947-954. PMID: 11108633, DOI: 10.1002/1522-2594(200012)44:6<947::aid-mrm17>3.0.co;2-5.Peer-Reviewed Original ResearchMeasurement of the planum temporale (PT) on magnetic resonance imaging scans: temporal PT alone and with parietal extension
Honeycutt N, Musick A, Barta P, Pearlson G. Measurement of the planum temporale (PT) on magnetic resonance imaging scans: temporal PT alone and with parietal extension. Psychiatry Research 2000, 98: 103-116. PMID: 10762736, DOI: 10.1016/s0925-4927(00)00043-3.Peer-Reviewed Original ResearchMeSH KeywordsAdultFemaleFunctional LateralityHumansMagnetic Resonance ImagingMaleMiddle AgedParietal LobeReproducibility of ResultsTemporal Lobe
1997
Measurement of frontal lobe volume on magnetic resonance imaging scans
Aylward E, Augustine A, Li Q, Barta P, Pearlson G. Measurement of frontal lobe volume on magnetic resonance imaging scans. Psychiatry Research 1997, 75: 23-30. PMID: 9287371, DOI: 10.1016/s0925-4927(97)00026-7.Peer-Reviewed Original Research
1993
MRI-guided region of interest placement on emission computed tomograms
Harris G, Pearlson G. MRI-guided region of interest placement on emission computed tomograms. Psychiatry Research 1993, 50: 57-63. PMID: 8511224, DOI: 10.1016/0925-4927(93)90024-c.Peer-Reviewed Original ResearchAnalysis of VarianceBrainHumansMagnetic Resonance ImagingReproducibility of ResultsTomography, Emission-Computed, Single-Photon