Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering
Ji Y, Pearlson G, Bustillo J, Kochunov P, Turner J, Jiang R, Shao W, Zhang X, Fu Z, Li K, Liu Z, Xu X, Zhang D, Qi S, Calhoun V. Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering. Schizophrenia Research 2023, 264: 130-139. PMID: 38128344, DOI: 10.1016/j.schres.2023.12.013.Peer-Reviewed Original ResearchPsychosis subtypesSchizoaffective disorderBipolar disorderClinical phenotypeFirst-degree relativesTemporal-occipital cortexAmygdala-hippocampusClinical symptomsNeuroimaging featuresBipolar-Schizophrenia NetworkBrain alterationsHealthy controlsIntermediate Phenotypes (B-SNIP) consortiumOccipital cortexDecreased connectivitySubtypesStructural covarianceFractional amplitudeSubtype IILow-frequency fluctuationsNeurobiological heterogeneityGreater predispositionPsychosis spectrumGroup differencesDiagnostic classificationSymmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter
Colmenares A, Hefner M, Calhoun V, Salerno E, Fanning J, Gothe N, McAuley E, Kramer A, Burzynska A. Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter. Frontiers In Neurology 2023, 14: 1094313. PMID: 37139071, PMCID: PMC10149813, DOI: 10.3389/fneur.2023.1094313.Peer-Reviewed Original ResearchDiffusion tensor imagingAge differencesExamination of age differencesPattern of age differencesFractional anisotropyRadial diffusivityJoint independent component analysisCognitively healthy adultsWhite matterDiffusion tensor imaging parametersFluid abilitiesPrefrontal WMProcessing speedDiagnostic classificationWM pathologyDiffusion tensor imaging datasetsAging WMUnimodal analysisTensor imagingIndependent component analysisClinical samplesHealthy agingCognitionCorpus callosumHealthy adults