Prognostic Utility of Multivariate Morphometry in Schizophrenia

Conclusions: The baseline deficit in a covariance-based network-like spatial component comprising of insula and IFG is predictive of the clinical course of schizophrenia. We do not find any evidence to support the notion of symptoms per se being neurotoxic to gray matter tissue. If judiciously combined with other available predictors of clinical outcome, multivariate morphometric information can improve our ability to predict prognosis in schizophrenia.IntroductionOver the last five decades, several neuroimaging studies have reported numerous morphological abnormalities in the brain, especially in the gray matter volume (GMV), in patients with schizophrenia. Meta-analytical syntheses of whole-brain voxel-based morphometric studies have found consistent reduction in gray matter volume (GMV) of the anterior insula, anterior cingulate cortex (ACC), superior temporal gyrus (STG), middle and inferior frontal gyrus, and thalamus, even at the time of first-episode psychosis (1, 2). Such repeated observations have raised the promise of morphometric signatures being utilized as biomarkers for clinical use (3, 4), though, to date, this promise is yet to be realized (5).The nonspecific nature of GMV deficits across various psychiatric disorders has reduced the diagnostic utility of morphometry (6, 7). Nevertheless, the pathophysiological and outcome-related heterogeneity of schizophrenia raises the prospect of using morphometric variations to predict prognosis. Given that more stable ou...
Source: Frontiers in Psychiatry - Category: Psychiatry Source Type: research

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