ORCID Profile
0000-0002-8027-6546
Current Organisations
University of Cantabria
,
Instituto de Investigación Marqués de Valdecilla
,
Universidad Nacional de Educación a Distancia
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Publisher: Wiley
Date: 08-09-2021
DOI: 10.1002/HBM.25625
Abstract: Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta‐analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between in iduals with schizophrenia and healthy control participants. The study analyzed data from 2,833 in iduals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippoc us, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1‐weighted structural MRI scans. Mass univariate meta‐analyses revealed more‐concave‐than‐convex shape differences in the hippoc us, amygdala, accumbens, and thalamus in in iduals with schizophrenia compared with control participants, more‐convex‐than‐concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippoc us, amygdala, and thalamus in in iduals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta‐analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.
Publisher: Oxford University Press (OUP)
Date: 19-08-2019
Abstract: In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small s les and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.
Publisher: American Medical Association (AMA)
Date: 2021
Publisher: Elsevier BV
Date: 05-2018
DOI: 10.1016/J.SCHRES.2017.08.032
Abstract: Recovery in psychotic disorders remains a major challenge across mental health. Identifying predictors of recovery in first psychotic episodes is a priority in order to increase knowledge on underlying mechanisms of the illness and to obtain objective severity markers at initial phases. In this study we gathered sociodemographic, clinical and cognitive data to explore predictive variables of recovery after three years follow-up in a s le of 399 patients with a first episode of psychosis (FEP). This is a longitudinal study including patients with a FEP. A dichotomic variable of recovery was created according to symptomatic and functional outcome after 3years follow-up. Significant variables in univariate analysis were entered into a binary logistic regression to obtain a multivariate prediction model of recovery. The predictive model was statistically significant and classified an overall of 76% of patients correctly, specifically 86.7% of patients that would not recover and 55% of the patients that would recover. From all the variables that where significantly different between recovered and not recovered patients, only speed of processing, executive functions and premorbid adjustment were found to be significant predictors of recovery. This study provides evidence that the degree of basal impairment in cognitive functions related to the Prefrontal Cortex and a worst premorbid adaptation predict in a significant way which patients are less likely to recover three years after a FEP.
Publisher: Elsevier BV
Date: 10-2020
No related grants have been discovered for Esther Setién-Suero.