Objective: This multicenter study aimed to verify whether the historical and psychopathological characteristics of a large group of patients with deficit schizophrenia were consistent with those reported in previous studies. The authors also tested the hypothesis that neurological and neuropsychological indices sensitive to frontoparietal dysfunction, but not those sensitive to temporal lobe dysfunction, are more impaired in patients with deficit schizophrenia than in those with non-deficit schizophrenia.
Method: For each patient with deficit schizophrenia enrolled in the study, a matched subject with non-deficit schizophrenia was recruited. Historical, psychopathological, neurological, and neuropsychological evaluations were carried out for all patients.
Results: Patients with deficit schizophrenia, compared with patients with non-deficit schizophrenia, had a similar severity of positive symptoms and disorganization and less hostility. They had poorer premorbid adjustment during childhood and early adolescence and exhibited more impairment in general cognitive abilities. The deficit state was associated with impairment in sequencing of complex motor acts, which suggests frontoparietal dysfunction.
Conclusions: Previous reports of differences in historical, psychopathological, and neuropsychological characteristics between patients with deficit schizophrenia and those with non-deficit schizophrenia were mostly supported by the present findings. Neurological findings suggest that frontoparietal functioning is more impaired in patients with deficit schizophrenia. Deficit schizophrenia might represent a neurodevelopmental subtype of schizophrenia in which significant behavioral and cognitive impairment since childhood compromises the development of basic capacities relevant to subsequent cognitive and social functioning.
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http://dx.doi.org/10.1176/appi.ajp.159.6.983 | DOI Listing |
Comput Med Imaging Graph
January 2025
University of Electronic Science and Technology of China, Chengdu, Sichuan, China. Electronic address:
In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parcellation. The key innovation of DDEvENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI.
View Article and Find Full Text PDFSci Data
January 2025
University of Bergen, Department of Clinical Medicine, Bergen, 5009, Norway.
Mental health is vital to human well-being, and prevention strategies to address mental illness have a significant impact on the burden of disease and quality of life. With the recent developments in body-worn sensors, it is now possible to continuously collect data that can be used to gain insights into mental health states. This has the potential to optimize psychiatric assessment, thereby improving patient experiences and quality of life.
View Article and Find Full Text PDFSchizophr Bull
January 2025
Department of Psychology, University of Maryland, College Park, MD 20742, United States.
Background And Hypothesis: Among individuals living with psychotic disorders, social impairment is common, debilitating, and challenging to treat. While the roots of this impairment are undoubtedly complex, converging lines of evidence suggest that social motivation and pleasure (MAP) deficits play a central role. Yet most neuroimaging studies have focused on monetary rewards, precluding decisive inferences.
View Article and Find Full Text PDFJ Neuropsychol
January 2025
Department of Child and Adolescent Psychiatry, Hacettepe University, Ankara, Türkiye.
This study aims to demonstrate that children and adolescents diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) who exhibit autism traits have a more severe clinical profile in terms of emotion regulation, clinical features related to ADHD, and functionality, compared to those diagnosed with ADHD without these traits. 50 patients with and 64 patients without autism traits between the ages of 8-16 were recruited for the study among the children and adolescents diagnosed with ADHD. The Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version, DSM-5-2016-Turkish Adaptation (K-SADS-PL-DSM-5-T) was used to exclude the diagnosis of Autism Spectrum Disorder (ASD) and detect comorbid psychiatric diagnosis.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Department of Psychiatry, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia.
The ( C677T gene polymorphism is associated with neurological disorders and schizophrenia. Patients diagnosed with schizophrenia and schizoaffective disorder and controls ( 134) had data collected for risk factors, molecular and neuro-sensory variables, symptoms, and functional outcomes. Promising gene variant-related predictive biomarkers were identified for diagnosis by Receiver Operating Characteristics and for illness duration by linear regression.
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