Background: Impaired cognition is a prominent feature of schizophrenia. To what extent the heterogeneous cognitive impairments can be accounted for by considering only a single underlying impairment or a small number of core impairments remains elusive. This study examined whether cognitive impairments in antipsychotic-naïve, first-episode schizophrenia patients may be determined by a relative slower speed of information processing.
Method: Forty-eight antipsychotic-naïve patients with first-episode schizophrenia and 48 matched healthy controls were administered a comprehensive battery of neuropsychological tests to assess domains of cognitive impairments in schizophrenia. Composite scores were calculated, grouping tests into cognitive domains.
Results: There were significant differences between patients and healthy controls on global cognition and all cognitive domains, including verbal intelligence, processing speed, sustained attention, working memory, reasoning and problem solving, verbal learning and memory, visual learning and memory, and reaction time. All these significant differences, except for verbal intelligence and global cognition, disappeared when processing speed was included as a covariate.
Conclusion: At the first stage of illness, antipsychotic-naïve patients with schizophrenia display moderate/severe impairments in all the cognitive domains assessed. The results support the contention of a global cognitive dysfunction in schizophrenia that to some extent may be determined by impaired processing speed.
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http://dx.doi.org/10.1016/j.eurpsy.2012.06.003 | DOI Listing |
Neurology
February 2025
School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Background And Objectives: Lipid metabolism in older adults is affected by various factors including biological aging, functional decline, reduced physiologic reserve, and nutrient intake. The dysregulation of lipid metabolism could adversely affect brain health. This study investigated the association between year-to-year intraindividual lipid variability and subsequent risk of cognitive decline and dementia in community-dwelling older adults.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Chemical and Biomolecular Engineering, Lehigh University, 124 E. Morton Street, Bethlehem, Pennsylvania 18015, United States.
Quantum dot (QD) light-emitting diodes (QLEDs) are promising candidates for next-generation displays because of their high efficiency, brightness, broad color gamut, and solution-processability. Large-scale solution-processing of electroluminescent QLEDs poses significant challenges, particularly concerning the precise control of the active layer's thickness and uniformity. These obstacles directly impact charge transport, leading to current leakage and reduced overall efficiency.
View Article and Find Full Text PDFChild Neuropsychol
January 2025
Child Development Center, University Children's Hospital Zurich, Zurich, Switzerland.
Executive function (EF) impairments are prevalent in survivors of neonatal critical illness such as children born very preterm (VPT) or with complex congenital heart disease (cCHD). This paper aimed to describe EF profiles in school-aged children born VPT or with cCHD and in typically developing peers, to identify child-specific and family-environmental factors associated with these profiles and to explore links to everyday-life outcomes. Data from eight EF tests assessing working memory, inhibition, cognitive flexibility, switching, and planning in = 529 children aged between 7 and 16 years was subjected into a latent profile analysis.
View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFFront Child Adolesc Psychiatry
January 2025
Bronx-Lebanon Hospital Center, New York, NY, United States.
Background: Autism spectrum disorder is a neurodevelopmental condition characterized by persistent challenges in social communication and restricted, repetitive behaviors. Emotion recognition deficits are a core feature of ASD, impairing social functioning and quality of life. This meta-analysis evaluates emotion recognition accuracy and response time in individuals with autism spectrum disorder compared to neurotypical individuals and those with other neurodevelopmental disorders.
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