Background: People with prodromal symptoms have a very high risk of developing psychosis.
Aims: To use functional magnetic resonance imaging to examine the neurocognitive basis of this vulnerability.
Method: Cross-sectional comparison of regional activation in individuals with an'at-risk mental state' (at-risk group: n=17), patients with first-episode schizophreniform psychosis (psychosis group: n=10) and healthy volunteers (controls: n=15) during an overt verbal fluency task and an N-back working memory task.
Results: A similar pattern of between-group differences in activation was evident across both tasks. Activation in the at-risk group was intermediate relative to that in controls and the psychosis group in the inferior frontal and anterior cingulate cortex during the verbal fluency task and in the inferior frontal, dorsolateral prefrontal and parietal cortex during the N-back task.
Conclusions: The at-risk mental state is associated with abnormalities of regional brain function that are qualitatively similar to, but less severe than, those in patients who have recently presented with psychosis.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1192/bjp.bp.107.046789 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Shaping the structural dynamics of motor learning through cueing during sleep.
Sleep
January 2025
UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences and UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium.
Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted Memory Reactivation (TMR), involving cueing learned material during post-training sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and post-training sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during post-training sleep affects performance gains and delayed microstructural remodeling, using both standard Diffusion Tensor Imaging (DTI) and advanced Neurite Orientation Dispersion & Density Imaging (NODDI).
View Article and Find Full Text PDFThe association between multilingual experience factors and cognitive functioning in older adults: A Lifelines study.
J Gerontol B Psychol Sci Soc Sci
January 2025
Linguistics and English as a Second Language, Faculty of Arts, University of Groningen, Groningen, the Netherlands.
Objectives: The complex life experience of speaking two or more languages has been suggested to preserve cognition in older adulthood. This study aimed to investigate this further by examining the relationship between multilingual experience variables and cognitive functioning in a large cohort of older adults in the diversely multilingual north of the Netherlands.
Method: 11,332 older individuals participating in the Lifelines Cohort Study completed a language experience questionnaire.
Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks.
Sensors (Basel)
January 2025
Development Adaptation Handicap (DevAH) Research Unit, Université de Lorraine, 54000 Nancy, France.
Analyzing performance in rowing, e.g., analyzing force and power output profiles produced either on ergometer or on boat, is a priority for trainers and athletes.
View Article and Find Full Text PDFStudy on Long-Term Temperature Variation Characteristics of Concrete Bridge Tower Cracks Based on Deep Learning.
Sensors (Basel)
January 2025
Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China.
Monitoring existing cracks is a critical component of structural health monitoring in bridges, as temperature fluctuations significantly influence crack development. The study of the Huai'an Bridge indicated that concrete cracks predominantly occur near the central tower, primarily due to temperature variations between the inner and outer surfaces. This research aims to develop a deep learning model utilizing Long Short-Term Memory (LSTM) neural networks to predict crack depth based on the thermal variations experienced by the main tower.
View Article and Find Full Text PDFDiagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM.
Sensors (Basel)
December 2024
College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.
Early detection of autism spectrum disorder (ASD) is particularly important given its insidious qualities and the high cost of the diagnostic process. Currently, static functional connectivity studies have achieved significant results in the field of ASD detection. However, with the deepening of clinical research, more and more evidence suggests that dynamic functional connectivity analysis can more comprehensively reveal the complex and variable characteristics of brain networks and their underlying mechanisms, thus providing more solid scientific support for computer-aided diagnosis of ASD.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!