Disrupted sleep is more common in older adults (OLD) than younger adults (YOUNG), often co-morbid with other conditions. How these sleep disturbances affect cognitive performance is an area of active study. We examined whether brain activation during verbal encoding correlates with sleep quantity and quality the night before testing in a group of healthy OLD and YOUNG. Twenty-seven OLD (ages 59-82) and 27 YOUNG (ages 19-36) underwent one night of standard polysomnography. Twelve hours post-awakening, subjects performed a verbal encoding task while undergoing functional magnetic resonance imaging. Analyses examined the group (OLD vs. YOUNG) by prior sleep quantity (total sleep time, TST) or quality (sleep efficiency, SE) interaction on cerebral activation, controlling for performance. Longer TST promoted higher levels of activation in the bilateral anterior parahippocampal in OLD and lower activation levels in the left anterior parahippocampus in YOUNG. Greater SE promoted higher activation levels in the left posterior parahippocampus and right inferior frontal gyrus in YOUNG, but not in OLD. The roles of these brain regions in verbal encoding suggest, in OLD, longer sleep duration may be linked to the ability to engage in functional compensation during cognitive challenges. By contrast, in YOUNG, shorter sleep duration may necessitate functional compensation to maintain cognitive performance, similar to what is seen following acute sleep deprivation. Additionally, in YOUNG, better sleep quality may improve semantic retrieval processes, thereby aiding encoding.
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http://dx.doi.org/10.3389/fneur.2012.00049 | DOI Listing |
J Cogn
January 2025
Department of Psychology, Humboldt-Universität zu Berlin, Berlin, DE.
Visual working memory and verbal storage are often investigated independently of one another. However, a growing body of evidence suggests that naming visual stimuli can provide an advantage in performance during visual working memory tasks. On the other hand, there is also evidence that labeling could lead to biases in recall.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFFront Neuroinform
December 2024
Institute of Theoretical Physics, Jagiellonian University, Kraków, Poland.
Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging.
View Article and Find Full Text PDFFront Psychiatry
December 2024
Department of Information Science, University of Regensburg, Regensburg, Germany.
Background: Up to 13% of adolescents suffer from depressive disorders. Despite the high psychological burden, adolescents rarely decide to contact child and adolescent psychiatric services. To provide a low-barrier alternative, our long-term goal is to develop a chatbot for early identification of depressive symptoms.
View Article and Find Full Text PDFNeurology
January 2025
Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
Background And Objectives: Mesial temporal lobe epilepsy (mTLE) is generally associated with focal brain atrophy, but little knowledge exists on possible disease-related hypertrophy of brain structures. We hypothesized that repeated seizures or adaptive plasticity may lead to focal brain hypertrophy and aimed to investigate associated clinical correlates.
Methods: In this cohort study, we included patients with mTLE undergoing detailed epilepsy evaluations and matched healthy volunteers (HVs) from 2 tertiary centers (discovery and validation cohorts).
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