Sleep preferentially preserves aspects of memory that are most salient and valuable to remember at the expense of memory for less relevant details. Daytime naps and nocturnal sleep enhance this emotional memory trade-off effect, with memory for emotional components correlated with slow-wave sleep during the day and rapid eye movement sleep overnight. However, these studies have primarily sampled from young adult populations. Sleep and memory are altered by middle age, and the aim of the present study was to examine how age affects sleep-based mechanisms of emotional memory prioritization, using a daytime nap protocol to compare young to middle-aged adults. In both age groups, a nap soon after encoding scenes that contained a negative or neutral object on a neutral background led to superior retention of emotional object memory at the expense of memory for the related backgrounds. Sleep spindle activity during slow-wave sleep was related to memory for this emotionally salient information across the age range.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5993606 | PMC |
| http://dx.doi.org/10.1016/j.neurobiolaging.2018.03.030 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Preserved brain youthfulness: longitudinal evidence of slower brain aging in superagers.
Geroscience
January 2025
Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Background: Superagers, older adults with exceptional cognitive abilities, show preserved brain structure compared to typical older adults. We investigated whether superagers have biologically younger brains based on their structural integrity.
Methods: A cohort of 153 older adults (aged 61-93) was recruited, with 63 classified as superagers based on superior episodic memory and 90 as typical older adults, of whom 64 were followed up after two years.
Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer's disease.
Brain Inform
January 2025
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
Cognitive resilience (CR) describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer's disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals.
View Article and Find Full Text PDFCryogenic in-memory computing using magnetic topological insulators.
Nat Mater
January 2025
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state.
View Article and Find Full Text PDFThe trajectory of crime: Integrating mouse-tracking into concealed memory detection.
Behav Res Methods
January 2025
Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, 999078, Macau, China.
The autobiographical implicit association test (aIAT) is an approach of memory detection that can be used to identify true autobiographical memories. This study incorporates mouse-tracking (MT) into aIAT, which offers a more robust technique of memory detection. Participants were assigned to mock crime and then performed the aIAT with MT.
View Article and Find Full Text PDFMachine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms.
NPJ Digit Med
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training.
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!