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Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and MEG recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuospatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/nonmatch decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants' behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared with younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants' clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211717 | PMC |
http://dx.doi.org/10.1523/JNEUROSCI.1883-23.2024 | DOI Listing |
Front Psychiatry
December 2024
Department of Research, Innlandet Hospital Trust, Brumunddal, Norway.
Objective: We aimed to explore how specific cognitive processes, such as attention and executive functions, account for variance in decision-making measured by Iowa Gambling Task (IGT) performance among individuals with schizophrenia spectrum disorders.
Methods: Adults ( = 65, = 25.4) with schizophrenia spectrum disorders participating in a clinical trial (registered at clinicaltrials.
Front Psychiatry
December 2024
Department of Anatomy and Neurosciences, Amsterdam University Medical Center, Amsterdam, Netherlands.
Introduction: Improved effectiveness and treatment adherence is needed in smoking cessation (SC) therapies. Another important challenge is to disrupt maladaptive drug-related memories. To achieve these goals, we developed a novel treatment strategy on the basis of motion-assisted memory desensitization and reprocessing (3MDR).
View Article and Find Full Text PDFWater Res X
January 2025
Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
Sudden shocking load events featuring significant increases in inflow quantities or concentrations of wastewater treatment plants (WWTPs), are a major threat to the attainment of treated effluents to discharge quality standards. To aid in real-time decision-making for stable WWTP operations, this study developed a probabilistic deep learning model that comprises encoder-decoder long short-term memory (LSTM) networks with added capacity of producing probability predictions, to enhance the robustness of real-time WWTP effluent quality prediction under such events. The developed probabilistic encoder-decoder LSTM (P-ED-LSTM) model was tested in an actual WWTP, where bihourly effluent quality prediction of total nitrogen was performed and compared with classical deep learning models, including LSTM, gated recurrent unit (GRU) and Transformer.
View Article and Find Full Text PDFHeliyon
December 2024
Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia.
The agriculture sector is confronted with numerous challenges in the quest for accurate crop yield estimation, which is essential for efficient resource management and mitigating food scarcity in a rapidly growing global population. This research paper delves into the application of advanced Artificial Intelligence (AI) techniques to enhance crop yield estimation in the context of diverse agricultural challenges. Through a systematic literature review and analysis of relevant studies, this paper explores the role of AI methods, such as Machine Learning (ML) and Deep Learning (DL), in addressing the complexities posed by geographical variations, crop diversity, and cultivation areas.
View Article and Find Full Text PDFHear Res
December 2024
Clinics of Otolaryngology, Hannover Medical School, Hearing Center Hannover (DHZ), Karl-Wiechert-Allee 3, 30625 Hannover, Germany; Institute of AudioNeuroTechnology (VIANNA) & Dept. of Experimental Otology, Hannover Medical School, Stadtfelddamm 34, 30625 Hannover, Germany. Electronic address:
Objective: We investigated auditory working-memory using behavioural measures and electroencephalography (EEG) in adult Cochlear Implant (CI) users with varying degrees of CI performance.
Methods: 24 adult CI listeners (age: M = 61.38, SD = 12.
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