Background: Impaired auditory verbal working memory is a diagnostic hallmark and integral driver of the clinical phenotype in logopenic variant primary progressive aphasia (lvPPA). However, the physiology of the working memory buffer in this syndrome is poorly characterised. Here we addressed the temporal dynamics of auditory verbal working memory in patients with lvPPA and typical Alzheimer's disease (tAD).
Method: In a cohort of 8 patients with lvPPA, 17 patients with tAD and 18 healthy age-matched controls, we assessed how temporal manipulations of a standard auditory verbal working memory tasks (forward digit span and phrasal repetition) affected performance. We varied tempo of delivery and inter-trial gap and performed a detailed analysis of error types. Participants also underwent pure tone audiometry to assess peripheral hearing function and a comprehensive general neuropsychological assessment.
Result: Compared with healthy controls, patients with lvPPA and tAD showed increased sensitivity to temporal manipulation of auditory verbal working memory tasks and error profiles suggesting a dynamic physiological lesion of the working memory buffer. These effects were particularly marked in the lvPPA group.
Conclusion: Our findings open a novel physiological window on working memory dynamics in Alzheimer's disease syndromes. Further work is warranted to assess how this dynamic deficit impacts communication in patients' daily lives, how it can best inform management interventions and its potential as a novel, rapid read-out of neural function in the era of disease-modifying therapies.
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http://dx.doi.org/10.1002/alz.091482 | DOI Listing |
Sci Rep
January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
View Article and Find Full Text PDFMed Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
View Article and Find Full Text PDFSeizure
December 2024
University College Hospital, London, UK; UCL Queen Square Institute of Neurology: Department of Clinical and Experimental Epilepsy, London WC1N 3BG, UK. Electronic address:
Objective: Professional bodies recommend the use of performance validity tests (PVTs) to aid the interpretation of scores obtained in neuropsychological assessments, but base rates of failure differ according to neurological diagnosis and the associated impairments. This review summarises the PVT literature in people with epilepsy with the aim of establishing base rates of PVT failure and the factors associated with PVT performance in this population.
Methods: Ovid and PubMed databases were searched for studies reporting PVT test performance in people with epilepsy.
Comput Biol Med
January 2025
LMA Laboratory, University of Bejaia, Bejaia 06000, Algeria. Electronic address:
Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especially in times of crisis. This study aims to use a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-based hybrid model for health monitoring and health crisis forecasting. It consists of efficiently retrieving safe content from multiple social media sources.
View Article and Find Full Text PDFInt J Eat Disord
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Objective: Difficulty updating information in working memory has been proposed to underlie ruminative thinking in individuals with anorexia nervosa (AN). However, evidence regarding updating difficulties in AN remains inconclusive, particularly among adolescents. It has been proposed that exposure to negative emotion and disorder-salient stimuli may uniquely influence updating in AN.
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