The effects of age and gender on electroencephalographic (EEG) activity during a short-term memory task were assessed in a group of 40 healthy participants aged 22-63 years. Multi-channel EEG was recorded in 20 younger (mean = 24.65-year-old, 10 male) and 20 middle-aged participants (mean = 46.40-year-old, 10 male) during performance of a Sternberg task. EEG power and coherence measures were analyzed in different frequency bands. Significant interactions emerged between age and gender in memory performance and concomitant EEG parameters, suggesting that the aging process differentially influences men and women. Middle-aged women showed a lower short-term memory performance compared to young women, which was accompanied by decreasing delta and theta power and increasing brain connectivity with age in women. In contrast, men showed no age-related decline in short-term memory performance and no changes in EEG parameters. These results provide first evidence of age-related alterations in EEG activity underlying memory processes, which were already evident in the middle years of life in women but not in men.
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http://dx.doi.org/10.1016/j.neurobiolaging.2016.01.015 | DOI Listing |
Narra J
December 2024
Biological Models Laboratory, Department of Biochemistry and Molecular Biology, College of Medicine, University of the Philippines Manila, Manila, Philippines.
Sodium metabisulfite is widely used as a preservative in many food and beverage products, yet its potential effects on cognitive and motor functions at low concentrations remain poorly understood. Evaluating learning, short-term memory, and motor activity is essential, as these functions are critical indicators of neurological health and could be impacted by low-level exposure to sodium metabisulfite. The aim of this study was to investigate the effects of sublethal concentrations of sodium metabisulfite on cognitive and motor functions using (fruit flies) as the model organism.
View Article and Find Full Text PDFSci Rep
January 2025
College of computer science and technology, China University of Petroleum (East China), No.66 Changjiang West Road, Huangdao, Qingdao, 266580, Shandong, China.
Addressing the issues of inadequate information exchange among subsequences in the operational time series of water injection pumps, leading to low accuracy and high false alarm rates in anomaly detection, this paper proposes a multidimensional time series anomaly detection method for water injection pump operations, leveraging Long Short-Term Memory Autoencoder augmented with Attention Mechanism (LSTMA-AE) and mechanistic constraints. The LSTMA-AE framework encompasses three primary modules: a Time Feature Extraction Module (Encoder), an Attention Layer, and a Data Reconstruction Module (Decoder). The Encoder captures temporal dependencies and features within the input sequences, mapping the input data into a higher-dimensional space.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, USA.
Childhood cognitively stimulating activities have been associated with higher cognitive function in late life. Whether activities in early or late childhood are more salient, and whether activities are associated with specific cognitive domains is unknown. Participants retrospectively reported cognitively stimulating activities at ages 6, 12, and 18 years.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Tianjin University, Centre for advanced Mechanisms and Robotics, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, China., Tianjin, 300072, CHINA.
This study proposes a real-time tumor position prediction-based multi-dimensional respiratory motion compensation puncture method to accurately track real-time lung tumors and achieve precise needle puncture. Approach: A hybrid model framework integrating prediction and correlation models is developed to enable real-time tumor localization. A Long Short-Term Memory neural network with bidirectional and attention modules (Bi-LSTM-ATT) is employed for predicting external respiratory signals.
View Article and Find Full Text PDFPLoS One
January 2025
School of Electronic Information Engineering, Inner Mongolia University, Hohhot, Inner Mongolia, China.
Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN's local feature extraction capability and LSTM's advantage in handling sequential data to optimize sensing accuracy and efficiency.
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