The current study used a magnetoencephalogram to investigate the relationship between high-gamma (52-100 Hz) activity within an attention network and individual differences in behavioral performance among healthy elderly adults. We analyzed brain activity in 41 elderly subjects performing a 3-stimulus visual oddball task. In addition to the average amplitude of event-related fields in the left intraparietal sulcus (IPS), high-gamma power in the left middle frontal gyrus (MFG), the strength of high-gamma imaginary coherence between the right MFG and the left MFG, and those between the right MFG and the left thalamus predicted individual differences in reaction time. In addition, high-gamma power in the left MFG was correlated with task accuracy, whereas high-gamma power in the left thalamus and left IPS was correlated with individual processing speed. The direction of correlations indicated that higher high-gamma power or coherence in an attention network was associated with better task performance and, presumably, higher cognitive function. Thus, high-gamma activity in different regions of this attention network differentially contributed to attentional processing, and such activity could be a fundamental process associated with individual differences in cognitive aging.
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http://dx.doi.org/10.1016/j.neuroimage.2014.06.037 | DOI Listing |
Alzheimers Dement
December 2024
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Background: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Calgary, Calgary, AB, Canada.
Background: The overuse of antipsychotics in persons with dementia in long-term care (LTC) has been a source of clinical concern, public attention, and policy intervention for over 30 years. Targeted quality improvement, broader awareness of risks, and other initiatives have resulted in substantial reductions in antipsychotic use in LTC settings in North America and elsewhere. Limited evidence suggests that reductions in antipsychotic use may be resulting in unintended consequences, such as substitution with alternate, but similarly harmful, psychotropic medications.
View Article and Find Full Text PDFFront Neurorobot
December 2024
Department of Fine Arts, Bozhou University, Bozhou, Anhui, China.
Introduction: Segmentation tasks in computer vision play a crucial role in various applications, ranging from object detection to medical imaging and cultural heritage preservation. Traditional approaches, including convolutional neural networks (CNNs) and standard transformer-based models, have achieved significant success; however, they often face challenges in capturing fine-grained details and maintaining efficiency across diverse datasets. These methods struggle with balancing precision and computational efficiency, especially when dealing with complex patterns and high-resolution images.
View Article and Find Full Text PDFBiomed Eng Lett
January 2025
School of Information Science and Engineering, LinYi University, Linyi, 276000 Shandong China.
Deep learning-based image registration methods offer advantages of time efficiency and registration outcomes by automatically extracting enough image features. Currently, more and more scholars choose to use cascaded networks to achieve coarse-to-fine registration. Although cascaded networks take a lot of time in the training and inference stages, they can improve registration performance.
View Article and Find Full Text PDFBiomed Eng Lett
January 2025
Electronics and Communication Engineering, IFET College of Engineering, Villupuram, Tamilnadu India.
Unlabelled: Breast cancer (BC) remains a significant global health issue, necessitating innovative methodologies to improve early detection and diagnosis. Despite the existence of intelligent deep learning models, their efficacy is often limited due to the oversight of small-sized masses, leading to false positive and false negative outcomes. This research introduces a novel segmentation-guided classification model developed to increase BC detection accuracy.
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