High-gamma activity in an attention network predicts individual differences in elderly adults' behavioral performance.

Neuroimage

Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan; Smart Ageing International Research Center, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan; Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan. Electronic address:

Published: October 2014

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.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2014.06.037DOI Listing

Publication Analysis

Top Keywords

attention network
16
individual differences
16
high-gamma power
16
power left
12
high-gamma
8
high-gamma activity
8
activity attention
8
behavioral performance
8
mfg left
8
left mfg
8

Similar Publications

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 PDF

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 PDF

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 PDF

Gaussianmorph: deformable medical image registration with Gaussian noise constraints.

Biomed 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 PDF

Innovative breast cancer detection using a segmentation-guided ensemble classification framework.

Biomed 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.

View Article and Find Full Text PDF

Want 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!