Infra-slow oscillation (ISO) is a kind of brain rhythm between 0.01 and 0.5 Hz. ISO is widely distributed in multiple brain regions. As an important psychophysiological activity, the ISO interacts with high-frequency neural rhythm via cross-frequency coupling, but has different activity patterns from high-frequency neural activity. Physiologically, the ISO may be generated by the dynamic activity of thalamus, glia, and ions. Psychologically, the frequency, amplitude, and phase of ISO could all regulate cognitive activities, but in different ways. Investigations on the ISO expands the neural rhythm research to lower frequency range, further promoting the construction of rhythmic theory of brain function.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
A novel pulse-current waveform circuit for low-energy consumption and low-noise transcranial magnetic stimulation.
Front Neurosci
January 2025
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan City, China.
Introduction: Transcranial magnetic stimulation (TMS) is widely used for the noninvasive activation of neurons in the human brain. It utilizes a pulsed magnetic field to induce electric pulses that act on the central nervous system, altering the membrane potential of nerve cells in the cerebral cortex to treat certain mental diseases. However, the effectiveness of TMS can be compromised by significant heat generation and the clicking noise produced by the pulse in the TMS coil.
View Article and Find Full Text PDFBibliometric analysis of research on the application of deep learning to ophthalmology.
Quant Imaging Med Surg
January 2025
Department of Ophthalmology, the Fourth Affiliated Hospital of China Medical University, Shenyang, China.
Background: Recently, deep learning has become a popular area of research, and has revolutionized the diagnosis and prediction of ocular diseases, especially fundus diseases. This study aimed to conduct a bibliometric analysis of deep learning in the field of ophthalmology to describe international research trends and examine the current research directions.
Methods: This cross-sectional bibliometric analysis examined the development of research on deep learning in the field of ophthalmology and its sub-topics from 2015 to 2024.
A comparative study on different machine learning approaches with periodic items for the forecasting of GPS satellites clock bias.
Sci Rep
January 2025
College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.
Accurately predicting satellite clock deviation is crucial for improving real-time location accuracy in a GPS navigation system. Therefore, to ensure high levels of real-time positioning accuracy, it is essential to address the challenge of enhancing satellite clock deviation prediction when high-precision clock data is unavailable. Given the high frequency, sensitivity, and variability of space-borne GPS satellite atomic clocks, it is important to consider the periodic variations of satellite clock bias (SCB) in addition to the inherent properties of GPS satellite clocks such as frequency deviation, frequency drift, and frequency drift rate to improve SCB prediction accuracy and gain a better understanding of its characteristics.
View Article and Find Full Text PDFPallidothalamic Circuit-Selective Manipulation Ameliorates Motor Symptoms in A Rat Model of Parkinsonian.
J Neurosci
January 2025
Department of Biomedical Engineering, Michigan Technological University, 1400 Townsend Dr. Houghton, MI 49931.
Deep brain stimulation (DBS) effectively treats motor symptoms of advanced Parkinson's disease (PD), with the globus pallidus interna (GPi) commonly targeted. However, its therapeutic mechanisms remain unclear. We employed optogenetic stimulation in the entopeduncular nucleus (EP), the rat homologue of GPi, in a unilateral 6-OHDA lesioned female Sprague Dawley rat model of PD.
View Article and Find Full Text PDFA comprehensive graph neural network method for predicting triplet motifs in Disease-Drug-Gene interactions.
Bioinformatics
January 2025
College of Artificial Intelligence, Nankai University, Tianjin, 300350, China.
Motivation: The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph motifs of (disease, drug, gene) triplets. Among them, the triangle is a steady and important motif structure in the network, and other various motifs different from the triangle also indicate rich semantic relationships.
View Article and Find Full Text PDFWant 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!