Gamma oscillations of large scale electrical activity are used in electrophysiological studies as markers for neural activity and functional processes in the cortex, yet the nature of this mass neural phenomenon and its relation to the evoked response potentials (ERP) are still not well understood. Many studies associated the gamma oscillations with oscillators around the 40 Hz frequency, yet recent studies have shown that gamma frequencies may be part of a broadband phenomenon ranging from 30 Hz up to 250 Hz. In this study we have examined the possibility that a simple model, based on available neurophysiological parameters, involving an increase in asynchronous (Poisson distributed) neural firing may be sufficient to generate the observed gamma power increases. Our simulation shows a roughly linear increase in gamma power as a function of the aggregated firing rate of the neural population, while the influence of the synchronization level within the neurons on the gamma power is limited. Our model supports the viewpoint that the broadband gamma response is mainly driven by the summed, asynchronous, activity of the neural population. We show that the time frequency spectrogram of the stimulus response can be reconstructed by combining two different phenomena-the broadband gamma power increase due to local processing and the more spatially distributed event related desynchronization (ERD). Our model thus raises the possibility that the broadband gamma response is closely linked to the aggregate population firing rate of the recorded neurons.
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http://dx.doi.org/10.1016/j.neunet.2013.01.004 | DOI Listing |
Geroscience
January 2025
Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow, 125047, Russia.
Age-related dependencies of electric and spectral powers in conventional frequency bands were studied by the newly proposed method of detailed spectral analysis. The magnetic encephalograms (MEG) and magnetic resonance images (MRI) of the head were obtained from the open archive Cam-CAN. The spatial distributions of elementary spectral components (MEG-based functional tomograms) were reconstructed from MEG for 501 subjects (248 males and 253 females, ages 18-88 years, mean age 54.
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January 2025
Department of Biobehavioral Health, Bennett Pierce Prevention Research Center, Pennsylvania State University.
Objective: Drinking intention is a predictor of heavy-drinking episodes and could serve as a real-time target for preventive interventions. However, the association is inconsistent and relatively weak. Considering the affective context when intentions are formed might improve results by revealing conditions in which intention-behavior links are strongest and the predictive power of intentions is greatest.
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Radiation Epidemiology Branch, National Cancer Institute, MD 20892-9778, USA; Faculty of Health, Science and Technology, Oxford Brookes University, Headington Campus, OX3 0BP, UK.
Biological effects of ionizing radiation vary not merely with total dose but also with temporal dose distribution. Sparing dose protraction effects, in which dose protraction reduces effects of radiation have widely been accepted and generally assumed in radiation protection, particularly for stochastic effects (e.g.
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CREATIS, INSA de Lyon, Bâtiment Blaise Pascal, 7 Avenue Jean Capelle, Villeurbanne, 69621 Cedex , FRANCE.
Compton cameras are imaging devices that may improve observation of sources of γ photons. We present CoReSi, a Compton Reconstruction and Simulation software implemented in Python and powered by PyTorch to leverage multi-threading and for easy interfacing with image processing and deep learning algorithms. The code is mainly dedicated to medical imaging and for near-field experiments where the images are reconstructed in 3D.
View Article and Find Full Text PDFMed J Malaysia
January 2025
National University of Malaysia, Faculty of Medicine, Department of Medicine, Kuala Lumpur, Malaysia.
Introduction: Stroke is a major cause of morbidity and mortality worldwide. While electroencephalography (EEG) offers valuable data on post-stroke brain activity, qualitative EEG assessments may be misinterpreted. Therefore, we examined the potential of quantitative EEG (qEEG) to identify key band frequencies that could serve as potential electrophysiological biomarkers in stroke patients.
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