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      Ensemble of coupling forms and networks among brain rhythms as function of states and cognition

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          Abstract

          The current paradigm in brain research focuses on individual brain rhythms, their spatiotemporal organization, and specific pairwise interactions in association with physiological states, cognitive functions, and pathological conditions. Here we propose a conceptually different approach to understanding physiologic function as emerging behavior from communications among distinct brain rhythms. We hypothesize that all brain rhythms coordinate as a network to generate states and facilitate functions. We analyze healthy subjects during rest, exercise, and cognitive tasks and show that synchronous modulation in the micro-architecture of brain rhythms mediates their cross-communications. We discover that brain rhythms interact through an ensemble of coupling forms, universally observed across cortical areas, uniquely defining each physiological state. We demonstrate that a dynamic network regulates the collective behavior of brain rhythms and that network topology and links strength hierarchically reorganize with transitions across states, indicating that brain-rhythm interactions play an essential role in generating physiological states and cognition.

          Abstract

          Chen et al. analyze healthy human subjects during rest, exercise and cognitive tasks to show that synchronous modulation in the micro-architecture of brain rhythms mediates their cross-communications. They demonstrate that a dynamic network regulates the brain rhythm behaviour and that brain-rhythm interactions play an essential role in generating physiological states and cognition.

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          Complex brain networks: graph theoretical analysis of structural and functional systems.

          Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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            EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis.

            Evidence is presented that EEG oscillations in the alpha and theta band reflect cognitive and memory performance in particular. Good performance is related to two types of EEG phenomena (i) a tonic increase in alpha but a decrease in theta power, and (ii) a large phasic (event-related) decrease in alpha but increase in theta, depending on the type of memory demands. Because alpha frequency shows large interindividual differences which are related to age and memory performance, this double dissociation between alpha vs. theta and tonic vs. phasic changes can be observed only if fixed frequency bands are abandoned. It is suggested to adjust the frequency windows of alpha and theta for each subject by using individual alpha frequency as an anchor point. Based on this procedure, a consistent interpretation of a variety of findings is made possible. As an example, in a similar way as brain volume does, upper alpha power increases (but theta power decreases) from early childhood to adulthood, whereas the opposite holds true for the late part of the lifespan. Alpha power is lowered and theta power enhanced in subjects with a variety of different neurological disorders. Furthermore, after sustained wakefulness and during the transition from waking to sleeping when the ability to respond to external stimuli ceases, upper alpha power decreases, whereas theta increases. Event-related changes indicate that the extent of upper alpha desynchronization is positively correlated with (semantic) long-term memory performance, whereas theta synchronization is positively correlated with the ability to encode new information. The reviewed findings are interpreted on the basis of brain oscillations. It is suggested that the encoding of new information is reflected by theta oscillations in hippocampo-cortical feedback loops, whereas search and retrieval processes in (semantic) long-term memory are reflected by upper alpha oscillations in thalamo-cortical feedback loops. Copyright 1999 Elsevier Science B.V.
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              Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration.

              Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This Perspective considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity. Copyright © 2014 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                plamen@buphy.bu.edu
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                21 January 2022
                21 January 2022
                2022
                : 5
                : 82
                Affiliations
                [1 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Keck Laboratory for Network Physiology, Department of Physics, , Boston University, ; Boston, MA 02215 USA
                [2 ]GRID grid.4489.1, ISNI 0000000121678994, Mind, Brain and Behaviour Research Center, Department of Experimental Psychology, Faculty of Psychology, , University of Granada, ; Campus de la Cartuja, Granada, 18071 Spain
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Sleep Medicine, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA 02115 USA
                [4 ]GRID grid.493309.4, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. Georgi Bonchev Str. Block 21, ; Sofia, 1113 Bulgaria
                Author information
                http://orcid.org/0000-0003-2701-8342
                http://orcid.org/0000-0001-7629-762X
                Article
                3017
                10.1038/s42003-022-03017-4
                8782865
                35064204
                2f6b19e4-aa6b-4ae2-bb62-84f5910f1300
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 August 2021
                : 23 December 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000888, W. M. Keck Foundation (W.M. Keck Foundation);
                Funded by: FundRef https://doi.org/10.13039/100006221, United States - Israel Binational Science Foundation (BSF);
                Award ID: 2020020
                Award Recipient :
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                © The Author(s) 2022

                neurophysiology,network models
                neurophysiology, network models

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