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      Developmental atlas of phase-amplitude coupling between physiologic high-frequency oscillations and slow waves

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          Abstract

          We investigated the developmental changes in high-frequency oscillation (HFO) and Modulation Index (MI) – the coupling measure between HFO and slow-wave phase. We generated normative brain atlases, using subdural EEG signals from 8251 nonepileptic electrode sites in 114 patients (ages 1.0–41.5 years) who achieved seizure control following resective epilepsy surgery. We observed a higher MI in the occipital lobe across all ages, and occipital MI increased notably during early childhood. The cortical areas exhibiting MI co-growth were connected via the vertical occipital fasciculi and posterior callosal fibers. While occipital HFO rate showed no significant age-association, the temporal, frontal, and parietal lobes exhibited an age-inversed HFO rate. Assessment of 1006 seizure onset sites revealed that z-score normalized MI and HFO rate were higher at seizure onset versus nonepileptic electrode sites. We have publicly shared our intracranial EEG data to enable investigators to validate MI and HFO-centric presurgical evaluations to identify the epileptogenic zone.

          Abstract

          It remains unclear how cortical high-frequency oscillations (HFOs) and their relation with slow waves change with age. Here, the authors found that while HFO rate widely decreases over time, its coupling with slow waves strengthens in the occipital lobe during childhood.

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          Most cited references67

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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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            Cortical plasticity: from synapses to maps.

            It has been clear for almost two decades that cortical representations in adult animals are not fixed entities, but rather, are dynamic and are continuously modified by experience. The cortex can preferentially allocate area to represent the particular peripheral input sources that are proportionally most used. Alterations in cortical representations appear to underlie learning tasks dependent on the use of the behaviorally important peripheral inputs that they represent. The rules governing this cortical representational plasticity following manipulations of inputs, including learning, are increasingly well understood. In parallel with developments in the field of cortical map plasticity, studies of synaptic plasticity have characterized specific elementary forms of plasticity, including associative long-term potentiation and long-term depression of excitatory postsynaptic potentials. Investigators have made many important strides toward understanding the molecular underpinnings of these fundamental plasticity processes and toward defining the learning rules that govern their induction. The fields of cortical synaptic plasticity and cortical map plasticity have been implicitly linked by the hypothesis that synaptic plasticity underlies cortical map reorganization. Recent experimental and theoretical work has provided increasingly stronger support for this hypothesis. The goal of the current paper is to review the fields of both synaptic and cortical map plasticity with an emphasis on the work that attempts to unite both fields. A second objective is to highlight the gaps in our understanding of synaptic and cellular mechanisms underlying cortical representational plasticity.
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              High gamma power is phase-locked to theta oscillations in human neocortex.

              We observed robust coupling between the high- and low-frequency bands of ongoing electrical activity in the human brain. In particular, the phase of the low-frequency theta (4 to 8 hertz) rhythm modulates power in the high gamma (80 to 150 hertz) band of the electrocorticogram, with stronger modulation occurring at higher theta amplitudes. Furthermore, different behavioral tasks evoke distinct patterns of theta/high gamma coupling across the cortex. The results indicate that transient coupling between low- and high-frequency brain rhythms coordinates activity in distributed cortical areas, providing a mechanism for effective communication during cognitive processing in humans.
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                Author and article information

                Contributors
                easano@med.wayne.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 October 2023
                13 October 2023
                2023
                : 14
                : 6435
                Affiliations
                [1 ]GRID grid.254444.7, ISNI 0000 0001 1456 7807, Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, , Wayne State University, ; Detroit, MI 48201 USA
                [2 ]Department of Neurosurgery, Rush University Medical Center, ( https://ror.org/01j7c0b24) Chicago, IL 60612 USA
                [3 ]Department of Neurosurgery, University of Tsukuba, ( https://ror.org/02956yf07) Tsukuba, 3058575 Japan
                [4 ]Department of Epileptology, Tohoku University Graduate School of Medicine, ( https://ror.org/01dq60k83) Sendai, 9808575 Japan
                [5 ]Department of Neurosurgery, Yokohama City University, ( https://ror.org/0135d1r83) Yokohama-shi, 2360004 Japan
                [6 ]Department of Neurosurgery, Juntendo University, ( https://ror.org/01692sz90) Tokyo, 1138421 Japan
                [7 ]Department of Physiology, Wayne State University, ( https://ror.org/01070mq45) Detroit, MI 48201 USA
                [8 ]GRID grid.254444.7, ISNI 0000 0001 1456 7807, Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, , Wayne State University, ; Detroit, MI 48201 USA
                [9 ]Department of Pediatrics, Central Michigan University, ( https://ror.org/02xawj266) Mount Pleasant, MI 48858 USA
                [10 ]GRID grid.254444.7, ISNI 0000 0001 1456 7807, Department of Neurosurgery, Children’s Hospital of Michigan, Detroit Medical Center, , Wayne State University, ; Detroit, MI 48201 USA
                Author information
                http://orcid.org/0000-0002-2759-3518
                http://orcid.org/0000-0001-8156-5385
                http://orcid.org/0000-0003-3677-9581
                http://orcid.org/0000-0001-8391-4067
                Article
                42091
                10.1038/s41467-023-42091-y
                10575956
                37833252
                c5efa515-6673-4d22-90f5-acb02a0b654c
                © Springer Nature Limited 2023

                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
                : 10 April 2023
                : 28 September 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: NS064033
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001691, MEXT | Japan Society for the Promotion of Science (JSPS);
                Award ID: JP22J23281
                Award Recipient :
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                © Springer Nature Limited 2023

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