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      Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence

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          Abstract

          Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be transiently suppressed via transcranial electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained shortly after the end of the stimulation and can be phenomenologically predicted. Finally, we use feature-based statistical-learning and neurophysiological-modelling to show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the aberrant oscillations in the olivocerebellar loop, thus establishing its causal role. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may in the future represent a powerful neuromodulatory strategy to treat brain disorders.

          Abstract

          Aberrant synchronous oscillations have been associated with numerous brain disorders, including essential tremor. The authors show that synchronous cerebellar activity can casually affect essential tremor and that its underlying mechanism may be related to the temporal coherence of the tremulous movement.

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          CircStat: AMATLABToolbox for Circular Statistics

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            How common is the most common adult movement disorder? Update on the worldwide prevalence of essential tremor.

            Essential tremor (ET) is among the more prevalent neurological disorders, yet prevalence estimates have varied enormously, making it difficult to establish prevalence with precision. We: (1) reviewed the worldwide prevalence of ET in population-based epidemiological studies, (2) derived as precisely as possible an estimate of disease prevalence, and (3) examined trends and important differences across studies. We identified 28 population-based prevalence studies (19 countries). In a meta-analysis, pooled prevalence (all ages) = 0.9%, with statistically significant heterogeneity across studies (I(2) = 99%, P or= 65 years) = 4.6%, and in additional descriptive analyses, median crude prevalence (age >or= 60-65) = 6.3%. In one study of those age >or= 95 years, crude prevalence = 21.7%. Several studies reported ethnic differences in prevalence, although more studies are needed. Greater than one-third of studies show a gender difference, with most demonstrating a higher prevalence among men. This possible gender preference is interesting given clinical, epidemiological, and pathological associations between ET and Parkinson's disease. Precise prevalence estimates such as those we provide are important because they form the numerical basis for planned public health initiatives, provide data on the background occurrence of disease for family studies, and offer clues about the existence of environmental or underlying biological factors of possible mechanistic importance. (c) 2010 Movement Disorder Society.
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              Information theory and neural coding.

              Information theory quantifies how much information a neural response carries about the stimulus. This can be compared to the information transferred in particular models of the stimulus-response function and to maximum possible information transfer. Such comparisons are crucial because they validate assumptions present in any neurophysiological analysis. Here we review information-theory basics before demonstrating its use in neural coding. We show how to use information theory to validate simple stimulus-response models of neural coding of dynamic stimuli. Because these models require specification of spike timing precision, they can reveal which time scales contain information in neural coding. This approach shows that dynamic stimuli can be encoded efficiently by single neurons and that each spike contributes to information transmission. We argue, however, that the data obtained so far do not suggest a temporal code, in which the placement of spikes relative to each other yields additional information.
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                Author and article information

                Contributors
                k.bhatia@ucl.ac.uk
                nirg@ic.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 January 2021
                13 January 2021
                2021
                : 12
                : 363
                Affiliations
                [1 ]GRID grid.83440.3b, ISNI 0000000121901201, Institute of Neurology, Department of Clinical and Movement Neuroscience, , Queen Square, University College London (UCL), ; London, WC1N 3BG UK
                [2 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Computer Science and Artificial Intelligence Laboratory, , Massachussetts Institute of Technology (MIT), ; Cambridge, MA 02139 USA
                [3 ]NuVu studio Inc, Cambridge, MA 02139 USA
                [4 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Mathematics and EPSRC Centre for Mathematics of Precision Healthcare, , Imperial College London, ; London, SW7 2AZ UK
                [5 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Brain Sciences, , Imperial College London, ; London, W12 0HS UK
                [6 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, UK Dementia Research Institute (UK DRI) at Imperial College London, ; London, W12 0NN UK
                [7 ]GRID grid.63054.34, ISNI 0000 0001 0860 4915, Biomedical Engineering Department, , University of Connecticut, ; Storrs, CT 06269 USA
                [8 ]GRID grid.63054.34, ISNI 0000 0001 0860 4915, CT Institute for the Brain and Cognitive Sciences, , University of Connecticut, ; Storrs, CT 06269 USA
                [9 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Physics, , Imperial College London, ; London, SW7 2AZ UK
                [10 ]GRID grid.443853.d, IT’IS Foundation for Research on Information Technologies in Society, ; 8004 Zurich, Switzerland
                [11 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Media Arts and Sciences, , MIT, ; Cambridge, MA 02139 USA
                [12 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, McGovern Institute for Brain Research, MIT, ; Cambridge, MA 02139 USA
                [13 ]GRID grid.413575.1, ISNI 0000 0001 2167 1581, Howard Hughes Medical Institute, ; Cambridge, MA 02142 USA
                [14 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Biological Engineering, , MIT, ; Cambridge, MA 02139 USA
                [15 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Brain and Cognitive Sciences, , MIT, ; Cambridge, MA 02139 USA
                [16 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Centre for Neurobiological Engineering, , MIT, ; Cambridge, MA 02139 USA
                [17 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Koch Institute for Integrative Cancer Research, MIT, ; Cambridge, MA 02139 USA
                [18 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, , Imperial College London, ; London, SW7 2AZ UK
                [19 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Centre for Neurotechnology, , Imperial College London, ; London, SW7 2AZ UK
                Author information
                http://orcid.org/0000-0002-4129-5808
                http://orcid.org/0000-0002-8738-5825
                http://orcid.org/0000-0002-6210-5997
                http://orcid.org/0000-0003-1988-7767
                http://orcid.org/0000-0001-5137-6313
                http://orcid.org/0000-0003-3375-7440
                http://orcid.org/0000-0002-1089-5675
                http://orcid.org/0000-0002-2133-9471
                http://orcid.org/0000-0003-1367-6467
                http://orcid.org/0000-0001-8185-286X
                Article
                20581
                10.1038/s41467-020-20581-7
                7806740
                33441542
                b2a5c332-408e-441c-8ff0-72e2c8ee06da
                © The Author(s) 2021

                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
                : 20 July 2020
                : 4 December 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Funded by: UK Dementia Research Institute
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                network models,diseases of the nervous system,neural circuits,movement disorders,applied mathematics

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