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      Changes in EEG Complexity with Electroconvulsive Therapy in a Patient with Autism Spectrum Disorders: A Multiscale Entropy Approach

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          Abstract

          Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental disorders that are reportedly characterized by aberrant neural networks. Recently developed multiscale entropy analysis (MSE) can characterize the complexity inherent in electroencephalography (EEG) dynamics over multiple temporal scales in the dynamics of neural networks. We encountered an 18-year-old man with ASD whose refractory catatonic obsessive–compulsive symptoms were improved dramatically after electroconvulsive therapy (ECT). In this clinical case study, we strove to clarify the neurophysiological mechanism of ECT in ASD by assessing EEG complexity using MSE. Along with ECT, the frontocentral region showed decreased EEG complexity at higher temporal scales, whereas the occipital region expressed an increase at lower temporal scales. Furthermore, these changes were associated with clinical improvement associated with the elevation of brain-derived neurotrophic factor, which is a molecular hypothesis of ECT, playing key roles in ASD pathogenesis. Changes in EEG complexity in a region-specific and temporal scale-specific manner that we found might reflect atypical EEG dynamics in ASD. Although MSE is not a direct approach to measuring neural connectivity and the results are from only a single case, they might reflect specific aberrant neural network activity and the therapeutic neurophysiological mechanism of ECT in ASD.

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          Most cited references34

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          Multiscale entropy analysis of complex physiologic time series.

          There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.
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            Why the frontal cortex in autism might be talking only to itself: local over-connectivity but long-distance disconnection.

            Although it has long been thought that frontal lobe abnormality must play an important part in generating the severe impairment in higher-order social, emotional and cognitive functions in autism, only recently have studies identified developmentally early frontal lobe defects. At the microscopic level, neuroinflammatory reactions involving glial activation, migration defects and excess cerebral neurogenesis and/or defective apoptosis might generate frontal neural pathology early in development. It is hypothesized that these abnormal processes cause malformation and thus malfunction of frontal minicolumn microcircuitry. It is suggested that connectivity within frontal lobe is excessive, disorganized and inadequately selective, whereas connectivity between frontal cortex and other systems is poorly synchronized, weakly responsive and information impoverished. Increased local but reduced long-distance cortical-cortical reciprocal activity and coupling would impair the fundamental frontal function of integrating information from widespread and diverse systems and providing complex context-rich feedback, guidance and control to lower-level systems.
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              Sample entropy analysis of neonatal heart rate variability.

              Abnormal heart rate characteristics of reduced variability and transient decelerations are present early in the course of neonatal sepsis. To investigate the dynamics, we calculated sample entropy, a similar but less biased measure than the popular approximate entropy. Both calculate the probability that epochs of window length m that are similar within a tolerance r remain similar at the next point. We studied 89 consecutive admissions to a tertiary care neonatal intensive care unit, among whom there were 21 episodes of sepsis, and we performed numerical simulations. We addressed the fundamental issues of optimal selection of m and r and the impact of missing data. The major findings are that entropy falls before clinical signs of neonatal sepsis and that missing points are well tolerated. The major mechanism, surprisingly, is unrelated to the regularity of the data: entropy estimates inevitably fall in any record with spikes. We propose more informed selection of parameters and reexamination of studies where approximate entropy was interpreted solely as a regularity measure.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                26 February 2015
                2015
                : 9
                : 106
                Affiliations
                [1] 1Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui , Fukui, Japan
                [2] 2Department of Child and Adolescent Mental Health, National Center of Neurology and Psychiatry, National Institute of Mental Health , Tokyo, Japan
                [3] 3Research Center for Child Mental Development, Kanazawa University , Kanazawa, Japan
                Author notes

                Edited by: Robert Coben, Integrated Neuroscience Services, USA

                Reviewed by: Koichi Sameshima, Universidade de São Paulo, Brazil; Iman Mohammad-Rezazadeh, UCLA David Geffen School of Medicine, USA

                *Correspondence: Tetsuya Takahashi, Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, 23-3 Matsuokashimoaizuki, Eiheiji-cho, Yoshida-gun, Fukui 910-1193, Japan e-mail: takahash@ 123456u-fukui.ac.jp

                This article was submitted to the journal Frontiers in Human Neuroscience.

                Article
                10.3389/fnhum.2015.00106
                4341548
                25767444
                e72e5d51-7863-4832-95a0-1c3522323d22
                Copyright © 2015 Okazaki, Takahashi, Ueno, Takahashi, Ishitobi, Kikuchi, Higashima and Wada.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 May 2014
                : 12 February 2015
                Page count
                Figures: 3, Tables: 1, Equations: 2, References: 47, Pages: 7, Words: 5537
                Funding
                Funded by: Fukui Prefecture League Project for the Promotion of Science
                Funded by: Grants-in-Aid for Scientific Research
                Award ID: 25461755
                Award ID: 90647778
                Categories
                Neuroscience
                Clinical Case Study

                Neurosciences
                autism spectrum disorders,brain-derived neurotrophic factor,eeg complexity,electroconvulsive therapy,multiscale entropy,obsessive–compulsive disorder

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