Sleep, Memory & Brain Rhythms
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Learning Alters Theta-Nested Gamma Oscillations in Inferotemporal Cortex
Learning alters theta-nested gamma oscillations in inferotemporal cortex Keith M Kendrick1, Yang Zhan1, Hanno Fischer1 Ali U Nicol1 Xuejuan Zhang 2 & Jianfeng Feng3 1Cognitive and Behavioural Neuroscience, The Babraham Institute, Cambridge, CB22 3AT, UK 2Mathematics Department, Zhejiang Normal University, Jinhua 321004, Zhejian Province, PR China 3Department of Computer Science, Warwick University, Coventry CV4 7AL UK and Centre for Computational Systems Biology, Fudan University, Shanghai, PR China. Corresponding authors: [email protected] [email protected] 1 How coupled brain rhythms influence cortical information processing to support learning is unresolved. Local field potential and neuronal activity recordings from 64- electrode arrays in sheep inferotemporal cortex showed that visual discrimination learning increased the amplitude of theta oscillations during stimulus presentation. Coupling between theta and gamma oscillations, the theta/gamma ratio and the regularity of theta phase were also increased, but not neuronal firing rates. A neural network model with fast and slow inhibitory interneurons was developed which generated theta nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity similar learning-evoked changes could be produced. The model revealed that altered theta nested gamma could potentiate downstream neuron responses by temporal desynchronization of excitatory neuron output independent of changes in overall firing frequency. This learning-associated desynchronization was also exhibited by inferotemporal cortex neurons. Changes in theta nested gamma may therefore facilitate learning-associated potentiation by temporal modulation of neuronal firing. The functions of both low and high frequency oscillations in the brain have been the subject of considerable speculation1. Low frequency theta oscillations (4-8Hz) have been observed to increase in terms of power and phase-locked discharge of single neurons in a visual memory task2. -
Measuring Sleep Quality from EEG with Machine Learning Approaches
Measuring Sleep Quality from EEG with Machine Learning Approaches Li-Li Wang, Wei-Long Zheng, Hai-Wei Ma, and Bao-Liang Lu∗ Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University, Shanghai, 200240, China Abstract—This study aims at measuring last-night sleep quality Index [2], Epworth Sleep Scale [3], and Sleep Diaries [4]. from electroencephalography (EEG). We design a sleep ex- However, we have no way of knowing whether the respondents periment to collect waking EEG signals from eight subjects are honest, conscientious, responsible for self assessment and under three different sleep conditions: 8 hours sleep, 6 hours sleep, and 4 hours sleep. We utilize three machine learning related records and in many cases they may provide false approaches, k-Nearest Neighbor (kNN), support vector machine information or fill in the questionnaire not seriously. Secondly, (SVM), and discriminative graph regularized extreme learning the subjective evaluation method is more troublesome, time- machine (GELM), to classify extracted EEG features of power consuming and laborious, which requires participants to se- spectral density (PSD). The accuracies of these three classi- riously think about, a detailed answer, record relevant infor- fiers without feature selection are 36.68%, 48.28%, 62.16%, respectively. By using minimal-redundancy-maximal-relevance mation in time, in the meantime, experiment personnel also (MRMR) algorithm and the brain topography, the classification need a manual investigation and analysis of the information accuracy of GELM with 9 features is improved largely and in order to give a sleep quality evaluation and judgment of increased to 83.57% in average. -
Impact Factor: 3.958/ICV: 4.10 ISSN: 0976-7908 306 BRAINWAVES AS
Impact factor: 3.958/ICV: 4.10 ISSN: 0976-7908 306 Pharma Science Monitor 8(2), Apr-Jun 2017 PHARMA SCIENCE MONITOR AN INTERNATIONAL JOURNAL OF PHARMACEUTICAL SCIENCES Journal home page: http://www.pharmasm.com BRAINWAVES AS NEUROPHYSIOLOGICAL OUTCOMES IN NEUROSEDATION Nikhil C. Shah1, Srabona Lahiri1 and Dhrubo Jyoti Sen2* 1Bethany Mission School, Near Ashirwad Flat, Behind Hingraj Society, Mehsana-384002, Gujarat, India 2Shri Sarvajanik Pharmacy College, Gujarat Technological University, Arvind Baug, Mehsana-384001, Gujarat, India ABSTRACT Neural oscillation is rhythmic or repetitive neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well- known example of macroscopic neural oscillations is alpha activity. Neural oscillations were observed by researchers as early as 1924 (by Hans Berger). More than 50 years later, intrinsic oscillatory behavior was encountered in vertebrate neurons, but its functional role is still not fully understood. The possible roles of neural oscillations include feature binding, information transfer mechanisms and the generation of rhythmic motor output. -
Nutritional and Herbal Therapies for Children and Adolescents
Nutritional and Herbal Therapies for Children and Adolescents A Handbook for Mental Health Clinicians Nutritional and Herbal Therapies for Children and Adolescents A Handbook for Mental Health Clinicians George M. Kapalka Associate Professor, Monmouth University West Long Branch, NJ and Director, Center for Behavior Modifi cation Brick, NJ AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier Academic Press is an imprint of Elsevier 32 Jamestown Road, London NW1 7BY, UK 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA Copyright © 2010 Elsevier Inc. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (44) (0) 1865 843830; fax (44) (0) 1865 853333; email: [email protected]. Alternatively, visit the Science and Technology Books website at www.elsevierdirect.com/rights for further information Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material -
Machine Learning Techniques for Detection of Nocturnal Epileptic
Università degli Studi di Cagliari PHD DEGREE Industrial Engineering Cycle XXX Machine Learning Techniques for Detection of Nocturnal Epileptic Seizures from Electroencephalographic Signals Scientific Disciplinary Sector ING-IND/31 PhD Student: Barbara Pisano Coordinator of the PhD Programme Prof. Francesco Aymerich Supervisor Prof. Alessandra Fanni Final exam. Academic Year 2016 – 2017 Thesis defence: March 2018 Session Università degli Studi di Cagliari PHD DEGREE Industrial Engineering Cycle XXX Machine Learning Techniques for Detection of Nocturnal Epileptic Seizures from Electroencephalographic Signals Scientific Disciplinary Sector ING-IND/31 PhD Student: Barbara Pisano Coordinator of the PhD Programme Prof. Francesco Aymerich Supervisor Prof. Alessandra Fanni Final exam. Academic Year 2016 – 2017 Thesis defence: March 2018 Session Barbara Pisano gratefully acknowledges Sardinia Regional Government for the financial support of her PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2007-2013 - Axis IV Human Resources, Objective l.3, Line of Activity l.3.1.)”. Questa Tesi può essere utilizzata, nei limiti stabiliti dalla normativa vigente sul Diritto d’Autore (Legge 22 aprile 1941 n. 633 e succ. modificazioni e articoli da 2575a 2583 del Codice civile) ed esclusivamente per scopi didattici e di ricerca; è vietato qualsiasi utilizzo per fini commerciali. In ogni caso tutti gli utilizzi devono riportare la corretta citazione delle fonti. La traduzione, l'adattamento totale e parziale, sono riservati per tutti i Paesi. I documenti depositati sono sottoposti alla legislazione italiana in vigore nel rispetto del Diritto di Autore, da qualunque luogo essi siano fruiti. III ACKNOWLEDGEMENTS A few words to thank all the people who made this work possible. -
Thalamocortical Oscillations - Scholarpedia Page 1 of 14
Thalamocortical oscillations - Scholarpedia Page 1 of 14 Thalamocortical oscillations Maxim Bazhenov, The Salk Institute, San Diego, California Igor Timofeev, Laval University, Quebec, Canada Oscillatory activity is an emerging property of the thalamocortical system. The various oscillatory rhythms generated in the thalamocortical system are mediated by two types of mechanisms: intrinsic mechanisms, which depend on the interplay between specific intrinsic currents. extrinsic or network mechanisms, which require the interaction of excitatory and inhibitory neurons within a population. Intrinsic and network mechanisms can work alone (e.g., thalamic delta oscillations depend on the intrinsic properties of thalamic relay cells, cortical slow oscillation depends on network properties) or in combination (e.g., spindles depend on the interaction between thalamic relay and reticular neurons as well as on their intrinsic properties). The patterns and the dominant frequencies of thalamocortical oscillations depend on the functional state of the brain. Oscillations Normal thalamocortical oscillatory activities include infra-slow : 0.02-0.1 Hz, slow : 0.1-15 Hz (present mainly during slow-wave sleep or anesthesia), which are further divided on slow oscillation (0.2-1 Hz), delta (1-4 Hz), spindle (7-15Hz), theta , which is generated in the limbic system and described elsewhere, fast : 20-60 Hz, ultra-fast : 100-600 Hz. The fast and ultra-fast activities may be present in various states of vigilance including sleep and frequently coexist with slower rhythms (e.g., fast gamma oscillations may be found during depolarized phases of slow sleep oscillations). Spontaneous brain rhythms during different states of vigilance may lead to increased responsiveness and plastic changes in the strength of connections among neurons, thus affecting information flow in the thalamocortical system. -
WHO Technical Meeting on Sleep and Health
WHO technical meeting on sleep and health Bonn Germany, 22-24 January 2004 World Health Organization Regional Office for Europe European Centre for Environment and Health Bonn Office ABSTRACT Twenty-one world experts on sleep medicine and epidemiologists met to review the effects on health of disturbed sleep. Invited experts reviewed the state of the art in sleep parameters, sleep medicine and, long-term effects on health of disturbed sleep in order to define a position on the secondary and long- term effects of noise on sleep for adults, children and other risk groups. This report gives definitions of normal sleep, of indicators of disturbance (arousals, awakenings, sleep deficiency and fragmentation); it describes the main sleep pathologies and disorders and recommends that when evaluating the health impact of chronic long-term sleep disturbance caused by noise exposure, a useful model is the health impact of chronic insomnia. Keywords SLEEP ENVIRONMENTAL HEALTH NOISE Address requests about publications of the WHO Regional Office to: • by e-mail [email protected] (for copies of publications) [email protected] (for permission to reproduce them) [email protected] (for permission to translate them) • by post Publications WHO Regional Office for Europe Scherfigsvej 8 DK-2100 Copenhagen Ø, Denmark © World Health Organization 2004 All rights reserved. The Regional Office for Europe of the World Health Organization welcomes requests for permission to reproduce or translate its publications, in part or in full. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. -
Dyssomnia Sleep Disorders.Pdf
Dyssomnia Sleep Disorders Dyssomnia Sleep Disorders By Yolanda Smith, BPharm Dyssomnia is a broad type of sleep disorders involving difficulty falling or remaining asleep, which can lead to excessive sleepiness during the day due to the reduced quantity, quality or timing of sleep. This is distinct from parasomnias, which involves abnormal behavior of the nervous system during sleep. Symptoms indicative a dyssomnia sleep disorder may include difficulty falling asleep, intermittent awakenings during the night or waking up earlier than usual. As a result of the reduced or disrupted sleep, most patients do not feel well rested and have less ability to perform during the day. In many cases, lifestyle habits have an impact on the disorder, including stress, physical pain or discomfort, napping during the day, bedtime or use of stimulants. Dyssomnia Sleep Disorders There are two main types of dyssomnia sleep disorders according to the origin or cause or the disorder: extrinsic and intrinsic. Both of these are covered in more detail below, in addition to general principles in the diagnosis and treatment of the disorders. Extrinsic dyssomnias are sleep disorders that originate from external causes and may include: Insomnia Sleep apnea Narcolepsy Restless legs syndrome Periodic Limb movement disorder Hypersomnia Toxin-induced sleep disorder Kleine-Levin syndrome Intrinsic dyssomnias are sleep disorders that originate from internal causes and may include: Altitude insomnia Substance use insomnia Sleep-onset association disorder Nocturnal paroxysmal dystonia Limit-setting sleep disorder Diagnosis The differential diagnosis between the types of dyssomnias usually begins with a consultation about the sleep history of the individual, including the onset, frequency and duration of sleep. -
Mechanisms of Gamma Oscillations
NE35CH10-Buzsaki ARI 22 May 2012 13:18 Mechanisms of Gamma Oscillations Gyorgy¨ Buzsaki´ 1,2 and Xiao-Jing Wang3 1Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey 07102 2The Neuroscience Institute, New York University, School of Medicine, New York, NY 10016; email: [email protected] 3Department of Neurobiology and Kavli Institute of Neuroscience, Yale University School of Medicine, New Haven, Connecticut 06520; email: [email protected] Annu. Rev. Neurosci. 2012. 35:203–25 Keywords First published online as a Review in Advance on inhibitory interneurons, interneuronal network, excitatory-inhibitory March 20, 2012 loop, spike timing, dynamical cell assembly, irregular spiking, The Annual Review of Neuroscience is online at cross-frequency coupling, long-distance communication neuro.annualreviews.org This article’s doi: Abstract 10.1146/annurev-neuro-062111-150444 Gamma rhythms are commonly observed in many brain regions during Copyright c 2012 by Annual Reviews. both waking and sleep states, yet their functions and mechanisms remain All rights reserved a matter of debate. Here we review the cellular and synaptic mechanisms 0147-006X/12/0721-0203$20.00 underlying gamma oscillations and outline empirical questions and controversial conceptual issues. Our main points are as follows: First, gamma-band rhythmogenesis is inextricably tied to perisomatic inhibi- by New York University - Bobst Library on 03/16/13. For personal use only. tion. Second, gamma oscillations are short-lived and typically emerge Annu. Rev. Neurosci. 2012.35:203-225. Downloaded from www.annualreviews.org from the coordinated interaction of excitation and inhibition, which can be detected as local field potentials. -
Reading List Final 2020
Society of Behavioral Sleep Medicine (SBSM) Reading List The purpose of this document is to provide a reference with content-specific reading options for: 1) those who are preparing for the Diplomate of Behavioral Sleep Medicine (DBSM) exam; 2) participants in a BSM training program; or 3) anyone interested in learning more about a certain topic in BSM. Please note that this list is not intended to serve as a comprehensive study guide for exam preparation. For those preparing for the DBSM exam, we do not anticipate that it is feasible to review all of the materials on this list. At the same time, there may be exam content that is not covered by the materials below. This document is prepared and maintained by the SBSM Education Committee. The SBSM is independent from the organization that oversees the exam itself (Board of Behavioral Sleep Medicine; BBSM), and Education Committee members do not have access to the exam. The readings were not designed to serve as preparatory material for the exam, but to provide an overview of various topics to learners. Thus, the SBSM cannot guarantee that the information presented in the readings is up-to-date and comprehensive for exam preparation. We would love your feedback! If you perceive that any articles did not cover the information in a category, or included inaccurate information, please let us know. If you would like to propose additional articles for inclusion in this list, we will consider these as well. Please email [email protected] with your feedback. Instructions: Click on the number to access the publication reference, which can also be found in the alphabetical reference list below. -
Sleep and Dreaming
The Journal of Neuroscience, February 1990, fO(2): 371-382 Feature Article Sleep and Dreaming J. Allan Hobson Laboratory of Neurophysiology, Department of Psychiatry, Harvard Medical School, Boston, Massachusetts 02115 With its increasing emphasis upon functional questions, sleep mals to activate their brains periodically in sleep. The only research has entered a new and exciting third phase. Since sleep exceptions are birds, who show brief REM episodes in the first had never been objectively studied in any detail prior to the few days after hatching. They lose this sleep state as they mature, beginning of its first phase in about 1950, it was to be expected thus paralleling the dramatic decline in sleep-and especially that much of the early work in the field would be descriptive. REM-in all young mammals (Roffwarg et al., 1966). Amphibia (For reviews ofthe early work, see Jouvet, 1972; Moruzzi, 1972.) have none of the sleep features of mammals and, unless their Sleep proved a more complex behavior than such distinguished temperature falls, they remain constantly alert even when im- physiologists as Pavlov (1960) and Sherrington (1955) had imag- mobile and relaxed for long periods of time. ined, and, even today, new discoveries continue to be made, I begin this essay by describing the most recent findings re- especially in the clinical realm. In the second phase of sleep garding sleep function, not only because they are exciting in research, beginning about 1960, specific mechanistic theories their novelty, but because they strongly support some of our began to be enunciated and tested. New cellular and molecular commonsense notions about the importance of sleep. -
Sleep and Wake Disorders Following Traumatic Brain Injury: Impact on Recovery of Cognition and Communication
Sleep and Wake Disorders Following Traumatic Brain Injury: Impact on Recovery of Cognition and Communication by Catherine Anne Wiseman-Hakes A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Rehabilitation Science University of Toronto © Copyright by Catherine A. Wiseman-Hakes 2012 Sleep and Wake Disorders Following Traumatic Brain Injury: Impact on Recovery of Cognition and Communication Catherine Anne Wiseman-Hakes Ph.D. Reg. CASLPO Graduate Department of Rehabilitation Science University of Toronto 2012 Abstract Objective: To examine sleep and wake disorders following traumatic brain injury (TBI) and their impact on recovery of cognition, communication and mood. Research Design: This three-manuscript thesis comprises an introduction to sleep in the context of human function and development. It is followed by a systematic review of the literature pertaining to sleep and wake disorders following TBI, and then explores the relationship between sleep and arousal disturbance and functional recovery of cognitive-communication through a single case study, pre–post intervention. Finally, a larger study longitudinally explores the impact of treatment to optimize sleep and wakefulness on recovery of cognition, communication and mood through objective and subjective measures, pre-post intervention. The thesis concludes with a chapter that addresses the implications of findings for rehabilitation from the perspective of the International Classification of Functioning, Disability and Health (ICF), and a presentation of future research directions for the field Methods: The first manuscript involved a systematic review and rating of the quality of evidence. The second manuscript involved the evaluation of sleep and ii wakefulness by objective measures, and longitudinally by self-report through the Daily Cognitive-Communication and Sleep Profile (DCCASP, © Wiseman-Hakes 2008, see Appendix S).