DOCSLIB.ORG

Optogenetic Investigation of Neuronal Excitability and Sensory

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Optogenetic Investigation of Neuronal Excitability and Sensory OPTOGENETIC INVESTIGATION OF NEURONAL EXCITABILITY AND SENSORY- MOTOR FUNCTION FOLLOWING A TRANSIENT GLOBAL ISCHEMIA IN MICE by Yicheng Xie Msc, The University of Missouri-Columbia, 2011 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Neuroscience) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2015 © Yicheng Xie, 2015 Abstract Global ischemia occurs during cardiac arrest and has been implicated as a complication that can occur during cardiac surgery. It induces delayed neuronal death in human and animal models, particularly in the hippocampus, while it also can affect the cortex. Other than morphology and measures of cell death, relatively few studies have examined neuronal networks and motor- sensory function following reversible global ischemia in vivo. Optogenetics allows the combination of genetics and optics to control or monitor cells in living tissues. Here, I adapted optogenetics to examine neuronal excitability and motor function in the mouse cortex following a transient global ischemia. Following optogenetic stimulation, I recorded electrical signals from direct stimulation to targeted neuronal populations before and after a 5 min transient global ischemia. I found that both excitatory and inhibitory neuronal network in the somatosensory cortex exhibited prolonged suppression of synaptic transmission despite reperfusion, while the excitability and morphology of neurons recovered rapidly and more completely. Next, I adapted optogenetic motor mapping to investigate the changes of motor processing, and compared to the changes of sensory processing following the transient global ischemia. I found that both sensory and motor processing showed prolonged impairments despite of the recovery of neuronal excitability following reperfusion, presumably due to the unrestored synaptic transmission. Interestingly, motor processing recovered faster and more completely than sensory processing. My results suggest a uniform suppression of synaptic transmission, both in excitatory and inhibitory network, despite the rapid recovery of neuronal excitability and morphology, following a global ischemia and reperfusion. This prolonged suppression of synaptic transmission might impede the recovery of sensory and motor processing with differential ii severity. Besides, I extended tools for mesoscopic imaging using novel optogenetic sensors, including genetically encoded Ca2+ indicators - GCaMPs, and extracellular glutamate sensor - iGluSnFR. I found that iGluSnFR has fastest kinetics for reporting both sensory and spontaneous activity in the cortex, which can resolve temporal features of sensory processing that were not readily observed with GECIs. I suggest that iGluSnFR tools have potential utility in normal physiology, and neurologic pathologies in which abnormalities in glutamatergic signaling are implicated, such as stroke. iii Preface This dissertation is mainly based on works previously published: The pioneer work was led by Shangbin Chen and was published in Journal of Neuroscience (a version of Chapter 2). Chen, S., Mohajerani, M. H., Xie, Y., & Murphy, T. H. (2012). Optogenetic analysis of neuronal excitability during global ischemia reveals selective deficits in sensory processing following reperfusion in mouse cortex. The Journal of Neuroscience, 32(39), 13510-13519. Chapter 3 is based on a publication in the Journal of Neuroscience. Xie, Y*., Chen, S*., Wu, Y., & Murphy, T. H. (2014). Prolonged deficits in parvalbumin neuron stimulation-evoked network activity despite recovery of dendritic structure and excitability in the somatosensory cortex following global ischemia in mice. The Journal of Neuroscience, 34(45), 14890-14900. (*Equal contributors). Chapter 4 is based on a publication in Journal of Cerebral Blood Flow & Metabolism. Xie, Y., Chen, S., Anenberg, E., & Murphy, T. H. (2013). Resistance of optogenetically evoked motor function to global ischemia and reperfusion in mouse in vivo. Journal of Cerebral Blood Flow & Metabolism, 33(8), 1148-1152. A version of Chapter 5 has been accepted for publication by the Journal of Neuroscience. Yicheng Xie, Allen W Chan, Alexander McGirr, Songchao Xue, Dongsheng Xiao, Hongkui iv Zeng, and Timothy H. Murphy. Resolution of high-frequency mesoscale intracortical maps using the genetically-encoded glutamate sensor – iGluSnFR. I conducted the majority of experimental procedures and data analysis, and wrote the draft of manuscripts for the optogenetic characterization of PV-neuronal excitability (Chapter 3) and motor mapping (Chapter 4) during a transient global ischemia, as well as mesoscopic cortical imaging with iGluSnFR (Chapter 5). I conducted experimental procedures and data analysis for the optogenetic characterization of cortical pyramidal neuronal excitability during a transient ischemia, and worked with former post-doctor Shangbin Chen to finish the submission and revision (Chapter 2). Jeff LeDue helped with microscopy set-up for imaging experiments. Pumin Wang helped with animal surgery. Cindy Jiang helped with the management of mouse colony. Dr. Jamie Boyd developed software used for operating previously designed hardware (Ayling et al., 2009). Dr. Timothy H. Murphy supervised the project, edited the published manuscripts, and provided materials and financial supports. Approval for animal experiments was issued by the Animal Care Committee of the University of British Columbia (Protocols A09-0665 and A10-0140). v Table of Contents Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................xv List of Figures ............................................................................................................................. xvi List of Abbreviations ................................................................................................................. xix Acknowledgements .................................................................................................................. xxiii Dedication ................................................................................................................................. xxiv Chapter 1: General Introduction ................................................................................................ 1 1.1 Stroke .......................................................................................................................... 1 1.1.1 Etiology of Stroke and Its Complications ............................................................... 1 1.1.2 The Importance of Stroke Study ............................................................................. 2 1.1.3 General Pathophysiology of Ischemic Stroke ......................................................... 3 1.1.4 Transient Global Ischemia ...................................................................................... 6 1.2 Brain Excitability and Its Alterations after Transient Ischemia ................................ 10 vi 1.2.1 Assessing Brain Excitability ................................................................................. 10 1.2.2 Brain Excitability after Transient Global Ischemia .............................................. 11 1.3 Optogenetic Tools for Investigating Brain Excitability after Cerebral Ischemia ..... 14 1.3.1 Optogenetic Activators and Inhibitors .................................................................. 14 1.3.2 Optogenetic Sensors for Imaging Brain Activity .................................................. 16 1.3.3 Techniques for Genetic Targeting ........................................................................ 20 1.3.4 Light-Based Optogenetic Mapping and Its Application in Probing Spatial and Temporal Changes in Neuronal Excitability and Motor Output ....................................... 24 1.3.5 Optogenetic Mapping following Stroke ................................................................ 25 1.3.6 Using Optogenetics to Study Brain Plasticity and Diseases ................................. 27 1.4 Hypothesis and Objectives ........................................................................................ 28 Chapter 2: Optogenetic Analysis of Neuronal Excitability during Global Ischemia Reveals Selective Deficits in Sensory Processing following Reperfusion in Mouse Cortex ................ 30 2.1 Introduction ............................................................................................................... 30 2.2 Materials and Methods .............................................................................................. 31 2.2.1 Transgenic Mice .................................................................................................... 31 2.2.2 Surgical Procedures .............................................................................................. 32 vii 2.2.3 Global Ischemia Model ......................................................................................... 32 2.2.4 ChR2 Stimulation.................................................................................................
Load more

Recommended publications
  • Towards an Objective Evaluation of EEG/MEG Source Estimation Methods: the Linear Tool Kit
    bioRxiv preprint doi: https://doi.org/10.1101/672956; this version posted June 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Towards an Objective Evaluation of EEG/MEG Source Estimation Methods: The Linear Tool Kit Olaf Hauk1, Matti Stenroos2, Matthias Treder3 1 MRC Cognition and Brain Sciences Unit, University of Cambridge, UK 2 Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland 3 School of Computer Sciences and Informatics, Cardiff University, Cardiff, UK Corresponding author: Olaf Hauk MRC Cognition and Brain Sciences Unit University of Cambridge 15 Chaucer Road Cambridge CB2 7EF [email protected] Key words: inverse problem, point-spread function, cross-talk function, resolution matrix, minimum-norm estimation, spatial filter, beamforming, localization error, spatial dispersion, relative amplitude 1 bioRxiv preprint doi: https://doi.org/10.1101/672956; this version posted June 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract The question “What is the spatial resolution of EEG/MEG?” can only be answered with many ifs and buts, as the answer depends on a large number of parameters. Here, we describe a framework for resolution analysis of EEG/MEG source estimation, focusing on linear methods.
    [Show full text]
  • Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals
    sensors Article Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals Dong-Woo Koh 1, Jin-Kook Kwon 2 and Sang-Goog Lee 1,* 1 Department of Media Engineering, Catholic University of Korea, 43 Jibong-ro, Bucheon-si 14662, Korea; [email protected] 2 CookingMind Cop. 23 Seocho-daero 74-gil, Seocho-gu, Seoul 06621, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-2-2164-4909 Abstract: Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing.
    [Show full text]
  • Electroencephalography (EEG) Technology Applications and Available Devices
    applied sciences Review Electroencephalography (EEG) Technology Applications and Available Devices Mahsa Soufineyestani , Dale Dowling and Arshia Khan * Department of Computer Science, University of Minnesota Duluth, Duluth, MN 55812, USA; soufi[email protected] (M.S.); [email protected] (D.D.) * Correspondence: [email protected] Received: 18 September 2020; Accepted: 21 October 2020; Published: 23 October 2020 Abstract: The electroencephalography (EEG) sensor has become a prominent sensor in the study of brain activity. Its applications extend from research studies to medical applications. This review paper explores various types of EEG sensors and their applications. This paper is for an audience that comprises engineers, scientists and clinicians who are interested in learning more about the EEG sensors, the various types, their applications and which EEG sensor would suit a specific task. The paper also lists the details of each of the sensors currently available in the market, their technical specs, battery life, and where they have been used and what their limitations are. Keywords: EEG; EEG headset; EEG Cap 1. Introduction An electroencephalography (EEG) sensor is an electronic device that can measure electrical signals of the brain. EEG sensors typically measure the varying electrical signals created by the activity of large groups of neurons near the surface of the brain over a period of time. They work by measuring the small fluctuations in electrical current between the skin and the sensor electrode, amplifying the electrical current, and performing any filtering, such as bandpass filtering [1]. Innovations in the field of medicine began in the early 1900s, prior to which there was little innovation due to the uncollaborative nature of the field of medicine.
    [Show full text]
  • Analysis of EEG Signal Processing Techniques Based on Spectrograms
    ISSN 1870-4069 Analysis of EEG Signal Processing Techniques based on Spectrograms Ricardo Ramos-Aguilar, J. Arturo Olvera-López, Ivan Olmos-Pineda Benemérita Universidad Autónoma de Puebla, Faculty of Computer Science, Puebla, Mexico [email protected],{aolvera, iolmos}@cs.buap.mx Abstract. Current approaches for the processing and analysis of EEG signals consist mainly of three phases: preprocessing, feature extraction, and classifica- tion. The analysis of EEG signals is through different domains: time, frequency, or time-frequency; the former is the most common, while the latter shows com- petitive results, implementing different techniques with several advantages in analysis. This paper aims to present a general description of works and method- ologies of EEG signal analysis in time-frequency, using Short Time Fourier Transform (STFT) as a representation or a spectrogram to analyze EEG signals. Keywords. EEG signals, spectrogram, short time Fourier transform. 1 Introduction The human brain is one of the most complex organs in the human body. It is considered the center of the human nervous system and controls different organs and functions, such as the pumping of the heart, the secretion of glands, breathing, and internal tem- perature. Cells called neurons are the basic units of the brain, which send electrical signals to control the human body and can be measured using Electroencephalography (EEG). Electroencephalography measures the electrical activity of the brain by recording via electrodes placed either on the scalp or on the cortex. These time-varying records produce potential differences because of the electrical cerebral activity. The signal generated by this electrical activity is a complex random signal and is non-stationary [1].
    [Show full text]
  • Analysis of EEG Signals for EEG-Based Brain-Computer Interface
    Analysis of EEG Signals For EEG-based Brain-Computer Interface Jessy Parokaran Varghese School of Innovation, Design and Technology Mälardalen University Vasteras, Sweden Supervisor and Examiner: Dr. BARAN ÇÜRÜKLÜ Date: July 2009 Abstract Advancements in biomedical signal processing techniques have led Electroencephalography (EEG) signals to be more widely used in the diagnosis of brain diseases and in the field of Brain Computer Interface(BCI). BCI is an interfacing system that uses electrical signals from the brain (eg: EEG) as an input to control other devices such as a computer, wheel chair, robotic arm etc. The aim of this work is to analyse the EEG data to see how humans can control machines using their thoughts.In this thesis the reactivity of EEG rhythms in association with normal, voluntary and imagery of hand movements were studied using EEGLAB, a signal processing toolbox under MATLAB. In awake people, primary sensory or motor cortical areas often display 8-12 Hz EEG activity called ’Mu’ rhythm ,when they are not engaged in processing sensory input or produce motor output. Movement or preparation of movement is typically accompanied by a decrease in this mu rhythm called ’event-related desynchronization’(ERD). Four males, three right handed and one left handed participated in this study. There were two sessions for each subject and three possible types : Imagery, Voluntary and Normal. The EEG data was sampled at 256Hz , band pass filtered between 0.1 Hz and 50 Hz and then epochs of four events : Left button press , Right button press, Right arrow ,Left arrow were extracted followed by baseline removal.After this preprocessing of EEG data, the epoch files were studied by analysing Event Related Potential plots, Independent Component Analysis, Power spectral Analysis and Time- Frequency plots.
    [Show full text]
  • Proquest Dissertations
    INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMi films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6” x 9” black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. UMI Bell & Howell Information and Learning 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA 800-521-0600 MULTIMODALITY ELECTROPHYSIOLOGICAL MONITORING IN THE NEUROINTENSIVE GARE UNIT DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By André van der Kouwe, M.Eng.
    [Show full text]
  • Analysis and Classification of EEG Signals Using Probabilistic Models
    Analysis and Classification of EEG Signals using Probabilistic Models for Brain Computer Interfaces THESE` No 3547 (2006) present´ee `ala Facult´esciences et techniques de l’ing´enieur Ecole´ Polytechnique F´ed´erale de Lausanne pour l’obtention du grade de docteur `es sciences par Silvia Chiappa Laurea Degree in Mathematics, University of Bologna, Italy accept´ee sur proposition du jury: Prof. Jos´edel R. Mill´an, directeur de th`ese Dr. Samy Bengio, co-directeur de th`ese Dr. David Barber, rapporteur Dr. Sara Gonzalez Andino, rapporteur Prof. Klaus-Robert M¨uller, rapporteur Lausanne, EPFL 2006 2 Riassunto Questa tesi esplora l’utilizzo di modelli probabilistici a variabili nascoste per l’analisi e la clas- sificazione dei segnali elettroencefalografici (EEG) usati in sistemi Brain Computer Interface (BCI). La prima parte della tesi esplora l’utilizzo di modelli probabilistici per la classificazione. Iniziamo con l’analizzare la differenza tra modelli generativi e discriminativi. Allo scopo di tenere in considerazione la natura temporale del segnale EEG, utilizziamo due modelli dinamici: il modello generativo hidden Markov model e il modello discriminativo input-output hidden Markov model. Per quest’ultimo modello, introduciamo un nuovo algoritmo di apprendimento che `edi particolare beneficio per il tipo di sequenze EEG utilizzate. Analizziamo inoltre il vantaggio nell’utilizzare questi modelli dinamici verso i loro equivalenti statici. In seguito, analizziamo l’introduzione di informazione pi`uspecifica circa la struttura del seg- nale EEG. In particolare, un’assunzione comune nell’ambito di ricerca relativa al segnale EEG `eil fatto che il segnale sia generato da una trasformazione lineare di sorgenti indipendenti nel cervello e altre componenti esterne.
    [Show full text]
  • Multiple Mechanisms Link Prestimulus Neural Oscillations to Sensory Responses
    RESEARCH ARTICLE Multiple mechanisms link prestimulus neural oscillations to sensory responses Luca Iemi1,2,3*, Niko A Busch4,5, Annamaria Laudini6, Saskia Haegens1,7, Jason Samaha8, Arno Villringer2,6, Vadim V Nikulin2,3,9,10* 1Department of Neurological Surgery, Columbia University College of Physicians and Surgeons, New York City, United States; 2Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; 3Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russian Federation; 4Institute of Psychology, University of Mu¨ nster, Mu¨ nster, Germany; 5Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Mu¨ nster, Mu¨ nster, Germany; 6Berlin School of Mind and Brain, Humboldt- Universita¨ t zu Berlin, Berlin, Germany; 7Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; 8Department of Psychology, University of California, Santa Cruz, Santa Cruz, United States; 9Department of Neurology, Charite´-Universita¨ tsmedizin Berlin, Berlin, Germany; 10Bernstein Center for Computational Neuroscience, Berlin, Germany Abstract Spontaneous fluctuations of neural activity may explain why sensory responses vary across repeated presentations of the same physical stimulus. To test this hypothesis, we recorded electroencephalography in humans during stimulation with identical visual stimuli and analyzed how prestimulus neural oscillations modulate different stages of sensory processing reflected by distinct components of the event-related potential (ERP). We found that strong prestimulus alpha- and beta-band power resulted in a suppression of early ERP components (C1 and N150) and in an *For correspondence: amplification of late components (after 0.4 s), even after controlling for fluctuations in 1/f aperiodic [email protected] (LI); signal and sleepiness.
    [Show full text]
  • EEG Correlates of Old/New Discrimination Performance Involving Abstract Figures and Non-Words
    brain sciences Article EEG Correlates of Old/New Discrimination Performance Involving Abstract Figures and Non-Words Monika Toth * , Anke Sambeth and Arjan Blokland Department of Neuropsychology and Psychopharmacology, Maastricht University, 6229 ER Maastricht, The Netherlands; [email protected] (A.S.); [email protected] (A.B.) * Correspondence: [email protected] Abstract: The processing of pre-experimentally unfamiliar stimuli such as abstract figures and non- words is poorly understood. Here, we considered the role of memory strength in the discrimination process of such stimuli using a three-phase old/new recognition memory paradigm. Memory strength was manipulated as a function of the levels of processing (deep vs. shallow) and repetition. Behavioral results were matched to brain responses using EEG. We found that correct identification of the new abstract figures and non-words was superior to old item recognition when they were merely studied without repetition, but not when they were semantically processed or drawn. EEG results indicated that successful new item identification was marked by a combination of the absence of familiarity (N400) and recollection (P600) for the studied figures. For both the abstract figures and the non-words, the parietal P600 was found to differentiate between the old and new items (late old/new effects). The present study extends current knowledge on the processing of pre-experimentally unfamiliar figurative and verbal stimuli by showing that their discrimination depends on experimentally induced memory strength and that the underlying brain processes differ. Nevertheless, the P600, Citation: Toth, M.; Sambeth, A.; similar to pre-experimentally familiar figures and words, likely reflects improved recognition memory Blokland, A.
    [Show full text]
  • Non Invasive Brain-Machine Interfaces Final Report
    Non Invasive Brain-Machine Interfaces Final Report Authors: José del R. Millán, Pierre W. Ferrez, Anna Buttfield Affiliation: IDIAP Research Institute ESA Research Fellow/Technical Officer: Carlo Menon Contacts: Prof. Dr. José del R. Millán Tel: +41-27-7217770 Fax: +41-27-7217712 e-mail: [email protected] Dr. Carlo Menon Tel: +31(0)715658675 Fax: +31(0)715658018 e-mail: [email protected] Ariadna ID: 05/6402 Available on the ACT website Study Duration: 2 months http://www.esa.int/act Contract Number: 19708/06/NL/HE Table of Contents 1. Introduction............................................................................................. 3 2. EEG-based Brain-Computer Interfaces: Methodologies ........................ 6 2.1 Evoked BCIs ................................................................................... 6 2.2 Spontaneous BCI............................................................................. 7 2.2.1 Slow Potentials................................................................. 8 2.2.2 Rhythmic Activity............................................................ 9 2.3 Operant Conditioning & Machine Learning ................................. 11 2.4 Synchronous vs. Asynchronous BCI............................................. 12 2.5 Spatial Filtering............................................................................. 13 3. Hardware............................................................................................... 14 4. Current Applications............................................................................
    [Show full text]
  • Time Series Analysis Applied to EEG Shows Increased Global Connectivity During Motor Activation Detected in PD Patients Compared to Controls
    applied sciences Article Time Series Analysis Applied to EEG Shows Increased Global Connectivity during Motor Activation Detected in PD Patients Compared to Controls Ana María Maitín 1,†, Ramiro Perezzan 2,†, Diego Herráez-Aguilar 2, José Ignacio Serrano 3 , María Dolores Del Castillo 3, Aida Arroyo 2, Jorge Andreo 2 and Juan Pablo Romero 2,4,* 1 CEIEC, Universidad Francisco de Vitoria, 28223 Madrid, Spain; [email protected] 2 Facultad de Ciencias Experimentales, Universidad Francisco de Vitoria, 28223 Madrid, Spain; [email protected] (R.P.); [email protected] (D.H.-A.); [email protected] (A.A.); [email protected] (J.A.) 3 Neural and Cognitive Engineering Group, Center for Automation and Robotics (CAR) CSIC, 28500 Arganda del Rey, Spain; [email protected] (J.I.S.); [email protected] (M.D.D.C.) 4 Brain Damage Unit, Hospital Beata María Ana, 28007 Madrid, Spain * Correspondence: [email protected] † Both authors contributed equally to this work. Abstract: Background: Brain connectivity has shown to be a key characteristic in the study of both Parkinson’s Disease (PD) and the response of the patients to the dopaminergic medication. Time series analysis has been used here for the first time to study brain connectivity changes during motor activation in PD. Methods: A 64-channel EEG signal was registered during unilateral motor activation and resting-state in 6 non-demented PD patients before and after the administration of levodopa and in 6 matched healthy controls. Spectral entropy correlation, coherence, and interhemispheric divergence differences among PD patients and controls were analyzed under the assumption of Citation: Maitín, A.M.; Perezzan, R.; stationarity of the time series.
    [Show full text]
  • Quantitative Features for Newborn EEG Using Matlab
    NEURAL: quantitative features for newborn EEG using Matlab John M. O' Toolea,∗, Geraldine B. Boylana aNeonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork, Ireland Abstract Background: For newborn infants in critical care, continuous monitoring of brain function can help identify infants at-risk of brain injury. Quantitative features allow a consistent and reproducible approach to EEG analysis, but only when all implementation aspects are clearly defined. Methods: We detail quantitative features frequently used in neonatal EEG analysis and present a Matlab software package together with exact implementation details for all features. The feature set includes stationary features that capture amplitude and frequency characteristics and features of inter-hemispheric connectivity. The software, a Neonatal Eeg featURe set in mAtLab (NEURAL), is open source and freely available. The software also includes a pre-processing stage with a basic artefact removal procedure. Conclusions: NEURAL provides a common platform for quantitative analysis of neonatal EEG. This will support reproducible research and enable comparisons across independent studies. These features present summary measures of the EEG that can also be used in automated methods to determine brain development and health of the newborn in critical care. Keywords: neonate, preterm infant, electroencephalogram, quantitative analysis, feature extraction, spectral analysis, inter-burst interval Abbreviations: EEG, electroencephalogram; NEURAL, neonatal EEG feature set in Matlab; NICU, neonatal intensive care unit; aEEG, amplitude-integrated EEG; rEEG, range EEG; PSD, power spectral density; BSI, brain symmetry index; FIR, finite impulse response; IIR, infinite impulse response; DFT, discrete Fourier transform; CC, correlation coefficient. 1. Introduction Electroencephalography (EEG) is used in the neonatal intensive care environment to monitor brain function of critically-ill newborns.
    [Show full text]