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      New Protocol for Quantitative Analysis of Brain Cortex Electroencephalographic Activity in Patients With Psychiatric Disorders

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          Abstract

          The interview is still the main and most important tool in psychiatrist's work. The neuroimaging methods such as CT or MRI are widely used in other fields of medicine, for instance neurology. However, psychiatry lacks effective quantitative methods to support of diagnosis. A novel neuroinformatic approach to help clinical patients by means of electroencephalographic technology in order to build foundations for finding neurophysiological biomarkers of psychiatric disorders is proposed. A cohort of 30 right-handed patients (21 males, 9 females) with psychiatric disorders (mainly with panic and anxiety disorder, Asperger syndrome as well as with phobic anxiety disorders, schizophrenia, bipolar affective disorder, obsessive-compulsive disorder, nonorganic hypersomnia, and moderate depressive episode) were examined using the dense array EEG amplifier in the P300 experiment. The results were compared with the control group of 30 healthy, right-handed male volunteers. The quantitative analysis of cortical activity was conducted using the sLORETA source localization algorithm. The most active Brodmann Areas were pointed out and a new quantitative observable of electrical charge flowing through the selected Brodmann Area is proposed. The precise methodology and research protocol for collecting EEG data as well as the roadmap of future investigations in this area are presented. The essential result of this study is the idea proven by the initial results of our experiments that it is possible to determine quantitatively biomarkers of particular psychiatric disorders in order to support the process of diagnosis and hopefully choose most appropriate medical treatment later.

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          Most cited references47

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          Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain.

          This paper presents a new method for localizing the electric activity in the brain based on multichannel surface EEG recordings. In contrast to the models presented up to now the new method does not assume a limited number of dipolar point sources nor a distribution on a given known surface, but directly computes a current distribution throughout the full brain volume. In order to find a unique solution for the 3-dimensional distribution among the infinite set of different possible solutions, the method assumes that neighboring neurons are simultaneously and synchronously activated. The basic assumption rests on evidence from single cell recordings in the brain that demonstrates strong synchronization of adjacent neurons. In view of this physiological consideration the computational task is to select the smoothest of all possible 3-dimensional current distributions, a task that is a common procedure in generalized signal processing. The result is a true 3-dimensional tomography with the characteristic that localization is preserved with a certain amount of dispersion, i.e., it has a relatively low spatial resolution. The new method, which we call Low Resolution Electromagnetic Tomography (LORETA) is illustrated with two different sets of evoked potential data, the first showing the tomography of the P100 component to checkerboard stimulation of the left, right, upper and lower hemiretina, and the second showing the results for the auditory N100 component and the two cognitive components CNV and P300. A direct comparison of the tomography results with those obtained from fitting one and two dipoles illustrates that the new method provides physiologically meaningful results while dipolar solutions fail in many situations. In the case of the cognitive components, the method offers new hypotheses on the location of higher cognitive functions in the brain.
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            Evoked-potential correlates of stimulus uncertainty.

            The average evoked-potential waveforms to sound and light stimuli recorded from scalp in awake human subjects show differences as a function of the subject's degree of uncertainty with respect to the sensory modality of the stimulus to be presented. Differences are also found in the evoked potential as a function of whether or not the sensorymodality of the stimulus was anticipated correctly. The major waveform alteration is in the amplitude of a positive-going component which reaches peak amplitude at about 300 milliseconds.
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              Biocybernetic system evaluates indices of operator engagement in automated task.

              A biocybernetic system has been developed as a method to evaluate automated flight deck concepts for compatibility with human capabilities. A biocybernetic loop is formed by adjusting the mode of operation of a task set (e.g., manual/automated mix) based on electroencephalographic (EEG) signals reflecting an operator's engagement in the task set. A critical issue for the loop operation is the selection of features of the EEG to provide an index of engagement upon which to base decisions to adjust task mode. Subjects were run in the closed-loop feedback configuration under four candidate and three experimental control definitions of an engagement index. The temporal patterning of system mode switching was observed for both positive and negative feedback of the index. The indices were judged on the basis of their relative strength in exhibiting expected feedback control system phenomena (stable operation under negative feedback and unstable operation under positive feedback). Of the candidate indices evaluated in this study, an index constructed according to the formula, beta power/(alpha power + theta power), reflected task engagement best.
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                Author and article information

                Contributors
                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                24 May 2018
                2018
                : 12
                : 27
                Affiliations
                [1] 1Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science–Department of Neuroinformatics, Maria Curie-Sklodowska University in Lublin , Lublin, Poland
                [2] 2Neurophysiological Independent Unit of the Department of Psychiatry, Medical University of Lublin , Lublin, Poland
                Author notes

                Edited by: Antonio Fernández-Caballero, Universidad de Castilla-La Mancha, Spain

                Reviewed by: Antonio Ivano Triggiani, University of Foggia, Italy; Ali Yadollahpour, Ahvaz Jundishapur University of Medical Sciences, Iran

                *Correspondence: Grzegorz M. Wojcik gmwojcik@ 123456live.umcs.edu.pl
                Article
                10.3389/fninf.2018.00027
                5976787
                29881339
                f8b28ff8-6ca7-4cde-ae60-36770183545b
                Copyright © 2018 Wojcik, Masiak, Kawiak, Schneider, Kwasniewicz, Polak and Gajos-Balinska.

                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) and the copyright owner 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
                : 27 February 2018
                : 02 May 2018
                Page count
                Figures: 4, Tables: 4, Equations: 2, References: 51, Pages: 9, Words: 5689
                Categories
                Neuroscience
                Original Research

                Neurosciences
                electroencephalography,sloreta,psychiatric disorders,quantitative analysis,biomarkers,p300

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