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      Cortical Tracking of the Speech Envelope in Logopenic Variant Primary Progressive Aphasia

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          Abstract

          Logopenic variant primary progressive aphasia (lvPPA) is a neurodegenerative language disorder primarily characterized by impaired phonological processing. Sentence repetition and comprehension deficits are observed in lvPPA and linked to impaired phonological working memory, but recent evidence also implicates impaired speech perception. Currently, neural encoding of the speech envelope, which forms the scaffolding for perception, is not clearly understood in lvPPA. We leveraged recent analytical advances in electrophysiology to examine speech envelope encoding in lvPPA. We assessed cortical tracking of the speech envelope and in-task comprehension of two spoken narratives in individuals with lvPPA ( n = 10) and age-matched ( n = 10) controls. Despite markedly reduced narrative comprehension relative to controls, individuals with lvPPA had increased cortical tracking of the speech envelope in theta oscillations, which track low-level features (e.g., syllables), but not delta oscillations, which track speech units that unfold across a longer time scale (e.g., words, phrases, prosody). This neural signature was highly correlated across narratives. Results indicate an increased reliance on acoustic cues during speech encoding. This may reflect inefficient encoding of bottom-up speech cues, likely as a consequence of dysfunctional temporoparietal cortex.

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          "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician.

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            EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

            We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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              Nonparametric statistical testing of EEG- and MEG-data.

              In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.
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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
                06 January 2021
                2020
                : 14
                : 597694
                Affiliations
                [1] 1Aphasia Research and Treatment Lab, Department of Speech, Language, and Hearing Sciences, University of Texas at Austin , Austin, TX, United States
                [2] 2SoundBrain Lab, Department of Communication Science and Disorders, University of Pittsburgh , Pittsburgh, PA, United States
                [3] 3Language Neurobiology Laboratory, Department of Neurology, Memory and Aging Center, University of California, San Francisco , San Francisco, CA, United States
                [4] 4Center for Neuroscience, University of Pittsburgh , Pittsburgh, PA, United States
                [5] 5Department of Neurology, Dell Medical School, University of Texas at Austin , Austin, TX, United States
                Author notes

                Edited by: Kirrie J. Ballard, The University of Sydney, Australia

                Reviewed by: Roeland Hancock, University of Connecticut, United States; David Jackson Morris, University of Copenhagen, Denmark

                *Correspondence: Bharath Chandrasekaran b.chandra@ 123456pitt.edu

                This article was submitted to Speech and Language, a section of the journal Frontiers in Human Neuroscience

                Article
                10.3389/fnhum.2020.597694
                7815818
                33488371
                bb1441ac-dfde-469c-b54b-1196e844ec8a
                Copyright © 2021 Dial, Gnanateja, Tessmer, Gorno-Tempini, Chandrasekaran and Henry.

                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(s) 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
                : 21 August 2020
                : 19 November 2020
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 53, Pages: 9, Words: 6716
                Funding
                Funded by: National Institute on Deafness and Other Communication Disorders 10.13039/100000055
                Categories
                Human Neuroscience
                Brief Research Report

                Neurosciences
                logopenic variant,logopenic variant of primary progressive aphasia (lvppa),cortical tracking of speech,temporal response function (trf),speech perception,speech envelope,speech envelope tracking

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