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      Electrophysiology of Inhibitory Control in the Context of Emotion Processing in Children With Autism Spectrum Disorder

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          Abstract

          Autism Spectrum Disorder (ASD) is an increasingly common developmental disorder that affects 1 in 59 children. Despite this high prevalence of ASD, knowledge regarding the biological basis of its associated cognitive difficulties remains scant. In this study, we aimed to identify altered neurophysiological responses underlying inhibitory control and emotion processing difficulties in ASD, together with their associations with age and various domains of cognitive and social function. This was accomplished by assessing electroencephalographic recordings during an emotional go/nogo task alongside parent rating scales of behavior. Event related potential (ERP) N200 component amplitudes were reduced in children with ASD compared to typically developing (TD) children. No group differences were found, however, for task performance, P300 amplitude or latency, or N170 amplitude or latency, suggesting that individuals with ASD may only present conflict monitoring abnormalities, as reflected by the reduced N200 component, compared to TD individuals. Consistent with previous findings, increased age correlated with improved task performance scores and reduced N200 amplitude in the TD group, indicating that as these children develop, their neural systems become more efficient. These associations were not identified in the ASD group. Results also showed significant associations between increased N200 amplitudes and improved executive control abilities and decreased autism traits in TD children only. The newly discovered findings of decreased brain activation in children with ASD, alongside differences in correlations with age compared to TD children, provide a potential neurophysiological indicator of atypical development of inhibitory control mechanisms in these individuals.

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          Most cited references51

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          Conflict monitoring and cognitive control.

          A neglected question regarding cognitive control is how control processes might detect situations calling for their involvement. The authors propose here that the demand for control may be evaluated in part by monitoring for conflicts in information processing. This hypothesis is supported by data concerning the anterior cingulate cortex, a brain area involved in cognitive control, which also appears to respond to the occurrence of conflict. The present article reports two computational modeling studies, serving to articulate the conflict monitoring hypothesis and examine its implications. The first study tests the sufficiency of the hypothesis to account for brain activation data, applying a measure of conflict to existing models of tasks shown to engage the anterior cingulate. The second study implements a feedback loop connecting conflict monitoring to cognitive control, using this to simulate a number of important behavioral phenomena.
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            Influence of cognitive control and mismatch on the N2 component of the ERP: a review.

            Recent years have seen an explosion of research on the N2 component of the event-related potential, a negative wave peaking between 200 and 350 ms after stimulus onset. This research has focused on the influence of "cognitive control," a concept that covers strategic monitoring and control of motor responses. However, rich research traditions focus on attention and novelty or mismatch as determinants of N2 amplitude. We focus on paradigms that elicit N2 components with an anterior scalp distribution, namely, cognitive control, novelty, and sequential matching, and argue that the anterior N2 should be divided into separate control- and mismatch-related subcomponents. We also argue that the oddball N2 belongs in the family of attention-related N2 components that, in the visual modality, have a posterior scalp distribution. We focus on the visual modality for which components with frontocentral and more posterior scalp distributions can be readily distinguished.
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              Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review.

              Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging. Copyright © 2010 Elsevier Inc. All rights reserved.
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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
                12 March 2019
                2019
                : 13
                : 78
                Affiliations
                [1] 1Department of Biomedical Physiology and Kinesiology, Simon Fraser University , Burnaby, BC, Canada
                [2] 2Behavioural and Cognitive Neuroscience Institute, Simon Fraser University , Burnaby, BC, Canada
                [3] 3CTF MEG International Services , Vancouver, BC, Canada
                [4] 4School of Engineering Science, Simon Fraser University , Surrey, BC, Canada
                [5] 5NeuroTech Laboratory, Surrey Memorial Hospital , Surrey, BC, Canada
                [6] 6Digital Health Hub, Simon Fraser University , Surrey, BC, Canada
                [7] 7AGE-WELL National Innovation Hub: Digital Health Circle , Surrey, BC, Canada
                [8] 8Surrey Memorial Hospital, Health Sciences and Innovation , Surrey, BC, Canada
                [9] 9Department of Psychology, Simon Fraser University , Burnaby, BC, Canada
                [10] 10Department Pediatrics and Psychiatry, University of British Columbia , Vancouver, BC, Canada
                [11] 11BC Children’s Hospital Research Institute , Vancouver, BC, Canada
                [12] 12Department of School of Interactive Art and Technology, Simon Fraser University , Surrey, BC, Canada
                Author notes

                Edited by: Francesco Di Russo, Foro Italico University of Rome, Italy

                Reviewed by: Alexandra Key, Vanderbilt University Medical Center, United States; Hidetoshi Takahashi, National Center of Neurology and Psychiatry, Japan

                *Correspondence: Justine R. Magnuson, jrhorne@ 123456sfu.ca

                Co-senior authors

                Article
                10.3389/fnhum.2019.00078
                6422887
                30914937
                aaf16677-9628-495d-bf1f-f52ce580b535
                Copyright © 2019 Magnuson, Peatfield, Fickling, Nunes, Christie, Vakorin, D’Arcy, Ribary, Iarocci, Moreno and Doesburg.

                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
                : 23 November 2018
                : 14 February 2019
                Page count
                Figures: 4, Tables: 2, Equations: 0, References: 64, Pages: 12, Words: 0
                Funding
                Funded by: Canadian Institutes of Health Research 10.13039/501100000024
                Categories
                Neuroscience
                Original Research

                Neurosciences
                autism spectrum disorder,electroencephalography,event related potentials,inhibitory control,emotion processing

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