Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment

We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle’s valid and invalid syllogisms, and how these differen...

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Autores principales:	Anastasia Papaioannou, Eva Kalantzi, Christos C. Papageorgiou, Kalliopi Korombili, Anastasia Bokou, Artemios Pehlivanidis, Charalabos C. Papageorgiou, George Papaioannou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multiscale entropy Partial Least Square Correlation PLSC Aristotle’s syllogism ASD-ADHD cognitive load Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/efe982fb39514f5c868b7b017e85597e
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id	oai:doaj.org-article:efe982fb39514f5c868b7b017e85597e
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN
topic	multiscale entropy Partial Least Square Correlation PLSC Aristotle’s syllogism ASD-ADHD cognitive load Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	multiscale entropy Partial Least Square Correlation PLSC Aristotle’s syllogism ASD-ADHD cognitive load Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Anastasia Papaioannou Eva Kalantzi Christos C. Papageorgiou Kalliopi Korombili Anastasia Bokou Artemios Pehlivanidis Charalabos C. Papageorgiou George Papaioannou Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment
description	We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle’s valid and invalid syllogisms, and how these differences correlate with brain regions and behavioral data for each subject. We recorded EEGs from 14 scalp electrodes (channels) in 21 adults with ADHD, 21 with ASD, and 21 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), Aristotle’s two types of syllogism mentioned above. A set of 39 questions were given to participants related to valid–invalid syllogisms as well as a separate set of questionnaires, in order to collect a number of demographic and behavioral data, with the aim of detecting shared information with values of a feature extracted from EEG, <b>the multiscale entropy (MSE)</b>, in the 14 channels (‘brain regions’). MSE, a nonlinear information-theoretic measure of complexity, was computed to extract a feature that quantifies the complexity of the EEG. <b>Behavior-Partial Least Squares Correlation, PLSC</b>, is the method to detect the correlation between two sets of data, brain, and behavioral measures. <b>-PLSC</b>, a variant of PLSC, was applied to build <b>a functional connectivity</b> of the brain regions involved in the reasoning tasks. <b>Graph-theoretic measures</b> were used to quantify the complexity of the functional networks. Based on the results of the analysis described in this work, a mixed 14 × 2 × 3 ANOVA showed significant <b>main effects of</b> <b>group factor</b> and <b>brain region* syllogism</b> <b>factor</b>, as well as a significant <b>brain region* group interaction</b>. <b>There are significant differences between the means of MSE (complexity) values at the 14 channels of the members of the ‘pathological’ groups of participants, i.e., between ASD and ADHD, while the difference in means of MSE between both ASD and ADHD and that of the control group is not significant. In conclusion, the valid–invalid type of syllogism generates significantly different complexity values, MSE, between ASD and ADHD. The complexity of activated brain regions of ASD participants increased significantly when switching from a valid to an invalid syllogism, indicating the need for more resources to ‘face’ the task escalating difficulty in ASD subjects. This increase is not so evident in both ADHD and control.</b> Statistically significant differences were found also in the behavioral response of ASD and ADHD, compared with those of control subjects, based on the principal brain and behavior saliences extracted by PLSC. Specifically, two behavioral measures, the emotional state and the degree of confidence of participants in answering questions in Aristotle’s valid–invalid syllogisms, and one demographic variable, age, statistically and significantly discriminate the three groups’ ASD. The seed-PLC generated <b>functional connectivity networks</b> for ASD, ADHD, and control, were ‘projected’ on the regions of the <b>Default Mode Network (DMN)</b>, the ‘reference’ connectivity, of which the structural changes were found significant in distinguishing the three groups. The contribution of this work lies in the examination of the relationship between brain activity and behavioral responses of healthy and ‘pathological’ participants in the case of cognitive reasoning of the type of Aristotle’s valid and invalid syllogisms, using PLSC, a machine learning approach combined with MSE, a nonlinear method of extracting a feature based on EEGs that captures a broad spectrum of EEGs linear and nonlinear characteristics. The results seem promising in adopting this type of reasoning, in the future, after further enhancements and experimental tests, as a supplementary instrument towards examining the differences in brain activity and behavioral responses of ASD and ADHD patients. The application of the combination of these two methods, after further elaboration and testing as new and complementary to the existing ones, may be considered as a tool of analysis in helping detecting more effectively such types of disorders.
format	article
author	Anastasia Papaioannou Eva Kalantzi Christos C. Papageorgiou Kalliopi Korombili Anastasia Bokou Artemios Pehlivanidis Charalabos C. Papageorgiou George Papaioannou
author_facet	Anastasia Papaioannou Eva Kalantzi Christos C. Papageorgiou Kalliopi Korombili Anastasia Bokou Artemios Pehlivanidis Charalabos C. Papageorgiou George Papaioannou
author_sort	Anastasia Papaioannou
title	Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment
title_short	Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment
title_full	Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment
title_fullStr	Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment
title_full_unstemmed	Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment
title_sort	differences in performance of asd and adhd subjects facing cognitive loads in an innovative reasoning experiment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/efe982fb39514f5c868b7b017e85597e
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spelling	oai:doaj.org-article:efe982fb39514f5c868b7b017e85597e2021-11-25T16:59:12ZDifferences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment10.3390/brainsci111115312076-3425https://doaj.org/article/efe982fb39514f5c868b7b017e85597e2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3425/11/11/1531https://doaj.org/toc/2076-3425We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle’s valid and invalid syllogisms, and how these differences correlate with brain regions and behavioral data for each subject. We recorded EEGs from 14 scalp electrodes (channels) in 21 adults with ADHD, 21 with ASD, and 21 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), Aristotle’s two types of syllogism mentioned above. A set of 39 questions were given to participants related to valid–invalid syllogisms as well as a separate set of questionnaires, in order to collect a number of demographic and behavioral data, with the aim of detecting shared information with values of a feature extracted from EEG, <b>the multiscale entropy (MSE)</b>, in the 14 channels (‘brain regions’). MSE, a nonlinear information-theoretic measure of complexity, was computed to extract a feature that quantifies the complexity of the EEG. <b>Behavior-Partial Least Squares Correlation, PLSC</b>, is the method to detect the correlation between two sets of data, brain, and behavioral measures. <b>-PLSC</b>, a variant of PLSC, was applied to build <b>a functional connectivity</b> of the brain regions involved in the reasoning tasks. <b>Graph-theoretic measures</b> were used to quantify the complexity of the functional networks. Based on the results of the analysis described in this work, a mixed 14 × 2 × 3 ANOVA showed significant <b>main effects of</b> <b>group factor</b> and <b>brain region* syllogism</b> <b>factor</b>, as well as a significant <b>brain region* group interaction</b>. <b>There are significant differences between the means of MSE (complexity) values at the 14 channels of the members of the ‘pathological’ groups of participants, i.e., between ASD and ADHD, while the difference in means of MSE between both ASD and ADHD and that of the control group is not significant. In conclusion, the valid–invalid type of syllogism generates significantly different complexity values, MSE, between ASD and ADHD. The complexity of activated brain regions of ASD participants increased significantly when switching from a valid to an invalid syllogism, indicating the need for more resources to ‘face’ the task escalating difficulty in ASD subjects. This increase is not so evident in both ADHD and control.</b> Statistically significant differences were found also in the behavioral response of ASD and ADHD, compared with those of control subjects, based on the principal brain and behavior saliences extracted by PLSC. Specifically, two behavioral measures, the emotional state and the degree of confidence of participants in answering questions in Aristotle’s valid–invalid syllogisms, and one demographic variable, age, statistically and significantly discriminate the three groups’ ASD. The seed-PLC generated <b>functional connectivity networks</b> for ASD, ADHD, and control, were ‘projected’ on the regions of the <b>Default Mode Network (DMN)</b>, the ‘reference’ connectivity, of which the structural changes were found significant in distinguishing the three groups. The contribution of this work lies in the examination of the relationship between brain activity and behavioral responses of healthy and ‘pathological’ participants in the case of cognitive reasoning of the type of Aristotle’s valid and invalid syllogisms, using PLSC, a machine learning approach combined with MSE, a nonlinear method of extracting a feature based on EEGs that captures a broad spectrum of EEGs linear and nonlinear characteristics. The results seem promising in adopting this type of reasoning, in the future, after further enhancements and experimental tests, as a supplementary instrument towards examining the differences in brain activity and behavioral responses of ASD and ADHD patients. The application of the combination of these two methods, after further elaboration and testing as new and complementary to the existing ones, may be considered as a tool of analysis in helping detecting more effectively such types of disorders.Anastasia PapaioannouEva KalantziChristos C. PapageorgiouKalliopi KorombiliAnastasia BokouArtemios PehlivanidisCharalabos C. PapageorgiouGeorge PapaioannouMDPI AGarticlemultiscale entropyPartial Least Square Correlation PLSCAristotle’s syllogismASD-ADHDcognitive loadNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENBrain Sciences, Vol 11, Iss 1531, p 1531 (2021)

Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment

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