Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions

In this study, a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder (ASD) from healthy controls. The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions. Therefore, the potential use of s...

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Main Authors:	Khosrowabadi, Reza, Quek, Chai, Kai, Keng Ang, Abdul Rahman, Abdul Wahab, Annabel Chang, Shen-Sing
Format:	Article
Language:	English English
Published:	Elsevier 2015
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/49856/ http://irep.iium.edu.my/49856/ http://irep.iium.edu.my/49856/1/49856_Dynamic_screening_of_autistic_children_in_various_mental_states_using_pattern.pdf http://irep.iium.edu.my/49856/2/49856_Dynamic_screening_of_autistic_children_in_various_mental_states_using_pattern_SCOPUS.pdf

id	iium-49856
recordtype	eprints
spelling	iium-498562016-12-05T08:28:50Z http://irep.iium.edu.my/49856/ Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions Khosrowabadi, Reza Quek, Chai Kai, Keng Ang Abdul Rahman, Abdul Wahab Annabel Chang, Shen-Sing T Technology (General) In this study, a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder (ASD) from healthy controls. The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions. Therefore, the potential use of such abnormality for autism screening is investigated. The connectivity patterns are estimated from electroencephalogram (EEG) data collected from 8 brain regions under various mental states. The EEG data of 12 healthy controls and 6 autistic children (age matched in 7–10) were collected during eyes-open and eyes-close resting states as well as when subjects were exposed to affective faces (happy, sad and calm). Subsequently, the subjects were classified as autistic or healthy groups based on their brain connectivity patterns using pattern recognition techniques. Performance of the proposed system in each mental state is separately evaluated. The results present higher recognition rates using functional connectivity features when compared against other existing feature extraction methods. Elsevier 2015-07 Article PeerReviewed application/pdf en http://irep.iium.edu.my/49856/1/49856_Dynamic_screening_of_autistic_children_in_various_mental_states_using_pattern.pdf application/pdf en http://irep.iium.edu.my/49856/2/49856_Dynamic_screening_of_autistic_children_in_various_mental_states_using_pattern_SCOPUS.pdf Khosrowabadi, Reza and Quek, Chai and Kai, Keng Ang and Abdul Rahman, Abdul Wahab and Annabel Chang, Shen-Sing (2015) Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions. Applied Soft Computing, 32. pp. 335-346. ISSN 1568-4946 http://www.sciencedirect.com/science/article/pii/S156849461500188X
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Khosrowabadi, Reza Quek, Chai Kai, Keng Ang Abdul Rahman, Abdul Wahab Annabel Chang, Shen-Sing Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions
description	In this study, a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder (ASD) from healthy controls. The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions. Therefore, the potential use of such abnormality for autism screening is investigated. The connectivity patterns are estimated from electroencephalogram (EEG) data collected from 8 brain regions under various mental states. The EEG data of 12 healthy controls and 6 autistic children (age matched in 7–10) were collected during eyes-open and eyes-close resting states as well as when subjects were exposed to affective faces (happy, sad and calm). Subsequently, the subjects were classified as autistic or healthy groups based on their brain connectivity patterns using pattern recognition techniques. Performance of the proposed system in each mental state is separately evaluated. The results present higher recognition rates using functional connectivity features when compared against other existing feature extraction methods.
format	Article
author	Khosrowabadi, Reza Quek, Chai Kai, Keng Ang Abdul Rahman, Abdul Wahab Annabel Chang, Shen-Sing
author_facet	Khosrowabadi, Reza Quek, Chai Kai, Keng Ang Abdul Rahman, Abdul Wahab Annabel Chang, Shen-Sing
author_sort	Khosrowabadi, Reza
title	Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions
title_short	Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions
title_full	Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions
title_fullStr	Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions
title_full_unstemmed	Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions
title_sort	dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions
publisher	Elsevier
publishDate	2015
url	http://irep.iium.edu.my/49856/ http://irep.iium.edu.my/49856/ http://irep.iium.edu.my/49856/1/49856_Dynamic_screening_of_autistic_children_in_various_mental_states_using_pattern.pdf http://irep.iium.edu.my/49856/2/49856_Dynamic_screening_of_autistic_children_in_various_mental_states_using_pattern_SCOPUS.pdf
first_indexed	2023-09-18T21:10:26Z
last_indexed	2023-09-18T21:10:26Z
_version_	1777411216270950400

Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions

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