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

Khosrowabadi, Reza; Quek, Chai; Ang, Kai Keng; Wahab, Abdul; Chen, Shen-Hsing Annabel

doi:10.1016/j.asoc.2015.03.030

Cited by 8 publications

(4 citation statements)

References 83 publications

(146 reference statements)

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“…Moreover, different age groups ranging from infants [132,143,169], children [6,43,75,84,89,103,125,126,[128][129][130]140,183,184,[210][211][212][213], adolescents [59,65,70,135,139], and elderly [105,214] in different experiments were considered as study samples. Some studies were conducted on a single gender, such as those investigating women's emotions during motherhood and parental status [133,134,164] and a study of men [215].…”

Section: Mahnobmentioning

confidence: 99%

“…Besides, existing signals are not enough for high accuracy feature extraction. Several approaches introduce more features in different analysis domains to capture extra information about the state of the brain [107,117,200,203,213,216,224]. Consequently, feature extraction is one of the major challenges in designing BCI systems; it is determined based on the features and on the appropriate transformation.…”

Section: Eeg Correlates Of Emotion (Signals)mentioning

confidence: 99%

“…Other medical cases include autism [6,21,75,140,212,213], bipolar disorder [124,145], epilepsy [230,234,235], attention-deficit/hyperactivity disorder [103,165], bulimia nervosa [33,168], borderline personality disorder [167], pervasive developmental disorder [81], and eye movement desensitization and reprocessing [125].…”

Section: Othermentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Mahnobmentioning

confidence: 99%

Section: Eeg Correlates Of Emotion (Signals)mentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…Studies on Asperger's syndrome have also shown GM abnormality in cingulate gyrus (Kwon, Ow, Pedatella, Lotspeich, & Reiss, 2004), cerebellum, putamen, precuneus and amygdala (Kevin & McAlonan, 2011). Although GM abnormalities have been reported in ASD and AS individuals, but, much less is known about differentiation between them (Khosrowabadi, Quek, Ang, Wahab, & Chen, 2015) (Sadeghi et al, 2017b). Faridi and khosrowabadi reviwed some of the important factors, including effect of age, gender and intelligence Quotiont in Asperger syndrome.…”

Section: Introductionmentioning

confidence: 99%

Brain Structural Covariance Network in Asperger Syndrome Differs From Those in Autism Spectrum Disorder and Healthy Controls

Faridi¹,

Seyedebrahimi²,

Khosrowabadi³

2022

BCN

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Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD). In this study, we compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age= 14-50, IQ= 92-132), 16 AS (age=7-58, IQ=93-133) and 28 HC (age=9-39, IQ=95-144). Results of one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is decreased in ASD. This attenuated structural differentiation could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.

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Alteration of perceived emotion and brain functional connectivity by changing the musical rhythmic pattern

2019

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

Cited by 8 publications

References 83 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Brain Structural Covariance Network in Asperger Syndrome Differs From Those in Autism Spectrum Disorder and Healthy Controls

Alteration of perceived emotion and brain functional connectivity by changing the musical rhythmic pattern

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