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Article: Recognition of autism spectrum disorder in children based on electroencephalogram network topology

TitleRecognition of autism spectrum disorder in children based on electroencephalogram network topology
Authors
KeywordsAutism spectrum disorder
Classification
Electroencephalogram
Spatial pattern of the network topology
Issue Date12-Apr-2023
PublisherSpringer
Citation
Cognitive Neurodynamics, 2023 How to Cite?
Abstract

Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.


Persistent Identifierhttp://hdl.handle.net/10722/331666
ISSN
2023 Impact Factor: 3.1
2023 SCImago Journal Rankings: 0.762
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Fali-
dc.contributor.authorZhang, Shu-
dc.contributor.authorJiang, Lin-
dc.contributor.authorDuan, Keyi-
dc.contributor.authorFeng, Rui-
dc.contributor.authorZhang, Yingli-
dc.contributor.authorZhang, Gao-
dc.contributor.authorZhang, Yangsong-
dc.contributor.authorLi, Peiyang-
dc.contributor.authorYao, Dezhong-
dc.contributor.authorXie, Jiang-
dc.contributor.authorXu, Wenming-
dc.contributor.authorXu, Peng-
dc.date.accessioned2023-09-21T06:57:49Z-
dc.date.available2023-09-21T06:57:49Z-
dc.date.issued2023-04-12-
dc.identifier.citationCognitive Neurodynamics, 2023-
dc.identifier.issn1871-4080-
dc.identifier.urihttp://hdl.handle.net/10722/331666-
dc.description.abstract<p>Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.<br></p>-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofCognitive Neurodynamics-
dc.subjectAutism spectrum disorder-
dc.subjectClassification-
dc.subjectElectroencephalogram-
dc.subjectSpatial pattern of the network topology-
dc.titleRecognition of autism spectrum disorder in children based on electroencephalogram network topology-
dc.typeArticle-
dc.identifier.doi10.1007/s11571-023-09962-y-
dc.identifier.scopuseid_2-s2.0-85152356202-
dc.identifier.eissn1871-4099-
dc.identifier.isiWOS:000969137300002-
dc.identifier.issnl1871-4080-

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