Identification of Autism Spectrum Disorder With Functional Graph Discriminative Network

Li, Jingcong; Wang, Fei; Pan, Jiahui; Wen, Zhenfu

doi:10.3389/fnins.2021.729937

Cited by 9 publications

(4 citation statements)

References 44 publications

(85 reference statements)

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“…This can be associated with significant synchronization and temporal coherence dysfunction, decreasing large cortical networks between the two hemispheres from binding temporally and spatially. This can result in a functional disconnection syndrome ( 88 , 102 , 135 , 137 , 279 , 289 , 290 ).…”

Section: Discussionmentioning

confidence: 99%

“…Individuals with ASD and other neurobehavioral disorders have also evidenced a reduction of interregional brain connectivity (25,27,43,44,94,95,102,(134)(135)(136)(137)(138)(139). The corpus callosum appears to be the brain area associated with the reduced cortical connectivity found in individuals with ASD (140).…”

Section: Asymmetric Development Can Lead To Underconnectivity Desynch...mentioning

confidence: 99%

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Retained Primitive Reflexes and Potential for Intervention in Autistic Spectrum Disorders

et al. 2022

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We provide evidence to support the contention that many aspects of Autistic Spectrum Disorder (ASD) are related to interregional brain functional disconnectivity associated with maturational delays in the development of brain networks. We think a delay in brain maturation in some networks may result in an increase in cortical maturation and development in other networks, leading to a developmental asynchrony and an unevenness of functional skills and symptoms. The paper supports the close relationship between retained primitive reflexes and cognitive and motor function in general and in ASD in particular provided to indicate that the inhibition of RPRs can effect positive change in ASD.

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Section: Discussionmentioning

confidence: 99%

Section: Asymmetric Development Can Lead To Underconnectivity Desynch...mentioning

confidence: 99%

Retained Primitive Reflexes and Potential for Intervention in Autistic Spectrum Disorders

et al. 2022

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“…Finally, 82% classification accuracy was obtained [ 47 ]. Li et al used ABIDE data set to evaluate a functional diagram discrimination network for ASD classification, which proved that this method can effectively distinguish between ASD patients and healthy controls [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on Autism Spectrum Disorder Neuroimaging

Lü

Zhang

et al. 2022

Disease Markers

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Background. Autism spectrum disorder (ASD) is a chronic developmental disability caused by differences in the brain. The gold standard for the diagnosis of this condition is based on behavioral science, but research on the application of neurological detection to diagnose the atypical nervous system of ASD is ongoing. ASD neuroimaging research involves the examination of the brain’s structure, functional connections, and neurometabolic. However, limited medical resource and the unique heterogeneity of ASD have resulted in many challenges when neuroimaging is utilized. Objective. This bibliometric study is aimed at summarizing themes and trends in research on autism spectrum disorder neuroimaging and at proposing potential directions for future inquiry. Methods. Citations were downloaded from the Web of Science Core Collection database on neuroimaging published from January 1, 2012, to December 31, 2021. The retrieved information was analyzed using Bibliometric.com, CiteSpace.5.8. R3, and VOS viewer. Results. A total of 1,363 papers were published across 58 regions. The United States was the leading source of publications. The League of European Research Universities published the largest number of articles (171). Burst keywords from 2018 to 2021 include identification and network. The clusters of references that continued into 2020 included graph theory, functional connectivity, and classification, which represent key research topics. Conclusions. Imaging data is being used to identify neuro-network models with higher accuracy for ASD discrimination. Functional near-infrared imaging is advantageous compared to other neuroimaging. In the future, research on systematic and accurate computer-aided diagnosis technology should be encouraged. Moreover, the study of neuroimaging of ASD in different psychological and behavioral states can inspire new ideas about the diagnosis and intervention training of ASD and should be explored.

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“…In recent decades, lots of related studies have paid great efforts to pursue the pathological explanations for the sociocommunicative and other impairments in individuals with ASD [2]- [5]. As demonstrated in previous studies, both individuals with ASD and those deemed to be typically developing (TD) display a wide range of ASD symptoms in a variety of behaviors [6]. This variability may mask neural abnormalities associated with these symptoms, and limit the success of finding the potential biomarkers for understanding this neurodevelopmental disorder.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Symptom Severity in Autism Spectrum Disorder Based on EEG Metrics

Zhang

Chen

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Autism spectrum disorder (ASD) is associated with the impaired integrating and segregating of related information that is expanded within the large-scale brain network. The varying ASD symptom severities have been explored, relying on their behaviors and related brain activity, but how to effectively predict ASD symptom severity needs further exploration. In this study, we aim to investigate whether the ASD symptom severity could be predicted with electroencephalography (EEG) metrics. Based on a publicly available dataset, the EEG brain networks were constructed, and four types of EEG metrics were calculated. Then, we statistically compared the brain network differences among ASD children with varying severities, i.e., high/low autism diagnostic observation schedule (A-DOS) scores, as well as with the typically developing (T-D) children. Thereafter, the EEG metrics were utilized to validate whether they could facilitate the prediction of the ASD symptom severity. The results demonstrated that both high-and low-scoring ASD children showed the decreased long-range frontal-occipital connectivity, increased anterior frontal connectivity and altered network properties. Furthermore, we found that the four types of EEG metrics are significantly correlated with the ADOS scores, and their combination can serve as the features to effectively predict the ASD symptom severity. The current findings will expand our knowledge of network dysfunction in ASD children and provide new EEG metrics for predicting the symptom severity.

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Identification of Autism Spectrum Disorder With Functional Graph Discriminative Network

Cited by 9 publications

References 44 publications

Retained Primitive Reflexes and Potential for Intervention in Autistic Spectrum Disorders

Retained Primitive Reflexes and Potential for Intervention in Autistic Spectrum Disorders

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on Autism Spectrum Disorder Neuroimaging

Predicting the Symptom Severity in Autism Spectrum Disorder Based on EEG Metrics

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