Complex Network Measures in Autism Spectrum Disorders

Recent studies have suggested abnormal brain network organization in subjects with Autism Spectrum Disorders (ASD). Here we applied spectral clustering algorithm, diverse centrality measures (betweenness (BC), clustering (CC), eigenvector (EC), and degree (DC)), and also the network entropy (NE) to...

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Vydané v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 15; číslo 2; s. 581 - 587
Hlavní autori: Sato, Joao Ricardo, Calebe Vidal, Maciel, de Siqueira Santos, Suzana, Brauer Massirer, Katlin, Fujita, Andre
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.03.2018
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ISSN:1545-5963, 1557-9964
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Shrnutí:Recent studies have suggested abnormal brain network organization in subjects with Autism Spectrum Disorders (ASD). Here we applied spectral clustering algorithm, diverse centrality measures (betweenness (BC), clustering (CC), eigenvector (EC), and degree (DC)), and also the network entropy (NE) to identify brain sub-systems associated with ASD. We have found that BC increases in the following ASD clusters: in the somatomotor, default-mode, cerebellar, and fronto-parietal. On the other hand, CC, EC, and DC decrease in the somatomotor, default-mode, and cerebellar clusters. Additionally, NE decreases in ASD in the cerebellar cluster. These findings reinforce the hypothesis of under-connectivity in ASD and suggest that the difference in the network organization is more prominent in the cerebellar system. The cerebellar cluster presents reduced NE in ASD, which relates to a more regular organization of the networks. These results might be important to improve current understanding about the etiological processes and the development of potential tools supporting diagnosis and therapeutic interventions.
Bibliografia:ObjectType-Article-1
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ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2015.2476787