Representing Temporal Network based on dDTF of EEG signals in Children with Autism and Healthy Children

Ali Motie-Nasrabadi | Shabnam Ghahari | Fatemeh Salehi | Naemeh Farahani | Robert Coben

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URL :   http://research.shahed.ac.ir/WSR/WebPages/Report/PaperView.aspx?PaperID=148134
Date :  2020/09/16
Publish in :    Biomedical Signal Processing and Control
DOI :  https://doi.org/https://doi.org/10.1016/j.bspc.2020.102139
Link :  http://dx.doi.org/https://doi.org/10.1016/j.bspc.2020.102139
Keywords :AutismElectroencephalographydirect Directed Transfer FunctionEffective ConnectivityTemporal Network TheoryDirected Temporal Network

Abstract :

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During the recent decade, there is a growing interest in the use of neuroimaging methods and different data analysis approaches to recognize and understand neuropsychiatric disorders. In this study, we investigated resting-state Electroencephalography (EEG) data of children with autism and healthy children. The direct Directed Transfer Function (dDTF) method was used to estimate the effective connectivity. We introduced and applied the directed temporal network measures for quantifying the effective brain connections in frequency bands of Alpha, Beta1, Beta2, Delta, Theta, and Gamma. Our results showed that each of the global measures was able to demonstrate a significant distinction at least in one frequency band, between the healthy and Autistic Spectrum Disorder (ASD) groups. The burstiness properties of edges and the directed temporal centrality properties of nodes were different in all the frequency bands in both groups. Also, the significant edges and nodes were determined in each group. The number of significant bursty edges in ASD was less than the healthy group, in Alpha, Delta, Beta1, and Theta bands. Finally, we could show how autism changes the pattern of the brain network across time.