Representing Temporal Network based on dDTF of EEG signals in Children with Autism and Healthy Children. (September 2020)

• Record Type: Journal Article
• Authors: Ghahari, Shabnam; Salehi, Fatemeh; Farahani, Naemeh; Coben, Robert; Motie Nasrabadi, Ali
• Is Part Of: Biomedical signal processing and control. Volume 62(2020)
• Publication Date: 2020-09
• Subjects: Autism -- Electroencephalography -- direct Directed Transfer Function -- Effective Connectivity -- Temporal Network Theory -- Directed Temporal Network; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102139
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Using the temporal network theory to quantify the effective connections derived from the dDTF method in resting-state EEG data, we were able to represent how the brain network pattern changes through time in healthy and ASD children. Each measure of the directed temporal network, depending on its definition, was able to show a different aspect of brain function in healthy and ASD children. In the global measures, the results from directed reachability latency and directed temporal efficiency in six frequency bands, directed volatility in all bands except Delta, and directed fluctuability in Gamma band represented a significant distinction between healthy and ASD groups. The different burstiness properties were observed in the edges for healthy and ASD groups in all the frequency bands. The results showed that the number of significant bursty edges in the ASD group is less than healthy group in the frequency bands of Alpha, Beta1, Delta, and Theta. In the measures of directed temporal centrality, the results indicated that the nodes have different centrality properties in healthy and ASD groups. Abstract: 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.