Social Anxiety Disorder Evaluation using Effective Connectivity Measures: EEG Phase Slope Index Study

• Main Authors: Al-Ezzi, A., Yahya, N., Kamel, N., Faye, I., Alsaih, K., Gunaseli, E.
• Format: Conference or Workshop Item
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104823382&doi=10.1109%2fIECBES48179.2021.9398819&partnerID=40&md5=108e02c56714652179015ada80aadcc7; http://eprints.utp.edu.my/30419/

Social Anxiety Disorder (SAD) is a prevalent, debilitating, and psychiatric condition marked by intense anxiety of being evaluated of negative appraisal or criticism in social events, which results in greater functional impairment in the brain. The main objective of this study is to quantify the severity of SAD by using effective connectivity (EC). Electroencephalography (EEG) is a suitable estimation mechanism to assess the EC network underlying the SAD data, due to its high temporal resolution. EEG data were acquired from 20 subjects divided to 5 severe, 5 average, 5 mild, and 5 healthy control (HC) in the anticipation time (before delivering a speech in public). The EEG data was used to estimate the EC network using the phase slope index (PSI) algorithm in the alpha band (8-13 Hz). The difference between the PSI metrics in all the SAD groups was significant (\mathrmp < 0.024). EEG results showed that the severe and average groups have greater EC in the left hemisphere for alpha networks, while mild and HC groups have shown greater EC networks in the right hemisphere. The midparietal lobe has shown to be the main brain hub in the severe group, while the right frontal cortex has shown to be the major brain hub for HC. The current results confirm that the involvement of the PSI algorithm in alpha oscillations is providing higher recognition of SAD level due to its sensitivity to characterize mental illness such as SAD and depression. © 2021 IEEE.