Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures

Bibliographic Data

ID15528971
AuthorsAbdulhakim Al‐Ezzi (0000-0003-1627-2994, Universiti Teknologi Petronas), Abdulhakim Al-Ezzi, Nidal Kamel (0000-0002-9638-6379, VinUniversity, corresponding author), Amal A Al-Shargabi (0000-0002-7312-9003, Qassim University), Fares Al-Shargie (0000-0002-5792-8032, Abu Dhabi University), Alaa Al-Shargabi (Qassim University, corresponding author), Norashikin Yahya (0000-0002-9812-0435, Universiti Teknologi Petronas, corresponding author), Mohammed Isam Al-Hiyali (0000-0001-7861-7412, Universiti Teknologi Petronas)
Year2023
Volume14
Publication date2023-05-09
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2023.1155812
OpenAlexW4378782902
LanguageEN
Citations received2
References cited68

Introduction The early diagnosis and classification of social anxiety disorder (SAD) are crucial clinical support tasks for medical practitioners in designing patient treatment programs to better supervise the progression and development of SAD. This paper proposes an effective method to classify the severity of SAD into different grading (severe, moderate, mild, and control) by using the patterns of brain information flow with their corresponding graphical networks. Methods We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta). The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with different severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC). Results PDC results have found significant differences between SAD groups and HCs in theta and alpha frequency bands ( p < 0.05). Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92.78%), sensitivity (95.25%), and specificity (94.12%) using Support Vector Machine (SVM). Discussion Based on the results, it can be concluded that the combination of graph theory features and PDC values may be considered an effective tool for SAD identification. Our outcomes may provide new insights into developing biomarkers for SAD diagnosis based on topological brain networks and machine learning algorithms

Anxiety · Cognitive psychology · Electroencephalography · Functional connectivity · Graph · Graph theory · Machine learning · Pattern recognition (psychology · Psychiatry · Resting state fMRI · Social anxiety · Support vector machine · Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes · Computer Science · Functional Brain Connectivity Studies · Mathematics · Mental Health Research Topics · Neuroscience · Psychology · Artificial Intelligence · Theoretical Computer Science

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