Investigation of Functional Brain Networks in MDD Patients Based on EEG Signals Processing

• Published in: 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) pp. 1 - 5
• Main Authors: Hasanzadeh, Fatemeh, Mohebbi, Maryam, Rostami, Reza
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2017
• Subjects: Biomedical engineering; Characteristic path length; Clustering coefficient; Coherence; EEG; Electroencephalography; Functional brain network; Graph theory; Information processing; MDD; Phase lag index; Phase measurement
• DOI: 10.1109/ICBME.2017.8430273

Analysis of functional brain networks using graph theory metrics reveals informative aspects of brain functions. Major depressive disorder (MDD) which is a widespread disorder worldwide cause disruption in some brain functions and thus leads to brain network changes. To study the abnormality of brain function networks in MDD, functional brain networks were constructed from resting state EEG data of 16 MDD patients and 16 normal subjects. The networks based on phase lag index (PLI) were extracted in delta, theta, alpha, beta and total frequency bands. The extracted networks were binarized by Minimum Connected Component (MCC) technique. Average clustering coefficient, average characteristic path length and node degree for two groups were extracted. Results show significantly lower average characteristic path length in depressed group in alpha and total frequency bands. No significant differences in average clustering coefficient between two groups were observed. Higher average degree and higher average PLI in depressed group in alpha, beta and total frequency bands were also observed that may indicate over activation in some brain networks in depressed individuals.