Brain Network Analysis by Stable and Unstable EEG Components.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Electroencephalography (EEG) brain networks (BNs) reflect individual health status.
• Novel methods for BN analysis are crucial for advancing disease diagnosis.
• Existing BN analysis methods require further development for clinical application.

Purpose of the Study:

• To develop and validate novel BNs using stable and unstable EEG components for disease diagnosis.
• To assess the efficacy of these new BNs in differentiating Parkinson's disease (PD) patients from healthy controls (HC).
• To compare the diagnostic performance of the novel BNs against traditional brain networks (TBNs).

Main Methods:

• EEG signals were decomposed into dynamic modes (DMs) to identify stable and unstable components across frequency bands (delta, theta, alpha, beta).
• Novel BNs, including stable brain network (SBN), unstable brain network (UBN), and inter-connected brain network (IBN), were constructed.
• Topological attributes of the novel BNs and TBNs were extracted for comparative analysis between PD patients and HC.

Main Results:

• Most topological attributes of SBN, UBN, and IBN significantly differentiated PD patients from HC (p < 0.05).
• The SBN analysis demonstrated significantly higher area under the curve (AUC), precision, and recall values compared to TBN.
• The proposed method offers a new perspective on EEG BN analysis with potential for improved diagnostic accuracy.

Conclusions:

• The newly developed BNs, based on stable and unstable EEG components, show significant potential for disease diagnosis.
• SBN analysis outperformed traditional methods in differentiating PD from HC, indicating its diagnostic utility.
• These novel BNs possess biological significance and broad applicability in medical and engineering fields.