Dynamic Changes of Brain Networks during Working Memory Tasks in Schizophrenia

Area of Science:

• Neuroscience
• Computational Psychiatry
• Brain Imaging

Background:

• Electroencephalograph (EEG) signals and graph theory are used to analyze brain functional networks.
• Analyzing these networks from EEG data is computationally intensive.
• Schizophrenia's impact on brain function during working memory tasks requires efficient analytical methods.

Purpose of the Study:

• To differentiate brain functional networks in schizophrenia patients versus healthy controls during working memory tasks.
• To evaluate the efficiency of microstate time series (MTS) analysis compared to original time series (OTS) analysis for EEG brain network characterization.

Main Methods:

• Utilized microstates to reduce EEG signal dimensionality during working memory tasks.
• Compared brain functional networks derived from MTS and OTS in schizophrenia patients and healthy individuals.
• Calculated correlations between electrodes over time series to construct functional networks.

Main Results:

• Schizophrenia patients exhibited reduced neuronal activity in right frontal and parietal-occipital regions during working memory tasks compared to controls.
• MTS analysis significantly reduced the time required for constructing brain functional networks compared to OTS.
• Both MTS and OTS successfully distinguished brain functional networks between the two groups.

Conclusions:

• Microstate time series (MTS) analysis is a viable method for characterizing EEG brain functional networks.
• MTS analysis effectively differentiates schizophrenia patients from healthy individuals during working memory tasks.
• MTS analysis offers a significant reduction in time complexity for EEG brain network studies.