Dynamic Changes of Brain Networks during Working Memory Tasks in Schizophrenia

Area of Science:

• Neuroscience
• Computational Psychiatry
• Brain Network Analysis

Background:

• Electroencephalograph (EEG) signals and graph theory are standard for characterizing brain functional networks.
• Analyzing these networks from EEG data is computationally intensive due to high time complexity.
• Distinguishing brain networks in schizophrenia patients during cognitive tasks remains a challenge.

Purpose of the Study:

• To differentiate brain functional networks between schizophrenia patients and healthy individuals during working memory tasks.
• To evaluate the efficacy of microstate analysis in reducing the complexity of EEG data for network analysis.
• To compare the performance of microstate time series (MTS) with original time series (OTS) in network construction and analysis.

Main Methods:

• Utilized microstates, defined by unique electric potential topographies, to reduce EEG signal dimensionality.
• Constructed and analyzed brain functional networks using both microstate time series (MTS) and original time series (OTS).
• Compared network construction time and discriminative power between MTS and OTS for schizophrenia patients and healthy controls.

Main Results:

• Schizophrenia patients exhibited reduced neuronal activity in right frontal and parietal-occipital regions during working memory tasks compared to controls.
• Microstate time series (MTS) significantly reduced the time required for brain functional network construction compared to original time series (OTS).
• Both MTS and OTS successfully distinguished brain functional networks between schizophrenia patients and healthy individuals.

Conclusions:

• Microstate analysis (MTS) provides an effective method for characterizing EEG brain functional networks, similar to OTS.
• MTS significantly decreases the time complexity associated with constructing and analyzing EEG brain functional networks.
• MTS offers a viable alternative for analyzing EEG data, particularly in clinical populations like schizophrenia during cognitive tasks.