Aberrant information flow within resting-state triple network model in schizophrenia-An EEG effective connectivity...

Area of Science:

• Neuroscience
• Psychiatry
• Computational Neuroscience

Background:

• Schizophrenia presents complex neurobiological underpinnings.
• The triple-network model offers a framework for understanding resting-state brain activity abnormalities.
• Investigating effective connectivity within these networks is crucial for schizophrenia research.

Purpose of the Study:

• To explore differences in resting-state effective connectivity within the default mode (DMN), central executive (CEN), and salience (SN) networks in individuals with schizophrenia compared to controls.
• To utilize source-localization-based Directed Transfer Function (DTF) for analyzing EEG data.
• To identify potential neurobiological markers associated with schizophrenia.

Main Methods:

• Recruited 30 individuals with chronic schizophrenia and 30 healthy controls.
• Recorded resting-state electroencephalography (EEG) data.
• Applied source-localization-based Directed Transfer Function (DTF) to analyze multiband effective connectivity within the DMN, CEN, and SN.

Main Results:

• Significant multiband effective connectivity disturbances were observed in the DMN, CEN, and SN of schizophrenia patients.
• Global default mode network (DMN) hyperconnectivity was a prominent finding.
• Central executive network (CEN) showed low-band hyperconnectivity and high-band hypoconnectivity, alongside aberrant information flow in the salience network (SN).

Conclusions:

• Schizophrenia is characterized by widespread disruptions in resting-state effective connectivity across key brain networks.
• Aberrant information flow directionality between the SN, DMN, and CEN hubs represents a significant finding.
• These connectivity alterations may serve as potential neuroimaging biomarkers for schizophrenia.