Identifying Critical Nodes in the Cognitive Decline Process through EEG Network Community Detection Based on...

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Cognitive impairment, a neurodegenerative disease, involves cognitive decline.
• Non-pharmacological interventions like physical stimuli are promising but lack precise target selection methods.
• Understanding neural network alterations in Mild Cognitive Impairment (MCI) is crucial for effective treatment.

Purpose of the Study:

• To develop a method for identifying critical brain regions in MCI progression.
• To investigate the efficacy of targeting these identified regions with Transcranial Magnetic Stimulation (TMS).
• To provide insights for early diagnosis and non-pharmacological treatment of neurodegenerative diseases.

Main Methods:

• Utilized autoencoders to analyze resting-state Electroencephalography (EEG) network structure and community information.
• Integrated node behavior analysis to calculate dynamic influence and identify critical brain regions.
• Applied Transcranial Magnetic Stimulation (TMS) to the identified key brain regions in MCI patients.

Main Results:

• Successfully encoded neural network structure and community information from EEG data.
• Identified specific critical brain regions dynamically influenced during MCI progression.
• Demonstrated significant cognitive performance improvement in MCI patients following targeted TMS treatment.

Conclusions:

• Introduced a novel method for discovering critical brain regions in neurodegenerative diseases.
• Validated the therapeutic potential of targeting identified key nodes with TMS for MCI.
• Provided valuable insights into neural network dynamics for future early diagnosis and treatment strategies.