Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and...

Area of Science:

• Systems neuroscience
• Neuroimaging
• Brain-computer interfaces

Background:

• Resting-state networks (RSNs) influence cognition and clinical symptoms.
• Brain stimulation targets are often selected based on RSNs.
• Current EEG methods lack spatial resolution for network-level characterization during TMS.

Purpose of the Study:

• Map brain networks with high spatial and temporal precision.
• Assess the impact of network-level states on Transcranial Magnetic Stimulation (TMS) outcomes.
• Explore the relationship between large-scale brain network dynamics and corticospinal excitability.

Main Methods:

• Hidden Markov Models applied to pre-stimulus, high-density EEG data during TMS.
• Source space analysis of EEG data targeting the left primary motor cortex.
• Correlation analysis between identified brain states, RSNs (Yeo atlas), and corticospinal excitability.

Main Results:

• Identified fast-dynamic, large-scale brain states with distinct spatiotemporal and spectral features mirroring RSNs.
• Demonstrated significant influence of different network engagements on corticospinal excitability.
• Observed increased motor evoked potentials when the sensorimotor network dominated baseline activity.

Conclusions:

• Advanced characterization of brain networks in EEG-TMS studies with high spatial and temporal resolution.
• Highlighted the importance of integrating large-scale network dynamics into TMS experimental design.
• Provided a foundation for more precise and effective TMS applications.