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A quantitative method for evaluating cortical responses to electrical stimulation.

Lawrence J Crowther1, Peter Brunner2, Christoph Kapeller3

  • 1National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, USA.

Journal of Neuroscience Methods
|October 8, 2018
PubMed
Summary
This summary is machine-generated.

We developed SIGNI, a new automated method to analyze brain network connectivity using electrical stimulation. This technique objectively measures cortical responses, improving the study of brain communication and aiding pre-surgical planning.

Keywords:
ConnectivityCortico-cortical evoked potentialsElectrical stimulationElectrocorticography

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Electrical stimulation via subdural electrodes elicits cortico-cortical evoked potentials (CCEPs) to map brain connectivity.
  • Previous CCEP evaluation methods were often subjective and susceptible to artifacts, limiting systematic investigation.

Purpose of the Study:

  • To develop an automated, quantitative method (SIGNI) for evaluating CCEPs, minimizing artifacts.
  • To enable objective and systematic investigation of cortical connectivity and neurophysiology.

Main Methods:

  • Developed SIGNI (Stimulation-Induced Gamma-based Network Identification), an automated method for analyzing electrocorticographic (ECoG) data.
  • Applied SIGNI to ECoG data from 8 human subjects with 978 subdural electrodes, delivering electrical stimuli to 64 electrode pairs.

Main Results:

  • SIGNI effectively quantifies cortical population-level responses to electrical stimulation.
  • Demonstrated relationships between evoked activity magnitude and stimulation amplitude, and between latency and distance, validating SIGNI's efficacy.

Conclusions:

  • SIGNI reveals timing and amplitude of cortical responses and underlying structural connectivity.
  • Enables new research into cortical communication neurophysiology and supports pre-surgical planning.