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Automated TMS hotspot-hunting using a closed loop threshold-based algorithm.

Jonna Meincke1, Manuel Hewitt1, Giorgi Batsikadze1

  • 1Department of Clinical Neurophysiology, Georg August University of Göttingen, University Medical Center, 37075 Göttingen, Germany.

Neuroimage
|September 20, 2015
PubMed
Summary
This summary is machine-generated.

We developed an automated hotspot-hunting procedure (AHH) that uses resting motor thresholds (RMTs) for greater accuracy. This method significantly reduces variability in transcranial magnetic stimulation (TMS) coil positioning compared to manual or MEP-amplitude-based approaches.

Keywords:
AccuracyAutomationHotspotMappingNeuronavigationRobotTMS reliabilityThresholdTranscranial magnetic stimulation

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Medical Imaging

Background:

  • Neuronavigation is used for optimizing transcranial magnetic stimulation (TMS) coil positioning.
  • Current methods suffer from unsatisfactory inter-session reliability in hotspot location.
  • Variability in motor evoked potentials (MEPs) and investigator bias contribute to unreliability.

Purpose of the Study:

  • To introduce a novel automated hotspot-hunting procedure (AHH) to enhance reliability and accuracy.
  • To develop an investigator-independent method for precise TMS hotspot identification.

Main Methods:

  • The automated hotspot-hunting procedure (AHH) utilizes resting motor thresholds (RMTs) instead of MEP amplitudes.
  • AHH combines robotic coil positioning with a closed-loop algorithm for independent operation.
  • It identifies MEP-positive targets based on RMTs and then determines the motor hotspot by measuring RMTs across these targets.

Main Results:

  • AHH significantly reduced inter-session center of gravity (CoG) shift (1.4mm) compared to robotic MEP amplitude mapping (MAM) (7.0mm) and manual hotspot search (MHS) (9.6mm).
  • No significant difference was found between MAM and MHS in terms of CoG shift.
  • Resting motor thresholds (RMTs) demonstrated high reliability between sessions.

Conclusions:

  • AHH is the first fully automated and investigator-independent TMS hotspot-hunting procedure.
  • Using RMTs instead of MEP amplitudes significantly improves the accuracy and reliability of CoG locations.
  • AHH fulfills the original definition of a motor hotspot by assessing stimulation thresholds.