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Quantifying Cerebellar Signal Detectability in MEG and EEG in Epilepsy Using Anatomically Informed Source Modeling.

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Quantifying cerebellar signal detectability in MEG and EEG in epilepsy using anatomically informed source modeling.

Teppei Matsubara1, Abbas Sohrabpour1, Seppo P Ahlfors1

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.

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Summary

New sensor layouts significantly improve detection of cerebellar brain activity using magnetoencephalography (MEG) and electroencephalography (EEG). This advancement enhances non-invasive brain imaging for conditions like epilepsy.

Keywords:
CerebellumEEGEpilepsyMEGOPMSNR

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Area of Science:

  • Neuroscience
  • Biophysics
  • Medical Imaging

Background:

  • The cerebellum's role in brain networks, particularly in epilepsy, is increasingly recognized.
  • Non-invasive electrophysiological characterization of the cerebellum is limited due to its depth, folding, and source orientation.
  • Conventional magnetoencephalography (MEG) and electroencephalography (EEG) face challenges in detecting cerebellar signals.

Purpose of the Study:

  • To quantitatively assess cerebellar signal detectability across different non-invasive recording modalities and sensor configurations.
  • To investigate the impact of anatomical factors and sensor placement on signal-to-noise ratio (SNR) for cerebellar activity.
  • To evaluate novel on-scalp optically pumped magnetometer (OPM) configurations for improved cerebellar signal detection.

Main Methods:

  • Anatomically informed source modeling was used with data from 54 epilepsy patients undergoing presurgical evaluation.
  • Subject-specific anatomical models from MRI were used for consistent forward modeling.
  • Signal-to-noise ratio (SNR) was estimated for clinical SQUID-MEG, EEG, and simulated OPM configurations, including optimized layouts.

Main Results:

  • Routine clinical MEG and EEG showed consistently lower cerebellar SNR compared to superficial cortical areas.
  • Placing OPMs at SQUID-equivalent locations did not improve cerebellar SNR, highlighting depth and geometry constraints over proximity.
  • Cerebellum-optimized OPM layouts demonstrated substantial SNR gains in posterior cerebellar regions, especially in smaller head sizes.

Conclusions:

  • Cerebellar signal detectability is primarily determined by anatomical depth and geometry, not just sensor proximity.
  • Optimized OPM sensor layouts offer a promising approach to enhance MEG/EEG sensitivity to cerebellar activity.
  • This framework aids in improving non-invasive mapping of deep and complex brain structures beyond epilepsy.