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Optimizing Magnetoencephalographic Imaging Estimation of Language Lateralization for Simpler Language Tasks.

Leighton B N Hinkley¹, Elke De Witte^1,2, Megan Cahill-Thompson¹

¹Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.

Frontiers in Human Neuroscience

|June 6, 2020

Summary

Magnetoencephalographic imaging (MEGI) offers new, simpler language tasks for precise preoperative language lateralization in neurosurgery. Combining tasks like non-word repetition and picture naming with verb generation improves accuracy for patient-specific mapping.

Keywords:

MEG Wada language lateralization language tasks non-word repetition picture naming tumor patients verb generation

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Published on: October 24, 2012

Area of Science:

Neuroimaging
Neurosurgery
Cognitive Neuroscience

Background:

Magnetoencephalographic imaging (MEGI) is a non-invasive tool for preoperative language lateralization.
Current methods using complex tasks like auditory verb generation (VG) may be challenging for patients with impaired language function.
Optimizing simpler tasks is crucial for broader clinical application of MEGI.

Purpose of the Study:

To optimize MEGI laterality analyses for non-word repetition (NWR) and picture naming (PN) tasks.
To compare the language lateralization results of NWR and PN tasks with the established VG task.
To validate the optimized MEGI parameters in independent cohorts.

Main Methods:

Defined MEGI reconstruction parameters (regions and time windows) for NWR and PN in a training cohort, benchmarking against VG laterality indices.
Identified optimal time windows and regions of interest (ROIs) by analyzing beta-band oscillations (12-30 Hz).
Included specific ROIs like MTG/ITG for NWR and the postcentral gyrus for both NWR and PN.

Main Results:

Optimized time windows for NWR: 650-850 ms (stimulus-locked) and -350 to -150 ms (response-locked).
Optimized time windows for PN: -450 to -250 ms (response-locked).
High concordance was found between VG and PN laterality (82%) and VG and NWR laterality (87%) in a validation cohort.
In a test cohort, a composite LI of VG + NWR + PN showed 100% concordance with the intracarotid amobarbital procedure (IAP).

Conclusions:

Optimized NWR and PN tasks provide viable options for non-invasive language mapping using MEGI.
MEGI language mapping can be calibrated to individual patient abilities.
Combining laterality estimates from multiple tasks enhances the accuracy of MEGI-based language mapping.