MEG language mapping using a novel automatic ECD algorithm in comparison with MNE, dSPM, and DICS beamformer

Abbas Babajani-Feremi; Haatef Pourmotabbed; William A Schraegle; Clifford S Calley; Dave F Clarke; Andrew C Papanicolaou

doi:10.3389/fnins.2023.1151885

MEG language mapping using a novel automatic ECD algorithm in comparison with MNE, dSPM, and DICS beamformer

Front Neurosci. 2023 Jun 2:17:1151885. doi: 10.3389/fnins.2023.1151885. eCollection 2023.

Authors

Abbas Babajani-Feremi^{1

2

3}, Haatef Pourmotabbed³, William A Schraegle^{3

4

5}, Clifford S Calley^{3

4

5

6}, Dave F Clarke^{3

4

5

6}, Andrew C Papanicolaou⁷

Affiliations

¹ Department of Neurology, University of Florida, Gainesville, FL, United States.
² Magnetoencephalography (MEG) Lab, The Norman Fixel Institute of Neurological Diseases, University of Florida Health, Gainesville, FL, United States.
³ Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX, United States.
⁴ Comprehensive Pediatric Epilepsy Center, Dell Children's Medical Center, Austin, TX, United States.
⁵ Department of Pediatrics, Dell Medical School, University of Texas at Austin, Austin, TX, United States.
⁶ Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, TX, United States.
⁷ Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States.

Abstract

Introduction: The single equivalent current dipole (sECD) is the standard clinical procedure for presurgical language mapping in epilepsy using magnetoencephalography (MEG). However, the sECD approach has not been widely used in clinical assessments, mainly because it requires subjective judgements in selecting several critical parameters. To address this limitation, we developed an automatic sECD algorithm (AsECDa) for language mapping.

Methods: The localization accuracy of the AsECDa was evaluated using synthetic MEG data. Subsequently, the reliability and efficiency of AsECDa were compared to three other common source localization methods using MEG data recorded during two sessions of a receptive language task in 21 epilepsy patients. These methods include minimum norm estimation (MNE), dynamic statistical parametric mapping (dSPM), and dynamic imaging of coherent sources (DICS) beamformer.

Results: For the synthetic single dipole MEG data with a typical signal-to-noise ratio, the average localization error of AsECDa was less than 2 mm for simulated superficial and deep dipoles. For the patient data, AsECDa showed better test-retest reliability (TRR) of the language laterality index (LI) than MNE, dSPM, and DICS beamformer. Specifically, the LI calculated with AsECDa revealed excellent TRR between the two MEG sessions across all patients (Cor = 0.80), while the LI for MNE, dSPM, DICS-event-related desynchronization (ERD) in the alpha band, and DICS-ERD in the low beta band ranged lower (Cor = 0.71, 0.64, 0.54, and 0.48, respectively). Furthermore, AsECDa identified 38% of patients with atypical language lateralization (i.e., right lateralization or bilateral), compared to 73%, 68%, 55%, and 50% identified by DICS-ERD in the low beta band, DICS-ERD in the alpha band, MNE, and dSPM, respectively. Compared to other methods, AsECDa's results were more consistent with previous studies that reported atypical language lateralization in 20-30% of epilepsy patients.

Discussion: Our study suggests that AsECDa is a promising approach for presurgical language mapping, and its fully automated nature makes it easy to implement and reliable for clinical evaluations.

Keywords: dynamic imaging of coherent sources beamformer; dynamic statistical parametric mapping; language lateralization; magnetoencephalography; minimum norm estimation; single equivalent current dipole.