1
|
Bhutada AS, Cai C, Mizuiri D, Findlay A, Chen J, Tay A, Kirsch HE, Nagarajan SS. Clinical Validation of the Champagne Algorithm for Evoked Response Source Localization in Magnetoencephalography. Brain Topogr 2021; 35:96-107. [PMID: 34114168 PMCID: PMC8664897 DOI: 10.1007/s10548-021-00850-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 05/05/2021] [Indexed: 11/06/2022]
Abstract
Magnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.
Collapse
Affiliation(s)
- Abhishek S Bhutada
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Chang Cai
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Danielle Mizuiri
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Anne Findlay
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Jessie Chen
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Ashley Tay
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Heidi E Kirsch
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA. .,Department of Neurology, Epilepsy Center, UCSF, 94143, San Francisco, CA, USA.
| | - Srikantan S Nagarajan
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA.
| |
Collapse
|