Estimates of cortical column orientation improve MEG source inversion

James J Bonaiuto,Fardin Afdideh,Maxime Ferez,Konrad Wagstyl,Jérémie Mattout,Mathilde Bonnefond,Gareth R Barnes,Sven Bestmann

doi:10.1016/j.neuroimage.2020.116862

Abstract

Determining the anatomical source of brain activity non-invasively measured from EEG or MEG sensors is challenging. In order to simplify the source localization problem, many techniques introduce the assumption that current sources lie on the cortical surface. Another common assumption is that this current flow is orthogonal to the cortical surface, thereby approximating the orientation of cortical columns. However, it is not clear which cortical surface to use to define the current source locations, and normal vectors computed from a single cortical surface may not be the best approximation to the orientation of cortical columns. We compared three different surface location priors and five different approaches for estimating dipole vector orientation, both in simulations and visual and motor evoked MEG responses. We show that models with source locations on the white matter surface and using methods based on establishing correspondences between white matter and pial cortical surfaces dramatically outperform models with source locations on the pial or combined pial/white surfaces and which use methods based on the geometry of a single cortical surface in fitting evoked visual and motor responses. These methods can be easily implemented and adopted in most M/EEG analysis pipelines, with the potential to significantly improve source localization of evoked responses.

Highlights

Non-invasive measures of brain activity such as magnetoencephalography (MEG) and electroencephalography (EEG) are powerful tools for generating insights into human brain function with millisecond-scale temporal resolution
Two commonly used source localization constraints based on this assumption are that the locations of source dipoles are restricted to locations on a mesh of the white matter surface as is it is closest to the deep cortical layers (Dale and Sereno, 1993; Henson et al, 2009; Hillebrand and Barnes, 2003, 2002; Mattout et al, 2007), and that the orientation of dipoles is orthogonal to this surface (H€am€al€ainen and Ilmoniemi, 1984, 1994; Henson et al, 2009; Hillebrand and Barnes, 2003; Lin et al, 2006; Salmelin et al, 1995), thereby approximating the orientation of cortical columns (Nunez and Srinivasan, 2006; Okada et al, 1997)
In this paper we show that methods for computing dipole orientation which are based on establishing correspondences between white matter and pial cortical surfaces dramatically outperform methods based on the geometry of a single cortical surface in fitting evoked visual and motor responses

Summary

Introduction

Non-invasive measures of brain activity such as magnetoencephalography (MEG) and electroencephalography (EEG) are powerful tools for generating insights into human brain function with millisecond-scale temporal resolution. In order to simplify the source localization problem, many techniques introduce constraints to the dimensionality of source space These constraints embody assumptions about how the brain generates the signals which we can measure from outside of the head. One of these assumptions is that signals measured by M/EEG sensors are predominantly generated by large pyramidal neurons in deep cortical layers, which are arranged in parallel columns so that their cumulative activity produces a measurable extracranial signal (Baillet, 2017; Buzsaki et al, 2012; Murakami and Okada, 2006; Okada et al, 1997). Two commonly used source localization constraints based on this assumption are that the locations of source dipoles are restricted to locations on a mesh of the white matter surface as is it is closest to the deep cortical layers (Dale and Sereno, 1993; Henson et al, 2009; Hillebrand and Barnes, 2003, 2002; Mattout et al, 2007), and that the orientation of dipoles is orthogonal to this surface (H€am€al€ainen and Ilmoniemi, 1984, 1994; Henson et al, 2009; Hillebrand and Barnes, 2003; Lin et al, 2006; Salmelin et al, 1995), thereby approximating the orientation of cortical columns (Nunez and Srinivasan, 2006; Okada et al, 1997)

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Discussion

Conclusion