A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high‐resolution atlas: Performance, precision, and parcellation

Abstract

Noninvasive functional neuroimaging of the human brain can give crucial insight into the mechanisms that underpin healthy cognition and neurological disorders. Magnetoencephalography (MEG) measures extracranial magnetic fields originating from neuronal activity with high temporal resolution, but requires source reconstruction to make neuroanatomical inferences from these signals. Many source reconstruction algorithms are available, and have been widely evaluated in the context of localizing task‐evoked activities. However, no consensus yet exists on the optimum algorithm for resting‐state data. Here, we evaluated the performance of six commonly‐used source reconstruction algorithms based on minimum‐norm and beamforming estimates. Using human resting‐state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor‐level data. Next, we proposed a data‐driven approach to reduce the atlas from the Human Connectome Project's multi‐modal parcellation of the human cortex based on metrics such as MEG signal‐to‐noise‐ratio and resting‐state functional connectivity gradients. This procedure produced a reduced cortical atlas with 230 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners. Our results show that there is no “one size fits all” algorithm, and make recommendations on the appropriate algorithms depending on the data and aimed analyses. Our comprehensive comparisons and recommendations can serve as a guide for choosing appropriate methodologies in future studies of resting‐state MEG.

MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

“…The first step of the pipeline is therefore source reconstruction of the M/EEG data. A range of methodologies for source reconstruction are currently available and the choice of methodology chosen should be data specific ( Tait et al., 2021 ). Here, eLORETA ( Pascual-Marqui , Pascual-Marqui, 2009 ) was used for source-reconstruction.…”

“…This was done through parcellation of source time courses into regions of interest. Here, the HCP230 atlas ( Tait et al., 2021 ), a version of the Human Connectome Project’s multimodal parcellation ( Glasser et al., 2016 ) optimized for resting-state MEG, was chosen to reduce the source data from approximately 10,000 voxels to 230 ROI time courses. Source-reconstructed data was band-pass filtered 1–30 Hz in line with sensor-EEG microstate studies ( Michel and Koenig, 2018 ).…”

“…From the T1-weighted MRI image, extraction of the scalp, brain, and cortical surfaces was performed with Freesurfer ( Dale et al., 1999 ). Vertices of the cortical surface were labelled according to the HCP230 atlas optimized for MEG studies ( Tait et al., 2021 ). The scalp surface was used to align the structural data with the MEG digitizers.…”

Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors

“…Motivated by the challenging task of defining a ground truth, few studies have dedicated to systematically compare the performance of reconstruction strategies. The prevalent finding is that there is no (universal) optimal solution that encompasses all the contexts and designs, with few studies listing a set of recommendations to guide the choice of the most appropriate method based on the application (Belardinelli et al 2012;Hedrich et al 2017;Hincapié et al 2017;Anzolin et al 2019;Tait et al 2021). Particularly, (Hedrich et al 2017;Hincapié et al 2017;Tait et al 2021) have carried a more intensive comparison, however several existing methods were not addressed, e.g.…”

Generative Models of Brain Dynamics -- A review

“…Distinct dynamics is observed in a wide set of settings from resting-state activity [109] to task-specific experiments [116]. These insights are also useful in a wide set of modularities including fMRI [117], EEG [118], MEG [119], and Calcium imaging [120]. One common formulation is to build the dynamical graph models of the cortex based on the anatomical, functional, or effective connectivity as described in Table 1.…”