Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG
Introduction
High-density Electroencephalography (hdEEG), defined here as EEG with 256 electrodes, and Magnetoencephalography (MEG) are two complementary and non-invasive neurophysiology modalities used to depict electromagnetic brain activity (Stefan, 2009, Ebersole and Ebersole, 2010, Ahlfors et al., 2011). Electrical and magnetic source imaging consist in solving a so-called inverse problem, localizing the generators of scalp EEG or MEG signals into the brain (Darvas et al., 2004).
The inverse problem is ill-posed by nature and a unique solution can only be found if specific constraints are added for regularization (Panday et al., 2009). Multiple inverse solution approaches are nowadays available, including equivalent current dipole fitting, dipole scanning approaches and distributed source models (see Wendel et al., 2009, Klamer et al., 2014).
The spatial accuracy of source imaging techniques is influenced by several factors, including the choice of modality (Pittau et al., 2014), the number of sensors (Sohrabpour et al., 2014), the orientation of the generator (Ahlfors et al., 2010), the conductivity of the biological tissues (Aydin et al., 2014) and the source imaging technique used to solve the inverse problem (Hauk et al., 2011).
The objective of this study was to compare the intrinsic spatial resolutions of different source imaging techniques applied to hdEEG and MEG signals, when considering these two modalities with approximately the same number of sensors. We analyzed the resolution matrix, whose application in the neuroimaging field was originally proposed by Menendez et al. (1997), to characterize the spatial properties of a linear inverse operator. The spatial resolution matrix is a square matrix, whose size is the number of dipolar sources, providing the following two features (Schoffelen and Gross, 2009):
- 1.
The columns of the resolution matrix quantify the Point Spread Functions (PSF) of every dipolar source of the source space. Each PSF is assessing the solution of the source imaging method for the activation a single cortical dipole, when considering noise-free data. Analyzing the localization error and the spatial extent of the PSF provides information about the intrinsic spatial property of a source imaging technique.
- 2.
The rows of the resolution matrix represent the crosstalk functions (CT) which reflect the influence a single dipolar source may have on the estimation of the generators in its neighborhood. Hence, the spatial extent of the CT informs on the amount of “source leakage” and the potential bias in the estimation of functional connectivity patterns leading to spurious local coherence (Schoffelen and Gross, 2009).
An ideal resolution matrix should be the identity matrix. In practice, any source estimate is subject to blurring (if large amplitude values are found in off-diagonal terms in the matrix) and mislocation (if off-diagonal values were higher amplitude than diagonal values).
Once PSF and CT maps are constructed for each dipolar source, one can assess their spatial properties through evaluation metrics such as Dipole Localization Error (DLE) and Spatial Dispersion (SD) (Liu et al., 2002, Molins et al., 2008). DLE measures the Euclidean distance between the maximum of the PSF or CT maps and the true source location, whereas SD quantifies the spatial spread around the true source location.
Beyond the theoretical analysis of the resolution matrix, a validation of the comparison between the intrinsic spatial resolution can be achieved by studying real data acquired under well-controlled paradigms, for which the location of generator is known a priori. To that motive, we measures somatosensory evoked responses measured after electrical stimulation of the median nerve. This paradigm is known to generate evoked response exhibiting the activation of the contralateral primary sensory hand region (Balzamo et al., 2004). In this context, for which the generator is located in a predefined focal brain region, properties of source imaging techniques and comparison between them could also be evaluated using DLE and SD metrics, as proposed by Molins et al. (2008).
We chose to evaluate and compare four distributed sources localization methods. Three of them are well-known linear operators: Minimum Norm Estimate (MNE) (Hämäläinen and Ilmoniemi, 1994), dynamic Statistical Parametric Mapping (dSPM) (Dale and Sereno, 1993) and standardized Low-Resolution Electromagnetic Tomography (sLORETA) (Pascual-Marqui, 2002). The fourth one is the coherent Maximum Entropy on the Mean (cMEM) (Amblard and Lapalme, 2004, Grova et al., 2006), which is a novel non-linear method specifically evaluated for its sensitivity to recover the spatial extent of the underlying cortical generators (Chowdhury et al., 2013, Heers et al., 2016, Grova et al., 2016). Whereas the calculation of the resolution matrix is straightforward for the linear methods (MNE, sLORETA and dSPM), specific estimation of the resolution matrix for the non-linear method cMEM was performed with the iterative reconstruction of the PSF of every dipolar source.
We proposed a systematic and quantitative assessment of these source imaging techniques based on two strategies: (i) theoretical analysis of the resolution matrix; (ii) study of the source estimated from hdEEG and MEG responses evoked by electrical median nerve stimulation, in the primary somatosensory cortex.
Section snippets
Subjects selection
Five right-handed healthy subjects (3 males, mean age ± standard deviation ) were selected for this study. The study was approved by the Research Ethics Board of the Montreal Neurological Institute and Hospital and a written informed consent was signed by all participants prior to the procedures.
Electrical Median Nerve Stimulation
Electrical Median Nerve Stimulation (MNS) was performed using a Digitimer system (Digitimer DS7A, Letchworth Garden City, U.K). 600 stimuli were delivered to the left and right median nerves
Analysis of DLE and SD distributions
For each source localization technique and each modality, the distributions of DLE and SD metrics over all dipolar sources along the cortical surface are presented in Fig. 2, using boxplot representations. As expected (see Analysis of the intrinsic properties of the resolution matrix), all metrics values for MNE PSF and MNE CT were identical. Similarly, all metrics values for MNE CT, dSPM CT, and sLORETA CT were the same. Moreover DLE values for sLORETA PSF were correctly found to be zero.
The
Discussion
In this study, we applied the concept of resolution matrix to compare four source imaging methods, namely MNE, dSPM, sLORETA and cMEM on the basis of spatial accuracy measured with PSF and CT scores. The spatial properties of the imaging techniques were also analyzed by applying source localization of the N20 peak of the somatosensory evoked response to electrical stimulation of the median nerve.
Whereas most methods can reach overall good performance in accurately localizing the main peak of
Conclusion
We used the resolution matrix to compare different source imaging techniques (MNE, dSPM, sLORETA, and cMEM) in terms of PSF (related to the intrinsic spatial resolution of the source reconstruction) and CT, an important feature to be characterized before assessing functional connectivity between different regions. In this study combining theoretical analysis of resolution matrices and localization of real somatosensory data, we carefully evaluated both MEG and hdEEG data using similar amount of
Acknowledgments
This work was supported by NSERC Discovery grant (RGPIN/356610-2013) and CIHR (MOP-93614). TH was supported by the Irma Bauer Fellowship (McGill University, Faculty of Medicine). GP was supported by the Richard and Edith Strauss Canada Foundation. The authors would like to thank Ümit Aydin for his help in the correction of this manuscript.
References (58)
- et al.
Short and middle-latency Median Nerve (MN) SEPs recorded by depth electrodes in human pre-SMA and SMA-proper
Clin. Neurophysiol. : Off. J. Int. Fed. Clin. Neurophysiol.
(2005) - et al.
Short-latency components of evoked potentials to median nerve stimulation recorded by intracerebral electrodes in the human pre- and postcentral areas
Clin. Neurophysiol. : Off. J. Int. Fed. Clin. Neurophysiol.
(2004) - et al.
Dynamic statistical parametric neurotechnique mapping: combining fMRI and MEG for high-resolution imaging of cortical activity
Neuron
(2000) - et al.
Multiple sparse priors for the M/EEG inverse problem
NeuroImage
(2008) - et al.
Neuromagnetic source imaging with FOCUSSa recursive weighted minimum norm algorithm
Electroencephalogr. Clin. Neurophysiol.
(1995) - et al.
Brain activity is related to individual differences in the number of items stored in auditory short-term memory for pitch: evidence from magnetoencephalography
NeuroImage
(2014) - et al.
Evaluation of EEG localization methods using realistic simulations of interictal spikes
NeuroImage
(2006) - et al.
Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics
NeuroImage
(2011) - et al.
Selecting forward models for {MEG} source-reconstruction using model-evidence
Neuroimage
(2009) - et al.
The sensory somatotopic map of the human hand demonstrated at 4 Tesla
NeuroImage
(1999)
Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation
NeuroImage
Geodesic photogrammetry for localizing sensor positions in dense-array EEG
Clin. Neurophysiol.
Magnetic source imaging
Rev. Neurol.
A guideline for head volume conductor modeling in EEG and MEG
NeuroImage
Sensitivity of MEG and EEG to source orientation
Brain Topogr.
Cancellation of EEG and MEG signals generated by extended and distributed sources
Hum. Brain Mapp.
Biomagnetic source detection by maximum entropy and graphical models
IEEE Trans. Bio-Med. Eng.
Combining EEG and MEG for the reconstruction of epileptic activity using a calibrated realistic volume conductor model
PLoS One
MEG source localization of spatially extended generators of epileptic activity: comparing Entropic and Hierarchical Bayesian approaches
PLoS One
MEG - EEG information fusion and electromagnetic source imaging: from theory to clinical application in epilepsy
Brain Topogr.
Combining MEG and EEG source modeling in epilepsy evaluations
J. Clin. Neurophysiol. : Off. Publ. Am. Electroencephalogr. Soc.
OpenMEEG: opensource software for quasistatic bioelectromagnetics
Biomed. Eng. Online
Intracranial EEG potentials estimated from MEG sources: a new approach to correlate MEG and iEEG data in epilepsy
Hum. Brain Mapp.
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