Fig 1.
Locations of the simulated sources, S1, S2, and S3, are shown from left to right, respectively.
Simulated EEG sources’ subject-based coordinates are S1 = [82, 77, 82] and S2 = [57, 117, 84]. fMRI active regions are simulated in their spatial neighbourhood and comprise deep brain locations in the limbic lobe and posterior cingulate for S1, and sub-lobar, and insula, regions for S2. The fMRI extra source S3 is not related to any EEG source and comprises voxels in the temporal lobe and superior temporal areas.
Fig 2.
Schematic of SIMF-informed EEG source localization.
High-frequency spatial components extracted from the fMRI map and the EEG signals recorded at the same condition are used for finding the activated sources with high spatial and temporal resolution. We start with computing the weighting matrix from the fMRI activation map. A) The 3D-EMD method is applied to the fMRI activation map to decompose it into its B) SIMFs. C) The local highly activated voxels are specified using the high frequency SIMFs and the activation threshold is then applied to the high-frequency-based fMRI activation map. D) The resultant fMRI map is registered to the same head model used for EEG inverse modeling, or vise versa. E) Each diagonal element of the weighting matrix R is computed according to its correspondence voxel activity in the fMRI activation map registered to the head model of the EEG inverse modeling. We used a weight of 0.1 for the regions that are not activated based on the fMRI map. F) For a single EEG band (Y) recorded at EEG sensors, the gain matrix (G) and the covariance matrix (C) are computed. G) We solve the fMRI-informed-EEG inverse modeling equation by adding the diagonal weighting matrix R, computed from high-frequency SIMFs, as a coefficient for the gain matrix G. H) The SIMF-informed EEG source localization result is computed.
Fig 3.
fMRI activation map and its SIMFs around one of the simulated sources.
Figures demonstrate A) an fMRI active spot in the brain, B) SIMF1, C) SIMF2, D) SIMF3, and E) Residue, applying the 3D-EMD method. SIMF1 specifies the local maxima of fMRI intensities, and the residue presents the intensity trend of the fMRI activation map’s voxels.
Table 1.
Mean DLE values ± standard deviation for different fMRI extra active area’s amplitude when the whole fMRI signal and high spatial frequencies of fMRI are considered as prior for EEG source localization with p-value <0.05.
Lower DLE means better source localization. The level of the DLE improvement comparing the SIMF-based with the fMRI-based EEG source localization is represented in the third column of the table.
Table 2.
Mean Spatial Dispersion values ± standard deviation for different fMRI extra active area’s amplitude when the whole fMRI signal and high spatial frequencies of fMRI are considered as prior for EEG source localization with p-value <0.05.
Lower Spatial Dispersion means better source localization. The third column of the table represents the spatial Dispersion improvement percentage, comparing the fMRI and SIMF-based EEG source localization.
Table 3.
Mean accuracy values ± standard deviation for different fMRI extra active area’s amplitude when the whole fMRI signal and high spatial frequencies of fMRI are considered as prior for EEG source localization with p-value <0.05.
Accuracy evaluates a method for identifying true activation. A higher value of the Accuracy means better identification of the sources. The third column of the table represents the accuracy improvement percentage, comparing the fMRI and SIMF-based EEG source localization.
Fig 4.
fMRI-informed source localization results when the mean intensity of the fMRI extra active area is 0.3 μ.
Axial brain images from top left to bottom right show the sources’ activity from 0 ms to 996.1 ms with 66.5 ms apart.
Fig 5.
High-SIMF informed source localization results when the mean intensity of the fMRI extra active area is 0.3 μ.
Axial brain images from top left to bottom right demonstrate the sources’ activity from 0 ms to 996.1 ms with 66.5 ms apart.
Fig 6.
A) Uninformed, B) fMRI- and C)SIMF-informed MEG data source localization for the left toe movements. D) Restricted SIMF-informed MEG source localization, in which voxels with intensities lower than ten percent of the maximum activity on the fMRI map are removed from the weighting matrix added to the MEG inverse modeling. Uninformed source localization results show activation on broad brain areas and more on the cortex surface area. Active areas in fMRI- and SIMF-informed source localization are more spatially specified and in accordance with the expected activity shown in refs. [58, 59]. Active areas localized by the guide of SIMF are more spatially detailed, even without applying any threshold on the fMRI activation map. Figure (D) shows the source localization result when a small threshold (10 percent of the maximum activation intensity) is applied on the SIMF-based activation map and weighting matrix. Brain images from top left to bottom right, for each part of A to D, represent source localization results with ≈125 ms apart.
Fig 7.
MEG data source localization, A) uninformed, and B, C) informed by their associated fMRI and SIMF data, for the working memory. Informed source localization methods could localize more spatially specified areas and in accordance with the expected activity shown in refs. [58, 59] compare to the uninformed source localization. According to the source localization results shown in figure (C), SIMF-informed MEG source localization leads to more spatially detailed activated areas. The source localization results at each part of A to D, from top left to bottom right, are ≈125 ms apart.