Joint Estimation of Effective Brain Wave Activation Modes Using EEG/MEG Sensor Arrays and Multimodal MRI Volumes

Abstract

In this paper we present a new method for integration of sensor-based multi-frequency bands of EEG and MEG datasets into a voxel-based structural-temporal MRI analysis by utilizing the general Joint ESTimation using Entropy Regularization (JESTER) framework. This allows the enhancement of the spatial-temporal localization of brain function and the ability to relate it to morphological features and structural connectivity. This method has broad implications for both basic neuroscience research and clinical neuroscience focused on identifying disease-relevant biomarkers by enhancing the spatial-temporal resolution of the estimates derived from current neuroimaging modalities thereby providing a better picture of the normal human brain in basic neuroimaging experiments and variations associated with disease states.

JEDI:JointEstimationDiffusionImaging of macroscopic and microscopic tissue properties

“…It has been used to demonstrate, for example, that tractography using GO‐ESP combined with spherical wave decomposition (SWD) from high resolution anatomical (HRA) data can produce tracts of spatial resolution an order of magnitude greater than the scanner resolution and resolve crossing fibers at or below angular resolution . This procedure is general and not limited to MRI data, as we have demonstrated in combining fMRI data with cortical activity data from EEG, using both HRA and dMRI to model tissue conductivity information in a variant we call CORT‐JESTER . This demonstrated the capability of achieving the spatial resolution of fMRI with the temporal resolution of EEG.…”

“…This could be considered more a limitation of implementation, rather than the method, for in other applications involving more complicated spatiotemporal phenomena we have employed higher order correlations . Nor have we employed any very specific model of the diffusion process, although again in other works we have incorporated much more sophisticated models of physical phenomena . Multimodal coupling may also present a challenge in finding and/or selecting the “best” possible relation between heterogeneous data properties available from different modalities.…”

Brain Waves: Emergence of Localized, Persistent, Weakly Evanescent Cortical Loops

“…Our approach allows straightforward incorporation and integration of the realistic human brain morphological features and structural connectivity with brain functional dynamics using, presented here, a realistic inhomogeneous/anisotropic tissue framework derived from Maxwell equations. A study based on this mechanism (Galinsky et al, 2018) applied to multimodal MRI/EEG/MEG resting and task-based datasets showed good repeatability and similarity between subjects, with the intraclass correlation coefficient ranging from .9 to higher than .99, thus demonstrating potentially broad implications for both basic neuroscience and clinical studies by enhancing the spatial-temporal resolution of the estimates derived from current neuroimaging modalities.…”

“…The data include task and resting state EEG and FMRI recordings at 2 msec for about 30 min in total and HRA T1 volumes. We followed the procedure for generation of functional EEG modes from combined EEG and FMRI data described in detail in Galinsky, Martinez, Paulus, and Frank (2018).…”

Brain connections derived from diffusion MRI tractography can be highly anatomically accurate—if we know where white matter pathways start, where they end, and where they do not go

“…Major progress in the connectomics and bundle-segmentation field has taken place with advanced filtering and/or spatial priors based on anatomy [13,35,[71][72][73], microstructure [74], and the diffusion signal itself (conservation of density) [75,76]. We believe the next big steps involve multimodal integration of these and orthogonal techniques used to probe the human connectome -for example myelin [77,78], BOLD contrasts [52,54,[79][80][81], functional imaging [82,83], and quantitative microdissection [84], which will lead to a better understanding of the fundamental rules governing the structural organization and connectivity of the brain and endeavors to fully incorporate these into tractography algorithms. In essence, all of these facilitate the adoption of rules, for example ways to include, exclude, or generate streamlines in the same way approached through this study, which can lead to breakthroughs in the anatomical accuracy of tractographyas quantitatively shown in this study.…”