Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources

Abstract

Brain networks are spatiotemporal phenomena that dynamically vary over time. Functional imaging approaches strive to noninvasively estimate these underlying processes. Here, we propose a novel source imaging approach that uses high-density EEG recordings to map brain networks. This approach objectively addresses the long-standing limitations of conventional source imaging techniques, namely, difficulty in objectively estimating the spatial extent, as well as the temporal evolution of underlying brain sources. We validate our approach by directly comparing source imaging results with the intracranial EEG (iEEG) findings and surgical resection outcomes in a cohort of 36 patients with focal epilepsy. To this end, we analyzed a total of 1,027 spikes and 86 seizures. We demonstrate the capability of our approach in imaging both the location and spatial extent of brain networks from noninvasive electrophysiological measurements, specifically for ictal and interictal brain networks. Our approach is a powerful tool for noninvasively investigating large-scale dynamic brain networks.

Noninvasive high-frequency oscillations riding spikes delineates epileptogenic sources

“…Second, the source imaging analysis of pHFOs can provide estimation of the underlying EZ in individual patients with focal epilepsy, which could further facilitate in planning for surgical intervention, such as resection or neuromodulation, and provide evaluation of the treatment outcomes. Third, our results raise the possibility of accurately investigating epilepsy without having to record seizures, which is resource consuming and prone to induce additional risks of secondary generalization due to medication reduction or cessation for prolonged video-monitoring prior to neurosurgery (89). Together, our findings suggest that the utility of concurrent HFOs with spikes in scalp recordings is highly desirable, which might be a critical resource for clinical translation in numerous patients and extend the application from presurgical diagnosis to monitoring of disease severity, tracking therapeutic progress, and providing postsurgical evaluation in vulnerable patient populations.…”

Virtual intracranial electroencephalography for epilepsy surgery using ictal magnetoencephalography

“…These 11 seizures might be amenable to source reconstruction with corresponding ictal HDEEG signals (the subject of our future work in a larger cohort). Ictal source signals have been reconstructed using ictal scalp EEG in prior studies 47,48,49 .…”

“…However, as opposed to MEG, scalp EEG signals are more distorted when the electrical eld propagates through inhomogeneous head tissue. More sophisticated techniques are needed to process and analyse the EEG signals in source space 49 .…”

SIFNet: Electromagnetic Source Imaging Framework Using Deep Neural Networks

“…The dipole source localization methods need to know the number of sources a priori [4], and when that information is not available, distributed-source models can be used [5] with regularization priors to restrict the solution space. Regularization terms could be based on imposing constraints on solutions' energy [6][7][8], covariance [9,10], sparsity level [11][12][13] or multiple different constrains [14]. These methods require parameter tuning to balance between fitting the recorded data and satisfying the regularization constraints when solving every instance of a given problem, i.e.…”

“…Estimating the underlying sources' extent is also an important issue that needs further investigation. Some recent developments in the field suggest that underlying source sizes can be reliably estimated from noninvasive scalp measurements such as EEG [13]. Additionally, estimating the temporal dynamics of estimated sources, rather than just identifying the location of activity in the brain, is of utmost importance when studying a dynamic system like the brain.…”