Voxel-based dipole orientation constraints for distributed current estimation

A Novel Bayesian Approach for EEG Source Localization

We propose a new method for EEG source localization. An efficient solution to this problem requires choosing an appropriate regularization term in order to constraint the original problem. In our work, we adopt the Bayesian framework to place constraints; hence, the regularization term is closely connected to the prior distribution. More specifically, we propose a new sparse prior for the localization of EEG sources. The proposed prior distribution has sparse properties favoring focal EEG sources. In order to obtain an efficient algorithm, we use the variational Bayesian (VB) framework which provides us with a tractable iterative algorithm of closed-form equations. Additionally, we provide extensions of our method in cases where we observe group structures and spatially extended EEG sources. We have performed experiments using synthetic EEG data and real EEG data from three publicly available datasets. The real EEG data are produced due to the presentation of auditory and visual stimulus. We compare the proposed method with well-known approaches of EEG source localization and the results have shown that our method presents state-of-the-art performance, especially in cases where we expect few activated brain regions. The proposed method can effectively detect EEG sources in various circumstances. Overall, the proposed sparse prior for EEG source localization results in more accurate localization of EEG sources than state-of-the-art approaches.

Lesion-Constrained Electrical Source Imaging: A Novel Approach in Epilepsy Surgery for Tuberous Sclerosis Complex

PURPOSE

Electrical source imaging may yield ambiguous results in multilesional epilepsy. The aim of this study was to test the clinical utility of lesion-constrained electrical source imaging in epilepsy surgery in children with tuberous sclerosis complex.

METHODS

Lesion-constrained electrical source imaging is a novel method based on a proposed head model in which the source solution is constrained to lesions. Using a goodness of fit analysis, we rank-ordered individual tubers by their ability to approximate interictal and ictal EEG data. The overlap with the surgical resection cavity was determined qualitatively, and placed findings in the context of epilepsy surgical outcome, and compared with the low-resolution brain electromagnetic tomography solution.

RESULTS

Low-resolution brain electromagnetic tomography predicted the surgical cavity in only one patient with good outcome (true positive) and localized to outside of the cavity in two patients with a good outcome (false negative). In one patient with a poor outcome, the interictal low-resolution brain electromagnetic tomography solution overlapped with the cavity (false positive). Lesion-constrained electrical source imaging of ictal EEG data identified tubers concordant with the resection zone in three patients with a good surgical outcome (true positive) and appropriately discordant in three other patients with a poor outcome (true negative).

CONCLUSIONS

Lesion-constrained electrical source imaging on low-resolution EEG data provides complementary information in the presurgical workup for patients with tuberous sclerosis complex, although further validation is required. In the appropriate clinical context, the yield of source localization on low-resolution EEG data may be increased by reduction of the solution space.

Multi-Resolution Graph Based Volumetric Cortical Basis Functions From Local Anatomic Features

OBJECTIVE

Modern clinical MRI collects millimeter scale anatomic information, but scalp electroencephalography source localization is ill posed, and cannot resolve individual sources at that resolution. Dimensionality reduction in the space of cortical sources is needed to improve computational and storage complexity, yet volumetric methods still employ simplistic grid coarsening that eliminates fine scale anatomic structure. We present an approach to extend near-arbitrary spatial scaling to volumetric localization.

METHODS

Starting from a voxelwise brain parcellation, sub-parcels are identified from local cortical connectivity with an iterated graph cut approach. Spatial basis functions in each parcel are constructed using either a decomposition of the local leadfield matrix or spectral basis functions of local cortical connectivity graphs.

RESULTS

We present quantitative evaluation with extensive simulations and use multiple sets of real data to highlight how parameter changes impact computed reconstructions. Our results show that volumetric basis functions can improve accuracy by as much as 30%, while reducing computational complexity by over two orders of magnitude. In real data from epilepsy surgical candidates, accurate localization of seizure onset regions is demonstrated.

CONCLUSION

Spatial dimensionality reduction with volumetric basis functions improves reconstruction accuracy while reducing computational complexity.

SIGNIFICANCE

Near-arbitrary spatial dimensionality reduction will enable volumetric reconstruction with modern computationally intensive algorithms and anatomically driven multi-resolution methods.

Dynamic Electrical Source Imaging (DESI) of Seizures and Interictal Epileptic Discharges Without Ensemble Averaging

We propose an algorithm for electrical source imaging of epileptic discharges that takes a data-driven approach to regularizing the dynamics of solutions. The method is based on linear system identification on short time segments, combined with a classical inverse solution approach. Whereas ensemble averaging of segments or epochs discards inter-segment variations by averaging across them, our approach explicitly models them. Indeed, it may even be possible to avoid the need for the time-consuming process of marking epochs containing discharges altogether. We demonstrate that this approach can produce both stable and accurate inverse solutions in experiments using simulated data and real data from epilepsy patients. In an illustrative example, we show that we are able to image propagation using this approach. We show that when applied to imaging seizure data, our approach reproducibly localized frequent seizure activity to within the margins of surgeries that led to patients' seizure freedom. The same approach could be used in the planning of epilepsy surgeries, as a way to localize potentially epileptogenic tissue that should be resected.