An in-vivo validation of ESI methods with focal sources

Abstract

Electrical source imaging (ESI) aims at reconstructing the electrical brain activity from measurements of the electric field on the scalp. Even though the localization of single focal sources should be relatively straightforward, different methods provide diverse solutions due to the different underlying assumptions. Furthermore, their input parameter(s) further affects the solution provided by each method, making localization even more challenging. In addition, validations and comparisons are typically performed either on synthetic data or through post-operative outcomes, in both cases with considerable limitations. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are known. We compare ten different ESI methods under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of the parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with more accurate methods outperforming less accurate ones by 1 cm, on average. Expectedly, dipolar methods tend to outperform distributed methods. Sensitivity to input parameters varies widely between methods. Depth weighting played no role for three out of six methods implementing it. In terms of regularization parameters, for several distributed methods SNR=1 unexpectedly turned out to be the best choice among the tested ones. Our data show similar levels of accuracy of ESI techniques when applied to "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings. Overall findings reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.

EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy

“…Larger localization errors (mean ~ 15–20 mm) were found for distributed source imaging methods such as the MNE ω , sLORETA, eLORETA (Pascual‐Marqui et al, 2006) and Beamformer (Van Veen et al, 1997), with large localization errors associated with deeper sources, even after applying depth weighting. In our HD‐EEG simulations, we observed a similar range of localization error and SD for MNE ω as reported by Pascarella et al (2023). We found the lowest localization error (4.8 ± 6.8 mm) for cMEM ω .…”

“…This is consistent with the findings reported in previous studies using EEG (Krings et al, 1999; Mikulan et al, 2020; Unnwongse et al, 2023; Whittingstall et al, 2003) and MEG (Chowdhury et al, 2015). Using simultaneously acquired HD‐EEG and intracerebral stimulation as ground truth, Pascarella et al (2023) compared 10 source imaging methods and explored different depth weighting parameters. They found the lowest localization error (within 10 mm) was obtained for dipolar and sparsity‐promoting localization methods.…”

“…Both cMEM and cMEM ω provided much lower SD compared to MNE ω , consistent with our previous studies (Chowdhury et al, 2013; Chowdhury et al, 2016; Pellegrino, Hedrich, et al, 2020) reporting the ability of the MEM method to recover the spatial extent accurately. Pascarella et al (2023) used real intracerebral stimulations resulting in very focal generators associated with high SNR, whereas we used realistic numerical simulations involving spatially extended generators and different SNR levels. Another important difference is we considered a fixed value of depth weighting factor (ω = 0.5) for cMEM ω and MNE ω in the whole study.…”

Evaluation of Long-Short Term Memory Networks for M/EEG Source Imaging with Simulated and Real EEG Data

“…The a priori hard-to-solve problem with the evaluation of real M/EEG recordings, however, is the missing ground truth. An intermediate step to approach this problem may be the use of EEG data from epilepsy patients with implanted electrodes undergoing brain stimulation (e.g., Mikulan et al, 2020), which was already successfully used to evaluate various inverse solutions (Pascarella et al, 2021).…”

Recent advances in clinical electroencephalography

“…One of the challenges validating ESI is the lack of a perfect gold standard for the localization, since all approaches have advantages and disadvantages. This was circumvented by analyzing the location of single pulse electric stimulation [11,12]. Although this is an artificial signal (as opposed to epileptiform discharges generated in the cortical neurons), the location of the source (i.e., the cortically placed electrode contacts) is certain, and these studies provide important information about the accuracy and biophysical limitations of ESI.…”