An in-vivo validation of ESI methods with focal sources.

Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements of the electric field on the scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to the ill-posedness of the underlying mathematical problem. However, it is difficult to find systematic comparisons involving a wide variety of methods. Further, existing comparisons rarely take into account the variability of the results with respect to the input parameters. Finally, comparisons are typically performed using either synthetic data, or in-vivo data where the ground-truth is only roughly known. 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 precisely known. We compare ten different ESI methods, using their implementation in the MNE-Python package: MNE, dSPM, LORETA, sLORETA, eLORETA, LCMV beamformers, irMxNE, Gamma Map, SESAME and dipole fitting. We perform comparisons under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of such parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with most accurate methods hitting an average localization error of 1.2 cm and outperforming least accurate ones erring by 2.5 cm. As expected, dipolar and sparsity-promoting methods tend to outperform distributed methods. For several distributed methods, the best regularization parameter turned out to be the one in principle associated with low SNR, despite the high SNR of the available dataset. Depth weighting played no role for two out of the six methods implementing it. Sensitivity to input parameters varied widely between methods. While one would expect high variability being associated with low localization error at the best solution, this is not always the case, with some methods producing highly variable results and high localization error, and other methods producing stable results with low localization error. In particular, recent dipolar and sparsity-promoting methods provide significantly better results than older distributed methods. As we repeated the tests with "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings, we observed little impact of the number of channels on localization accuracy; however, for distributed methods denser montages provide smaller spatial dispersion. Overall findings confirm that EEG is a reliable technique for localization of point sources and therefore 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.

Identifying the epileptogenic zone by four non-invasive imaging techniques versus stereo-EEG in MRI-negative pre-surgery epilepsy patients.

OBJECTIVE: We evaluated four imaging techniques, i.e. Electroencephalography (EEG)-functional Magnetic Resonance Imaging (MRI) (EEG-fMRI), High-resolution EEG (HR-EEG), Magnetoencephalography (MEG) and 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (PET), for the identification of the epileptogenic zone (EZ) in 41 patients with negative MRI, candidate to neurosurgery. METHODS: For each technique, results were compared to the Stereo-EEG. Diagnostic measures were calculated with respect to the post-surgical outcome, either for all the patients (39/41, two patients excluded) and for the subgroup of patients with the EZ involving more than one lobe (20/41). RESULTS: When considered individually, each functional technique showed accuracy values ranging 54,6%-63,2%, having PET, MEG and HR-EEG higher sensitivity, and EEG-fMRI higher specificity. In patients with multilobar epileptogenic zone, functional techniques achieved the best accuracies (up to 80%) when three techniques, including EEG-fMRI, were considered together. CONCLUSIONS: The study highlights the accuracy of a combination of functional imaging techniques in the identification of EZ in MRI negative focal epilepsy. The best diagnostic yield was obtained if the combination of PET, MEG (or HR-EEG as alternative), EEG-fMRI were considered together. SIGNIFICANCE: The functional imaging techniques may improve the presurgical workup of MRI negative focal epilepsy, if epileptogenic zone involves more than one lobe.

Multiscale Functional Clustering Reveals Frequency Dependent Brain Organization in Type II Focal Cortical Dysplasia With Sleep Hypermotor Epilepsy.

OBJECTIVE: A multiscale functional clustering approach is proposed to investigate the organization of the epileptic networks during different sleep stages and in relation with the occurrence of seizures. METHOD: Stereo-electroencephalographic signals from seven pharmaco-resistant epileptic patients (focal cortical dysplasia type II) were analyzed. The discrete wavelet transform provided a multiscale framework on which a data-driven functional clustering procedure was applied, based on multivariate measures of integration and mutual information. The most interacting functional clusters (FCs) within the sampled brain areas were extracted. RESULTS: FCs characterized by strongly integrated activity were observed mostly in the beta and alpha frequency bands, immediately before seizure onset and in deep sleep stages. These FCs generally included the electrodes from the epileptogenic zone. Furthermore, repeatable patterns were found across ictal events in the same patient. CONCLUSION: In line with previous studies, our findings provide evidence of the important role of beta and alpha activity in seizures generation and support the relation between epileptic activity and sleep stages. SIGNIFICANCE: Despite the small number of subjects included in the study, the present results suggest that the proposed multiscale functional clustering approach is a useful tool for the identification of the frequency-dependent mechanisms underlying seizure generation.

Correlating Interictal Spikes with Sigma and Delta Dynamics during Non-Rapid-Eye-Movement-Sleep.

Interictal spikes (IS) are one of the major hallmarks of epilepsy. Understanding the factors promoting or suppressing IS would increase our comprehension of epilepsy and possibly open new avenues for therapy. Sleep strongly influences epileptic activity, and the modulatory effects of the different sleep stages on IS have been studied for decades. However, several aspects are still disputed, in particular the role of sleep spindles and slow waves in the activation of IS during Non-REM sleep. Here, we correlate the rate of IS with quantitative measures derived from stereo-EEG during one Non-REM cycle in 10 patients suffering from drug-resistant epilepsy due to type 2 focal cortical dysplasia. We show that the IS rate (ISR) is positively correlated with sigma power (a surrogate for sleep-spindle density) but negatively correlated with delta power (surrogate for slow wave activity). In addition, we present two new indices for quantifying the spatial and temporal instability of sleep. We found that both instability indices are correlated with a high ISR. The main contribution of this study is to confirm the suppressive effect of stable deep sleep on IS. This result might influence future guidelines for therapy of patients suffering from epilepsy and sleep disorders.

A descriptive review of additional risk minimisation measures applied to EU centrally authorised medicines 2006-2015.

BACKGROUND: Guidance on therapeutic risk management, first released in EU in 2005, was updated in 2012-2013 with increased requirements on additional risk minimisation measures (aRMM). This study describes aRMM imposed at initial EU central marketing authorisation of medicines in 2006-2015. METHODS: Non-generic medicines authorised between 01/01/2006 and 31/12/2015 were identified in the European Public Assessment Report (EPAR) database. Data on aRMM, including effectiveness measures were extracted, tabulated and analysed with Excel 2016 for chi-square p value estimates and linear regression modelling as appropriate. RESULTS: The EPAR database encompassed 717 medicines, including 550 non-generic products authorised in 2006-2015. Those authorised with aRMM accounted for 26% (144/550). Yearly frequency ranged from 12% (6/49) in 2008 to 41% (13/32) in 2010, though no time-trend was detected. Antineoplastic/immunomodulating products were the most prevalent (26%, 38/144). All aRMM consisted of educational interventions, mostly targeting physicians/nurses (96%, 139/144). Patients were targeted in 50% of instances (72/144). Effectiveness evaluation was limited to 31% (44/144) of medicines, though an accelerated increase by year was detected. CONCLUSIONS: Through 2006-2015, aRMM were imposed to approximately a quarter of EU centrally authorised medicines. No time-trends were observed. Updated regulatory guidance did not appear to impact frequency of aRMM, type of interventions, or target population, however, further investigation is required. Effectiveness measurements, though increasing in time, remained limited to a minority of instances.