Multimodal integration of simultaneous acquired EEG and fMRI data to study cognitive processes

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A Bayesian model to estimate individual skull conductivity for EEG source imaging

EEG source imaging (ESI) techniques estimate 3D brain activity based on electrical activity measured on the scalp. In a clinical context, these techniques are typically used for the analysis of epileptiform activity. They play a central role in the pre-surgical planning prior to removal of the epileptic seizure focus, needed in about 30% of people with epilepsy [1]. ESI techniques make use of a parametric model of the geometry and electromagnetic properties of the subject’s head. While the geometry can be modelled precisely using an anatomical MR image of the head, there remains high uncertainty in the electrical conductivity of several types of tissue in the head (skull, white and gray matter, scalp etc.). Commonly, these conductivity values are set to a conventional value, based on previous studies. Because individual conductivity values can deviate radically from the conventional values (exceeding an order of magnitude) this can lead to errors that need to be avoided for accurate estimation of the epileptic focus location [2]. In this work, a first Bayesian model is proposed that is able to simultaneously estimate the source location and the subject specific skull conductivity from the measured EEG signals. The expectation-maximization algorithm was used to iteratively update the parameter estimation. As a first proof of concept, we used a three-layered spherical head model and a single dipole source to simulate electrical activity on the scalp, measured at 36 electrode positions, for a range of human skull conductivity values found in literature. We compared the source localization performance with our adaptive conductivity estimation to the performance with several conventional conductivity values used in previous studies. We found that, due to the high variation in individual skull conductivity values, the true source can be located more than 15mm away from the estimated source location using the conventional conductivity. Adaptive estimation of the conductivity with the Bayesian model lowers the maximum location error to only 3mm (see Figure 1). The first proof of concept looks promising and will be further deployed, including better probabilistic models for the variation in measured EEG, variation in dipole location and prior distribution of conductivity values. The final goal of this work is to estimate all tissue conductivity parameters, making the head model truly adaptive to the individual subject. [1] Strobbe G., Carrette E., Lopez J.D., Van Roost D., Meurs E., Vonck K., Boon P., Vandenberghe S., van Mierlo P. (2015) EEG source imaging of interictal spikes using multiple sparse volumetric priors for presurgical focus localization, NeuroImage, in preparation for submission. [2] Kassem A., Jackson D., Baumann S., Williams J., Wilton D., Fink P. and Prasky B. (1998) Effect of Conductivity Uncertainties and Modeling Errors on EEG Source Localization Using a 2-D Model, IEEE Transaction on Biomedical Engineering, vol. 45, no. 9, pp. 1135-114

Electrical source imaging and connectivity analysis to localize the seizure-onset zone based on high-density ictal scalp EEG recordings

Functional connectivity analysis of ictal intracranial EEG (icEEG) recordings can help with seizure-onset zone (SOZ) localization in patients with focal epilepsy1. However, it would be of high clinical value to be able to localize the SOZ based on non-invasive ictal EEG recordings to better target or avoid icEEG and improve surgical outcome. In this work, we propose an approach to localize the SOZ based on non-invasive ictal high- density EEG (hd-EEG) recordings. We considered retrospective ictal hd-EEG recordings of two patients who were rendered seizure free after surgery. Furthermore, we simulated 1000 ictal hd-EEG epochs of 10s with an underlying network consisting of 3 randomly placed epileptic patches in the brain. EEG source imaging (ESI) was performed in CARTOOL using an individual head model (LSMAC) to calculate the forward model2. We considered dipoles uniformly distributed in the brain with a spacing of 5mm. LORETA3 was used as inverse solution method. Center dipoles of clusters with high activation were determined as dipoles for which there was no higher power in their neighborhood. The time-varying connectivity pattern between the time series of these dipoles was calculated using the integrated, full-frequency, and spectrum-weighted Adaptive Directed Transfer Function4. This was done in the frequency band containing the seizure information, 3-30Hz. The outdegree of each selected dipole was determined as the sum over time of all outgoing connections. Around the dipole with the highest outdegree, we determined a region of dipoles that had a power that was at least 90% of the power of the center dipole. This region was then considered as the SOZ. We were able to successfully localize the driver in the resected zone for both patients. For the simulation data, the results can be quantified: in 71% of the simulations, the localization error remained below 25mm. If the selection of the dipole would be solely based on the highest power, the error would be more than 82mm. ESI in combination with connectivity analysis can successfully localize the SOZ in non- invasive ictal hd-EEG recordings and outperforms localization based on power. This could have important clinical relevance for the presurgical evaluation in focal epilepsy. References: 1. van Mierlo, P et al. (2014) Prog Neurobiol. 121:19-35. 2. Brunet, D. et al. (2011) Comput. Intell. Neurosci. 2. 3. Pascal-Marqui, R.D., et al. (1994) Int. J. Psychophysiol. 18(1):49-65. 4. van Mierlo, P. et al. (2013) Epilepsia 54.8:1409-1418

Seizure onset zone localization from ictal high-density EEG in five patients

Rationale Because epilepsy is a network disease, localization of the exact seizure onset zone (SOZ) is difficult because the epileptic activity can spread to other regions within milliseconds. Functional connectivity metrics quantify how the activity in different brain regions is interrelated. In the past, it has been shown that functional connectivity analysis of ictal intracranial EEG (icEEG) recordings can help with SOZ localization in patients with focal epilepsy (van Mierlo et al., 2014). However, it would be of high clinical value to be able to localize the SOZ based on non-invasive ictal EEG recordings to optimize the icEEG implantation scheme or to avoid invasive monitoring and improve surgical outcome. In this work, we propose an approach to localize the SOZ based on non-invasive ictal high-density EEG (hd-EEG) recordings. Methods We considered retrospective ictal epochs of 2.4 s up to 10 s recorded with hd-EEG (256 electrodes) in five patients who were rendered seizure free after surgery. From the 256 electrodes, the facial electrodes were removed, resulting in a subset of 204 electrodes. A 28-channel subset was constructed to mimic a low-density (ld) electrode setup used in clinical practice. EEG source imaging (ESI) was performed in the CARTOOL software using an individual head model (LSMAC) to calculate the forward model (Brunet et al., 2011). We considered sources uniformly distributed in the brain with a spacing of 5 mm. LORETA (Pascal-Marqui et al., 1994) was used as inverse solution method. In each cluster of activity, we determined a central source based on the criterion that there was no higher power in its neighborhood. The time-varying connectivity pattern between the time series of these sources was calculated using Granger causality (van Mierlo et al., 2013). This was done in the frequency band containing the fundamental seizure frequency, 3-30Hz. The outdegree of each selected dipole was determined as the sum over time of all outgoing connections. Around the dipole with the highest outdegree, we determined a region of dipoles that had a power that was at least 90% of the power of the center dipole. This region was then considered as the SOZ. Results We were able to successfully localize the driver in the resected zone for all patients based on ESI followed by connectivity analysis of the hd-EEG (mean localization error (LE) = 0 mm). If we chose the cluster with the highest power as driver, the mean LE was 59.69 mm. For the ld-EEG, ESI followed by connectivity analysis resulted in a mean LE of 23.30 mm and when selecting the cluster with the highest power as driver, the mean LE was 31.21 mm. Conclusions ESI in combination with connectivity analysis can successfully localize the SOZ in non-invasive ictal hd-EEG recordings and greatly outperforms localization based on power. For ld-EEG recordings, the localization error remains significant but still outperforms localization based on power. This could have important clinical relevance for the presurgical evaluation in focal epilepsy

Improved localization of seizure onset zones using spatiotemporal constraints and time-varying source connectivity

Presurgical evaluation of brain neural activity is commonly carried out in refractory epilepsy patients to delineate as accurately as possible the seizure onset zone (SOZ) before epilepsy surgery. In practice, any subjective interpretation of electroencephalographic (EEG) recordings is hindered mainly because of the highly stochastic behavior of the epileptic activity. We propose a new method for dynamic source connectivity analysis that aims to accurately localize the seizure onset zones by explicitly including temporal, spectral, and spatial information of the brain neural activity extracted from EEG recordings. In particular, we encode the source nonstationarities in three critical stages of processing: Inverse problem solution, estimation of the time courses extracted from the regions of interest, and connectivity assessment. With the aim to correctly encode all temporal dynamics of the seizure-related neural network, a directed functional connectivity measure is employed to quantify the information flow variations over the time window of interest. Obtained results on simulated and real EEG data confirm that the proposed approach improves the accuracy of SOZ localization