Influence of time-series normalization, number of nodes, connectivity and graph measure selection on seizure-onset zone localization from intracranial EEG

We investigated the influence of processing steps in the estimation of multivariate directed functional connectivity during seizures recorded with intracranial EEG (iEEG) on seizure-onset zone (SOZ) localization. We studied the effect of (i) the number of nodes, (ii) time-series normalization, (iii) the choice of multivariate time-varying connectivity measure: Adaptive Directed Transfer Function (ADTF) or Adaptive Partial Directed Coherence (APDC) and (iv) graph theory measure: outdegree or shortest path length. First, simulations were performed to quantify the influence of the various processing steps on the accuracy to localize the SOZ. Afterwards, the SOZ was estimated from a 113-electrodes iEEG seizure recording and compared with the resection that rendered the patient seizure-free. The simulations revealed that ADTF is preferred over APDC to localize the SOZ from ictal iEEG recordings. Normalizing the time series before analysis resulted in an increase of 25-35% of correctly localized SOZ, while adding more nodes to the connectivity analysis led to a moderate decrease of 10%, when comparing 128 with 32 input nodes. The real-seizure connectivity estimates localized the SOZ inside the resection area using the ADTF coupled to outdegree or shortest path length. Our study showed that normalizing the time-series is an important pre-processing step, while adding nodes to the analysis did only marginally affect the SOZ localization. The study shows that directed multivariate Granger-based connectivity analysis is feasible with many input nodes (> 100) and that normalization of the time-series before connectivity analysis is preferred

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

A new insight into sentence comprehension : the impact of word associations in sentence processing as shown by invasive EEG recording

The effect of word association on sentence processing is still a matter of debate. Some studies observe no effect while others found a dependency on sentence congruity or an independent effect. In an attempt to separate the effects of sentence congruity and word association in the spatio-temporal domain, we jointly recorded scalp- and invasive-EEG (iEEG). The latter provides highly localized spatial (unlike scalp-EEG) and high temporal (unlike fMRI) resolutions. We recorded scalp- and iEEG in three patients with refractory epilepsy. The stimuli consisted of 280 sentences with crossed factors of sentence congruity and within sentence word-association. We mapped semantic retrieval processes involved in sentence comprehension onto the left temporal cortex and both hippocampi, and showed for the first time that certain localized regions participate in the processing of word association in sentence context. Furthermore, simultaneous recording of scalp- and iEEG gave us a direct overview of signal change due to its propagation across the head tissues

Heart rate, electrodermal responses and frontal alpha asymmetry to accepted and non-accepted solutions and drinks

Consumers' physiological responses, such as heart rate, electrodermal responses and frontal alpha activity can enhance the understanding of the consumers' food experience. This study looked at physiological responses of the autonomic nervous system (heart rate, electrodermal responses) as a measure for level of arousal, and to responses of the central nervous system (frontal alpha asymmetry, FAA) as a measure for approach/withdrawal motivational tendency, to accepted (liked) and non-accepted (disliked) solutions and drinks. Participants (n = 32, age range: 18-34 years) were presented with a universally accepted (sucrose) and non-accepted (caffeine) solution, a personally selected accepted and non-accepted drink, and plain water. Heart rate, heart rate variability, electrodermal activity and electro-encephalography for FAA at F7 and F8 (10/20 system, 25 channels, 256 Hz) were registered during tasting. Statistical analysis consisted of linear mixed model analyses. We found a significantly higher heart rate during tasting of the personally selected non-accepted drink and a significantly lower latency of the electrodermal response during tasting of the universally non-accepted solution and personally selected non-accepted drink. No significant results were observed for FAA. This is one of the first studies that examined physiological responses including frontal alpha asymmetry during actual tasting. This study provides an exploratory method to obtain implicit measurement of acceptance and food product-elicited emotion through physiological responses and supports the importance of the inclusion of implicit measures, next to explicit measures, in sensory evaluation of food products

EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy

Visual inspection of the EEG to determine the seizure onset zone (SOZ) in the context of the presurgical evaluation in epilepsy is time-consuming and often challenging or impossible. We offer an approach that uses EEG source imaging (ESI) in combination with functional connectivity analysis (FC) to localize the SOZ from ictal EEG. Ictal low-density-scalp EEG from 111 seizures in 27 patients who were rendered-seizure free after surgery was analyzed. For every seizure, ESI (LORETA) was applied on an artifact-free epoch selected around the seizure onset. Additionally, FC was applied on the reconstructed sources. We estimated the SOZ in two ways: (i)the source with highest power after ESI and (ii)the source with the most outgoing connections after ESI and FC. For both approaches, the distance between the estimated SOZ and the resected zone (RZ) of the patient were calculated. Using ESI alone, the SOZ was estimated inside the RZ in 31% of the seizures and within 10mm from the border of the RZ in 42%. For 18.5% of the patients, all seizures were estimated within 10mm of the RZ. Using ESI and FC, 72% of the seizures were estimated inside the RZ, and 94% within 10mm. For 85% of the patients, all seizures were estimated within 10mm of the RZ. FC provided a significant added value to ESI alone (p<0.001). ESI combined with subsequent FC is able to localize the SOZ in a non-invasive way with high accuracy. Therefore it could be a valuable tool in the presurgical evaluation of epilepsy