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

Effects of transcranial Direct Current Stimulation (tDCS) on cortical activity: A computational modeling study.

International audienceAlthough it is well-admitted that transcranial Direct Current Stimulation (tDCS) allows for interacting with brain endogenous rhythms, the exact mechanisms by which externally-applied fields modulate the activity of neurons remain elusive. In this study a novel computational model (a neural mass model including subpopulations of pyramidal cells and inhibitory interneurons mediating synaptic currents with either slow or fast kinetics) of the cerebral cortex was elaborated to investigate the local effects of tDCS on neuronal populations based on an in-vivo experimental study. Model parameters were adjusted to reproduce evoked potentials (EPs) recorded from the somatosensory cortex of the rabbit in response to air-puffs applied on the whiskers. EPs were simulated under control condition (no tDCS) as well as under anodal and cathodal tDCS fields. Results first revealed that a feed-forward inhibition mechanism must be included in the model for accurate simulation of actual EPs (peaks and latencies). Interestingly, results revealed that externally-applied fields are also likely to affect interneurons. Indeed, when interneurons get polarized then the characteristics of simulated EPs become closer to those of real EPs. In particular, under anodal tDCS condition, more realistic EPs could be obtained when pyramidal cells were depolarized and, simultaneously, slow (resp. fast) interneurons became de- (resp. hyper-) polarized. Geometrical characteristics of interneurons might provide some explanations for this effect

Higher Order Direction Finding with Polarization Diversity: The PD-2q-MUSIC Algorithms

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Higher Order Direction Finding From Arrays With Diversely Polarized Antennas: The PD-2q-MUSIC Algorithms

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Doa Estimation Based on an Even Order Deflation Scheme

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Goniométrie aux ordres supérieurs pour réseaux d’antennes à polarisation multiple

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Automatic detection of fast ripples.

International audienceOBJECTIVE: We propose a new method for automatic detection of fast ripples (FRs) which have been identified as a potential biomarker of epileptogenic processes. METHODS: This method is based on a two-stage procedure: (i) global detection of events of interest (EOIs, defined as transient signals accompanied with an energy increase in the frequency band of interest 250-600Hz) and (ii) local energy vs. frequency analysis of detected EOIs for classification as FRs, interictal epileptic spikes or artifacts. For this second stage, two variants were implemented based either on Fourier or wavelet transform. The method was evaluated on simulated and real depth-EEG signals (human, animal). The performance criterion was based on receiving operator characteristics. RESULTS: The proposed detector showed high performance in terms of sensitivity and specificity. CONCLUSIONS: As designed to specifically detect FRs, the method outperforms any method simply based on the detection of energy changes in high-pass filtered signals and avoids spurious detections caused by sharp transient events often present in raw signals. SIGNIFICANCE: In most of epilepsy surgery units, huge data sets are generated during pre-surgical evaluation. We think that the proposed detection method can dramatically decrease the workload in assessing the presence of FRs in intracranial EEGs

Effects of transcranial direct current stimulation (tDCS) on sensory evoked potentials

We present a combined experimental/computational modeling approach aimed at studying the effects of transcranial Direct Current Stimulation (tDCS) on neuronal systems. More particularly, we introduce i) a neural mass model (neuronal population level) of the cerebral cortex and ii) a coupling model between the considered neuronal population and an externally-applied electric field. We then use this computational modeling approach to interpret evoked potentials (EPs) recorded from the somatosensory cortex of the rabbit under tDCS. Results showed that the model could accurately reproduce the time-course of actual EPs (polarity and latency of main peaks) recorded under control (i.e. “no tDCS stimulation”) condition. From real data, we also identified the “major” effects of tDCS on EPs in terms of shape modifications and we studied the necessary and sufficient conditions for which the model could reproduce these effects. We found that pyramidal cells should be depolarized (resp. hyperpolarized) in order to simulate the effects of anodal (resp. cathodal) currents. We also found that some interneurons are sensitive to externally-applied fields, indicating that modelling efforts need to also consider the role of these neurons to fully understand interactions between stimulation currents and underlying neuronal systems