Uncovering the underlying factors of ERP changes in the cyberball paradigm: A systematic review investigating the impact of ostracism and paradigm characteristics.

The Cyberball is the most commonly employed paradigm for the investigation of the effects of social exclusion, also called ostracism. The analysis of event-related potentials (ERPs), short-term stimulus-induced fluctuations in the EEG signal, has been employed for the identification of time-sensitive neural responses to ostracism-related information. Changes in ERPs during the Cyberball are normally attributed to the effect of ostracism, but it has been argued that characteristics of the paradigm, not ostracism, are the driving force for these changes. To elucidate the origin of the ERP changes in the Cyberball, we systematically reviewed the Cyberball-ERP literature of healthy, adult populations, and evaluated whether the social context of ostracism or characteristics of the paradigm are better suited for the explanation of the found results. Our results show that for many components no clear origin can be identified, but that expectancy violations, not ostracism, best explains the results of the P3 complex. Future research should therefore also employ other paradigms for the research into the effects of ostracism on ERPs.

Automated ictal EEG source imaging: A retrospective, blinded clinical validation study.

OBJECTIVE: EEG source imaging (ESI) is a validated tool in the multimodal workup of patients with drug resistant focal epilepsy. However, it requires special expertise and it is underutilized. To circumvent this, automated analysis pipelines have been developed and validated for the interictal discharges. In this study, we present the clinical validation of an automated ESI for ictal EEG signals. METHODS: We have developed an automated analysis pipeline of ictal EEG activity, based on spectral analysis in source space, using an individual head model of six tissues. The analysis was done blinded to all other data. As reference standard, we used the concordance with the resected area and one-year postoperative outcome. RESULTS: We analyzed 50 consecutive patients undergoing epilepsy surgery (34 temporal and 16 extra-temporal). Thirty patients (60%) became seizure-free. The accuracy of the automated ESI was 74% (95% confidence interval: 59.66-85.37%). CONCLUSIONS: Automated ictal ESI has a high accuracy for localizing the seizure onset zone. SIGNIFICANCE: Automating the ESI of the ictal EEG signals will facilitate implementation of this tool in the presurgical evaluation.

The Role of Skull Modeling in EEG Source Imaging for Patients with Refractory Temporal Lobe Epilepsy.

We investigated the influence of different skull modeling approaches on EEG source imaging (ESI), using data of six patients with refractory temporal lobe epilepsy who later underwent successful epilepsy surgery. Four realistic head models with different skull compartments, based on finite difference methods, were constructed for each patient: (i) Three models had skulls with compact and spongy bone compartments as well as air-filled cavities, segmented from either computed tomography (CT), magnetic resonance imaging (MRI) or a CT-template and (ii) one model included a MRI-based skull with a single compact bone compartment. In all patients we performed ESI of single and averaged spikes marked in the clinical 27-channel EEG by the epileptologist. To analyze at which time point the dipole estimations were closer to the resected zone, ESI was performed at two time instants: the half-rising phase and peak of the spike. The estimated sources for each model were validated against the resected area, as indicated by the postoperative MRI. Our results showed that single spike analysis was highly influenced by the signal-to-noise ratio (SNR), yielding estimations with smaller distances to the resected volume at the peak of the spike. Although averaging reduced the SNR effects, it did not always result in dipole estimations lying closer to the resection. The proposed skull modeling approaches did not lead to significant differences in the localization of the irritative zone from clinical EEG data with low spatial sampling density. Furthermore, we showed that a simple skull model (MRI-based) resulted in similar accuracy in dipole estimation compared to more complex head models (based on CT- or CT-template). Therefore, all the considered head models can be used in the presurgical evaluation of patients with temporal lobe epilepsy to localize the irritative zone from low-density clinical EEG recordings.