Electrical stimulation mapping (ESM) is the gold standard for identification of “eloquent” areas prior to resection of epileptogenic tissue, however, it is time consuming and may cause side effects, especially stimulation-induced seizures and after-discharges. Broadband gamma activity (55 – 200 Hz) recorded with subdural electrocorticography (ECoG) during cognitive tasks has been proposed as an attractive tool for mapping cortical areas with specific function but until now has not proven definitive clinical value. Fewer studies have addressed whether the alpha (8 – 12 Hz) and beta (15 – 25 Hz) band activity could also be used to improve eloquent cortex identification. We compared alpha, beta and broadband gamma activity, and their combination for the identification of eloquent cortical areas defined by ESM. Ten patients participated in a delayed-match-to-sample task, where syllable sounds were matched to visually presented letters and responses given by keyboard. We used a generalized linear model (GLM) approach to find the optimal weighting of low frequency bands and broadband gamma power to predict the ESM categories. Broadband gamma activity increased more in eloquent areas than in non-eloquent areas and this difference had a diagnostic ability (area under (AU) the receiving operating characteristic curve - AUROC) of ∼70%. Both alpha and beta power decreased more in eloquent areas. Alpha power had lower AUROC than broadband gamma while beta had similar AUROC. AUROC was enhanced by the combination of alpha and broadband gamma (3% improvement) and by the combination of beta and broadband gamma (7% improvement) over the use of broadband gamma alone. Further analysis showed that the relative performance of broadband gamma and low frequency bands depended on multiple factors including the time period of the cognitive task, the location of the electrodes and the patient’s attention to the stimulus. However, the combination of beta band and broadband gamma always gave the best performance. We show how ECoG power modulation from cognitive testing periods can be used to map the probability of eloquence by ESM and how this probability can be used as an aid for optimal ESM planning. We conclude that low frequency power during cognitive testing can contribute to the identification of eloquent areas in patients with focal refractory epilepsy improving its precision but does not replace the need of ESM. Highlights Gamma, alpha and beta band activity has significant diagnostic ability to identify ESM defined eloquent cortical areas. We present a novel method to combine gamma and low frequency activity for enhanced identification. We quantify how identification is dependent on analysis time window, cortical function, and patient’s attentional engagement. With further development, this approach may offer an alternative to ESM mapping with reduced burden for patients.

Objective: Deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) can improve seizure control for patients with drug-resistant epilepsy. Yet, one cannot overlook the discrepancies in the response rate to ANT-DBS, possibly resulting from differences in stimulation site. In this study, we tested the hypothesis that stimulation at the junction of the ANT and mammillothalamic tract (ANT-MTT junction) increases seizure control in DBS for drug-resistant epilepsy.

Patients with medically refractory epilepsy treated with deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) vary highly in their therapy response. Proper positioning of the DBS lead is crucial to maximize efficacy and minimize side effects. For a correct implantation, the ANT is anatomically located using pre-operative 3T MRI and perioperative microelectrode recordings (MER). Neurons in the ANT have highly variable, yet characteristic patterns of firing in bursts. During DBS lead implantation, we noted differences among patients' characteristic burst patterns along the electrode trajectory. In this study, we investigate whether electrophysiological characteristics of the target region could predict therapy response to DBS and could thus be used to improve ANT targeting during DBS surgery. To determine whether perioperative neurophysiological characteristics relate to therapy response in DBS for patients with medically refractory epilepsy. We included ten consecutive epilepsy patients planned for DBS surgery at Maastricht University Medical Center. All patients were diagnosed with medically refractory epilepsy and had incapacitating seizures. Patients failed trials of at least two reasonably tolerated and adequately chosen antiepileptic drug schedules. Using pre-operative 3T MRI, we planned an extraventricular approach to target. The ANT was defined as a grey matter structure at the top of the mamillothalamic tract. Along this trajectory, we performed stereotactic single cell MERs. The anatomical location of the recordings were verified using preoperative 3T MR images. We compared characteristics of the neural signals at different depths along the trajectory between DBS responders and non-responders. Responders were defined as patients with a seizure frequency reduction of more than 50% at one year follow-up. Using MER data from 19 electrode trajectories of ten patients (one unilateral and nine bilateral trajectories), we found high-amplitude neuronal bursts around the target area or ANT. Responders to DBS (n = 5) had higher normalized mean firing rates and mean burst rates near the target area compared to non-responders (n = 5), with a clearer delineation between the target region and surroundings. Electrode trajectories and lead localization did not differ between responders and non-responders. Single cell firing patterns in the ANT relate to therapy response in DBS for patients with medically refractory epilepsy. Analysis of single cell firing patterns using MER may guide targeting or contribute to predicting therapy response to ANT DBS. Further exploration into the use of electrophysiological recordings is warranted to improve targeting or predict outcome in DBS for epilepsy patients.

To understand the etiology of multigenic diseases like atherosclerosis, a polymerase chain reaction (PCR) based gene array containing 65 single nucleotide polymorphisms (SNPs) was analyzed. To asses the possibilities of pattern recognition techniques in detecting unfavorable genetic combinations, two approaches were analysed. A selection of these 65 SNPs formed the input both to binary logistic regression models and to self-learning artificial neural networks (ANNs). Repeated analyses showed that both methods performed equally well. Further research to improve the differentiating power of both methods should focus first on decreasing the number of otherwise indeterminable polymorphisms.

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