Quantifying trial-by-trial variability during cortico-cortical evoked potential mapping of epileptogenic tissue.

OBJECTIVE: Measuring cortico-cortical evoked potentials (CCEPs) is a promising tool for mapping epileptic networks, but it is not known how variability in brain state and stimulation technique might impact the use of CCEPs for epilepsy localization. We test the hypotheses that (1) CCEPs demonstrate systematic variability across trials and (2) CCEP amplitudes depend on the timing of stimulation with respect to endogenous, low-frequency oscillations. METHODS: We studied 11 patients who underwent CCEP mapping after stereo-electroencephalography electrode implantation for surgical evaluation of drug-resistant epilepsy. Evoked potentials were measured from all electrodes after each pulse of a 30 s, 1 Hz bipolar stimulation train. We quantified monotonic trends, phase dependence, and standard deviation (SD) of N1 (15-50 ms post-stimulation) and N2 (50-300 ms post-stimulation) amplitudes across the 30 stimulation trials for each patient. We used linear regression to quantify the relationship between measures of CCEP variability and the clinical seizure-onset zone (SOZ) or interictal spike rates. RESULTS: We found that N1 and N2 waveforms exhibited both positive and negative monotonic trends in amplitude across trials. SOZ electrodes and electrodes with high interictal spike rates had lower N1 and N2 amplitudes with higher SD across trials. Monotonic trends of N1 and N2 amplitude were more positive when stimulating from an area with higher interictal spike rate. We also found intermittent synchronization of trial-level N1 amplitude with low-frequency phase in the hippocampus, which did not localize the SOZ. SIGNIFICANCE: These findings suggest that standard approaches for CCEP mapping, which involve computing a trial-averaged response over a .2-1 Hz stimulation train, may be masking inter-trial variability that localizes to epileptogenic tissue. We also found that CCEP N1 amplitudes synchronize with ongoing low-frequency oscillations in the hippocampus. Further targeted experiments are needed to determine whether phase-locked stimulation could have a role in localizing epileptogenic tissue

TRANSFER FUNCTION MODELS OF CORTICO-CORTICAL EVOKED POTENTIALS FOR THE LOCALIZATION OF SEIZURES IN MEDICALLY REFRACTORY EPILEPSY PATIENTS

Surgical resection of the seizure onset zone (SOZ) could potentially lead to seizure-freedom in medically refractory epilepsy (MRE) patients. However, localizing the SOZ is a time consuming, subjective process involving visual inspection of intracranial electroencephalographic (iEEG) recordings captured during invasive passive patient monitoring. Cortical stimulation is currently performed on patients undergoing invasive EEG monitoring for the main purpose of mapping functional brain networks such as language and motor networks. We hypothesized that the evoked responses from single pulse electrical stimulation (SPES) can be used to localize the SOZ as they may express the natural frequencies and connectivity of the iEEG network. We constructed patient specific transfer function models from evoked responses recorded from 22 MRE patients that underwent SPES evaluation and iEEG monitoring. We then computed the frequency and connectivity dependent “peak gain” of the system, as measured by the H_∞ norm from systems theory, and the corresponding “floor gain,” which is the gain at which the H_∞ dipped 3dB below the DC gain. In cases for which clinicians had high confidence in localizing the SOZ, the highest peak gain transfer functions with the smallest “floor gains” corresponded to when the clinically annotated SOZ and early spread regions were stimulated. In more complex cases, there was a large spread of the peak gains when the clinically annotated SOZ was stimulated. Interestingly, for patients who had successful surgeries, our ratio of peak-to-floor (PF) gains, agreed with clinical localization, no matter the complexity of the case. For patients with failed surgeries, the PF ratio did not match clinical annotations. Our findings suggest that transfer function gains and their corresponding frequency responses computed from SPES evoked responses may improve SOZ localization and thus surgical outcomes

Diagnostic value of combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) in epilepsy.

Purpose: The main aim of the project is to estimate the value of combined TMS-EEG responses and EEG to increase the sensitivity and/or specificity of the routine EEG in the diagnosis of newly onset epilepsies. Methods: The project is a combined cross-sectional and longitudinal study involving 60 patients recruited from the First Seizure Clinic at Guy's and St Thomas Hospital NHS Foundation Trust who have had their first presumed epileptic seizure. All the participants had a sleep-deprived EEG (baseline EEG) followed by a combined TMS and EEG study (TMS-EEG). The EEG responses to TMS were visually analysed, looking for two different types of TMS-evoked responses or late responses: The delayed responses were assessed in the unprocessed EEG and the repetitive responses (RRs) after averaging the EEG signals synchronized with the TMS pulse. The late responses were compared between epileptic and non-epileptic patients, looking for responses associated with epilepsy. In patients where the baseline EEG was normal, the additional diagnostic value provided by TMS-EEG was estimated by their ability to predict the final diagnosis based on the clinical history and other tests. A quantitative analysis was performed to compare the power ratio in different frequency bands between epilepsy and no epilepsy cohorts and to select epilepsy-associated variables to generate a machine learning-based classification model for epilepsy prediction. Results: In patients with normal baseline EEG, abnormal TMS-EEG evoked responses (late responses) had no statistically significant association with the presence of epilepsy (Fisher’s exact test, p=0.063), but the late responses correctly classified as epilepsy the 36% of patients with a false-negative baseline EEG. The combined presence of late responses and interictal epileptiform discharges (IEDs) in TMS-EEG records has a higher sensitivity (74%) but lower specificity (85%) than baseline EEG alone. The grand average power-ratio differences between epilepsy and no-epilepsy cohorts were not statistically significant. The epilepsy-associated variables selected for machine learning-based classification were predominantly in the alpha-theta and gamma frequency ranges when TMS activation was present and, in the beta-gamma range with Sham. The TMS support vector machine (SVM)-classifier’s disease prediction over an independent cohort had a sensitivity of 83%. Conclusions: The TMS-EEG significantly increased the sensitivity of the baseline EEG and correctly classified as epilepsy approximately one-third of the patients with a false negative baseline EEG and a final clinical diagnosis of epilepsy. TMS stimulation modified the spectral and topographic properties of the epilepsy-associated variables used for disease detection with machine learning linear regression algorithms. The performance of the TMS SVM-classifier in the training cohort has a high sensitivity, high specificity and low misclassification rate. The TMS SVM-classifier performed better than the Sham as an epilepsy disease prediction model in an independent TMS-EEG cohort. The TMS SVM-classifier has a promising value for disease prediction in TMS-EEG datasets