Co-occurrence of high-frequency oscillations and delayed responses evoked by intracranial electrical stimulation in stereo-EEG studies

Delayed effective connectivity characterizes the epileptogenic zone during stereo-EEG

OBJECTIVE

Single-pulse electrical stimulations (SPES) can elicit normal and abnormal responses that might characterize the epileptogenic zone, including spikes, high-frequency oscillations and cortico-cortical evoked potentials (CCEPs). In this study, we investigate their association with the epileptogenic zone during stereoelectroencephalography (SEEG) in 28 patients with refractory focal epilepsy.

METHODS

Characteristics of CCEPs (distance-corrected or -uncorrected latency, amplitude and the connectivity index) and the occurrence of spikes and ripples were assessed. Responses within the epileptogenic zone and within the non-involved zone were compared using receiver operating characteristics curves and analysis of variance (ANOVA) either in all patients, patients with well-delineated epileptogenic zone, and patients older than 15 years old.

RESULTS

We found an increase in distance-corrected CCEPs latency after stimulation within the epileptogenic zone (area under the curve = 0.71, 0.72, 0.70, ANOVA significant after false discovery rate correction).

CONCLUSIONS

The increased distance-corrected CCEPs latency suggests that neuronal propagation velocity is altered within the epileptogenic network. This association might reflect effective connectivity changes at cortico-cortical or cortico-subcortico-cortical levels. Other responses were not associated with the epileptogenic zone, including the CCEPs amplitude, the connectivity index, the occurrences of induced ripples and spikes. The discrepancy with previous descriptions may be explained by different spatial brain sampling between subdural and depth electrodes.

SIGNIFICANCE

Increased distance-corrected CCEPs latency, indicating delayed effective connectivity, characterizes the epileptogenic zone. This marker could be used to help tailor surgical resection limits after SEEG.

A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies.

 Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics.

To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings.

Transfer Function Models for the Localization of Seizure Onset Zone From Cortico-Cortical Evoked Potentials

Surgical resection of the seizure onset zone (SOZ) could potentially lead to seizure-freedom in medically refractory epilepsy patients. However, localizing the SOZ can be a time consuming and tedious process involving visual inspection of intracranial electroencephalographic (iEEG) recordings captured during 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 evoked responses from single pulse electrical stimulation (SPES) can also be used to localize the SOZ as they may express the natural frequencies and connectivity of the iEEG network. To test our hypothesis, we constructed patient specific transfer function models from the evoked responses recorded from 22 epilepsy 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. We found that in cases for which clinicians had high confidence in localizing the SOZ, the highest peak gain transfer functions with the smallest “floor gain” (gain at which the dipped H∞   3dB below DC gain) 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-to-floor (PF) ratios when the clinically annotated SOZ was stimulated. Interestingly for patients who had successful surgeries, our ratio of 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.

Unsupervised Detection of High-Frequency Oscillations Using Time-Frequency Maps and Computer Vision

High-frequency oscillations >80 Hz (HFOs) have unique features distinguishing them from spikes and artifactual components that can be well-evidenced in the time-frequency representations. We introduce an unsupervised HFO detector that uses computer-vision algorithms to detect HFO landmarks on two-dimensional (2D) time-frequency maps. To validate the detector, we introduce an analytical model of the HFO based on a sinewave having a Gaussian envelope, for which analytical equations in time-frequency space can be derived, allowing us to establish a direct correspondence between common HFO detection criteria in the time domain with the ones in the frequency domain, used by the computer-vision detection algorithm. The detector identifies potential HFO events on the time-frequency representation, which are classified as true HFOs if criteria regarding the HFO's frequency, amplitude, and duration are met. The detector is validated on simulated HFOs according to the analytical model, in the presence of noise, with different signal-to-noise ratios (SNRs) ranging from −9 to 0 dB. The detector's sensitivity was 0.64 at an SNR of −9 dB, 0.98 at −6 dB, and >0.99 at −3 dB and 0 dB, while its positive prediction value was >0.95, regardless of the SNR. Using the same simulation dataset, our detector is benchmarked against four previously published HFO detectors. The F-measure, a combined metric that takes into account both sensitivity and positive prediction value, was used to compare detection algorithms. Our detector surpassed the other detectors at −6, −3, and 0 dB and had the second best F-score at −9 dB SNR after the MNI detector (0.77 vs. 0.83). The ability to detect HFOs in clinical recordings has been tested on a set of 36 intracranial electroencephalogram (EEG) channels in six patients, with 89% of the detections being validated by two independent reviewers. The results demonstrate that the unsupervised detection of HFOs based on their 2D features in time-frequency maps is feasible and has a performance comparable or better than the most used HFO detectors.

Comparing connectivity metrics in cortico-cortical evoked potentials using synthetic cortical response patterns

BACKGROUND

Cortico-Cortical Evoked Potentials (CCEPs) are a novel low frequency stimulation method used for brain mapping during intracranial epilepsy investigations. Only a handful of metrics have been applied to CCEP data to infer connectivity, and no comparison as to which is best has been performed.

NEW METHOD

We implement a novel method which involved superimposing synthetic cortical responses onto stereoelectroencephalographic (SEEG) data, and use this to compare several metric's ability to detect the simulated patterns. In this we compare two commonly employed metrics currently used in CCEP analysis against eight time series similarity metrics (TSSMs), which have been widely used in machine learning and pattern matching applications.

RESULTS

Root Mean Square (RMS), a metric commonly employed in CCEP analysis, was sensitive to a wide variety of response patterns, but insensitive to simulated epileptiform patterns. Autoregressive (AR) coefficients calculated by Burg's method were also sensitive to a wide range of patterns, but were extremely sensitive to epileptiform patterns. Other metrics which employed elastic warping techniques were less sensitive to the simulated response patterns.

COMPARISON WITH EXISTING METHODS

Our study is the first to compare CCEP connectivity metrics against one-another. Our results found that RMS, which has been used in many CCEP studies previously, was the most sensitive metric across a wide range of patterns.

CONCLUSIONS

Our novel method showed that RMS is a robust and sensitive measure, validating much of the findings of the SEEG-CCEP literature to date. Autoregressive coefficients may also be a useful metric to investigate epileptic networks.

Functional mapping and effective connectivity of the human operculum

The operculum, defined as the cortex adjacent to the insula, is a large structure encompassing three lobes, with a recognized role in a variety of neurologic and psychiatric conditions. Its complex functions include sensory, motor, autonomic and cognitive processing. In humans, these are extended with the addition of language. These functions are implemented by highly specialized neuronal populations and their widespread connections, which our study aims at mapping in detail. We studied a group of 31 patients that were explored with intracranial electrodes during the pre-surgical workup for drug-resistant epilepsy. We have selected the subset of contacts implanted in non-epileptogenic opercular cortex and we analyzed the neurophysiological and behavioral responses to direct electrical stimulation. The functional mapping was performed by applying 1 Hz and 50 Hz electrical stimulation on 252 contact pairs and recording the threshold for evoking clinical effects. The effective connectivity was assessed using cortico-cortical evoked potentials elicited by single-pulse electrical stimulation in a subset of 19 patients. The locations of the effects grouped in twelve distinct semiological classes were analyzed. The most frequent effects evoked by stimulation of the frontal operculum were language related (29%). The Rolandic area produced most often oropharyngeal symptoms (47%), the parietal operculum produced somatosensory effects (67%), while the temporal evoked auditory (58%) semiology. The connectivity pattern was complex, with these structures having widespread ipsilateral and contralateral projections. The local connections between the opercular subregions and with the insula, as well as with more distant areas like the cingulate gyrus, were distinguished by strength and between-subjects consistency. In conclusion, we demonstrate specific opercular functionality, distinct from the one of the insular cortex. The study is complemented by a literature review on the opercular functional connectome in human and non-human primates.