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Introduction: For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific in silico dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain. Methods: We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome. Results: Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%. Discussion: This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.

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OBJECTIVE: The purpose of this study was to understand the anatomical and functional connections between the paracentral lobule (PCL) and the primary motor cortex (M1) of the human brain. METHODS: This retrospective study included 16 patients who underwent resection of lesions located near M1. Nine patients had lesions in the dominant hemisphere. Tractography was performed to visualize the connectivity between two regions of interest (ROIs)-the convexity and the interhemispheric fissure-that were shown by functional MRI to be activated during a finger tapping task. The number, mean length, and fractional anisotropy (FA) of the fibers between the ROIs were estimated. During surgery, subdural electrodes were placed on the brain surface, including the ROIs, using a navigation system. Cortico-cortical evoked potentials (CCEPs) were evoked by applying electrical stimuli to the hand region of M1 using electrodes placed on the convexity and were measured with electrodes placed on the interhemispheric fissure. To verify CCEP bidirectionality, electrical stimuli were applied to electrodes on the interhemispheric fissure that showed CCEP responses. Correlations of CCEP amplitudes and latencies with the number, mean length, and mean FA value obtained from tractography were determined. The correlations between these parameters and perioperative motor functions were also analyzed. RESULTS: Fibers of 14 patients were visualized by diffusion tensor imaging (DTI). Unidirectional CCEPs between the PCL and M1 were measurable in all 16 patients, and bidirectional CCEPs between them were measurable in 14 patients. There was no significant difference between the two directions in the maximum CCEP amplitude or latency (amplitude, p = 0.391; latency, p = 0.583). Neither the amplitude nor latency showed any apparent correlation with the number, mean length, or mean FA value of the fibers obtained from tractography. Pre- and postoperative motor function of the hands was not significantly correlated with CCEP amplitude or latency. The number and mean FA value of fibers obtained by DTI, as well as the maximum CCEP amplitude, varied between patients. CONCLUSIONS: This study demonstrated an anatomical connection and a bidirectional functional connection between the PCL, including the supplementary motor area, and M1 of the human brain. The observed variability between patients suggests possible motor function plasticity. These findings may serve as a foundation for further studies.

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OBJECTIVE: We investigated the feasibility of recording cortico-cortical evoked potentials (CCEPs) in patients with low- and high-grade glioma. We compared CCEPs during awake and asleep surgery, as well as those stimulated from the functional Broca area and recorded from the functional Wernicke area (BtW), and vice versa (WtB). We also analyzed CCEP properties according to tumor location, histopathology, and aphasia. METHODS: We included 20 patients who underwent minimally invasive surgery in an asleep-awake-asleep setting. Strip electrode placement was guided by classical Penfield stimulation of positive language sites and fiber tracking of the arcuate fascicle. CCEPs were elicited with alternating monophasic single pulses of 1.1 Hz frequency and recorded as averaged signals. Intraoperatively, there was no post-processing of the signal. RESULTS: Ninety-seven CCEPs from 19 patients were analyzed. There was no significant difference in CCEP properties when comparing awake versus asleep, nor BtW versus WtB. CCEP amplitude and latency were affected by tumor location and histopathology. CCEP features after tumor resection correlated with short- and long-term postoperative aphasia. CONCLUSION: CCEP recordings are feasible during minimally invasive surgery. CCEPs might be surrogate markers for altered connectivity of the language tracts. SIGNIFICANCE: This study may guide the incorporation of CCEPs into intraoperative neurophysiological monitoring.

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OBJECTIVE: We aimed to develop a new approach for identifying the localization of the seizure onset zone (SOZ) based on corticocortical evoked potentials (CCEPs) and to compare the connectivity patterns in patients with different clinical phenotypes. METHODS: Fifty patients who underwent stereoelectroencephalography and CCEP procedures were included. Logistic regression was used in the model, and six CCEP metrics were input as features: root mean square of the first peak (N1RMS) and second peak (N2RMS), peak latency, onset latency, width duration, and area. RESULTS: The area under the curve (AUC) for localizing the SOZ ranged from 0.88 to 0.93. The N1RMS values in the hippocampus sclerosis (HS) group were greater than that of the focal cortical dysplasia (FCD) IIa group (p < 0.001), independent of the distance between the recorded and stimulated sites. The sensitivity of localization was higher in the seizure-free group than in the non-seizure-free group (p = 0.036). CONCLUSIONS: This new method can be used to predict the SOZ localization in various focal epilepsy phenotypes. SIGNIFICANCE: This study proposed a machine-learning approach for localizing the SOZ. Moreover, we examined how clinical phenotypes impact large-scale abnormality of the epileptogenic networks.

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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.

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BACKGROUND: Long and thin shaft electrodes are implanted intracerebrally for stereoelectroencephalography (SEEG) in patients with pharmacoresistant focal epilepsies. Two adjacent contacts of one of such electrodes can deliver a train of single pulse electrical stimulations (SPES), and evoked potentials (EPs) are recorded on other contacts. In this study we assess if stimulating and recording on the same shaft, as opposed to different shafts, has an impact on common EP features. NEW METHOD: We leverage the large volume of SEEG data gathered in the F-TRACT database and analyze data from nearly one thousand SEEG implantations in order to verify whether stimulation and recording from the same shaft influence the EP pattern. RESULTS: We found that when the stimulated and the recording contacts were located on the same shaft, the mean and median amplitudes of an EP are greater, and its mean and median latencies are smaller than when the contacts were located on different shafts. This effect is small (Cohen's d ∼ 0.1), but robust (p-value < 10-3) across the SEEG database. COMPARISON WITH EXISTING METHOD(S): Our study is the first one to address this question. Due to the choice of commonly used EP features, our method is congruent with other studies. CONCLUSIONS: The magnitude of the reported effect does not obligate all standard analyses to correct for it, unless they aim at high precision. The source of the effect is not clear. Manufacturers of SEEG electrodes could examine it and potentially minimize the effect in their future products.

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Over the past decades, studies of human brain networks have received growing attention as the assessment and modelling of connectivity in the brain is a topic of high impact with potential application in the understanding of human brain organization under both physiological as well as various pathological conditions. Under specific diagnostic settings, human neuronal signal can be obtained from intracranial EEG (iEEG) recording in epilepsy patients that allows gaining insight into the functional organisation of living human brain. There are two approaches to assess brain connectivity in the iEEG-based signal: evaluation of spontaneous neuronal oscillations during ongoing physiological and pathological brain activity, and analysis of the electrophysiological cortico-cortical neuronal responses, evoked by single pulse electrical stimulation (SPES). Both methods have their own advantages and limitations. The paper outlines available methodological approaches and provides an overview of current findings in studies of physiological and pathological human brain networks, based on intracranial EEG recordings.

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Patterned brain stimulation is commonly employed as a tool for eliciting plasticity in brain circuits and treating neuropsychiatric disorders. Although widely used in clinical settings, there remains a limited understanding of how stimulation-induced plasticity influences neural oscillations and their interplay with the underlying baseline functional architecture. To address this question, we applied 15 minutes of 10Hz focal electrical simulation, a pattern identical to 'excitatory' repetitive transcranial magnetic stimulation (rTMS), to 14 medically-intractable epilepsy patients undergoing intracranial electroencephalographic (iEEG). We quantified the spectral features of the cortico-cortical evoked potential (CCEPs) in these patients before and after stimulation. We hypothesized that for a given region the temporal and spectral components of the CCEP predicted the location and degree of stimulation-induced plasticity. Across patients, low frequency power (alpha and beta) showed the broadest change, while the magnitude of change was stronger in high frequencies (beta and gamma). Next we demonstrated that regions with stronger baseline evoked spectral responses were more likely to undergo plasticity after stimulation. These findings were specific to a given frequency in a specific temporal window. Post-stimulation power changes were driven by the interaction between direction of change in baseline power and temporal window of change. Finally, regions exhibiting early increases and late decreases in evoked baseline power exhibited power changes after stimulation and were independent of stimulation location. Together, these findings that time-frequency baseline features predict post-stimulation plasticity effects demonstrate properties akin to Hebbian learning in humans and extend this theory to the temporal and spectral window of interest. These findings can help improve our understanding of human brain plasticity and lead to more effective brain stimulation techniques.

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INTRODUCTION: Intraoperative neurophysiology (ION) has been established over the past three decades as a valuable discipline to improve the safety of neurosurgical procedures with the main goal of reducing neurological morbidity. Neurosurgeons have substantially contributed to the development of this field not only by implementing the use and refinement of ION in the operating room but also by introducing novel techniques for both mapping and monitoring of neural pathways. METHODS: This review provides a personal perspective on the evolution of ION in a variety of pediatric neurosurgical procedures: from brain tumor to brainstem surgery, from spinal cord tumor to tethered cord surgery. RESULTS AND DISCUSSION: The contribution of pediatric neurosurgeons is highlighted showing how our discipline has played a crucial role in promoting ION at the turn of the century. Finally, a view on novel ION techniques and their potential implications for pediatric neurosurgery will provide insights into the future of ION, further supporting the view of a functional, rather than merely anatomical, approach to pediatric neurosurgery.

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INTRODUCTION: The neural bases for language perception have been studied elsewhere using Transcranial Magnetic Stimulation, functional Magnetic Resonance Imaging and Direct Cortical Stimulation. However, to our knowledge, there is no previous report about a patient identifying the change in his voice tone, speed, and prosody because of right temporal cortical stimulation. Nor has there been a cortico-cortical evoked potential (CCEP) assessment of the network underlying this process. CASE REPORT: We present CCEP from a patient with right focal refractory temporal lobe epilepsy of tumoral etiology who reported changes in the perception of his own speech prosody during stimulation. This report will serve as a complement to the understanding of the neural networks of language and prosody. CONCLUSION: The present report shows that right superior temporal gyrus, transverse temporal gyrus, right amygdala, hippocampus, and fusiform gyrus (FG) are part of the neural network subjacent to own human voice perception.

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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.

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OBJECTIVE: In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings could supplement presurgical evaluations and improve care. Thus, the authors sought to localize SOZs by training a multichannel convolutional neural network on stereoelectroencephalography (SEEG) cortico-cortical evoked potentials. METHODS: The authors performed single-pulse electrical stimulation in 10 drug-resistant temporal lobe epilepsy patients implanted with SEEG. Using 500,000 unique poststimulation SEEG epochs, the authors trained a multichannel 1-dimensional convolutional neural network to determine whether an SOZ had been stimulated. RESULTS: SOZs were classified with mean sensitivity of 78.1% and specificity of 74.6% according to leave-one-patient-out testing. To achieve maximum accuracy, the model required a 0- to 350-msec poststimulation time period. Post hoc analysis revealed that the model accurately classified unilateral versus bilateral mesial temporal lobe seizure onset, as well as neocortical SOZs. CONCLUSIONS: This was the first demonstration, to the authors' knowledge, that a deep learning framework can be used to accurately classify SOZs with single-pulse electrical stimulation-evoked responses. These findings suggest that accurate classification of SOZs relies on a complex temporal evolution of evoked responses within 350 msec of stimulation. Validation in a larger data set could provide a practical clinical tool for the presurgical evaluation of drug-resistant epilepsy.

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Cohort studies of brain stimulations performed with stereo-electroencephalographic (SEEG) electrodes in epileptic patients allow to derive large scale functional connectivity. It is known, however, that brain responses to electrical or magnetic stimulation techniques are not always reproducible. Here, we study variability of responses to single pulse SEEG electrical stimulation. We introduce a second-order probability analysis, i.e. we extend estimation of connection probabilities, defined as the proportion of responses trespassing a statistical threshold (determined in terms of Z-score with respect to spontaneous neuronal activity before stimulation) over all responses and derived from a number of individual measurements, to an analysis of pairs of measurements.Data from 445 patients were processed. We found that variability between two equivalent measurements is substantial in particular conditions. For long ( > ~ 90 mm) distances between stimulating and recording sites, and threshold value Z = 3, correlation between measurements drops almost to zero. In general, it remains below 0.5 when the threshold is smaller than Z = 4 or the stimulating current intensity is 1 mA. It grows with an increase of either of these factors. Variability is independent of interictal spiking rates in the stimulating and recording sites.We conclude that responses to SEEG stimulation in the human brain are variable, i.e. in a subject at rest, two stimulation trains performed at the same electrode contacts and with the same protocol can give discrepant results. Our findings highlight an advantage of probabilistic interpretation of such results even in the context of a single individual.

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Intraoperative recording of cortico-cortical evoked potentials (CCEPs) enables studying effective connections between various functional areas of the cerebral cortex. The fundamental possibility of postoperative speech dysfunction prediction in neurosurgery based on CCEP signal variations could serve as a basis to develop the criteria for the physiological permissibility of intracerebral tumors removal for maximum preservation of the patients' quality of life. The aim of the study was to test the possibility of predicting postoperative speech disorders in patients with glial brain tumors by using the CCEP data recorded intraoperatively before the stage of tumor resection. Materials and Methods: CCEP data were reported for 26 patients. To predict the deterioration of speech functions in the postoperative period, we used four options for presenting CCEP data and several machine learning models: a random forest of decision trees, logistic regression, and support vector machine method with different types of kernels: linear, radial, and polynomial. Twenty variants of models were trained: each in 300 experiments with resampling. A total of 6000 tests were performed in the study. Results: The prediction quality metrics for each model trained in 300 tests with resampling were averaged to eliminate the influence of "successful" and "unsuccessful" data grouping. The best result with F1-score = 0.638 was obtained by the support vector machine with a polynomial kernel. In most tests, a high sensitivity score was observed, and in the best model, it reached a value of 0.993; the specificity of the best model was 0.370. Conclusion: This pilot study demonstrated the possibility of predicting speech dysfunctions based on CCEP data taken before the main stage of glial tumors resection; the data were processed using traditional machine learning methods. The best model with high sensitivity turned out to be insufficiently specific. Further studies will be aimed at assessing the changes in CCEP during the operation and their relationship with the development of postoperative speech deficit.

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In the surgery of gliomas, recent years have witnessed unprecedented theoretical and technical development, which extensively increased indication to surgery. On one hand, it has been solidly demonstrated the impact of gross total resection on life expectancy. On the other hand, the paradigm shift from classical cortical localization of brain function towards connectomics caused by the resurgence of awake surgery and the advent of tractography has permitted safer surgeries focused on subcortical white matter tracts preservation and allowed for surgical resections within regions, such as Broca's area or the primary motor cortex, which were previously deemed inoperable. Furthermore, new asleep electrophysiological techniques have been developed whenever awake surgery is not an option, such as operating in situations of poor compliance (including paediatric patients) or pre-existing neurological deficits. One such strategy is the use of intraoperative neurophysiological monitoring (IONM), enabling the identification and preservation of functionally defined, but anatomically ambiguous, cortico-subcortical structures through mapping and monitoring techniques. These advances tie in with novel challenges, specifically risk prediction and the impact of neuroplasticity, the indication for tumour resection beyond visible borders, or supratotal resection, and most of all, a reappraisal of the importance of the right hemisphere from early psychosurgery to mapping and preservation of social behaviour, executive control, and decision making.Here we review current advances and future perspectives in a functional approach to glioma surgery.

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OBJECTIVE: Resection of the dominant side of gliomas extending to the frontal operculum has high risk of severe language dysfunction. Here, we report recording cortico-cortical evoked potentials (CCEP) using only two 6-strand strip electrodes to monitor language-related fibers intraoperatively. We examined whether this simple procedure is useful for removing gliomas extending to the dominant side of frontal operculum. METHODS: This study included 7 cases of glioma extending to the left frontal operculum. The frontal language area (FLA) was first identified by functional mapping during awake craniotomy. Next, a 6-strand strip electrode was placed on the FLA, while on the temporal side, an electrode was placed so as to slide parallel to the sylvian fissure toward the posterior language area. Electrical stimulation was performed using the electrode on the frontal side, and CCEPs were measured from the electrode on the temporal side. RESULTS: CCEPs were detected in all cases. Immediately after surgery, all patients demonstrated language dysfunction to varying degree. CCEP decreased to 10% in 1 patient, who recovered language function after 24 months. CCEP decreased slightly 80% in 1, and, in the 5 other cases, CCEPs did not change. These 5 patients soon recovered language function within 2 weeks to 1 month. CONCLUSIONS: This study confirmed the utility of CCEP monitoring using only two 6-strand strip electrodes during one-step surgery. We believe this simple method helped in monitoring intraoperative language function and predicting its postoperative recovery in patients with gliomas extending to the dominant side of frontal operculum.

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BACKGROUND: High-frequency cortical electrical stimulations (HF-CES) are the gold standard for presurgical functional mapping. In the dominant ventral temporal cortex (VTC) HF-CES can elicit transient naming impairment (eloquent sites), defining a basal temporal language area (BTLA). OBJECTIVE: Whether naming impairments induced by HF-CES within the VTC are related to a specific pattern of connectivity of the BTLA within the temporal lobe remains unknown. We addressed this issue by comparing the connectivity of eloquent and non-eloquent sites from the VTC using cortico-cortical evoked potentials (CCEP). METHODS: Low frequency cortical electrical stimulations (LF-CES) were used to evoke CCEP in nine individual brains explored with Stereo-Electroencephalography. We compared the connectivity of eloquent versus non eloquent sites within the VTC using Pearson's correlation matrix. RESULTS: Overall, within the VTC, eloquent sites were associated with increased functional connectivity compared to non-eloquent sites. Among the VTC structures, this pattern holds true for the inferior temporal gyrus and the parahippocampal gyrus while the fusiform gyrus specifically showed a high connectivity in both non eloquent and eloquent sites. CONCLUSIONS: Our findings suggest that the cognitive effects of focal HF-CES are related to the functional connectivity properties of the stimulated sites, and therefore to the disturbance of a wide cortical network. They further suggest that functional specialization of a cortical region emerges from its specific pattern of functional connectivity. Cortical electrical stimulation functional mapping protocols including LF coupled to HF-CES could provide valuable data characterizing both local and distant functional architecture.

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Successful outcomes in epilepsy surgery rely on the accurate localization of the seizure onset zone. Localizing the seizure onset zone is often a costly and time-consuming process wherein a patient undergoes intracranial EEG monitoring, and a team of clinicians wait for seizures to occur. Clinicians then analyse the intracranial EEG before each seizure onset to identify the seizure onset zone and localization accuracy increases when more seizures are captured. In this study, we develop a new approach to guide clinicians to actively elicit seizures with electrical stimulation. We propose that a brain region belongs to the seizure onset zone if a periodic stimulation at a particular frequency produces large amplitude oscillations in the intracranial EEG network that propagate seizure activity. Such responses occur when there is 'resonance' in the intracranial EEG network, and the resonant frequency can be detected by observing a sharp peak in the magnitude versus frequency response curve, called a Bode plot. To test our hypothesis, we analysed single-pulse electrical stimulation response data in 32 epilepsy patients undergoing intracranial EEG monitoring. For each patient and each stimulated brain region, we constructed a Bode plot by estimating a transfer function model from the intracranial EEG 'impulse' or single-pulse electrical stimulation response. The Bode plots were then analysed for evidence of resonance. First, we showed that when Bode plot features were used as a marker of the seizure onset zone, it distinguished successful from failed surgical outcomes with an area under the curve of 0.83, an accuracy that surpassed current methods of analysis with cortico-cortical evoked potential amplitude and cortico-cortical spectral responses. Then, we retrospectively showed that three out of five native seizures accidentally triggered in four patients during routine periodic stimulation at a given frequency corresponded to a resonant peak in the Bode plot. Last, we prospectively stimulated peak resonant frequencies gleaned from the Bode plots to elicit seizures in six patients, and this resulted in an induction of three seizures and three auras in these patients. These findings suggest neural resonance as a new biomarker of the seizure onset zone that can guide clinicians in eliciting native seizures to more quickly and accurately localize the seizure onset zone.

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BACKGROUND: Intraoperative neuromonitoring of motor functions experienced a dramatical revolution in the last years thanks to significant advances in anesthesiology procedures and both preoperative and intraoperative mapping techniques. Asleep, awake, and combined intraoperative mapping techniques were responsible for an improvement in the functional outcomes in neurosurgery, providing reliable and reproducible mapping of both projection and association fibers involved in motor control. METHODS: We report inter-M1 cortico-cortical evoked potential (CCEP) recording during asleep resection of a bilateral parasagittal meningioma with intraoperative neuromonitoring and motor mapping. RESULTS: CCEPs were recorded between both M1 cortices with bipolar stimulations of both supplementary motor areas (10.5-11.5 µV). CONCLUSIONS: Here, we provide evidence of intraoperative mapping of commissural fibres involved in motor control in a patient with asleep technique as well as a review of the potential tracts involved in the connectivity underlying the motor function.

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The possibility of postoperative speech dysfunction prediction in neurosurgery based on intraoperative cortico-cortical evoked potentials (CCEP) might provide a new basis to refine the criteria for the extent of intracerebral tumor resection and preserve patients' quality of life. In this study, we aimed to test the quality of predicting postoperative speech dysfunction with machine learning based on the initial intraoperative CCEP before tumor removal. CCEP data were reported for 26 patients. We used several machine learning models to predict speech deterioration following neurosurgery: a random forest of decision trees, logistic regression, support vector machine with different types of the kernel (linear, radial, and polynomial). The best result with F1-score = 0.638 was obtained by a support vector machine with a polynomial kernel. Most models showed low specificity and high sensitivity (reached 0.993 for the best model). Our pilot study demonstrated the insufficient quality of speech dysfunction prediction by solely intraoperative CCEP recorded before glial tumor resection, grounding our further research of CCEP postresectional dynamics.

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