Transient beta events (TBE) during electroencephalography (EEG) reflect thalamocortical activity, bridging genotype to phenotype and impacting sensory responsivity. Compared to typically developing controls, we found elevated TBE rate in some children with idiopathic Autism Spectrum Disorder (ASD) and a majority of children with Phelan-McDermid Syndrome, Rett Syndrome, and SYNGAP1-related disorder. TBE rate thus offers promise as a stratification biomarker with divergent and convergent properties across ASD and neurogenetic conditions, respectively.

Objective. Epilepsy is characterized by dynamic disruptions in brain networks, yet the spatiotemporal organization of directional interactions underlying ictal transitions remains incompletely understood. This study investigates peri-ictal transition patterns in mesial temporal lobe epilepsy (MTLE) by characterizing directional connectivity dynamics from intracranial stereo-electroencephalography (SEEG) seizure recordings.Approach. We analyzed SEEG data from 10 patients with MTLE who subsequently achieved seizure freedom following surgical resection. We quantified directional connectivity using directed transfer function measures across preictal, ictal, and post-ictal phases. We then applied clustering analyses to identify reproducible dynamical connectivity patterns and evaluated their consistency across seizures and patients.Main results. We identified distinct out-degree connectivity patterns across seizure phases, each associated with specific anatomical regions. Regions corresponding to the epileptogenic zone exhibited elevated out-degree during the preictal and early ictal periods, whereas non-resected regions showed increased out-degree during ictal termination and postictal phases. These patterns remained consistent across patients and seizures, demonstrating a structured and phase-dependent organization of directional information flow during seizure evolution.Significance. This study identifies stereotypical and reproducible peri-ictal directional connectivity patterns in MTLE. By characterizing how information flow evolves across seizure phases, our findings advance network-level understanding of ictal transitions and provide a reproducible framework for future studies of seizure network dynamics.

Accurate segmentation of seizure phases in intracranial EEG is essential for characterizing seizure dynamics and supporting presurgical evaluation in drug-resistant focal epilepsy. This study examines whether a semi-supervised changepoint detection framework can reliably delineate ictal onset, intra-ictal transition, and seizure termination. A three-phase segmentation pipeline integrates multivariate envelope-based features, including root mean square amplitude, relative bandpower in the theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–80 Hz) bands, line length, and spectral entropy, with the Pruned Exact Linear Time algorithm. Features were extracted from sliding windows whose lengths and phase-specific weights were optimized using nested leave-one-subject-out cross-validation with Optuna. To ensure length invariance, analysis windows were randomly extended by 5–30 s before seizure onset and after seizure termination using real pre- and post-ictal data. Performance was evaluated on 179 seizure-onset-zone bipolar channels across 32 seizures from 10 patients. Mean absolute errors were 4.19 ± 2.69 s for seizure onset, 6.93 ± 5.75 s for intra-ictal transition, and 3.82 ± 4.24 s for seizure termination. Detection accuracies within ± 5 s were 71.6 κ = 0.35 –0.69) and provides an interpretable, data-driven approach for comprehensive seizure phase characterization, with potential utility in clinical decision-making.

OBJECTIVE:Epilepsy surgery is an effective treatment option for patients with medically refractory epilepsy due to mild malformation of cortical development with oligodendroglial hyperplasia (MOGHE). The success of surgery depends on the accurate localization of the epileptogenic zone, which can be challenging due to the subtle imaging features. The aim of this project was to provide an in-depth electro-clinical characterization of MOGHE in patients with medically intractable epilepsy, and to assess the role of stereo-electroencephalography (SEEG) in tailoring the resection and optimizing surgical outcome. METHODS:This single-center retrospective study analyzes a cohort of patients with medically intractable focal epilepsy who underwent surgery and had confirmed MOGHE on pathology evaluation. Clinical data, including demographics, electroclinical features (scalp EEG and invasive monitoring when available), surgical interventions, and postoperative outcomes were extracted from electronic medical records. RESULTS:Of 23 patients identified, 10 (43%) underwent SEEG as part of their standard care. Seizure outcome data were available for 22 patients in this series. Median post-operative follow-up duration was 3.8 years. Fourteen patients (64%) were seizure-free (Engel 1). Seizure freedom in the SEEG group was 80% (n = 8/10), in comparison to the non-SEEG group (50%, n = 6/12). Success rate was related to complete resection of the regions sampled by SEEG electrodes involved in ictal onset, and a more extensive resection of the lesion (or near total lobectomy). SIGNIFICANCE:Our results underscore the pivotal role of SEEG in enhancing surgical outcomes in patients with drug-resistant epilepsy due to MOGHE. SEEG proved particularly beneficial in defining resection margins, especially in cases where non-invasive data were discordant, scalp EEG patterns were generalized or poorly localized, and imaging findings were nonspecific, diffuse, or normal, making lesion identification challenging.

Alexopoulos et al. consider whether adding thalamic electrodes during SEEG improves patient outcomes. They conclude that the evidence remains limited and heterogeneous, and recommend cautious use within ethically and methodologically rigorous trials until the benefits and risks have been more clearly established.

OBJECTIVE:We evaluate the practical identifiability and clinical utility of local spectral graph model (SGM) parameters estimated from resting-state magnetoencephalography (MEG) in drug-resistant epilepsy. METHODS:A coupled excitatory-inhibitory SGM was fitted to MEG power spectra across 159 brain regions in 20 patients with temporal lobe epilepsy who achieved seizure freedom following surgery. Identifiability was assessed via boundary saturation analysis and inter-parameter correlations across four frequency bands. Identifiable parameters were tested for seizure onset zone (SOZ) discrimination. RESULTS:Model fit was excellent (mean $r = 0.976$) and significantly exceeded a $1/f^\beta$ baseline ($p < 10^{-10}$). Gain parameters ($g_{ei}$, $g_{ii}$) were robustly estimable, whereas the excitatory time constant ($\tau _{e}$) showed 74% boundary saturation in broadband fits, reduced to ${\sim }3\%$ when restricted to 1-50 Hz. SOZ regions exhibited elevated $g_{ei}$ (Cohen's $d = +0.67$, FDR-corrected $p = 0.024$) and reduced $g_{ii}$ ($d = -0.55$, $p = 0.003$), with a composite biomarker achieving 2.6-fold improvement over chance. CONCLUSION:Gain parameters are robustly identifiable from clinical MEG and capture excitatory-inhibitory imbalance in the SOZ, whereas time constants require band-limited fitting. These findings motivate an identifiability-aware framework in which only parameters demonstrably constrained by data are interpreted. SIGNIFICANCE:This is the first systematic assessment of neural mass model parameter identifiability in clinical epilepsy MEG, establishing practical guidelines for biophysical parameter interpretation.

Accurate localization of the seizure onset zone (SOZ) is a central determinant of surgical outcome in drug-resistant focal epilepsy, yet identifying it from stereo-electroencephalography (SEEG) remains a slow, subjective visual task. We developed a self-supervised CNN--Transformer encoder (CSOPE-Net; Contrastive Seizure-Onset Pattern Encoder) that learns contact-level peri-ictal representations from 60-second superlet spectrograms through InfoNCE contrastive pretraining. We evaluated this representation as a framework for SOZ localization, seizure-onset phenotype clustering, and identification of clinically labeled non-SOZ contacts with SOZ-like morphology in poor-outcome patients. Across 149 patients partitioned a priori into a development cohort (n=119) and an independent held-out cohort (n=30; 18 good-outcome subjects for classification validation and 12 poor-outcome subjects for SOZ-proximal replication), the model achieved aggregate ROC-AUC 0.854 under leave-one-subject-out cross-validation, 0.935 on held-out good-outcome subjects, and 0.822 on an independent external cohort (HUP iEEG dataset), with consistent performance across patients. The learned representation organized seizure onsets into reproducible phenotype families and, in poor-outcome patients, flagged clinically labeled non-SOZ contacts whose spectrotemporal features resembled those of high-confidence SOZ contacts. This signal reproduced in held-out data, and in a blinded re-review three experts endorsed these contacts as showing ictal-onset morphology at approximately 15-fold higher odds than matched non-SOZ controls. This framework augments expert SEEG review and surfaces candidate contacts for re-review in poor-outcome cases.

OBJECTIVE:Surgical decision-making in temporal lobe epilepsy (TLE) faces a critical challenge in determining whether the hippocampus can be safely spared during anterior temporal resection, particularly when surgery involves the language-dominant hemisphere. We investigated whether presurgical network control metrics derived from magnetoencephalography (MEG) can differentiate patients who achieved seizure freedom with hippocampal resection (HR) from those who achieved seizure freedom with hippocampal sparing (HS). METHODS:We analyzed presurgical spike-free interictal MEG in 25 TLE patients with seizure freedom after anterior temporal resection (19 with hippocampal resection, 6 with sparing). Functional networks constructed from MEG data were partitioned into communities using a Louvain-based consensus clustering algorithm. Within identified hippocampal-related communities from each patient, we applied control centrality analysis to quantify the influence of each node on community synchronizability. Group differences were assessed using Fisher's exact tests with false discovery rate (FDR) correction applied within each frequency band and hemisphere. Leave-one-subject-out (LOSO) sensitivity analysis assessed the robustness of findings to individual subjects. RESULTS:After FDR correction, synchronization of the contralateral anterior superior temporal sulcus (STS) in the HS group at beta band emerged as the primary finding (p = .001, q = .02). This finding showed 100% stability in LOSO and persisted in a subgroup analysis excluding patients with hippocampal seizure onset (p = .004). At p < .05, additional region-frequency differences formed a coherent anatomic pattern: the HR group showed higher synchronization fractions within ipsilateral limbic structures, whereas the HS group showed higher synchronization in the ipsilateral posterior hippocampus and contralateral temporal regions with 100% LOSO stability. SIGNIFICANCE:Presurgical MEG-based network control analysis identified contralateral anterior STS synchronization as a candidate biomarker for hippocampal sparing eligibility in TLE. This proof-of-concept finding warrants prospective validation as potential tools to support individualized surgical planning beyond localization-based approaches.

Accurate localization of the theoretical epileptogenic zone in cingulate epilepsy is particularly challenging due to the region's deep anatomical location and complex connectivity. While invasive stereoelectroencephalography (sEEG) methodology offers excellent spatiotemporal sampling of deep intracerebral structures, interpretation of these high-dimensional recordings remains largely qualitative and subject to interpretation by clinician experts. To address this limitation, we propose a quantitative, biomarker-based framework using phase-amplitude coupling (PAC) to investigate 25 seizures recorded from four patients with complex cingulate epilepsy who underwent sEEG followed by surgical treatment (either laser ablation or open resection), achieving ≥ 1 year of sustained seizure freedom. PAC values were computed from sEEG electrode contacts across multiple seizures during pre-ictal and ictal phases, employing wide-frequency and band-specific frequency coupling approaches. Among frequency pairs, theta-beta ([Formula: see text]-[Formula: see text]) coupling consistently demonstrated the most robust differentiation between surgically-treated and untreated contact sites. Our findings highlight frequency-specific PAC-based metrics as a potential tool for mapping dynamic epileptiform activity in brain networks, offering quantitative insight that may refine surgical planning and decision-making in challenging cases of cingulate epilepsy.

Objective.Multifractal formalism introduces an invaluable framework for the investigation of nonlinear, scale-invariant features across multiple time scales in non-stationary time series data.Approach.In this context, we sought to explore multifractal features defining spatiotemporal correlations in seizure activity, by applying multifractal detrended fluctuation analysis (MFDFA) to stereoelectroencephalography (sEEG) recordings from five patients with refractory, focal temporal epilepsy, who underwent subsequent surgical removal of the temporal lobe and achieved seizure freedom.Main results.To the best of our knowledge, we are the first to report evidence for a multifractal architecture underscoring sEEG-recorded epileptiform signalsin vivo, suggesting a fundamental propensity for scale-invariance in electrophysiological human brain recordings. Importantly, dynamical MFDFA-derived features captured altered spatiotemporal trends through the pre-ictal, ictal and post-ictal states, and also across anatomical brain regions. Larger fluctuations (deviations) in these metrics were observed to varying extents across resected temporal lobe structures, as compared to more constrained dynamics in non-resected networks.Significance.MFDFA-derived metrics were statistically analyzed and found to capture unique features from the sEEG data, with temporal variations across anatomical brain networks offering a potentially useful tool for the visualization, quantification and interpretation of network involvement in the onset and evolution of seizure activity. These results underscore the importance of investigating high-complexity dynamics in intracranial sEEG recordings and their potential utility towards surgical decision-making in patients with medically intractable epilepsy.

OBJECTIVE:To investigate whether local lesions created by stereo-electroencephalography (SEEG)-guided radiofrequency thermocoagulation (RFTC) affect distant brain connectivity and excitability in patients with focal, drug-resistant epilepsy (DRE). METHODS:Ten patients with focal DRE underwent SEEG implantation and subsequently 1 Hz bipolar repetitive electrical stimulation (RES) for 30 s before and after RFTC. Root mean square (RMS) of cortico-cortical evoked potentials (CCEPs) was calculated for 15 ms to 300 ms post-stimulation with baseline correction. Contact pairs were categorized as both coagulated, hybrid, or both non-coagulated. The data were divided into nine categories based on the stimulating and recording contact pair combinations. RMS of CCEPs was compared before and after (<12 h) RFTC using a two-sample t test (Hochberg corrected, p < 0.05) for each patient. Boost score, indicating power increase during seizures before RFTC relative to baseline, was analyzed in 4 s windows with 1 s overlap during seizure duration. RESULTS:RFTC altered connectivity across all categories. Of interest, decreases and increases in RMS were observed in connections between non-coagulated contacts distant from coagulation site (range: 1.09-85 mm, median = 17.7 mm, interquartile range [IQR] 10.1-32.3). Contact pairs involved in significantly altered non-coagulated connections showed a higher boost score correlation in the theta, beta, and gamma bands, as well as a stronger maximum correlation with coagulated sites in the delta band than contacts for which connectivity did not change after RFTC. SIGNIFICANCE:This study highlights how local lesions alter distant brain connectivity, providing insights for future research on epilepsy network changes and seizure outcomes following RFTC.

BACKGROUND:People with multiple sclerosis (MS) exhibit a different pattern of blood oxygenation level-dependent (BOLD) activation on functional magnetic resonance imaging (fMRI) studies when compared to healthy control (HC). PURPOSE:The objective of this study is to determine whether observed differences in BOLD activation between people with MS (pwMS) and HC participants are due to the differences of neurovascular coupling, cerebral blood flow (CBF) or actual neuronal activity. METHODS:We investigated the neuronal activation in pwMS (n = 11) and age- and sex-matched HC participants (n = 15) using simultaneous electroencephalogram (EEG) and fMRI measures during a visual task (VT) and hypercapnia condition. RESULTS:Significant neurovascular coupling is observed in both HC and pwMS. Neuro-vascular coupling ratios are not significantly different between groups. However, we observe significantly lower CBF increase during VT and higher quantitative CBF at a rest state in pwMS than in HC (p < 0.05). From the multiple regression model, in HC group, we found that the BOLD contrast change during VT is best predicted by the EEG power change during VT (Student t-score = 2.64, p = 0.022), and the CBF change during hypercapnia (Student t-score = 2.59, p = 0.024). In pwMS, the BOLD contrast change during VT is negatively predicted by the CBF change during VT (Student t-score = -4.02, p = 0.003). CONCLUSION:These findings could explain that BOLD activation in pwMS is mainly determined by the blood flow change during activation rather than the direct neuronal activation measures or hemodynamic vascular reactivity during hypercapnia challenge, suggesting that altered vasodilatory effects in response to task activation in pwMS might be linked to impaired cerebral hemodynamics, possibly leading to the widely observed abnormal BOLD activation in fMRI studies of pwMS.

OBJECTIVE:To characterize insular seizure semiology and correlate with stereoelectroencephalography (SEEG) seizure onset in a well-defined cohort, in particular examining differences between anterior and posterior insular seizures. METHODS:We documented all semiological signs and the timing of emergence for 45 patients with SEEG-confirmed insular epilepsy, along with the precise location of the seizure onset zone (SOZ) within the insula. Semiological signs and other non-invasive data were compared between those with anterior and posterior insula SOZ, with more detailed insular subregion description when appropriate. Co-occurrence patterns of insular semiological signs were also investigated. RESULTS:A total of 87% reported auras and the corresponding insular SOZ demonstrated an anterior-to-posterior gradient by aura type. The absence of aura was significantly associated with an anterior insula SOZ (p = 0.01). Late grunting/moaning (p = 0.07), symmetric mouth elementary motor (p = 0.1), blinking (p = 0.05), and chewing/repetitive swallowing (p = 0.09) also suggested an anterior insula SOZ. Unilateral non-painful somatosensory aura (p = 0.07) and early arm/hand elementary motor (p = 0.1) suggested a posterior insular SOZ. INTERPRETATION:We report the characteristics and semiological features in a large cohort of primary insular epilepsy patients. The type of aura is crucial for identifying the SOZ and the timing of semiological emergence contributes to the anatomo-electro-clinical localization of an insular SOZ. These findings can enhance identification of insular epilepsy, making targeted curative treatment feasible. ANN NEUROL 2025;98:1111-1124.

This study explores the feasibility of using commercial smartwatches and smartphones in patients with epilepsy admitted to the Epilepsy Monitoring Unit (EMU) for seizure data collection and seizure monitoring. The accelerometer and heart rate sensors in a Garmin Venu 3 smartwatch are used for data recording and a commercial smartphone is used as a wireless Hub for user registration, data storage and management. We addressed several challenges in developing a remote data collection pipeline, which includes raw sensor data synchronization, CSV file generation, data upload, extended battery life, as well as uninterrupted operation through foreground services and programmed background processes. With an optimized mobile application, continuous data collection, storage and transfer have been demonstrated for more than 5 days. In addition, we have performed preliminary testing of a Time-Series Foundation Model for seizure forecasting.

Objective.For medically-refractory epilepsy patients, stereoelectroencephalography (sEEG) is a surgical method using intracranial electrode recordings to identify brain networks participating in early seizure organization and propagation (i.e. the epileptogenic zone, EZ). If identified, surgical EZ treatment via resection, ablation or neuromodulation can lead to seizure-freedom. To date, quantification of sEEG data, including its visualization and interpretation, remains a clinical and computational challenge. Given elusiveness of physical laws or governing equations modelling complex brain dynamics, data science offers unique insight into identifying unknown patterns within high-dimensional sEEG data. We apply here an unsupervised data-driven algorithm, dynamic mode decomposition (DMD), to sEEG recordings from five focal epilepsy patients (three with temporal lobe, and two with cingulate epilepsy), who underwent subsequent resective or ablative surgery and became seizure free.Approach.DMD obtains a linear approximation of nonlinear data dynamics, generating coherent structures ('modes') defining important signal features, used to extract frequencies, growth rates and spatial structures. DMD was adapted to produce dynamic modal maps (DMMs) across frequency sub-bands, capturing onset and evolution of epileptiform dynamics in sEEG data. Additionally, we developed a static estimate of EZ-localized electrode contacts, termed the higher-frequency mode-based norm index (MNI). DMM and MNI maps for representative patient seizures were validated against clinical sEEG results and seizure-free outcomes following surgery.Main results.DMD was most informative at higher frequencies, i.e. gamma (including high-gamma) and beta range, successfully identifying EZ contacts. Combined interpretation of DMM/MNI plots best identified spatiotemporal evolution of mode-specific network changes, with strong concordance to sEEG results and outcomes across all five patients. The method identified network attenuation in other contacts not implicated in the EZ.Significance.This is the first application of DMD to sEEG data analysis, supporting integration of neuroengineering, mathematical and machine learning methods into traditional workflows for sEEG review and epilepsy surgical decision-making.

ObjectiveIctal Single Photon Emission Computed Tomography (SPECT) and stereo-electroencephalography (SEEG) are diagnostic techniques used for the management of patients with drug-resistant focal epilepsies. While hyperperfusion patterns in ictal SPECT studies reveal seizure onset and propagation pathways, the role of ictal hypoperfusion remains poorly understood. The goal of this study was to systematically characterize the spatio-temporal information flow dynamics between differently perfused brain regions using stereo-EEG recordings.MethodsWe identified seizure-free patients after resective epilepsy surgery who had prior ictal SPECT and SEEG investigations. We estimated directional connectivity between the epileptogenic-zone (EZ), non-resected areas of hyperperfusion, hypoperfusion, and baseline perfusion during the interictal, preictal, ictal, and postictal periods.ResultsCompared to the background, we noted significant information flow (1) during the preictal period from the EZ to the baseline and hyperperfused regions, (2) during the ictal onset from the EZ to all three regions, and (3) during the period of seizure evolution from the area of hypoperfusion to all three regions.ConclusionsHypoperfused brain regions were found to indirectly interact with the EZ during the ictal period.SignificanceOur unique study, combining intracranial electrophysiology and perfusion imaging, presents compelling evidence of dynamic changes in directional connectivity between brain regions during the transition from interictal to ictal states.

OBJECTIVE:To develop a multiparametric machine-learning (ML) framework using high-resolution 3 dimensional (3D) magnetic resonance (MR) fingerprinting (MRF) data for quantitative characterization of focal cortical dysplasia (FCD). MATERIALS:We included 119 subjects, 33 patients with focal epilepsy and histopathologically confirmed FCD, 60 age- and gender-matched healthy controls (HCs), and 26 disease controls (DCs). Subjects underwent whole-brain 3 Tesla MRF acquisition, the reconstruction of which generated T1 and T2 relaxometry maps. A 3D region of interest was manually created for each lesion, and z-score normalization using HC data was performed. We conducted 2D classification with ensemble models using MRF T1 and T2 mean and standard deviation from gray matter and white matter for FCD versus controls. Subtype classification additionally incorporated entropy and uniformity of MRF metrics, as well as morphometric features from the morphometric analysis program (MAP). We translated 2D results to individual probabilities using the percentage of slices above an adaptive threshold. These probabilities and clinical variables were input into a support vector machine for individual-level classification. Fivefold cross-validation was performed and performance metrics were reported using receiver-operating-characteristic-curve analyses. RESULTS:FCD versus HC classification yielded mean sensitivity, specificity, and accuracy of 0.945, 0.980, and 0.962, respectively; FCD versus DC classification achieved 0.918, 0.965, and 0.939. In comparison, visual review of the clinical magnetic resonance imaging (MRI) detected 48% (16/33) of the lesions by official radiology report. In the subgroup where both clinical MRI and MAP were negative, the MRF-ML models correctly distinguished FCD patients from HCs and DCs in 98.3% of cross-validation trials. Type II versus non-type-II classification exhibited mean sensitivity, specificity, and accuracy of 0.835, 0.823, and 0.83, respectively; type IIa versus IIb classification showed 0.85, 0.9, and 0.87. In comparison, the transmantle sign was present in 58% (7/12) of the IIb cases. INTERPRETATION:The MRF-ML framework presented in this study demonstrated strong efficacy in noninvasively classifying FCD from normal cortex and distinguishing FCD subtypes. ANN NEUROL 2024;96:944-957.

Responsive neurostimulation is a closed-loop neuromodulation therapy for drug resistant focal epilepsy. Responsive neurostimulation electrodes are placed near ictal onset zones so as to enable detection of epileptiform activity and deliver electrical stimulation. There is no standard approach for determining the optimal placement of responsive neurostimulation electrodes. Clinicians make this determination based on presurgical tests, such as MRI, EEG, magnetoencephalography, ictal single-photon emission computed tomography and intracranial EEG. Currently functional connectivity measures are not being used in determining the placement of responsive neurostimulation electrodes. Cortico-cortical evoked potentials are a measure of effective functional connectivity. Cortico-cortical evoked potentials are generated by direct single-pulse electrical stimulation and can be used to investigate cortico-cortical connections in vivo. We hypothesized that the presence of high amplitude cortico-cortical evoked potentials, recorded during intracranial EEG mon-itoring, near the eventual responsive neurostimulation contact sites is predictive of better outcomes from its therapy. We retrospectively reviewed 12 patients in whom cortico-cortical evoked potentials were obtained during stereoelectroencephalography evaluation and subsequently underwent responsive neurostimulation therapy. We studied the relationship between cortico-cortical evoked potentials, the eventual responsive neurostimulation electrode locations and seizure reduction. Directional connectivity indicated by cortico- cortical evoked potentials can categorize stereoelectroencephalography electrodes as either receiver nodes/in-degree (an area of greater inward connectivity) or projection nodes/out-degree (greater outward connectivity). The follow-up period for seizure reduction ranged from 1.3-4.8 years (median 2.7) after responsive neurostimulation therapy started. Stereoelectroencephalography electrodes closest to the eventual responsive neurostimulation contact site tended to show larger in-degree cortico-cortical evoked potentials, especially for the early latency cortico-cortical evoked potentials period (10-60 ms period) in six out of 12 patients. Stereoelectroencephalography electrodes closest to the responsive neurostimulation contacts (<= 5 mm) also had greater significant out-degree in the early cortico- cortical evoked potentials latency period than those further away (>= 10 mm) (P < 0.05). Additionally, significant correlation was noted between in-degree cortico-cortical evoked potentials and greater seizure reduction with responsive neurostimulation therapy at its most effective period (P < 0.05). These findings suggest that functional connectivity determined by cortico-cortical evoked potentials may provide additional information that could help guide the optimal placement of responsive neurostimulation electrodes.

Simultaneous electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) is a unique combined technique that provides synergy in the understanding and localization of seizure onset in epilepsy. However, reported experimental protocols for EEG-fMRI recordings fail to address details about conducting such procedures on epilepsy patients. In addition, these protocols are limited solely to research settings. To fill the gap between patient monitoring in an epilepsy monitoring unit (EMU) and conducting research with an epilepsy patient, we introduce a unique EEG-fMRI recording protocol of epilepsy during the interictal period. The use of an MR conditional electrode set, which can also be used in the EMU for a simultaneous scalp EEG and video recording, allows an easy transition of EEG recordings from the EMU to the scanning room for concurrent EEG-fMRI recordings. Details on the recording procedures using this specific MR conditional electrode set are provided. In addition, the study explains step-by-step EEG processing procedures to remove the imaging artifacts, which can then be used for clinical review. This experimental protocol promotes an amendment to the conventional EEG-fMRI recording for enhanced applicability in both clinical (i.e., EMU) and research settings. Furthermore, this protocol provides the potential to expand this modality to postictal EEG-fMRI recordings in the clinical setting.