Epileptic activity on foramen ovale electrodes is associated with sleep and tau pathology in Alzheimer's disease.

Both sleep alterations and epileptiform activity are associated with the accumulation of amyloid-ß and tau pathology and are currently investigated for potential therapeutic interventions in Alzheimer's disease (AD). However, a bidirectional intertwining relation between sleep and neuronal hyperexcitability might modulate the effects of AD pathology on the corresponding associations. To investigate this, we performed multiple day simultaneous foramen ovale (FO) plus scalp EEG and polysomnography (PSG) recordings and acquired 18F-MK6240 tau PET-MR in three patients in the prodromal stage of AD and in two patients with mild and moderate dementia due to AD, respectively. As an eligibility criterion for the present study, subjects either had a history of a recent seizure (n = 2) or subclinical epileptiform activity (SEA) on a previous scalp EEG taken in a research context (n = 3). The 18F-MK6240 standard uptake value ratio (SUVR) and asymmetry index (AI) were calculated in a priori defined volumes of interest (VOIs). Linear mixed effects models were used to study associations between interictal epileptiform discharges (IEDs), PSG parameters and 18F-MK6240 SUVR. Epileptiform activity was bilateral but asymmetrically present on FO electrodes in all patients and ≥ 95% of IEDs were not visible on scalp EEG. In one patient two focal seizures were detected on FO electrodes, both without visual scalp EEG correlate. We observed lateralized periodic discharges, brief potentially ictal rhythmic discharges and lateralized rhythmic delta activity on FO electrodes in four patients. Unlike scalp EEG, intracranial electrodes showed a lateralization of epileptiform activity. Although the amount of IEDs on intracranial electrodes was not associated to the 18F-MK6240 SUVR binding in different VOIs, there was a congruent asymmetry of the 18F-MK6240 binding towards the most epileptic hemisphere for the mesial (P = 0.007) and lateral temporal cortex (P = 0.006). IEDs on intracranial electrodes were most abundant during slow wave sleep (SWS) (92/h) and N2 (81/h), followed by N1 (33/h) and least frequent during wakefulness (17/h) and REM sleep (9/h). The extent of IEDs during sleep was not reflected in the relative time in each sleep stage spent (REM% (P = 0.415), N1% (P = 0.668), N2% (P = 0.442), SWS% (P = 0.988)), and not associated with the arousal index (P = 0.317), apnea-hypopnea index (P = 0.846) or oxygen desaturation index (P = 0.746). Together, our observations suggest a multi-directional interaction between sleep, epileptiform activity and tau pathology in AD.

Foramen ovale electrode investigation in the era of SEEG: Results and a reappraisal.

INTRODUCTION: Patients with medication-resistant disabling epilepsy should be considered for potential epilepsy surgery. If noninvasive techniques are unable to identify the location of the seizure onset zone (SOZ), it becomes necessary to consider intracranial investigations. Stereo-electroencephalography (SEEG) is currently the preferred method for such monitoring, however foramen ovale (FO) electrodes offer a less invasive alternative that may be suitable in certain situations. Previous studies have demonstrated the effectiveness of FO electrodes in suspected mesial temporal epilepsy, nevertheless, increased experience with FO electrode use could further enhance their safety and efficacy. Therefore, we conducted an analysis of recent FO electrode investigations to assess their utility in surgical decision making, post resection outcomes, and complication rates. METHODS: We conducted a retrospective analysis of 61 patients who underwent FO placement at Mass General Brigham between 2009 and 2020. Patient and seizure characteristics, preoperative investigation data, and seizures outcomes were collected. In addition, identified predictors of FO utility using logistic regression. RESULTS: A total of 61 patients were identified. FO evaluation localized the SOZ in 56 % of patients. Complications were encountered in 1.6 % of patients. Subsequent surgical resection was pursued by 49 % of patients, with 56 % becoming seizure free, and 67 % having favorable seizure outcomes at last follow-up. Multivariate analysis identified younger patients with a higher number of preoperative ASMs as more likely to undergo subsequent treatment, however, these features were not predictive features of SOZ localization, seizure freedom, or favorable seizure outcomes. In patients with bitemporal or cross-over onsets on scalp EEG, FO was able to identify the SOZ in 79 %, whereas in patients with discordant or unclear onset, the rates were 71 % and 45 %, respectively. CONCLUSION: In a contemporary cohort, FO electrode placement had a low complication rate and a high utility primarily in cases of unclear laterality of mesial temporal onsets or discordance between scalp EEG and other pre-FO investigation data in cases of suspected mesial temporal onsets.

Assessing expert reliability in determining intracranial EEG channel quality and introducing the Automated Bad Channel Detection algorithm.

OBJECTIVES: To evaluate the inter- and intra-rater reliability for the identification of bad channels among neurologists, EEG Technologists, and naïve research personnel, and to compare their performance with the automated bad channel detection (ABCD) algorithm for detecting bad channels. Methods: Six Neurologists, ten EEG Technologists, and six naïve research personnel (22 raters in total) were asked to rate 1440 real intracranial EEG channels as good or bad. Intra- and interrater kappa statistics were calculated for each group. We then compared each group to the ABCD algorithm which uses spectral and temporal domain features to classify channels as good or bad. Results: Analysis of channel ratings from our participants revealed variable intra-rater reliability within each group, with no significant differences across groups. Inter-rater reliability was moderate among neurologists and EEG Technologists but minimal among naïve participants. Neurologists demonstrated a slightly higher consistency in ratings than EEG Technologists. Both groups occasionally misclassified flat channels, and participants generally focused on low-frequency content for their assessments. The ABCD algorithm, in contrast, relied more on high-frequency content. A logistic regression model showed a linear relationship between the algorithm's ratings and user responses for predominantly good channels, but less so for channels rated as bad. Sensitivity and specificity analyses further highlighted differences in rating patterns among the groups, with neurologists showing higher sensitivity and naïve personnel higher specificity. Significance: Our study reveals the bias in human assessments of iEEG data quality and the tendency of even experienced professionals to overlook certain bad channels, highlighting the need for standardized, unbiased methods. The ABCD algorithm, outperforming human raters, suggests the potential of automated solutions for more reliable iEEG interpretation and seizure characterization, offering a reliable approach free from human biases. .

Incomplete resection of the intracranial electroencephalographic seizure onset zone is not associated with postsurgical outcomes.

Delineation of seizure onset regions using intracranial electroencephalography (icEEG) is vital in the surgical workup of drug-resistant epilepsy cases. However, it is unknown whether the complete resection of these regions is necessary for seizure freedom, or whether postsurgical seizure recurrence can be attributed to the incomplete removal of seizure onset regions. To address this gap, we retrospectively analyzed icEEG recordings from 63 subjects, identifying seizure onset regions visually and algorithmically. We assessed onset region resection and correlated this with postsurgical seizure control. The majority of subjects had more than half of their onset regions resected (82.46% and 80.65% of subjects using visual and algorithmic methods, respectively). There was no association between the proportion of the seizure onset zone (SOZ) that was subsequently resected and better surgical outcomes (area under the receiver operating characteristic curve [AUC] < .7). Investigating the spatial extent of onset regions, we found no substantial evidence of an association with postsurgical seizure control (all AUC < .7). Although seizure onset regions are typically resected completely or in large part, incomplete resection is not associated with worse postsurgical outcomes. We conclude that postsurgical seizure recurrence cannot be attributed to an incomplete resection of the icEEG SOZ alone. Other network mechanisms beyond icEEG seizure onset likely contribute.

Deep learning based decoding of single local field potential events.

How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.

Combined neuromodulation and resection for functional cortex epilepsy: a case series.

OBJECTIVE: Medically refractory epilepsy (MRE) often requires resection of the seizure onset zone (SOZ) for effective treatment. However, when the SOZ is in functional cortex (FC), achieving complete and safe resection becomes difficult, due to the seizure network overlap with function. The authors aimed to assess the safety and outcomes of a combined approach involving partial resection combined with focal neuromodulation for FC refractory epilepsy. METHODS: The authors performed a retrospective analysis of individuals diagnosed with MRE who underwent surgical intervention from January 2015 to December 2022. Patients whose SOZ was located in FC and were treated with resection combined with simultaneous implantation of a focal neuromodulation device (responsive neurostimulation [RNS] device) with more than 12 months of follow-up data were included. All patients underwent a standard epilepsy preoperative assessment including intracranial electroencephalography and extraoperative stimulation mapping. Resections were performed under general anesthesia, followed by the concurrent implantation of an RNS device. RESULTS: Seven patients (4 males, median age 32.3 years, all right-handed) were included. The median interval from seizure onset to surgery was 17.4 years. The epileptogenic network included sensorimotor areas (cases 2, 3, and 6), visual cortex (case 1), language areas (cases 4 and 7), and the insula (case 5). The median follow-up was 3 years (range 1-5.8 years). No significant changes in neuropsychological tests were reported. One permanent nondisabling planned neurological deficit (left inferior quadrantanopia) was observed. Six patients had stimulation activated at a median of 4.7 months after resection. All patients achieved good seizure outcomes (5 with Engel class I and 2 with Engel class II outcomes). CONCLUSIONS: Maximal safe resection combined with focal neuromodulation presents a promising alternative to stand-alone resections for MRE epileptogenic zones overlapping with functional brain. This combined approach prioritizes the preservation of function while improving seizure outcomes.

WONOEP 2022: Neurotechnology for the diagnosis of epilepsy.

Epilepsy's myriad causes and clinical presentations ensure that accurate diagnoses and targeted treatments remain a challenge. Advanced neurotechnologies are needed to better characterize individual patients across multiple modalities and analytical techniques. At the XVIth Workshop on Neurobiology of Epilepsy: Early Onset Epilepsies: Neurobiology and Novel Therapeutic Strategies (WONOEP 2022), the session on "advanced tools" highlighted a range of approaches, from molecular phenotyping of genetic epilepsy models and resected tissue samples to imaging-guided localization of epileptogenic tissue for surgical resection of focal malformations. These tools integrate cutting edge research, clinical data acquisition, and advanced computational methods to leverage the rich information contained within increasingly large datasets. A number of common challenges and opportunities emerged, including the need for multidisciplinary collaboration, multimodal integration, potential ethical challenges, and the multistage path to clinical translation. Despite these challenges, advanced epilepsy neurotechnologies offer the potential to improve our understanding of the underlying causes of epilepsy and our capacity to provide patient-specific treatment.

Introducing HiBoP: a Unity-based visualization software for large iEEG datasets.

BACKGROUND: Intracranial EEG data offer a unique spatio-temporal precision to investigate human brain functions. Large datasets have become recently accessible thanks to new iEEG data-sharing practices and tighter collaboration with clinicians. Yet, the complexity of such datasets poses new challenges, especially regarding the visualization and anatomical display of iEEG. NEW METHOD: We introduce HiBoP, a multi-modal visualization software specifically designed for large groups of patients and multiple experiments. Its main features include the dynamic display of iEEG responses induced by tasks/stimulations, the definition of Regions and electrodes Of Interest, and the shift between group-level and individual-level 3D anatomo-functional data. RESULTS: We provide a use-case with data from 36 patients to reveal the global cortical dynamics following tactile stimulation. We used HiBoP to visualize high-gamma responses [50-150 Hz], and define three major response components in primary somatosensory and premotor cortices and parietal operculum. COMPARISON WITH EXISTING METHODS(S): Several iEEG softwares are now publicly available with outstanding analysis features. Yet, most were developed in languages (Python/Matlab) chosen to facilitate the inclusion of new analysis by users, rather than the quality of the visualization. HiBoP represents a visualization tool developed with videogame standards (Unity/C#), and performs detailed anatomical analysis rapidly, across multiple conditions, patients, and modalities with an easy export toward third-party softwares. CONCLUSION: HiBoP provides a user-friendly environment that greatly facilitates the exploration of large iEEG datasets, and helps users decipher subtle structure/function relationships.

Mesial temporal lobe spiking reveals distinct patterns of blood oxygen level-dependent functional magnetic resonance imaging activation using simultaneous intracranial electroencephalography-functional magnetic resonance imaging.

OBJECTIVE: Temporal lobe epilepsy (TLE) has a high probability of becoming drug resistant and is frequently considered for surgical intervention. However, 30% of TLE cases have nonlesional magnetic resonance imaging (MRI) scans, which is associated with worse surgical outcomes. Characterizing interactions between temporal and extratemporal structures in these patients may help understand these poor outcomes. Simultaneous intracranial electroencephalography-functional MRI (iEEG-fMRI) can measure the hemodynamic changes associated with interictal epileptiform discharges (IEDs) recorded directly from the brain. This study was designed to characterize the whole brain patterns of IED-associated fMRI activation recorded exclusively from the mesial temporal lobes of patients with nonlesional TLE. METHODS: Eighteen patients with nonlesional TLE undergoing iEEG monitoring with mesial temporal IEDs underwent simultaneous iEEG-fMRI at 3 T. IEDs were marked, and statistically significant clusters of fMRI activation were identified. The locations of IED-associated fMRI activation for each patient were determined, and patients were grouped based on the location and pattern of fMRI activation. RESULTS: Two patterns of IED-associated fMRI activation emerged: primarily localized (n = 7), where activation was primarily located within the ipsilateral temporal lobe, and primarily diffuse (n = 11), where widespread bilateral extratemporal activation was detected. The primarily diffuse group reported significantly fewer focal to bilateral tonic-clonic seizures and had better postsurgical outcomes. SIGNIFICANCE: Simultaneous iEEG-fMRI can measure the hemodynamic changes associated with focal IEDs not visible on scalp EEG, such as those arising from the mesial temporal lobe. Significant fMRI activation associated with these IEDs was observed in all patients. Two distinct patterns of IED-associated activation were seen: primarily localized to the ipsilateral temporal lobe and more widespread, bilateral activation. Patients with widespread IED associated-activation had fewer focal to bilateral tonic-clonic seizures and better postsurgical outcome, which may suggest a neuroprotective mechanism limiting the spread of ictal events.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice.

Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Sidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.

CARLA: Adjusted common average referencing for cortico-cortical evoked potential data.

Human brain connectivity can be mapped by single pulse electrical stimulation during intracranial EEG measurements. The raw cortico-cortical evoked potentials (CCEP) are often contaminated by noise. Common average referencing (CAR) removes common noise and preserves response shapes but can introduce bias from responsive channels. We address this issue with an adjusted, adaptive CAR algorithm termed "CAR by Least Anticorrelation (CARLA)". CARLA was tested on simulated CCEP data and real CCEP data collected from four human participants. In CARLA, the channels are ordered by increasing mean cross-trial covariance, and iteratively added to the common average until anticorrelation between any single channel and all re-referenced channels reaches a minimum, as a measure of shared noise. We simulated CCEP data with true responses in 0-45 of 50 total channels. We quantified CARLA's error and found that it erroneously included 0 (median) truly responsive channels in the common average with ≤42 responsive channels, and erroneously excluded ≤2.5 (median) unresponsive channels at all responsiveness levels. On real CCEP data, signal quality was quantified with the mean R2 between all pairs of channels, which represents inter-channel dependency and is low for well-referenced data. CARLA re-referencing produced significantly lower mean R2 than standard CAR, CAR using a fixed bottom quartile of channels by covariance, and no re-referencing. CARLA minimizes bias in re-referenced CCEP data by adaptively selecting the optimal subset of non-responsive channels. It showed high specificity and sensitivity on simulated CCEP data and lowered inter-channel dependency compared to CAR on real CCEP data.

TMS provokes target-dependent intracranial rhythms across human cortical and subcortical sites.

BACKGROUND: Transcranial magnetic stimulation (TMS) is believed to alter ongoing neural activity and cause circuit-level changes in brain function. While the electrophysiological effects of TMS have been extensively studied with scalp electroencephalography (EEG), this approach generally evaluates low-frequency neural activity at the cortical surface. However, TMS can be safely used in patients with intracranial electrodes (iEEG), allowing for direct assessment of deeper and more localized oscillatory responses across the frequency spectrum. OBJECTIVE/HYPOTHESIS: Our study used iEEG to understand the effects of TMS on human neural activity in the spectral domain. We asked (1) which brain regions respond to cortically-targeted TMS, and in what frequency bands, (2) whether deeper brain structures exhibit oscillatory responses, and (3) whether the neural responses to TMS reflect evoked versus induced oscillations. METHODS: We recruited 17 neurosurgical patients with indwelling electrodes and recorded neural activity while patients underwent repeated trials of single-pulse TMS at either the dorsolateral prefrontal cortex (DLPFC) or parietal cortex. iEEG signals were analyzed using spectral methods to understand the oscillatory responses to TMS. RESULTS: Stimulation to DLPFC drove widespread low-frequency increases (3-8 Hz) in frontolimbic cortices and high-frequency decreases (30-110 Hz) in frontotemporal areas, including the hippocampus. Stimulation to parietal cortex specifically provoked low-frequency responses in the medial temporal lobe. While most low-frequency activity was consistent with phase-locked evoked responses, anterior frontal regions exhibited induced theta oscillations following DLPFC stimulation. CONCLUSIONS: By combining TMS with intracranial EEG recordings, our results suggest that TMS is an effective means to perturb oscillatory neural activity in brain-wide networks, including deeper structures not directly accessed by stimulation itself.

Depth versus surface: A critical review of subdural and depth electrodes in intracranial electroencephalographic studies.

Intracranial electroencephalographic (IEEG) recording, using subdural electrodes (SDEs) and stereoelectroencephalography (SEEG), plays a pivotal role in localizing the epileptogenic zone (EZ). SDEs, employed for superficial cortical seizure foci localization, provide information on two-dimensional seizure onset and propagation. In contrast, SEEG, with its three-dimensional sampling, allows exploration of deep brain structures, sulcal folds, and bihemispheric networks. SEEG offers the advantages of fewer complications, better tolerability, and coverage of sulci. Although both modalities allow electrical stimulation, SDE mapping can tessellate cortical gyri, providing the opportunity for a tailored resection. With SEEG, both superficial gyri and deep sulci can be stimulated, and there is a lower risk of afterdischarges and stimulation-induced seizures. Most systematic reviews and meta-analyses have addressed the comparative effectiveness of SDEs and SEEG in localizing the EZ and achieving seizure freedom, although discrepancies persist in the literature. The combination of SDEs and SEEG could potentially overcome the limitations inherent to each technique individually, better delineating seizure foci. This review describes the strengths and limitations of SDE and SEEG recordings, highlighting their unique indications in seizure localization, as evidenced by recent publications. Addressing controversies in the perceived usefulness of the two techniques offers insights that can aid in selecting the most suitable IEEG in clinical practice.

Voxeloc: A time-saving graphical user interface for localizing and visualizing stereo-EEG electrodes.

BACKGROUND: Thanks to its unrivalled spatial and temporal resolutions and signal-to-noise ratio, intracranial EEG (iEEG) is becoming a valuable tool in neuroscience research. To attribute functional properties to cortical tissue, it is paramount to be able to determine precisely the localization of each electrode with respect to a patient's brain anatomy. Several software packages or pipelines offer the possibility to localize manually or semi-automatically iEEG electrodes. However, their reliability and ease of use may leave to be desired. NEW METHOD: Voxeloc (voxel electrode locator) is a Matlab-based graphical user interface to localize and visualize stereo-EEG electrodes. Voxeloc adopts a semi-automated approach to determine the coordinates of each electrode contact, the user only needing to indicate the deep-most contact of each electrode shaft and another point more proximally. RESULTS: With a deliberately streamlined functionality and intuitive graphical user interface, the main advantages of Voxeloc are ease of use and inter-user reliability. Additionally, oblique slices along the shaft of each electrode can be generated to facilitate the precise localization of each contact. Voxeloc is open-source software and is compatible with the open iEEG-BIDS (Brain Imaging Data Structure) format. COMPARISON WITH EXISTING METHODS: Localizing full patients' iEEG implants was faster using Voxeloc than two comparable software packages, and the inter-user agreement was better. CONCLUSIONS: Voxeloc offers an easy-to-use and reliable tool to localize and visualize stereo-EEG electrodes. This will contribute to democratizing neuroscience research using iEEG.

High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams?

Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms, and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.

Evaluation of Utility of Invasive Electroencephalography for Definitive Surgery in Patients with Drug-Resistant Epilepsy: A Systematic Review and Meta-Analysis.

When noninvasive tests are unable to define the epileptogenic zone in patients, intracranial electroencephalography (iEEG) is a method of localizing the epileptogenic zone. Compared with noninvasive evaluations, it offers more precise information about patterns of epileptiform activity, which results in useful diagnostic information that supports surgical decision-making. The primary aim of the present study was to assess the utility of iEEG for definitive surgery for patients with drug-resistant epilepsy. Online databases such as PubMed, Medline, Embase, Scopus, Cochrane Library, Web of Science, and IEEE Xplore were searched for MeSH terms and free-text keywords. The ROBINS I (risk of bias in non-randomized studies - of interventions) critical appraisal tool was used for quality assessment. The prevalence from different studies was pooled together using the inverse variance heterogeneity method. Egger's regression analysis and funnel plot were used to evaluate publication bias. The systematic review included 18 studies, and the meta-analysis included 10 studies to estimate the prevalence of seizure freedom (Engel class I) in patients undergoing surgery after iEEG. A total of 526 patients were included in the meta-analysis. The follow-up period ranged from 1 to 10 years. The overall pooled estimate of the prevalence of seizure freedom (Engel class I) for patients undergoing surgery after iEEG was 53% (95% confidence interval, 44%-62%). The results additionally demonstrated that 12 studies had a moderate risk of bias and 6 had a low risk. Future studies are crucial to enhance our understanding of iEEG to guide patient choices and unravel their implications.

Intracranial Mapping of Response Latencies and Task Effects for Spoken Syllable Processing in the Human Brain.

Prior lesion, noninvasive-imaging, and intracranial-electroencephalography (iEEG) studies have documented hierarchical, parallel, and distributed characteristics of human speech processing. Yet, there have not been direct, intracranial observations of the latency with which regions outside the temporal lobe respond to speech, or how these responses are impacted by task demands. We leveraged human intracranial recordings via stereo-EEG to measure responses from diverse forebrain sites during (i) passive listening to /bi/ and /pi/ syllables, and (ii) active listening requiring /bi/-versus-/pi/ categorization. We find that neural response latency increases from a few tens of ms in Heschl's gyrus (HG) to several tens of ms in superior temporal gyrus (STG), superior temporal sulcus (STS), and early parietal areas, and hundreds of ms in later parietal areas, insula, frontal cortex, hippocampus, and amygdala. These data also suggest parallel flow of speech information dorsally and ventrally, from HG to parietal areas and from HG to STG and STS, respectively. Latency data also reveal areas in parietal cortex, frontal cortex, hippocampus, and amygdala that are not responsive to the stimuli during passive listening but are responsive during categorization. Furthermore, multiple regions-spanning auditory, parietal, frontal, and insular cortices, and hippocampus and amygdala-show greater neural response amplitudes during active versus passive listening (a task-related effect). Overall, these results are consistent with hierarchical processing of speech at a macro level and parallel streams of information flow in temporal and parietal regions. These data also reveal regions where the speech code is stimulus-faithful and those that encode task-relevant representations.

What do brain oscillations tell about the human sense of smell?

Brain activity may manifest itself as oscillations which are repetitive rhythms of neuronal firing. These local field potentials can be measured via intracranial electroencephalography (iEEG). This review focuses on iEEG used to map human brain structures involved in olfaction. After presenting the methodology of the review, a summary of the brain structures involved in olfaction is given, followed by a review of the literature on human olfactory oscillations in different contexts. A single case is provided as an illustration of the olfactory oscillations. Overall, the timing and sequence of oscillations found in the different structures of the olfactory system seem to play an important role for olfactory perception.

Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction.

Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches model to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.