Verbal memory network mapping in individual patients predicts postoperative functional impairments.

Verbal memory decline is a significant concern following temporal lobe surgeries in patients with epilepsy, emphasizing the need for precision presurgical verbal memory mapping to optimize functional outcomes. However, the inter-individual variability in functional networks and brain function-structural dissociations pose challenges when relying solely on group-level atlases or anatomical landmarks for surgical guidance. Here, we aimed to develop and validate a personalized functional mapping technique for verbal memory using precision resting-state functional MRI (rs-fMRI) and neurosurgery. A total of 38 patients with refractory epilepsy scheduled for surgical interventions were enrolled and 28 patients were analyzed in the study. Baseline 30-min rs-fMRI scanning, verbal memory and language assessments were collected for each patient before surgery. Personalized verbal memory networks (PVMN) were delineated based on preoperative rs-fMRI data for each patient. The accuracy of PVMN was assessed by comparing post-operative functional impairments and the overlapping extent between PVMN and surgical lesions. A total of 14 out of 28 patients experienced clinically meaningful declines in verbal memory after surgery. The personalized network and the group-level atlas exhibited 100% and 75.0% accuracy in predicting postoperative verbal memory declines, respectively. Moreover, six patients with extra-temporal lesions that overlapped with PVMN showed selective impairments in verbal memory. Furthermore, the lesioned ratio of the personalized network rather than the group-level atlas was significantly correlated with postoperative declines in verbal memory (personalized networks: r = -0.39, p = .038; group-level atlas: r = -0.19, p = .332). In conclusion, our personalized functional mapping technique, using precision rs-fMRI, offers valuable insights into individual variability in the verbal memory network and holds promise in precision verbal memory network mapping in individuals.

An automated algorithm for stereoelectroencephalography electrode localization and labelling.

PURPOSE: Stereoelectroencephalography (sEEG) is increasingly utilized for localization of seizure foci, functional mapping, and neurocognitive research due to its ability to target deep and difficult to reach anatomical locations and to study in vivo brain function with a high signal-to-noise ratio. The research potential of sEEG is constrained by the need for accurate localization of the implanted electrodes in a common template space for group analyses. METHODS: We present an algorithm to automate the grouping of sEEG electrodes by trajectories, labelled by target and insertion point. This algorithm forms the core of a pipeline that fully automates the entire process of electrode localization in standard space, using raw CT and MRI images to produce atlas labelled MNI coordinates. RESULTS: Across 196 trajectories from 20 patients, the pipeline successfully processed 190 trajectories with localizations within 0.25±0.55 mm of the manual annotation by two reviewers. Six electrode trajectories were not directly identified due to metal artifacts and locations were interpolated based on the first and last contact location and the number of contacts in that electrode as listed in the surgical record. CONCLUSION: We introduce our algorithm and pipeline for automatically localizing, grouping, and classifying sEEG electrodes from raw CT and MRI. Our algorithm adds to existing pipelines and toolboxes for electrode localization by automating the manual step of marking and grouping electrodes, thereby expedites the analyses of sEEG data, particularly in large datasets.

Optimized microburst VNS elicits fMRI responses beyond thalamic-specific response from standard VNS.

OBJECTIVE: In parallel to standard vagus nerve stimulation (VNS), microburst stimulation delivery has been developed. We evaluated the fMRI-related signal changes associated with standard and optimized microburst stimulation in a proof-of-concept study (NCT03446664). METHODS: Twenty-nine drug-resistant epilepsy patients were prospectively implanted with VNS. Three 3T fMRI scans were collected 2 weeks postimplantation. The maximum tolerated VNS intensity was determined prior to each scan starting at 0.125 mA with 0.125 mA increments. FMRI scans were block-design with alternating 30 sec stimulation [ON] and 30 sec no stimulation [OFF]: Scan 1 utilized standard VNS and Scan 3 optimized microburst parameters to determine target settings. Semi-automated on-site fMRI data processing utilized ON-OFF block modeling to determine VNS-related fMRI activation per stimulation setting. Anatomical thalamic mask was used to derive highest mean thalamic t-value for determination of microburst stimulation parameters. Paired t-tests corrected at P < 0.05 examined differences in fMRI responses to each stimulation type. RESULTS: Standard and microburst stimulation intensities at Scans 1 and 3 were similar (P = 0.16). Thalamic fMRI responses were obtained in 28 participants (19 with focal; 9 with generalized seizures). Group activation maps showed standard VNS elicited thalamic activation while optimized microburst VNS showed widespread activation patterns including thalamus. Comparison of stimulation types revealed significantly greater cerebellar, midbrain, and parietal fMRI signal changes in microburst compared to standard VNS. These differences were not associated with seizure responses. INTERPRETATION: While standard and optimized microburst VNS elicited thalamic activation, microburst also engaged other brain regions. Relationship between these fMRI activation patterns and clinical response warrants further investigation. CLINICAL TRIAL REGISTRATION: The study was registered with clinicaltrials.gov (NCT03446664).

Circadian changes in aperiodic activity are correlated with seizure reduction in patients with mesial temporal lobe epilepsy treated with responsive neurostimulation.

OBJECTIVES: Responsive neurostimulation (RNS) is an established therapy for drug-resistant epilepsy that delivers direct electrical brain stimulation in response to detected epileptiform activity. However, despite an overall reduction in seizure frequency, clinical outcomes are variable, and few patients become seizure-free. The aim of this retrospective study was to evaluate aperiodic electrophysiological activity, associated with excitation/inhibition balance, as a novel electrographic biomarker of seizure reduction to aid early prognostication of the clinical response to RNS. METHODS: We identified patients with intractable mesial temporal lobe epilepsy who were implanted with the RNS System between 2015 and 2021 at the University of Utah. We parameterized the neural power spectra from intracranial RNS System recordings during the first 3 months following implantation into aperiodic and periodic components. We then correlated circadian changes in aperiodic and periodic parameters of baseline neural recordings with seizure reduction at the most recent follow-up. RESULTS: Seizure reduction was correlated significantly with a patient's average change in the day/night aperiodic exponent (r = .50, p = .016, n = 23 patients) and oscillatory alpha power (r = .45, p = .042, n = 23 patients) across patients for baseline neural recordings. The aperiodic exponent reached its maximum during nighttime hours (12 a.m. to 6 a.m.) for most responders (i.e., patients with at least a 50% reduction in seizures). SIGNIFICANCE: These findings suggest that circadian modulation of baseline broadband activity is a biomarker of response to RNS early during therapy. This marker has the potential to identify patients who are likely to respond to mesial temporal RNS. Furthermore, we propose that less day/night modulation of the aperiodic exponent may be related to dysfunction in excitation/inhibition balance and its interconnected role in epilepsy, sleep, and memory.

Measurable transitions during seizures in intracranial EEG: A stereoelectroencephalography and SPECT study.

OBJECTIVE: Ictal 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. METHODS: We 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. RESULTS: Compared 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. CONCLUSIONS: Hypoperfused brain regions were found to indirectly interact with the EZ during the ictal period. SIGNIFICANCE: Our 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.

Distributed source modeling of stereoencephalographic measurements of ictal activity.

OBJECTIVES: Stereoelectroencephalography (SEEG) can define the epileptogenic zone (EZ). However, SEEG is susceptible to the sampling bias, where no SEEG recording is taken within a circumscribed EZ. METHODS: Nine patients with medically refractory epilepsy underwent SEEG recording, and brain resection got positive outcomes. Ictal neuronal currents were estimated by distributed source modeling using the SEEG data and individual's anatomical magnetic resonance imaging. Using a retrospective leave-one-out data sub-sampling, we evaluated the sensitivity and specificity of the current estimates using MRI after surgical resection or radio-frequency ablation. RESULTS: The sensitivity and specificity in detecting the EZ were indistinguishable from either the data from all electrodes or the sub-sampled data (rank sum test: rank sum = 23719, p = 0.13) when at least one remaining electrode contact was no more than 20 mm away. CONCLUSIONS: The distributed neuronal current estimates of ictal SEEG data can mitigate the challenge of delineating the boundary of the EZ in cases of missing an electrode implanted within the EZ and a required second SEEG exploration. SIGNIFICANCE: Distributed source modeling can be a tool for clinicians to infer the EZ by allowing for more flexible planning of the electrode implantation route and minimizing the number of electrodes.

Drug-resistant generalized epilepsies: Revisiting the frontiers of idiopathic generalized epilepsies.

The 2017 International League Against Epilepsy (ILAE) classification suggested that the term "genetic generalized epilepsies" (GGEs) should be used for the broad group of epilepsies with so-called "generalized" seizure types and "generalized" spike-wave activity on EEG, based on a presumed genetic etiology. Within this framework, idiopathic generalized epilepsies (IGEs) are described as a subset of GGEs and include only four epileptic syndromes: childhood absence epilepsy, juvenile absence epilepsy, juvenile myoclonic epilepsy, and epilepsy with generalized tonic-clonic seizures alone. The recent 2022 ILAE definition of IGEs is based on the current state of knowledge and reflects a community consensus and is designed to evolve as knowledge advances. The term "frontiers of IGEs" refers to the actual limits of our understanding of these four syndromes. Indeed, among patients presenting with a syndrome compatible with the 2022 definition of IGEs, we still observe a significant proportion of patients presenting with specific clinical features, refractory seizures, or drug-resistant epilepsies. This leads to the discussion of the boundaries of IGEs and GGEs, or what is accepted within a clinical spectrum of a definite IGE. Here, we discuss several entities that have been described in the literature for many years and that may either constitute rare features of IGEs or a distinct differential diagnosis. Their recognition by clinicians may allow a more individualized approach and improve the management of patients presenting with such entities.

Multiscale neuro-inspired models for interpretation of EEG signals in patients with epilepsy.

OBJECTIVE: The aim is to gain insight into the pathophysiological mechanisms underlying interictal epileptiform discharges observed in electroencephalographic (EEG) and stereo-EEG (SEEG, depth electrodes) recordings performed during pre-surgical evaluation of patients with drug-resistant epilepsy. METHODS: We developed novel neuro-inspired computational models of the human cerebral cortex at three different levels of description: i) microscale (detailed neuron models), ii) mesoscale (neuronal mass models) and iii) macroscale (whole brain models). Although conceptually different, micro- and mesoscale models share some similar features, such as the typology of neurons (pyramidal cells and three types of interneurons), their spatial arrangement in cortical layers, and their synaptic connectivity (excitatory and inhibitory). The whole brain model consists of a large-scale network of interconnected neuronal masses, with connectivity based on the human connectome. RESULTS: For these three levels of description, the fine-tuning of free parameters and the quantitative comparison with real data allowed us to reproduce interictal epileptiform discharges with a high degree of fidelity and to formulate hypotheses about the cell- and network-related mechanisms underlying the generation of fast ripples and SEEG-recorded epileptic spikes and spike-waves. CONCLUSIONS: The proposed models provide valuable insights into the pathophysiological mechanisms underlying the generation of epileptic events. The knowledge gained from these models effectively complements the clinical analysis of SEEG data collected during the evaluation of patients with epilepsy. SIGNIFICANCE: These models are likely to play a key role in the mechanistic interpretation of epileptiform activity.

Predictors of medical intractability in children with epilepsy onset during the first two years of life, excluding infantile epileptic spasm syndrome.

PURPOSE: Early childhood epilepsy presents a significant challenge, with approximately 30 % of individuals experiencing treatment failure. This study aimed to identify predictors of medical intractability in children with epilepsy onset during the first two years of life, excluding infantile epileptic spasm syndrome. METHODS: A total of 323 children were retrospectively evaluated. The analyses included a review of medical records for demographic, laboratory, radiological, and electroencephalographic (EEG) findings. Children were diagnosed with drug-resistant epilepsy (DRE) according to the ILAE diagnostic criteria. Twenty-one potential prognostic predictors were examined in relation to medical intractability. RESULTS: Among the 323 children (56.7 % male), 119 (36.8 %) had unknown epilepsy, 131 (40.6 %) had structural epilepsy, 53 (16.4 %) had genetic epilepsy, and 20 (6.2 %) had metabolic epilepsy. Over a median follow-up of 68 months, 55.4 % of the children achieved ≥6 months of seizure freedom, 33.1 % developed DRE, and the remaining 11.5 % had rare ongoing seizures but did not meet the criteria for DRE because they were only treated with one antiseizure medication at the last follow-up. Univariate logistic regression analyses identified ten risk factors significantly associated with DRE. Multivariate logistic regression analyses revealed that the presence of developmental delay at epilepsy onset (p = 0.000; OR 7.890; 95 %CI 2.713 to 22.945), history of status epilepticus (p = 0.000; OR 8.247; 95 %CI 3.619 to 18.793), number of antiseizure medications (ASMs) at the sixth month of diagnosis (p = 0.000; OR 20.585; 95 %CI 8.993 to 47.117), and initial EEG findings (p = 0.046; OR 2.366; 95 %CI 1.015 to 5.518) were predictors of medical intractability. Nineteen (5.9 %) children died during follow-up for various reasons, including progressive neurogenetic or neurodegenerative disorders. CONCLUSION: Developmental delay at epilepsy onset, a history of status epilepticus, the use of two or more ASMs in the sixth month of diagnosis, and abnormal initial EEG findings were associated with medical intractability.

Systematic 1 Hz direct electrical stimulation for seizure induction: A reliable method for localizing seizure onset zone and predicting seizure freedom.

OBJECTIVE: To prospectively investigate the utility of seizure induction using systematic 1 Hz stimulation by exploring its concordance with the spontaneous seizure onset zone (SOZ) and relation to surgical outcome; comparison with seizures induced by non-systematic 50 Hz stimulation was attempted as well. METHODS: Prospective cohort study from 2018 to 2021 with ≥ 1 y post-surgery follow up at Yale New Haven Hospital. With 1 Hz, all or most of the gray matter contacts were stimulated at 1, 5, and 10 mA for 30-60s. With 50 Hz, selected gray matter contacts outside of the medial temporal regions were stimulated at 1-5 mA for 0.5-3s. Stimulation was bipolar, biphasic with 0.3 ms pulse width. The Yale Brain Atlas was used for data visualization. Variables were analyzed using Fisher's exact, χ2, or Mann-Whitney test. RESULTS: Forty-one consecutive patients with refractory epilepsy undergoing intracranial EEG for localization of SOZ were included. Fifty-six percent (23/41) of patients undergoing 1 Hz stimulation had seizures induced, 83% (19/23) habitual (clinically and electrographically). Eighty two percent (23/28) of patients undergoing 50 Hz stimulation had seizures, 65% (15/23) habitual. Stimulation of medial temporal or insular regions with 1 Hz was more likely to induce seizures compared to other regions [15/32 (47%) vs. 2/41 (5%), p < 0.001]. Sixteen patients underwent resection; 11/16 were seizure free at one year and all 11 had habitual seizures induced by 1 Hz; 5/16 were not seizure free at one year and none of those 5 had seizures with 1 Hz (11/11 vs 0/5, p < 0.0001). No patients had convulsions with 1 Hz stimulation, but four did with 50 Hz (0/41 vs. 4/28, p = 0.02). SIGNIFICANCE: Induction of habitual seizures with 1 Hz stimulation can reliably identify the SOZ, correlates with excellent surgical outcome if that area is resected, and may be superior (and safer) than 50 Hz for this purpose. However, seizure induction with 1 Hz was infrequent outside of the medial temporal and insular regions in this study.

Evidence for interictal blood-brain barrier dysfunction in people with epilepsy.

OBJECTIVE: Interictal blood-brain barrier dysfunction in chronic epilepsy has been demonstrated in animal models and pathological specimens. Ictal blood-brain barrier dysfunction has been shown in humans in vivo using an experimental quantitative magnetic resonance imaging (MRI) protocol. Here, we hypothesized that interictal blood-brain barrier dysfunction is also present in people with drug-resistant epilepsy. METHODS: Thirty-nine people (21 females, mean age at MRI ± SD = 30 ± 8 years) with drug-resistant epilepsy were prospectively recruited and underwent interictal T1-relaxometry before and after administration of a paramagnetic contrast agent. Likewise, quantitative T1 was acquired in 29 people without epilepsy (12 females, age at MRI = 48 ± 18 years). Quantitative T1 difference maps were calculated and served as a surrogate imaging marker for blood-brain barrier dysfunction. Values of quantitative T1 difference maps inside hemispheres ipsilateral to the presumed seizure onset zone were then compared, on a voxelwise level and within presumed seizure onset zones, to the contralateral side of people with epilepsy and to people without epilepsy. RESULTS: Compared to the contralateral side, ipsilateral T1 difference values were significantly higher in white matter (corrected p < .05), gray matter (uncorrected p < .05), and presumed seizure onset zones (p = .04) in people with epilepsy. Compared to people without epilepsy, significantly higher T1 difference values were found in the anatomical vicinity of presumed seizure onset zones (p = .004). A subgroup of people with hippocampal sclerosis demonstrated significantly higher T1 difference values in the ipsilateral hippocampus and in regions strongly interconnected with the hippocampus compared to people without epilepsy (corrected p < .01). Finally, z-scores reflecting the deviation of T1 difference values within the presumed seizure onset zone were associated with verbal memory performance (p = .02) in people with temporal lobe epilepsy. SIGNIFICANCE: Our results indicate a blood-brain barrier dysfunction in drug-resistant epilepsy that is detectable interictally in vivo, anatomically related to the presumed seizure onset zone, and associated with cognitive deficits.

Epileptic tissue localization using graph-based networks in the high frequency oscillation range of intracranial electroencephalography.

PURPOSE: High frequency oscillations (HFOs) are an emerging biomarker of epilepsy. However, very few studies have investigated the functional connectivity of interictal iEEG signals in the frequency range of HFOs. Here, we study the corresponding functional networks using graph theory, and we assess their predictive value for automatic electrode classification in a cohort of 20 drug resistant patients. METHODS: Coherence-based connectivity analysis was performed on the iEEG recordings, and six different local graph measures were computed in both sub-bands of the HFO frequency range (80-250 Hz and 250-500 Hz). Correlation analysis was implemented between the local graph measures and the ripple and fast ripple rates. Finally, the WEKA software was employed for training and testing different predictive models on the aforementioned local graph measures. RESULTS: The ripple rate was significantly correlated with five out of six local graph measures in the functional network. For fast ripples, their rate was also significantly (but negatively) correlated with most of the local metrics. The results from WEKA showed that the Logistic Regression algorithm was able to classify highly HFO-contaminated electrodes with an accuracy of 82.5 % for ripples and 75.4 % for fast ripples. CONCLUSION: Functional connectivity networks in the HFO band could represent an alternative to the direct use of distinct HFO events, while also providing important insights about hub epileptic areas that can represent possible surgical targets. Automatic electrode classification through FC-based classifiers can help bypass the burden of manual HFO annotation, providing at the same time similar amount of information about the epileptic tissue.

The landscape of drug resistant absence seizures in adolescents and adults: Pathophysiology, electroclinical spectrum and treatment options.

The persistence of typical absence seizures (AS) in adolescence and adulthood may reduce the quality of life of patients with genetic generalized epilepsies (GGEs). The prevalence of drug resistant AS is probably underestimated in this patient population, and treatment options are relatively scarce. Similarly, atypical absence seizures in developmental and epileptic encephalopathies (DEEs) may be unrecognized, and often persist into adulthood despite improvement of more severe seizures. These two seemingly distant conditions, represented by typical AS in GGE and atypical AS in DEE, share at least partially overlapping pathophysiological and genetic mechanisms, which may be the target of drug and neurostimulation therapies. In addition, some patients with drug-resistant typical AS may present electroclinical features that lie in between the two extremes represented by these generalized forms of epilepsy.

Drug resistant epilepsies: A multicentre case series of steroid therapy.

PURPOSE: Our study aimed to evaluate the effectiveness of corticosteroids on seizure control in drug-resistant epilepsies (DREs). Our primary goal was to assess the response to steroids for various underlying etiologies, interictal electroencephalographic (EEG) patterns and electroclinical seizure descriptions. Our second goal was to compare steroid responsiveness to different treatment protocols. METHODS: This is a retrospective multicentre cohort study conducted according to the STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology). The following data were collected for each patient: epilepsy etiology, interictal EEG pattern, seizure types and type of steroid treatment protocol administered. RESULTS: Thirty patients with DRE were included in the study. After 6 months of therapy, 62.7 % of patients experienced reduced seizure frequency by 50 %, and 6.6 % of patients experienced complete seizure cessation. Findings associated with favourable response to steroids included structural/lesional etiology of epilepsy, immune/infectious etiology and focal interictal abnormalities on EEG. Comparing four different steroid treatment protocols, the most effective for seizure control was treatment with methylprednisolone at the dose of 30 mg/kg/day administered for 3 days, leading to greater than 50 % seizure reduction at 6 months in 85.7 % of patients. Treatment with dexamethasone 6 mg/day for 5 days decreased seizure frequency in 71.4 % of patients. Hydrocortisone 10 mg/kg administered for 3 months showed a good response to treatment in 71 %. CONCLUSIONS: In our study, two-thirds of patients with DRE experienced a significant seizure reduction following treatment with steroids. We suggest considering steroids as a potential therapeutic option in children with epilepsy not responding to conventional antiseizure medicines (ASM).

Ictal Central Apnea Is Predictive of Mesial Temporal Seizure Onset: An Intracranial Investigation.

OBJECTIVE: Ictal central apnea (ICA) is a semiological sign of focal epilepsy, associated with temporal and frontal lobe seizures. In this study, using qualitative and quantitative approaches, we aimed to assess the localizational value of ICA. We also aimed to compare ICA clinical utility in relation to other seizure semiological features of focal epilepsy. METHODS: We analyzed seizures in patients with medically refractory focal epilepsy undergoing intracranial stereotactic electroencephalographic (SEEG) evaluations with simultaneous multimodal cardiorespiratory monitoring. A total of 179 seizures in 72 patients with reliable artifact-free respiratory signal were analyzed. RESULTS: ICA was seen in 55 of 179 (30.7%) seizures. Presence of ICA predicted a mesial temporal seizure onset compared to those without ICA (odds ratio = 3.8, 95% confidence interval = 1.3-11.6, p = 0.01). ICA specificity was 0.82. ICA onset was correlated with increased high-frequency broadband gamma (60-150Hz) activity in specific mesial or basal temporal regions, including amygdala, hippocampus, and fusiform and lingual gyri. Based on our results, ICA has an almost 4-fold greater association with mesial temporal seizure onset zones compared to those without ICA and is highly specific for mesial temporal seizure onset zones. As evidence of symptomatogenic areas, onset-synchronous increase in high gamma activity in mesial or basal temporal structures was seen in early onset ICA, likely representing anatomical substrates for ICA generation. INTERPRETATION: ICA recognition may help anatomoelectroclinical localization of clinical seizure onset to specific mesial and basal temporal brain regions, and the inclusion of these regions in SEEG evaluations may help accurately pinpoint seizure onset zones for resection. ANN NEUROL 2024;95:998-1008.

Neurostimulation treatments for epilepsy: Deep brain stimulation, responsive neurostimulation and vagus nerve stimulation.

Epilepsy is a common and debilitating neurological disorder, and approximately one-third of affected individuals have ongoing seizures despite appropriate trials of two anti-seizure medications. This population with drug-resistant epilepsy (DRE) may benefit from neurostimulation approaches, such as vagus nerve stimulation (VNS), deep brain stimulation (DBS) and responsive neurostimulation (RNS). In some patient populations, these techniques are FDA-approved for treating DRE. VNS is used as adjuvant therapy for children and adults. Acting via the vagus afferent network, VNS modulates thalamocortical circuits, reducing seizures in approximately 50 â€‹% of patients. RNS uses an adaptive (closed-loop) system that records intracranial EEG patterns to activate the stimulation at the appropriate time, being particularly well-suited to treat seizures arising within eloquent cortex. For DBS, the most promising therapeutic targets are the anterior and centromedian nuclei of the thalamus, with anterior nucleus DBS being used for treating focal and secondarily generalized forms of DRE and centromedian nucleus DBS being applied for treating generalized epilepsies such as Lennox-Gastaut syndrome. Here, we discuss the indications, advantages and limitations of VNS, DBS and RNS in treating DRE and summarize the spatial distribution of neuroimaging observations related to epilepsy and stimulation using NeuroQuery and NeuroSynth.

Cortical maps of somatosensory perception in human.

Tactile and movement-related somatosensory perceptions are crucial for our daily lives and survival. Although the primary somatosensory cortex is thought to be the key structure of somatosensory perception, various cortical downstream areas are also involved in somatosensory perceptual processing. However, little is known about whether cortical networks of these downstream areas can be dissociated depending on each perception, especially in human. We address this issue by combining data from direct cortical stimulation (DCS) for eliciting somatosensation and data from high-gamma band (HG) elicited during tactile stimulation and movement tasks. We found that artificial somatosensory perception is elicited not only from conventional somatosensory-related areas such as the primary and secondary somatosensory cortices but also from a widespread network including superior/inferior parietal lobules and premotor cortex. Interestingly, DCS on the dorsal part of the fronto-parietal area including superior parietal lobule and dorsal premotor cortex often induces movement-related somatosensations, whereas that on the ventral one including inferior parietal lobule and ventral premotor cortex generally elicits tactile sensations. Furthermore, the HG mapping results of the movement and passive tactile stimulation tasks revealed considerable similarity in the spatial distribution between the HG and DCS functional maps. Our findings showed that macroscopic neural processing for tactile and movement-related perceptions could be segregated.

[Mechanisms of apoptosis in drug-resistant epilepsy]. / Mekhanizmy apoptoza pri farmakorezistentnoi epilepsii.

Epilepsy is a chronic neurological disease with regular spontaneous seizures associated with neuroinflammatory, autoimmune and neurodegenerative processes. Approximately 40% of patients suffer from drug-resistant epilepsy, which leads to an increased risk of premature death, injury, irreversible brain damage, psychosocial dysfunction, and reduced quality of life. Apoptosis of neurons and glial cells of the brain is of great importance in the pathogenesis of epilepsy, especially drug-resistant epilepsy. Investigation of the mechanisms of apoptosis is necessary for the creation of a new generation of neuroprotective and anticonvulsant drugs, effective, in particular, in the case of drug-resistant epilepsy. The aim of the study was to analyze the mechanisms and role of apoptosis in epileptogenesis and the development of resistance. The review considers current data on the main mechanisms of apoptosis in epilepsy, especially its drug-resistant forms.

Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings.

Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on the signals from the sensorimotor cortex, leaving subcortical regions and other cortical regions related to the movements largely unexplored. As an intracranial recording technique for the presurgical assessments of brain surgery, stereo-encephalography (SEEG) inserts depth electrodes containing multiple contacts into the brain and thus provides the unique opportunity for investigating movement-related neural representation throughout the brain. Although SEEG samples neural signals with high spatial-temporal resolutions, its potential of being used to build BCIs has just been realized recently, and the decoding of SEEG activity related to hand movements has not been comprehensively investigated yet. Here, we systematically evaluated the factors influencing the performance of movement decoding using SEEG signals recorded from 32 human subjects performing a visually-cued hand movement task. Our results suggest that multiple regions in both lateral and depth directions present significant neural selectivity to the task, whereas the sensorimotor area, including both precentral and postcentral cortex, carries the richest discriminative neural information for the decoding. The posterior parietal and prefrontal cortex contribute gradually less, but still rich sources for extracting movement parameters. The insula, temporal and occipital cortex also contains useful task-related information for decoding. Under the cortex layer, white matter presents decodable neural patterns but yields a lower accuracy (42.0 ± 0.8%) than the cortex on average (44.2 ± 0.8%, p<0.01). Notably, collectively using neural signals from multiple task-related areas can significantly enhance the movement decoding performance by 6.9% (p<0.01) on average compared to using a single region. Among the different spectral components of SEEG activity, the high gamma and delta bands offer the most informative features for hand movements reconstruction. Additionally, the phase-amplitude coupling strength between these two frequency ranges correlates positively with the performance of movement decoding. In the temporal domain, maximum decoding accuracy is first reached around 2 s after the onset of movement commands. In sum, this study provides valuable insights for the future motor BCIs design employing both SEEG recordings and other recording modalities.

A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG.

Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long-term EEG recording. Spiking neural networks (SNN) have emerged as optimal architectures for embedding in compact low-power signal processing hardware. We analyzed 20 scalp EEG recordings from 11 pediatric focal lesional epilepsy patients. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. We identified the optimal SNN parameters to detect EoI and reject artifacts automatically. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.90 CI [0.75 0.96], p < 0.0001, Spearman's correlation). The fully automated SNN detected clinically relevant HFO in the scalp EEG. This study is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.