Discordant Wada and fMRI language lateralization: a case report.

Functional MRI (fMRI) is gaining importance in the preoperative assessment of language for presurgical planning. However, inconsistencies with the Wada test might arise. This current case report describes a very rare case of an epileptic patient who exhibited bilateral distribution (right > left) in the inferior frontal gyrus (laterality index [LI] = -0.433) and completely right dominance in the superior temporal gyrus (LI = -1). However, the Wada test revealed a dissociation: his motor speech was located in the left hemisphere, while he could understand vocal instructions with his right hemisphere. A clinical implication is that the LIs obtained by fMRI should be cautiously used to determine Broca's area in atypical patients; for example, even when complete right dominance is found in the temporal cortex in right-handed patients. Theoretically, as the spatially separated functions of motor speech and language comprehension (by the combined results of fMRI and Wada) can be further temporally separated (by the intracarotid amobarbital procedure) in this case report, these findings might provide direct support to Broca's initial conclusions that Broca's area is associated with acquired motor speech impairment, but not language comprehension per se. Moreover, this current finding supports the idea that once produced, motor speech can be independent from language comprehension.

OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy.

Objective.Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the importance of precise prediction of treatment outcomes with the initial AED regimen in patients with epilepsy.Approach. We introduce OxcarNet, an end-to-end neural network framework developed to predict treatment outcomes in patients undergoing oxcarbazepine monotherapy. The proposed predictive model adopts a Sinc Module in its initial layers for adaptive identification of discriminative frequency bands. The derived feature maps are then processed through a Spatial Module, which characterizes the scalp distribution patterns of the electroencephalography (EEG) signals. Subsequently, these features are fed into an attention-enhanced Temporal Module to capture temporal dynamics and discrepancies. A channel module with an attention mechanism is employed to reveal inter-channel dependencies within the output of the Temporal Module, ultimately achieving response prediction. OxcarNet was rigorously evaluated using a proprietary dataset of retrospectively collected EEG data from newly diagnosed epilepsy patients at Nanjing Drum Tower Hospital. This dataset included patients who underwent long-term EEG monitoring in a clinical inpatient setting.Main results.OxcarNet demonstrated exceptional accuracy in predicting treatment outcomes for patients undergoing Oxcarbazepine monotherapy. In the ten-fold cross-validation, the model achieved an accuracy of 97.27%, and in the validation involving unseen patient data, it maintained an accuracy of 89.17%, outperforming six conventional machine learning methods and three generic neural decoding networks. These findings underscore the model's effectiveness in accurately predicting the treatment responses in patients with newly diagnosed epilepsy. The analysis of features extracted by the Sinc filters revealed a predominant concentration of predictive frequencies in the high-frequency range of the gamma band.Significance. The findings of our study offer substantial support and new insights into tailoring early AED selection, enhancing the prediction accuracy for the responses of AEDs.

Maintenance and transformation of representational formats during working memory prioritization.

Visual working memory depends on both material-specific brain areas in the ventral visual stream (VVS) that support the maintenance of stimulus representations and on regions in the prefrontal cortex (PFC) that control these representations. How executive control prioritizes working memory contents and whether this affects their representational formats remains an open question, however. Here, we analyzed intracranial EEG (iEEG) recordings in epilepsy patients with electrodes in VVS and PFC who performed a multi-item working memory task involving a retro-cue. We employed Representational Similarity Analysis (RSA) with various Deep Neural Network (DNN) architectures to investigate the representational format of prioritized VWM content. While recurrent DNN representations matched PFC representations in the beta band (15-29 Hz) following the retro-cue, they corresponded to VVS representations in a lower frequency range (3-14 Hz) towards the end of the maintenance period. Our findings highlight the distinct coding schemes and representational formats of prioritized content in VVS and PFC.

Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal-external attention switching.

Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.

A Compact Graph Convolutional Network With Adaptive Functional Connectivity for Seizure Prediction.

Seizure prediction using EEG has significant implications for the daily monitoring and treatment of epilepsy patients. However, the task is challenging due to the underlying spatiotemporal correlations and patient heterogeneity. Traditional methods often use large-scale models with independent components to capture the spatial and temporal features of EEG separately or explore shared patterns among patients with the help of pre-defined functional connectivity. In this paper, we propose a compact model, called the graph convolutional network based on adaptive functional connectivity (AFC-GCN), for seizure prediction. The model can adaptively infer evolution of functional connectivity in epilepsy patients during seizures through data-driven methods and synchronously analyze spatiotemporal response of functional connectivity in multiple topologies. On CHB-MIT datasets, the experimental results demonstrate that AFC-GCN achieves accurate and robust performance with low complexity. (AUC: 0.9820, accuracy: 0.9815, sensitivity: 0.9802, FPR: 0.0172). The proposed method has the potential to predict seizure during daily monitoring.

Modification of pre-ictal cortico-hippocampal oscillations by medial ganglionic eminence precursor cells grafting in the pilocarpine model of epilepsy.

Cell replacement therapies using medial ganglionic eminence (MGE)-derived GABAergic precursors reduce seizures by restoring inhibition in animal models of epilepsy. However, how MGE-derived cells affect abnormal neuronal networks and consequently brain oscillations to reduce ictogenesis is still under investigation. We performed quantitative analysis of pre-ictal local field potentials (LFP) of cortical and hippocampal CA1 areas recorded in vivo in the pilocarpine rat model of epilepsy, with or without intrahippocampal MGE-precursor grafts (PILO and PILO+MGE groups, respectively). The PILO+MGE animals had a significant reduction in the number of seizures. The quantitative analysis of pre-ictal LFP showed decreased power of cortical and hippocampal delta, theta and beta oscillations from the 5 min. interictal baseline to the 20 s. pre-ictal period in both groups. However, PILO+MGE animals had higher power of slow and fast oscillations in the cortex and lower power of slow and fast oscillations in the hippocampus compared to the PILO group. Additionally, PILO+MGE animals exhibited decreased cortico-hippocampal synchrony for theta and gamma oscillations at seizure onset and lower hippocampal CA1 synchrony between delta and theta with slow gamma oscillations compared to PILO animals. These findings suggest that MGE-derived cell integration into the abnormally rewired network may help control ictogenesis.

SPECT/CT imaging of poor sleep quality in people with epilepsy.

PURPOSE: To analyze the characteristics of cerebral blood flow changes of poor sleep quality in people with epilepsy(PWE). METHODS: 90 PWE treated in The General Hospital of Ningxia Medical University from December 2021 to September 2023 were divided into poor sleep quality group (PSQG) and good sleep quality group (GSQG) according to the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI), to compare the differences in cerebral perfusion between the two groups of patients, so as to summarize the characteristics of cerebral blood flow changes of poor sleep quality in PWE. RESULTS: The positive rate of interictal single-photon emission computed tomography/computed tomography (SPECT/CT) was 76.7 %(69/90), which showed localized cerebral hypoperfusion. There was no statistical difference between the two groups of PSQG (N=29) and GSQG (N=61) in terms of the positive rate of SPECT/CT, the number of hypoperfusion foci, and the range of hypoperfusion foci. In PSQG and GSQG, 9 patients(31.0 %) and 6 patients(9.8 %) showed hypoperfusion in the right parietal lobe, respectively, and the difference between the two groups was statistically significant (P=0.017). There was no statistical difference the rate of the interictal epileptiform discharges (IEDs) and the brain area of IEDs in electroencephalography(EEG) between the two groups. CONCLUSION: SPECT/CT of poor sleep quality in PWE demonstrated hypoperfusion in the right parietal lobe.

A novel universal deep learning approach for accurate detection of epilepsy.

Epilepsy claims the lives of many people, so researchers strive to build highly accurate diagnostic models. One of the limitations of obtaining high accuracy is the scarcity of Electroencephalography (EEG) data and the fact that they are from different devices in terms of the channels number and sampling frequency. The paper proposes universal epilepsy diagnoses with high accuracy from electroencephalography signals taken from any device. The novelty of the proposal is to convert VEEG video into images, separating some parts and unifying images taken from different devices. The images were tested by dividing the video into labeled frames of different periods. By adding the spatial attention layer to the deep learning in the new model, classification accuracy increased to 99.95 %, taking five seconds/frame. The proposed has high accuracy in detecting epilepsy from any EEG without being restricted to a specific number of channels or sampling frequencies.

A Comprehensive Review of Hardware Acceleration Techniques and Convolutional Neural Networks for EEG Signals.

This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroencephalogram (EEG) signals across various application areas, including emotion classification, motor imagery, epilepsy detection, and sleep monitoring. Previous reviews on EEG have mainly focused on software solutions. However, these reviews often overlook key challenges associated with hardware implementation, such as scenarios that require a small size, low power, high security, and high accuracy. This paper discusses the challenges and opportunities of hardware acceleration for wearable EEG devices by focusing on these aspects. Specifically, this review classifies EEG signal features into five groups and discusses hardware implementation solutions for each category in detail, providing insights into the most suitable hardware acceleration strategies for various application scenarios. In addition, it explores the complexity of efficient CNN architectures for EEG signals, including techniques such as pruning, quantization, tensor decomposition, knowledge distillation, and neural architecture search. To the best of our knowledge, this is the first systematic review that combines CNN hardware solutions with EEG signal processing. By providing a comprehensive analysis of current challenges and a roadmap for future research, this paper provides a new perspective on the ongoing development of hardware-accelerated EEG systems.

Utility of Various Activation Procedures in Provoking Ictal and Interictal Patterns, during Routine Electroencephalogram (rEEG) Recording.

BACKGROUND: Activation procedures (APs) are adopted during routine electroencephalography (rEEG) to provoke interictal epileptiform abnormalities (EAs). This study aimed to observe interictal and ictal (EAs) of different EEG patterns, provoked by various APs. METHODOLOGY: This cross-sectional study was performed in the neurology department of King Fahd hospital of university, Saudi Arabia. The EEGs and medical records of patients who presented for EEG recordings were screened initially, then 146 EEGs provoked EAs due to utilization of APs, were included for analysis. RESULTS: Among all EEGs with provoked EAs, Non-rapid eye movement sleep (NREM) provoked EAs in 93 (63.7%) patients with following patterns, focal spike wave discharges (FSWDs) 45 (P= 0.01), focal spike wave discharges with bilateral synchrony (FSWDBS) 27 (P=0.03) and generalized spike wave discharges (GSWDs) 46 (P=0.01). Intermittent photic stimulation (IPS) most significantly provoked FSWDs in 07 patient (P =0.01) and GSWDs in 30 patients (P=<0.001) 7 patients (P = 0.01) and GSWDs in 30 patients (P < 0.001). Hyperventilation (HV) was associated with a higher occurrence of GSWDs in 37 patients (P =0.01). Female sex 7 (P = 0.02), provoked GSWDs 3 (P = 0.03), NREM sleep 8 (P = 0.04), prolonged EEG record 3 (P = 0.02), clinical events during recording 5 (P ≤ 0.01), diagnosis of genetic 05 (P = 0.03), and immune-mediated epilepsies 2 (P = 0.001) were associated with the provocation of ictal EAs; however, in multiple logistic regression analysis, no statistically significant association of these variables (P ≥ 0.05 each) was noted. CONCLUSION: The provocation of EAs in rEEG with different APs varies according to circumstances, including seizure types, epilepsy etiology, and the type of AP applied. These clinical and procedural parameters affect the diagnostic yield of rEEG and need careful consideration during rEEG recordings. APs adopted during rEEG recording can induce FSWDs, FSWDBS, and GSWDs in the form of either interictal or ictal EAs in various etiologies of epilepsy. Ictal EAs may appear in the form of GSWDs, during NREM sleep, in prolonged EEG records; however, their independent association needs to be evaluated in larger sample studies. Further, prospective cohort studies with adequate sample sizes are warranted.

Résumé Contexte:Des procédures d'activation (AP) sont adoptées lors d'une électroencéphalographie de routine (rEEG) pour provoquer des anomalies épileptiformes (EA) intercritiques. Cette étude visait à observer les inter-critiques et critiques (EA) de différents modèles EEG, provoqués par divers PA.Méthodes:Cette étude transversale a été réalisée dans le département de neurologie de l'hôpital universitaire King Fahd de Khobar, en Arabie Saoudite. Les EEG et les dossiers médicaux des patients qui se sont présentés pour des enregistrements EEG ont été initialement examinés, puis 146 EEG avec des EA provoqués lors de l'utilisation des AP ont été inclus pour analyse.Résultats:Parmi tous les EEG avec des AE provoqués, le sommeil à mouvements oculaires non rapides (NREM) a provoqué des EA chez 93 (63,7 %) patients avec les schémas suivants : décharges d'ondes de pointe focales (FSWD) 45 ( P = 0,01), onde de pointe focale avec bilatéral synchronisation (FSWBS) 27 ( P = 0,03) et décharges d'ondes de pointe généralisées (GSWD) 46 ( P = 0,01). La stimulation photique intermittente (IPS) a provoqué de manière plus significative des FSWD chez 07 patients ( P = 0,01) et des GSWD chez 30 patients ( P = < 0,001) 7 patients ( P = 0,01) et des GSWD chez 30 patients ( P < 0,001). L'hyperventilation (HV) était associée à une fréquence plus élevée de GSWD chez 37 patients ( P = 0,01). Sexe féminin 07 ( P = 0,02), GSWD provoqués 03 ( P = 0,03), sommeil NREM 08 ( P = 0,04), enregistrement EEG prolongé 03 ( P = 0,02), événements cliniques lors de l'enregistrement 05 ( P = < 0,01), diagnostic des épilepsies génétiques 05 ( P = 0,03) et des épilepsies à médiation immunitaire 02 ( P = 0,001) étaient associées à la provocation d'EA critiques, cependant, dans l'analyse de régression logistique multiple, aucune association statistiquement significative de ces variables ( P = > 0,05 chacune) était noté.Conclusion:La provocation d'EA dans l'EEGr avec différents AP varie en fonction des circonstances, notamment des types de crises, de l'étiologie de l'épilepsie et du type d'AP appliqué. Ces paramètres cliniques et procéduraux affectent le rendement diagnostique du rEEG et doivent être soigneusement pris en compte lors des enregistrements rEEG. Les AP adoptés lors de l'enregistrement rEEG peuvent induire des FSWD, des FSWBS et des GSWD sous la forme d'EA inter-critiques ou critiques dans diverses étiologies de l'épilepsie. Les EA critiques peuvent apparaître sous forme de GSWD, pendant le sommeil NREM, dans les enregistrements EEG prolongés; cependant, leur association indépendante doit être évaluée dans des études sur un échantillon plus large. De plus, des études de cohortes prospectives avec des échantillons de taille adéquate sont justifiées.

Increased excitatory connectivity and epileptiform activity in thrombospondin1/2 knockout mice following cortical trauma.

Thrombospondins (TSPs) are astrocyte-secreted extracellular matrix proteins that play key roles as regulators of synaptogenesis in the central nervous system. We previously showed that TSP1/2 are upregulated in the partial neocortical isolation model ("undercut" or "UC" below) of posttraumatic epileptogenesis and may contribute to abnormal axonal sprouting, aberrant synaptogenesis and epileptiform discharges in the UC cortex. These results led to the hypothesis that posttraumatic epileptogeneis would be reduced in TSP1/2 knockout (TSP1/2 KO) mice. To test the hypothesis, we made UC lesions at P21, and subsequent experiments were conducted 14d later at P35. Ex vivo extracellular single or multi-electrode field potential recordings were obtained from layer V in cortical slices at P35 and in vivo video-EEGs of spontaneous epileptiform bursts were recorded to examine the effect of TSP1/2 deletion on epileptogenesis following cortical injury. Immunohistochemical experiments were performed to assess the effect of TSP1/2 KO + UC on the number of putative excitatory synapses and the expression of TSP4 and HEVIN, other astrocytic proteins known to up-regulate excitatory synapse formation. Unexpectedly, our results showed that, compared with WT + UC mice, TSP1/2 KO + UC mice displayed increased epileptiform activity, as indicated by 1) increased incidence and more rapid propagation of evoked and spontaneous epileptiform discharges in UC neocortical slices; 2) increased occurrence of spontaneous epileptiform discharges in vivo. There was an associated increase in the density of VLUT1/PSD95-IR colocalizations (putative excitatory synapses) and significantly upregulated TSP4- and HEVIN-IR in TSP1/2 KO + UC versus WT + UC mice. Results suggest that TSP1/2 deletion plays a potential epileptogenic role following neocortical injury, associated with compensatory upregulation of TSP4 and HEVIN, which may contribute to the increase in the density of excitatory synapses and resulting neural network hyperexcitability.

Use of sedation-awakening electroencephalography in dogs with epilepsy.

BACKGROUND: Electroencephalography (EEG) recording protocols have been standardized for humans. Although the utilization of techniques in veterinary medicine is increasing, a standard protocol has not yet been established. HYPOTHESIS: Assessment of a sedation-awakening EEG protocol in dogs. ANIMALS: Electroencephalography examination was performed in a research colony of 6 nonepileptic dogs (control [C]) and 12 dogs with epilepsy admitted to the clinic because of the epileptic seizures. METHODS: It was a prospective study with retrospective control. Dogs with epilepsy were divided into 2 equal groups, wherein EEG acquisition was performed using a "sedation" protocol (IE-S, n = 6) and a "sedation-awakening" protocol (IE-SA, n = 6). All animals were sedated using medetomidine. In IE-SA group, sedation was reversed 5 minutes after commencing the EEG recording by injecting atipamezole IM. Type of background activity (BGA) and presence of EEG-defined epileptiform discharges (EDs) were evaluated blindly. Statistical significance was set at P > 0.05. RESULTS: Epileptiform discharges were found in 1 of 6 of the dogs in group C, 4 of 6 of the dogs in IE-S group, and 5 of 6 of the dogs in IE-SA group. A significantly greater number of EDs (spikes, P = .0109; polyspikes, P = .0109; sharp waves, P = .01) were detected in Phase 2 in animals subjected to the "sedation-awakening" protocol, whereas there was no statistically significant greater number of discharges in sedated animals. CONCLUSIONS AND CLINICAL IMPORTANCE: A "sedation-awakening" EEG protocol could be of value for ambulatory use if repeated EEG recordings and monitoring of epilepsy in dogs is needed.

Correlation between semiautomated magnetic resonance imaging volumetry of the cingulate gyrus and interictal epileptiform discharge lateralization in dogs with idiopathic epilepsy.

BACKGROUND: Brain imaging suggests the involvement of the limbic system, particularly the cingulate gyrus (GC), in dogs with idiopathic epilepsy (IE). HYPOTHESIS: A correlation exists between the side of interictal epileptiform discharges (IEDs) and the volume of the ipsilateral GC (GCe) in dogs with IE. ANIMALS: Dogs admitted to the neurological consultation (32 with epileptic seizures and 13 control) were included. METHODS: This retrospective, blinded study followed the International Veterinary Epilepsy Task Force recommendations for diagnosing IE at the Tier III confidence level. The IE group included 18 and 14 dogs with IEDs in the left and right hemispheres, respectively (median age: 36 months, median weight: 19.5 kg), whereas the control group included 13 dogs (median age: 32 months, median weight: 20 kg). Whole-brain and GC-volumetric assessments were performed by a semiautomated method. RESULTS: In the control group, the volume of the GC was: left, from 743.63 to 1001.61 mm3, right, from 789.35 to 1015.86 mm3. In the study group, the volume of the GC was: left, from 720.88 to 1054.9 mm3 and right, from 566.29 to 987.77 mm3. In dogs with IE, GCe volume was significantly lower than the mean volume of the GC in the control group relative to total intracranial volume (TIV; P = .00044). CONCLUSIONS AND CLINICAL IMPORTANCE: Alterations in the volume of the GC provide insights into structural changes during IE. The use of semiautomatic volumetry provides an advantage by reducing the potential for human error.

Cohort Expansion and Genotype-Phenotype Analysis of RAB11A-Associated Neurodevelopmental Disorder.

BACKGROUND: GTPases of the Rab family are important orchestrators of membrane trafficking, and their dysregulation has been linked to a variety of neuropathologies. In 2017, we established a causal link between RAB11A variants and developmental and epileptic encephalopathy. In this study, we expand the phenotype of RAB11A-associated neurodevelopmental disorder and explore genotype-phenotype correlations. METHODS: We assessed 16 patients with pathogenic or likely pathogenic RAB11A variants, generally de novo, heterozygous missense variants. One individual had a homozygous nonsense variant, although concomitant with a pathogenic LAMA2 variant, which made their respective contributions to the phenotype difficult to discriminate. RESULTS: We reinforce the finding that certain RAB11A missense variants lead to intellectual disability and developmental delays. Other clinical features might include gait disturbances, hypotonia, magnetic resonance imaging abnormalities, visual anomalies, dysmorphisms, early adrenarche, and obesity. Epilepsy seems to be less common and linked to variants outside the binding sites. Individuals with variants in the binding sites seem to have a more multisystemic, nonepileptic phenotype. CONCLUSIONS: Similar to other Rab-related disorders, RAB11A-associated neurodevelopmental disorder can also impact gait, tonus, brain anatomy and physiology, vision, adrenarche, and body weight and structure. Epilepsy seems to affect the minority of patients with variants outside the binding sites.

Automated quantification of periodic discharges in human electroencephalogram.

Periodic discharges (PDs) are pathologic patterns of epileptiform discharges repeating at regular intervals, commonly detected in the human electroencephalogram (EEG) signals in patients who are critically ill. The frequency and spatial extent of PDs are associated with the tendency of PDs to cause brain injury, existing automated algorithms do not quantify the frequency and spatial extent of PDs. The present study presents an algorithm for quantifying frequency and spatial extent of PDs. The algorithm quantifies the evolution of these parameters within a short (10-14 second) window, with a focus on lateralized and generalized periodic discharges. We test our algorithm on 300 'easy', 300 'medium', and 240 'hard' examples (840 total epochs) of periodic discharges as quantified by interrater consensus from human experts when analyzing the given EEG epochs. We observe 95.0% agreement with a 95% confidence interval (CI) of [94.9%, 95.1%] between algorithm outputs with reviewer clincal judgement for easy examples, 92.0% agreement (95% CI [91.9%, 92.2%]) for medium examples, and 90.4% agreement (95% CI [90.3%, 90.6%]) for hard examples. The algorithm is also computationally efficient and is able to run in 0.385 ± 0.038 seconds for a single epoch using our provided implementation of the algorithm. The results demonstrate the algorithm's effectiveness in quantifying these discharges and provide a standardized and efficient approach for PD quantification as compared to existing manual approaches.

'Positive' inter-ictal clinical signs of functional neurological disorders are found in patients with functional dissociative seizures.

BACKGROUND AND PURPOSE: Prior studies highlighted the high diagnostic specificity (ranging from 92% to 100%) of clinical signs observed in functional neurological disorders (FNDs). However, these signs are rarely looked for by epileptologists when trying to distinguish between functional dissociative seizure (FDS) and epileptic seizure. The aim of this study was to determine the prevalence of inter-ictal clinical signs of FND in a cohort of patients with probable FDS. The secondary objective was to compare the prevalence of inter-ictal FND clinical signs in FDS patients with age- and gender-matched epileptic patients without FDS. METHODS: Patients diagnosed with FDS seen at two tertiary care centres and epileptic outpatients were included in the study. Each patient underwent a physical examination, searching for inter-ictal clinical signs of FND. RESULTS: In the FDS group, 79% of patients presented at least one sign of FND, compared to 16.6% of patients with epilepsy (p < 0.001). Moreover, 66.6% of FDS patients presented three or more FND signs, whereas only 4.1% of epileptic patients did (p < 0.001). The median number of FND clinical signs in the FDS group was four (SD 1.7; 5.5). Using the threshold of three signs or more, the specificity of detecting three or more FND signs was 83.3%, with a sensitivity of 79.2%. CONCLUSION: Inter-ictal clinical signs of FND are present in patients with FDS and should be looked for during neurological examination.

White matter brain-age in diverse forms of epilepsy and interictal psychosis.

Abnormal brain aging is suggested in epilepsy. Given the brain network dysfunction in epilepsy, the white matter tracts, which primarily interconnect brain regions, could be of special importance. We focused on white matter brain aging in diverse forms of epilepsy and comorbid psychosis. We obtained brain diffusion tensor imaging (DTI) data at 3 T-MRI in 257 patients with epilepsy and 429 healthy subjects. The tract-based fractional anisotropy values of the healthy subjects were used to build a brain-age prediction model, and we calculated the brain-predicted age difference (brain-PAD: predicted age-chronological age) of all subjects. As a result, almost all epilepsy categories showed significantly increased brain-PAD (p < 0.001), including temporal lobe epilepsy (TLE) with no MRI-lesion (+ 4.2 yr), TLE with hippocampal sclerosis (+ 9.1 yr), extratemporal focal epilepsy (+ 5.1 yr), epileptic encephalopathy or progressive myoclonus epilepsy (+ 18.4 yr), except for idiopathic generalized epilepsy (IGE). Patients with psychogenic non-epileptic seizures also presented increased brain-PAD. In TLE, interictal psychosis significantly raised brain-PAD by 8.7 years. In conclusion, we observed increased brain aging in most types of epilepsy, which was generally consistent with brain morphological aging results in previous studies. Psychosis may accelerate brain aging in TLE. These findings may suggest abnormal aging mechanisms in epilepsy and comorbid psychotic symptoms.

Epileptic network identification: insights from dynamic mode decomposition of sEEG data.

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.

Epileptiform Discharges Recorded as Monopoles and Dipoles in the EEG: A Practical Approach to Differentiate Tangential and Radial Generators Using Electrode Position Versus Voltage Graphs.

SUMMARY: Differentiating dipoles (tangential generators) from monopoles (radial generators) in routine EEG reading can be difficult. The polarity of sharp waves seen on surface EEG will change depending on the generator being located at the wall of the sulcus versus the crown of a gyrus. In this article, the authors introduce visual rules that may be used to determine polarity and estimate the localization of potentials during analysis of the EEG. They also review a practical approach to differentiate monopoles (radial generators) from dipoles (tangential dipoles) in the surface EEG using "electrode position versus voltage graphs." Finally, the authors illustrate examples of dipoles and monopoles with focal spikes located in the following locations: (1) bipolar spikes located in the anterior bank of the central sulcus, (2) bipolar spikes located in the posterior bank of the central sulcus, (3) monopolar spikes located in the crown of the precentral gyrus, (4) bipolar spikes with a vertically oriented dipole originated within the temporal (inferior) bank of the Sylvian fissure, and (5) monopolar spikes located in the convexity of a temporal gyrus. In summary, this article discusses electrographic features of spikes localized in various fissures and gyri and provides practical rules that permit the identification and location of dipoles and monopoles in standard scalp EEG recordings.

Left hemispheric epilepsy is more frequent and has worse clinical course that right hemispheric epilepsy.

INTRODUCTION: Few studies demonstrated that focal epilepsy (FE) with left hemispheric (LH) seizure onset is more frequent than with right hemispheric (RH) seizure onset. In addition, patients with LH seizure onset show worse clinical course compared to those with RH seizure onset. The aim of our study was to investigate both issues in a great cohort of FE patients. METHODS: In the retrospective study, we reviewed the clinical and paraclinical data of 682 patients with exclusively LH or RH seizure onset. We ascertained the laterality of seizure onset mainly by ictal and postictal semiology and ictal EEG findings. In the absence of ictal data, the basis of lateralization was the evidence of unilateral structural brain abnormality together with a corresponding interictal EEG finding. The endpoint of analysis of the clinical course was the presence/absence of five-year remission on drug treatment in the first ten years of treatment. RESULTS: Out of the 682 patients, 378 (55.4 per cent) had LH and 304 (44.6 per cent) had RH seizure onset. The difference was statistically significant (p = 0.04). Out of them, 213 LH and 156 RH patients were eligible to evaluate prognosis. Five-years-remission was attained by 71 patients (33.3 per cent) in the LH, and 65 (41.7 per cent) in the RH group. The difference was statistically significant (p = 0.05). CONCLUSION: We demonstrated the LH dominance of seizure onset and the worse clinical course of the patients with LH seizure onset. The findings are manifestations of the lateralized epileptic propensity of the brain. The dissimilar clinical course of the patient with LH and RH seizure onset may shape the general prognostic scheme in FE patients.