Missense variants in ANO4 cause sporadic encephalopathic or familial epilepsy with evidence for a dominant-negative effect.

Anoctamins are a family of Ca2+-activated proteins that may act as ion channels and/or phospholipid scramblases with limited understanding of function and disease association. Here, we identified five de novo and two inherited missense variants in ANO4 (alias TMEM16D) as a cause of fever-sensitive developmental and epileptic or epileptic encephalopathy (DEE/EE) and generalized epilepsy with febrile seizures plus (GEFS+) or temporal lobe epilepsy. In silico modeling of the ANO4 structure predicted that all identified variants lead to destabilization of the ANO4 structure. Four variants are localized close to the Ca2+ binding sites of ANO4, suggesting impaired protein function. Variant mapping to the protein topology suggests a preliminary genotype-phenotype correlation. Moreover, the observation of a heterozygous ANO4 deletion in a healthy individual suggests a dysfunctional protein as disease mechanism rather than haploinsufficiency. To test this hypothesis, we examined mutant ANO4 functional properties in a heterologous expression system by patch-clamp recordings, immunocytochemistry, and surface expression of annexin A5 as a measure of phosphatidylserine scramblase activity. All ANO4 variants showed severe loss of ion channel function and DEE/EE associated variants presented mild loss of surface expression due to impaired plasma membrane trafficking. Increased levels of Ca2+-independent annexin A5 at the cell surface suggested an increased apoptosis rate in DEE-mutant expressing cells, but no changes in Ca2+-dependent scramblase activity were observed. Co-transfection with ANO4 wild-type suggested a dominant-negative effect. In summary, we expand the genetic base for both encephalopathic sporadic and inherited fever-sensitive epilepsies and link germline variants in ANO4 to a hereditary disease.

Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection.

OBJECTIVE: This paper aims to investigate the possibility of detecting tonic-clonic seizures (TCSs) with behind-the-ear, two-channel wearable electroencephalography (EEG), and to evaluate its added value to non-EEG modalities in TCS detection. METHODS: We included 27 participants with a total of 44 TCSs from the European multicenter study SeizeIT2. The wearable Sensor Dot (SD; Byteflies) was used to measure behind-the-ear EEG, electromyography (EMG), electrocardiography (ECG), accelerometry (ACC) and gyroscope (GYR). We evaluated automatic unimodal detection of TCSs, using sensitivity, precision, false positive rate (FPR) and F1-score. Subsequently, we fused the different modalities and again assessed performance. Algorithm-labeled segments were then provided to two experts, who annotated true positive TCSs, and discarded false positives (FPs). RESULTS: Wearable EEG outperformed the other single modalities with a sensitivity of 100% and a FPR of 10.3/24h. The combination of wearable EEG and EMG proved most clinically useful, delivering a sensitivity of 97.7%, an FPR of 0.4/24h, a precision of 43%, and an F1-score of 59.7%. The highest overall performance was achieved through the fusion of wearable EEG, EMG, and ACC, yielding a sensitivity of 90.9%, an FPR of 0.1/24h, a precision of 75.5%, and an F1-score of 82.5%. CONCLUSIONS: In TCS detection with a wearable device, combining EEG with EMG, ACC or both resulted in a remarkable reduction of FPR, while retaining a high sensitivity. SIGNIFICANCE: Adding wearable EEG could further improve TCS detection, relative to extracerebral-based systems.

Learning Robust Representations of Tonic-Clonic Seizures with Cyclic Transformer.

Tonic-clonic seizures (TCSs) pose a significant risk for sudden unexpected death in epilepsy (SUDEP). Previous research has highlighted the potential of multimodal wearable seizure detection systems in accurately detecting TCSs through continuous monitoring, enabling timely alarms and potentially preventing SUDEP. However, such multimodal systems carry a higher risk of sensor malfunction. In this paper, we propose a cyclic transformer approach to address these challenges. The cyclic transformer learns a robust representation by performing circular modal translations between the source and target modalities. It leverages back-translation as regularization technique to enhance the discriminative power of the learned representation. Notably, the proposed cyclic transformer is trained on paired multimodal data but requires only a single source modality during deployment. This characteristic ensures the robustness of the cyclic transformer to perturbations or missing information in the target modality. Experimental results demonstrate that the proposed cyclic transformer achieves competitive performance compared with existing multimodal systems. While both approaches were trained using EEG and EMG data, the cyclic transformer exclusively employs EEG data for testing, diverging from the state-of-the-art's utilization of both EEG and EMG data during test. This showcases the effectiveness of the cyclic transformer in multimodal TCSs detection, offering a promising approach for enhancing the accuracy and robustness of seizure detection systems while mitigating the risks associated with sensor malfunction.

Ceftazidime-related neurotoxicity in a patient with renal impairment: a case report and literature review.

PURPOSE: We present the case of a 67-year-old woman with severely reduced renal clearance suffering from ceftazidime-induced encephalopathy. Subsequently, we search the literature to review and describe the neurotoxicity of ceftazidime. METHODS: A search string was developed to search PubMed for relevant cases from which relevant information was extracted. Using the collected data a ROC analysis was performed in R to determine a neurotoxicity threshold. RESULTS: Our patient suffered from progressive loss of consciousness and myoclonic seizures, with improvements noted a few days after discontinuation of treatment. The dose was not appropriately reduced to take into account her reduced renal function. The highest ceftazidime concentration recorded was 234.9 mg/mL. Using the Naranjo score we found a probable relationship between our patient's encephalopathy and ceftazidime administration. In the literature we found a total of 32 similar cases, most of which also had some form of renal impairment. Using our collected data and ceftazidime concentrations provided in the literature, a ROC analysis provided a neurotoxicity threshold of 78 mg/L for ceftazidime neurotoxicity. CONCLUSION: Ceftazidime-related neurotoxicity is a known issue, especially in patients with severe renal impairment. Yet no concrete toxicity threshold has been reported so far. We propose the first toxicity threshold for ceftazidime of 78 mg/L. Future prospective studies are needed to validate and optimize the neurotoxicity threshold as upper limit for ceftazidime therapeutic drug monitoring.

EANM practice guidelines for an appropriate use of PET and SPECT for patients with epilepsy.

Epilepsy is one of the most frequent neurological conditions with an estimated prevalence of more than 50 million people worldwide and an annual incidence of two million. Although pharmacotherapy with anti-seizure medication (ASM) is the treatment of choice, ~30% of patients with epilepsy do not respond to ASM and become drug resistant. Focal epilepsy is the most frequent form of epilepsy. In patients with drug-resistant focal epilepsy, epilepsy surgery is a treatment option depending on the localisation of the seizure focus for seizure relief or seizure freedom with consecutive improvement in quality of life. Beside examinations such as scalp video/electroencephalography (EEG) telemetry, structural, and functional magnetic resonance imaging (MRI), which are primary standard tools for the diagnostic work-up and therapy management of epilepsy patients, molecular neuroimaging using different radiopharmaceuticals with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) influences and impacts on therapy decisions. To date, there are no literature-based praxis recommendations for the use of Nuclear Medicine (NM) imaging procedures in epilepsy. The aims of these guidelines are to assist in understanding the role and challenges of radiotracer imaging for epilepsy; to provide practical information for performing different molecular imaging procedures for epilepsy; and to provide an algorithm for selecting the most appropriate imaging procedures in specific clinical situations based on current literature. These guidelines are written and authorized by the European Association of Nuclear Medicine (EANM) to promote optimal epilepsy imaging, especially in the presurgical setting in children, adolescents, and adults with focal epilepsy. They will assist NM healthcare professionals and also specialists such as Neurologists, Neurophysiologists, Neurosurgeons, Psychiatrists, Psychologists, and others involved in epilepsy management in the detection and interpretation of epileptic seizure onset zone (SOZ) for further treatment decision. The information provided should be applied according to local laws and regulations as well as the availability of various radiopharmaceuticals and imaging modalities.

Detecting Epileptic Seizures Using Hand-Crafted and Automatically Constructed EEG Features.

Epileptic seizure detection aims to replace unreliable seizure diaries by a model that automatically detects seizures based on electroencephalography (EEG) sensors. However, developing such a model is difficult and time consuming as it requires manually searching for relevant features from complex EEG data. Domain experts may have a partial understanding of the EEG characteristics that indicate seizures, but this knowledge is often not sufficient to exhaustively enumerate all relevant features. To address this challenge, we investigate how automated feature construction may complement hand-crafted features for epileptic seizure detection. By means of an empirical comparison on a real-world seizure detection dataset, we evaluate the ability of automated feature construction to come up with new relevant features. We show that combining hand-crafted and automated features results in more accurate models compared to using hand-crafted features alone. Our findings suggest that future studies on developing EEG-based seizure detection models may benefit from features constructed using a combination of hand-crafted and automated feature engineering.

Home recording of 3-Hz spike-wave discharges in adults with absence epilepsy using the wearable Sensor Dot.

OBJECTIVE: Home monitoring of 3-Hz spike-wave discharges (SWDs) in patients with refractory absence epilepsy could improve clinical care by replacing the inaccurate seizure diary with objective counts. We investigated the use and performance of the Sensor Dot (Byteflies) wearable in persons with absence epilepsy in their home environment. METHODS: Thirteen participants (median age = 22 years, 11 female) were enrolled at the university hospitals of Leuven and Freiburg. At home, participants had to attach the Sensor Dot and behind-the-ear electrodes to record two-channel electroencephalogram (EEG), accelerometry, and gyroscope data. Ground truth annotations were created during a visual review of the full Sensor Dot recording. Generalized SWDs were annotated if they were 3 Hz and at least 3 s on EEG. Potential 3-Hz SWDs were flagged by an automated seizure detection algorithm, (1) using only EEG and (2) with an additional postprocessing step using accelerometer and gyroscope to discard motion artifacts. Afterward, two readers (W.V.P. and L.S.) reviewed algorithm-labeled segments and annotated true positive detections. Sensitivity, precision, and F1 score were calculated. Patients had to keep a seizure diary and complete questionnaires about their experiences. RESULTS: Total recording time was 394 h 42 min. Overall, 234 SWDs were captured in 11 of 13 participants. Review of the unimodal algorithm-labeled recordings resulted in a mean sensitivity of .84, precision of .93, and F1 score of .89. Visual review of the multimodal algorithm-labeled segments resulted in a similar F1 score and shorter review time due to fewer false positive labels. Participants reported that the device was comfortable and that they would be willing to wear it on demand of their neurologist, for a maximum of 1 week or with intermediate breaks. SIGNIFICANCE: The Sensor Dot improved seizure documentation at home, relative to patient self-reporting. Additional benefits were the short review time and the patients' device acceptance due to user-friendliness and comfortability.

Subclinical epileptiform activity and sleep disturbances in Alzheimer's disease.

INTRODUCTION: Subclinical epileptiform activity (SEA) and sleep disturbances are frequent in Alzheimer's disease (AD). Both have an important relation to cognition and potential therapeutic implications. We aimed to study a possible relationship between SEA and sleep disturbances in AD. METHODS: In this cross-sectional study, we performed a 24-h ambulatory EEG and polysomnography in 48 AD patients without diagnosis of epilepsy and 34 control subjects. RESULTS: SEA, mainly detected in frontotemporal brain regions during N2 with a median of three spikes/night [IQR1-17], was three times more prevalent in AD. AD patients had lower sleep efficacy, longer wake after sleep onset, more awakenings, more N1%, less REM sleep and a higher apnea-hypopnea index (AHI) and oxygen desaturation index (ODI). Sleep was not different between AD subgroup with SEA (AD-Epi+) and without SEA (AD-Epi-); however, compared to controls, REM% was decreased and AHI and ODI were increased in the AD-Epi+ subgroup. DISCUSSION: Decreased REM sleep and more severe sleep-disordered breathing might be related to SEA in AD. These results could have diagnostic and therapeutic implications and warrant further study at the intersection between sleep and epileptiform activity in AD.

Wearable detection of tonic seizures in childhood epilepsy: An exploratory cohort study.

OBJECTIVE: To investigate the performance of a multimodal wearable device for the offline detection of tonic seizures (TS) in a pediatric childhood epilepsy cohort, with a focus on patients with Lennox-Gastaut syndrome. METHODS: Parallel with prolonged video-electroencephalography (EEG), the Plug 'n Patch system, a multimodal wearable device using the Sensor Dot and replaceable electrode adhesives, was used to detect TS. Multiple biosignals were recorded: behind-the-ear EEG, surface electromyography, electrocardiography, and accelerometer/gyroscope. Biosignals were annotated blindly by a neurologist. Seizure characteristics were described, and performance was assessed by sensitivity, positive predictive value (PPV), F1 score, and false alarm rate (FAR) per hour. Performance was compared to seizure diaries kept by the caretaker. RESULTS: Ninety-nine TS were detected in 13 patients. Seven patients (54%) had Lennox-Gastaut syndrome and six patients (46%) had other forms of (developmental) epileptic encephalopathies or drug-resistant epilepsy. All but one patient had intellectual disability. Overall sensitivity was 41%, with a PPV of 9%, an F1 score of 14%, and a median FAR per hour of 0.75. Performance increased to an F1 score of 66% for nightly seizures lasting at least 10 s (sensitivity 66%, PPV 66%) and 71% for nightly seizures lasting at least 20 s (sensitivity 62%, PPV 82%). For these seizures there were no false alarms in 10 of 13 patients. Sensitivity of seizure diaries reached a maximum of 52% for prolonged (≥20 s) nightly seizures, even though caretakers slept in the same room. SIGNIFICANCE: We showed that it is feasible to use a multimodal wearable device with multiple adhesive sites in children with epilepsy and intellectual disability. For prolonged nightly seizures, offline manual detection of TS outperformed seizure diaries. The recognition of seizure-specific signatures using multiple modalities can help in the development of automated TS detection algorithms.

The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG.

Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm's detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients.

Deep Brain Stimulation of the Anterior Nucleus of the Thalamus in Drug-Resistant Epilepsy in the MORE Multicenter Patient Registry.

BACKGROUND AND OBJECTIVES: The efficacy of deep brain stimulation of the anterior nucleus of the thalamus (ANT DBS) in patients with drug-resistant epilepsy (DRE) was demonstrated in the double-blind Stimulation of the Anterior Nucleus of the Thalamus for Epilepsy randomized controlled trial. The Medtronic Registry for Epilepsy (MORE) aims to understand the safety and longer-term effectiveness of ANT DBS therapy in routine clinical practice. METHODS: MORE is an observational registry collecting prospective and retrospective clinical data. Participants were at least 18 years old, with focal DRE recruited across 25 centers from 13 countries. They were followed for at least 2 years in terms of seizure frequency (SF), responder rate (RR), health-related quality of life (Quality of Life in Epilepsy Inventory 31), depression, and safety outcomes. RESULTS: Of the 191 patients recruited, 170 (mean [SD] age of 35.6 [10.7] years, 43% female) were implanted with DBS therapy and met all eligibility criteria. At baseline, 38% of patients reported cognitive impairment. The median monthly SF decreased by 33.1% from 15.8 at baseline to 8.8 at 2 years (p < 0.0001) with 32.3% RR. In the subgroup of 47 patients who completed 5 years of follow-up, the median monthly SF decreased by 55.1% from 16 at baseline to 7.9 at 5 years (p < 0.0001) with 53.2% RR. High-volume centers (>10 implantations) had 42.8% reduction in median monthly SF by 2 years in comparison with 25.8% in low-volume center. In patients with cognitive impairment, the reduction in median monthly SF was 26.0% by 2 years compared with 36.1% in patients without cognitive impairment. The most frequently reported adverse events were changes (e.g., increased frequency/severity) in seizure (16%), memory impairment (patient-reported complaint, 15%), depressive mood (patient-reported complaint, 13%), and epilepsy (12%). One definite sudden unexpected death in epilepsy case was reported. DISCUSSION: The MORE registry supports the effectiveness and safety of ANT DBS therapy in a real-world setting in the 2 years following implantation. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that ANT DBS reduces the frequency of seizures in patients with drug-resistant focal epilepsy. TRIAL REGISTRATION INFORMATION: MORE ClinicalTrials.gov Identifier: NCT01521754, first posted on January 31, 2012.

In-hospital and home-based long-term monitoring of focal epilepsy with a wearable electroencephalographic device: Diagnostic yield and user experience.

OBJECTIVE: The aim is to report the performance of an electroencephalogram (EEG) seizure-detector algorithm on data obtained with a wearable device (WD) in patients with focal refractory epilepsy and their experience. METHODS: Patients used a WD, the Sensor Dot (SD), to measure two channels of EEG using dry electrode patches during presurgical evaluation and at home for up to 8 months. An automated seizure detection algorithm flagged EEG regions with possible seizures, which we reviewed to evaluate the algorithm's diagnostic yield. In addition, we collected data on usability, side effects, and patient satisfaction with an electronic seizure diary application (Helpilepsy). RESULTS: Sixteen inpatients used the SD for up to 5 days and had 21 seizures. Sixteen outpatients used the device for up to 8 months and reported 101 focal impaired awareness seizures during the periods selected for analysis. Focal seizure detection sensitivity based on behind-the-ear EEG was 52% in inpatients and 23% in outpatients. False detections/h, positive predictive value (PPV), and F1 scores were 7.13%, .11%, and .002% for inpatients and 7.77%, .04%, and .001% for outpatients. Artifacts and low signal quality contributed to poor performance metrics. The seizure detector identified 19 nonreported seizures during sleep, when the signal quality was better. Regarding patients' experience, the likelihood of using the device at 6 months was 62%, and side effects were the main reason for dropping out. Finally, daily and monthly questionnaire completion rates were 33% and 65%, respectively. SIGNIFICANCE: Focal seizure detection sensitivity based on behind-the-ear EEG was 52% in inpatients and 23% in outpatients, with high false alarm rates and low PPV and F1 scores. This unobtrusive wearable seizure detection device was well received but had side effects. The current workflow and low performance limit its implementation in clinical practice. We suggest different steps to improve these performance metrics and patient experience.

Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG.

Objective.The goal of this paper is to investigate the limits of electroencephalography (EEG) sensor miniaturization in a set-up consisting of multiple galvanically isolated EEG units to record interictal epileptiform discharges (IEDs), referred to as 'spikes', in people with epilepsy.Approach.A dataset of high-density EEG recordings (257 channels) was used to emulate local EEG sensor units with short inter-electrode distances. A computationally efficient sensor selection and interictal spike detection algorithm was developed and used to assess the influence of the inter-electrode distance and the number of such EEG units on spike detection performance. Signal-to-noise ratio, correlation with a clinical-grade IEDs detector and Cohen's kappa coefficient of agreement were used to quantify performance. Bayesian statistics were used to confirm the statistical significance of the observed results.Main results.We found that EEG recording equipment should be specifically designed to measure the small signal power at short inter-electrode distance by providing an input referred noise<300 nV. We also found that an inter-electrode distance of minimum 5 cm between electrodes in a setup with a minimum of two EEG units is required to obtain near equivalent performance in interictal spike detection to standard EEG.Significance.These findings provide design guidelines for miniaturizing EEG systems for long term ambulatory monitoring of interictal spikes in epilepsy patients.

Efficacy and safety of adjunctive padsevonil in adults with drug-resistant focal epilepsy: Results from two double-blind, randomized, placebo-controlled trials.

OBJECTIVE: To characterize efficacy, safety/tolerability, and pharmacokinetics of padsevonil (PSL) administered concomitantly with ≤3 antiseizure medications (ASMs) for observable focal seizures in adults with drug-resistant epilepsy in two multicenter, randomized, double-blind, placebo-controlled, parallel-group trials. METHODS: The phase 2b dose-finding trial (EP0091/NCT03373383) randomized patients 1:1:1:1:1 to PSL 50/100/200/400 mg or placebo twice daily (b.i.d.). The phase 3 efficacy trial (EP0092/NCT03739840) randomized patients 1:1:1:1 to PSL 100/200/400 mg or placebo b.i.d. Patients with observable (focal aware with motor symptoms, focal impaired awareness, focal to bilateral tonic-clonic) focal seizures for ≥3 years, experiencing them ≥4 times per 28 days including during the 4-week baseline period despite treatment with ≥4 lifetime ASMs including current ASMs, were enrolled. RESULTS: In EP0091 and EP0092, 410 and 231 patients, respectively, were randomized and received at least one dose of trial medication. In patients in EP0091 on PSL 50/100/200/400 mg b.i.d. (n = 80/82/81/81, respectively) versus placebo (n = 81), outcomes included percentage reductions over placebo in observable focal seizure frequency during the 12-week maintenance period: 17.2%, 19.1% (p = 0.128), 19.2% (p = 0.128), 12.4% (p = 0.248); 75% responder rates (p-values for odds ratios): 13.8%, 12.2% (p = 0.192), 11.1% (p = 0.192), 16.0% (p = 0.124) versus 6.2%; 50% responder rates: 33.8% (p = 0.045), 31.7% (p = 0.079), 25.9% (p = 0.338), 32.1% (p = 0.087), versus 21.0%; TEAEs were reported by 82.7% (67/81), 78.3% (65/83), 74.4% (61/82), 90.1% (73/81) versus 78.3% (65/83). In patients in EP0092 on PSL 100/200/400 mg b.i.d. (n = 60/56/56, respectively) versus placebo (n = 54), outcomes included percentage reductions over placebo: -5.6% (p = 0.687), 6.5% (p = 0.687), 6.3% (p = 0.687); 75% responder rates: 15.3% (p = 0.989), 12.5% (p = 0.989), 14.3% (p = 0.989) versus 13.0%; 50% responder rates: 35.6% (p = 0.425), 33.9% (p = 0.625), and 42.9% (p = 0.125) versus 27.8%; TEAEs were reported by 80.0% (48/60), 78.9% (45/57), 83.1% (49/59) versus 67.3% (37/55). SIGNIFICANCE: In both trials, the primary outcomes did not reach statistical significance in any PSL dose group compared with placebo. PSL was generally well tolerated, and no new safety signals were identified.

Automatic annotation correction for wearable EEG based epileptic seizure detection.

Objective. Video-electroencephalography (vEEG), which defines the ground truth for the detection of epileptic seizures, is inadequate for long-term home monitoring. Thanks to advantages in comfort and unobtrusiveness, wearable EEG devices have been suggested as a solution for home monitoring. However, one of the challenges in data-driven automated seizure detection with wearable EEG data is to have reliable seizure annotations. Seizure annotations on the gold-standard 25-channel vEEG recordings may not be optimal to delineate seizure activity on the concomitantly recorded wearable EEG, due to artifacts or absence of ictal activity on the limited set of electrodes of the wearable EEG. This paper aims to develop an automatic approach to correct for imperfect annotations of seizure activity on wearable EEG, which can be used to train seizure detection algorithms.Approach. This paper first investigates the effectiveness of correcting the seizure annotations for the training set with a visual annotation correction. Then a novel approach has been proposed to automatically remove non-seizure data from wearable EEG in epochs annotated as seizures in gold-standard video-EEG recordings. The performance of the automatic annotation correction approach was evaluated by comparing the seizure detection models trained with (a) original vEEG seizure annotations, (b) visually corrected seizure annotations, and (c) automatically corrected seizure annotations.Main results. The automated seizure detection approach trained with automatically corrected seizure annotations was more sensitive and had fewer false-positive detections compared to the approach trained with visually corrected seizure annotations, and the approach trained with the original seizure annotations from gold-standard vEEG.Significance. The wearable EEG seizure detection approach performs better when trained with automatic seizure annotation correction.

Predictive value of metabolic and perfusion changes outside the seizure onset zone for postoperative outcome in patients with refractory focal epilepsy.

The value of functional molecular changes outside the seizure onset zone as independent predictive factors of surgical outcome has been scarcely evaluated. The aim of this retrospective study was to evaluate relative metabolic and perfusion changes outside the seizure onset zone as predictors of postoperative outcome in patients with unifocal refractory focal epilepsy. Eighty-six unifocal epilepsy patients who underwent 18F-FDG PET prior to surgery were included. Ictal and interictal perfusion SPECT was available in 65 patients. Good postoperative outcome was defined as the International League against Epilepsy class 1. Using univariate statistical analysis, the predictive ability of volume-of-interest based relative metabolism/perfusion for outcome classification was quantified by AUC ROC-curve, using composite, unilateral cortical (frontal, orbitofrontal, temporal, parietal, occipital) and central volumes-of-interest. The results were cross-validated, and a false discovery rate (FDR) correction was applied. As a secondary objective, a subgroup analysis was performed on temporal lobe epilepsy patients (N = 64). Increased relative ictal perfusion in the contralateral central volume-of-interest was significantly associated with the good surgical outcome both in the total population (AUC 0.79, pFDR = 0.009) and the temporal lobe epilepsy subgroup (AUC 0.80, pFDR = 0.028). No other significant associations between functional molecular changes and postoperative outcome were found. Increased relative ictal perfusion in the contralateral central region significantly predicted outcome after epilepsy surgery in patients with refractory focal epilepsy. We postulate that these relative perfusion changes could be an expression of better preoperative neuronal network integration and centralization in the contralateral central structures, which is suggested to be associated with better postoperative outcome.

Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis.

Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG-fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption.

Single-Unit Recordings Reveal the Selectivity of a Human Face Area.

The exquisite capacity of primates to detect and recognize faces is crucial for social interactions. Although disentangling the neural basis of human face recognition remains a key goal in neuroscience, direct evidence at the single-neuron level is limited. We recorded from face-selective neurons in human visual cortex in a region characterized by functional magnetic resonance imaging (fMRI) activations for faces compared with objects. The majority of visually responsive neurons in this fMRI activation showed strong selectivity at short latencies for faces compared with objects. Feature-scrambled faces and face-like objects could also drive these neurons, suggesting that this region is not tightly tuned to the visual attributes that typically define whole human faces. These single-cell recordings within the human face processing system provide vital experimental evidence linking previous imaging studies in humans and invasive studies in animal models.SIGNIFICANCE STATEMENT We present the first recordings of face-selective neurons in or near an fMRI-defined patch in human visual cortex. Our unbiased multielectrode array recordings (i.e., no selection of neurons based on a search strategy) confirmed the validity of the BOLD contrast (faces-objects) in humans, a finding with implications for all human imaging studies. By presenting faces, feature-scrambled faces, and face-pareidolia (perceiving faces in inanimate objects) stimuli, we demonstrate that neurons at this level of the visual hierarchy are broadly tuned to the features of a face, independent of spatial configuration and low-level visual attributes.

Accurate detection of typical absence seizures in adults and children using a two-channel electroencephalographic wearable behind the ears.

OBJECTIVE: Patients with absence epilepsy sensitivity <10% of their absences. The clinical gold standard to assess absence epilepsy is a 24-h electroencephalographic (EEG) recording, which is expensive, obtrusive, and time-consuming to review. We aimed to (1) investigate the performance of an unobtrusive, two-channel behind-the-ear EEG-based wearable, the Sensor Dot (SD), to detect typical absences in adults and children; and (2) develop a sensitive patient-specific absence seizure detection algorithm to reduce the review time of the recordings. METHODS: We recruited 12 patients (median age = 21 years, range = 8-50; seven female) who were admitted to the epilepsy monitoring units of University Hospitals Leuven for a 24-h 25-channel video-EEG recording to assess their refractory typical absences. Four additional behind-the-ear electrodes were attached for concomitant recording with the SD. Typical absences were defined as 3-Hz spike-and-wave discharges on EEG, lasting 3 s or longer. Seizures on SD were blindly annotated on the full recording and on the algorithm-labeled file and consequently compared to 25-channel EEG annotations. Patients or caregivers were asked to keep a seizure diary. Performance of the SD and seizure diary were measured using the F1 score. RESULTS: We concomitantly recorded 284 absences on video-EEG and SD. Our absence detection algorithm had a sensitivity of .983 and false positives per hour rate of .9138. Blind reading of full SD data resulted in sensitivity of .81, precision of .89, and F1 score of .73, whereas review of the algorithm-labeled files resulted in scores of .83, .89, and .87, respectively. Patient self-reporting gave sensitivity of .08, precision of 1.00, and F1 score of .15. SIGNIFICANCE: Using the wearable SD, epileptologists were able to reliably detect typical absence seizures. Our automated absence detection algorithm reduced the review time of a 24-h recording from 1-2 h to around 5-10 min.