Search

Abstract Objective Non‐invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure‐day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt. Methods Seventy‐eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non‐rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high‐frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo‐prospective seizure‐day forecasting, with forecasting horizons of 16‐ and 24‐h to include both diurnal and nocturnal seizures (24‐h) or diurnal seizures only (16‐h). The algorithm's performance was assessed using a nested leave‐one‐patient‐out cross‐validation approach. Results Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16‐ and 24‐h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16‐h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24‐h horizon. Significance Smart shirt‐based nocturnal sleep analysis holds promise as a non‐invasive approach for seizure‐day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long‐term recordings, could pave the way for the development of innovative and practical seizure forecasting devices. Plain Language Summary Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach.

Background: Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG. Methods: We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool. Results: We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures. Conclusion: The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG. Significance: We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

IntroductionOperculo-insular epilepsy (OIE) is a rare condition amenable to surgery in well-selected cases. Despite the high rate of neurological complications associated with OIE surgery, most postoperative deficits recover fully and rapidly. We provide insights into this peculiar pattern of functional recovery by investigating the longitudinal reorganization of structural networks after surgery for OIE in 10 patients.MethodsStructural T1 and diffusion-weighted MRIs were performed before surgery (t0) and at 6 months (t1) and 12 months (t2) postoperatively. These images were processed with an original, comprehensive structural connectivity pipeline. Using our method, we performed comparisons between the t0 and t1 timepoints and between the t1 and t2 timepoints to characterize the progressive structural remodeling.ResultsWe found a widespread pattern of postoperative changes primarily in the surgical hemisphere, most of which consisted of reductions in connectivity strength (CS) and regional graph theoretic measures (rGTM) that reflect local connectivity. We also observed increases in CS and rGTMs predominantly in regions located near the resection cavity and in the contralateral healthy hemisphere. Finally, most structural changes arose in the first six months following surgery (i.e., between t0 and t1).DiscussionTo our knowledge, this study provides the first description of postoperative structural connectivity changes following surgery for OIE. The ipsilateral reductions in connectivity unveiled by our analysis may result from the reversal of seizure-related structural alterations following postoperative seizure control. Moreover, the strengthening of connections in peri-resection areas and in the contralateral hemisphere may be compatible with compensatory structural plasticity, a process that could contribute to the recovery of functions seen following operculo-insular resections for focal epilepsy.

BackgroundRefractory (RSE) and super-refractory status epilepticus (SRSE) are serious neurological conditions requiring aggressive management. Beyond anesthetic agents, there is a lack of evidence guiding management in these patients. This systematic review and individual participant data meta-analysis (IPDMA) seeks to evaluate and compare the currently available surgical techniques for the acute treatment of RSE and SRSE.MethodsA systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Individual Participant Data (PRISMA-IPD). Only patients who underwent surgery while in RSE and SRSE were included. Descriptive statistics were used to compare various subgroups. Multivariable logistic regression models were constructed to identify predictors of status epilepticus (SE) cessation, long-term overall seizure freedom, and favorable functional outcome (i.e., modified Rankin score of 0–2) at last follow-up.ResultsA total of 87 studies including 161 participants were included. Resective surgery tended to achieve better SE cessation rate (93.9%) compared to non-resective techniques (83.9%), but this did not reach significance (p = 0.071). Resective techniques were also more likely to achieve seizure freedom (69.1% vs. 34.4%, p =

The insula is often considered the fifth lobe of the brain and is increasingly recognized as one of the most connected regions in the brain, with widespread connections to cortical and subcortical structures. As a follow-up to our previous tractography work, we investigated the resting-state functional connectivity (rsFC) profiles of insular subregions and assessed their concordance with structural connectivity. We used the CONN toolbox to analyze the rsFC of the same 19 insular regions of interest (ROIs) we used in our prior tractography work and regrouped them into six subregions based on their connectivity pattern similarity. Our analysis of 50 healthy participants confirms the known broad connectivity of the insula and shows novel and specific whole-brain and intra-connectivity patterns of insular subregions. By examining such subregions, our findings provide a more detailed pattern of connectivity than prior studies that may prove useful for comparison between patients.

Abstract Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55–0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.

Abstract Insular epilepsy (IE) is an increasingly recognized cause of drug‐resistant epilepsy amenable to surgery. However, concerns of suboptimal seizure control and permanent neurological morbidity hamper widespread adoption of surgery for IE. We performed a systematic review and individual participant data meta‐analysis to determine the efficacy and safety profile of surgery for IE and identify predictors of outcomes. Of 2483 unique citations, 24 retrospective studies reporting on 312 participants were eligible for inclusion. The median follow‐up duration was 2.58 years (range, 0‐17 years), and 206 (66.7%) patients were seizure‐free at last follow‐up. Younger age at surgery (≤18 years; HR = 1.70, 95% CI = 1.09‐2.66, P = .022) and invasive EEG monitoring (HR = 1.97, 95% CI = 1.04‐3.74, P = .039) were significantly associated with shorter time to seizure recurrence. Performing MR‐guided laser ablation or radiofrequency ablation instead of open resection (OR = 2.05, 95% CI = 1.08‐3.89, P = .028) was independently associated with suboptimal or poor seizure outcome (Engel II‐IV) at last follow‐up. Postoperative neurological complications occurred in 42.5% of patients, most commonly motor deficits (29.9%). Permanent neurological complications occurred in 7.8% of surgeries, including 5% and 1.4% rate of permanent motor deficits and dysphasia, respectively. Resection of the frontal operculum was independently associated with greater odds of motor deficits (OR = 2.75, 95% CI = 1.46‐5.15, P = .002). Dominant‐hemisphere resections were independently associated with dysphasia (OR = 13.09, 95% CI = 2.22‐77.14, P = .005) albeit none of the observed language deficits were permanent. Surgery for IE is associated with a good efficacy/safety profile. Most patients experience seizure freedom, and neurological deficits are predominantly transient. Pediatric patients and those requiring invasive monitoring or undergoing stereotactic ablation procedures experience lower rates of seizure freedom. Transgression of the frontal operculum should be avoided if it is not deemed part of the epileptogenic zone. Well‐selected candidates undergoing dominant‐hemisphere resection are more likely to exhibit transient language deficits; however, the risk of permanent deficit is very low.

Introduction The diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30 min routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyse the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy.Methods and analysis The protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyse the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area under the curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modelling, and comment on the reproducibility of the different studies.Ethics and dissemination Ethical approval was not required. Findings will be disseminated through peer-reviewed publication and presented at conferences related to this field.PROSPERO registration number CRD42022292261.

IntroductionWhile it is known that poor sleep is a seizure precipitant, this association remains poorly quantified. This study investigated whether seizures are preceded by significant changes in sleep efficiency as measured by a wearable equipped with an electrocardiogram, respiratory bands, and an accelerometer.MethodsNocturnal recordings from 47 people with epilepsy hospitalized at our epilepsy monitoring unit were analyzed (304 nights). Sleep metrics during nights followed by epileptic seizures (24 h post-awakening) were compared to those of nights which were not.ResultsLower sleep efficiency (percentage of sleep during the night) was found in the nights preceding seizure days (p < 0.05). Each standard deviation decrease in sleep efficiency and increase in wake after sleep onset was respectively associated with a 1.25-fold (95 % CI: 1.05 to 1.42, p < 0.05) and 1.49-fold (95 % CI: 1.17 to 1.92, p < 0.01) increased odds of seizure occurrence the following day. Furthermore, nocturnal seizures were associated with significantly lower sleep efficiency and higher wake after sleep onset (p < 0.05), as well as increased odds of seizure occurrence following wake (OR: 5.86, 95 % CI: 2.99 to 11.77, p < 0.001).DiscussionFindings indicate lower sleep efficiency during nights preceding seizures, suggesting that wearable sensors could be promising tools for sleep-based seizure-day forecasting in people with epilepsy.

It is increasingly recognized that deep understanding of epileptic seizures requires both localizing and characterizing the functional network of the region where they are initiated, i. e., the epileptic focus. Previous investigations of the epileptogenic focus' functional connectivity have yielded contrasting results, reporting both pathological increases and decreases during resting periods and seizures. In this study, we shifted paradigm to investigate the time course of connectivity in relation to interictal epileptiform discharges. We recruited 35 epileptic patients undergoing intracranial EEG (iEEG) investigation as part of their presurgical evaluation. For each patient, 50 interictal epileptic discharges (IEDs) were marked and iEEG signals were epoched around those markers. Signals were narrow-band filtered and time resolved phase-locking values were computed to track the dynamics of functional connectivity during IEDs. Results show that IEDs are associated with a transient decrease in global functional connectivity, time-locked to the peak of the discharge and specific to the high range of the gamma frequency band. Disruption of the long-range connectivity between the epileptic focus and other brain areas might be an important process for the generation of epileptic activity. Transient desynchronization could be a potential biomarker of the epileptogenic focus since 1) the functional connectivity involving the focus decreases significantly more than the connectivity outside the focus and 2) patients with good surgical outcome appear to have a significantly more disconnected focus than patients with bad outcomes.

Background: In temporal lobe epilepsy (TLE), advanced neuroimaging techniques reveal anomalies extending beyond the temporal lobe such as thinning of fronto-central cortices. Operculo-insular epilepsy (OIE) is an under-recognized and poorly characterized condition with the potential of mimicking TLE. In this work, we investigated insular and extra-insular cortical thickness (CT) changes in OIE. Methods: All participants (14 patients with refractory OIE, 9 age- and sex-matched patients with refractory TLE and 26 healthy controls) underwent a T1-weighted acquisition on a 3 T MRI. Anatomical images were processed with Advanced Normalization Tools. Between-group analysis of CT was performed using a two-sided t-test (threshold of p

Operculo-insular epilepsy (OIE) is an under-recognized condition that can mimic temporal and extratemporal epilepsies. Previous studies have revealed structural connectivity changes in the epileptic network of focal epilepsy. However, most reports use the debated streamline-count to quantify ‘connectivity strength’ and rely on standard tracking algorithms. We propose a sophisticated cutting-edge method that is robust to crossing fibers, optimizes cortical coverage, and assigns an accurate microstructure-reflecting quantitative conectivity marker, namely the COMMIT (Convex Optimization Modeling for Microstructure Informed Tractography)-weight. Using our pipeline, we report the connectivity alterations in OIE. COMMIT-weighted matrices were created in all participants (nine patients with OIE, eight patients with temporal lobe epilepsy (TLE), and 22 healthy controls (HC)). In the OIE group, widespread increases in ‘connectivity strength’ were observed bilaterally. In OIE patients, ‘hyperconnections’ were observed between the insula and the pregenual cingulate gyrus (OIE group vs. HC group) and between insular subregions (OIE vs. TLE). Graph theoretic analyses revealed higher connectivity within insular subregions of OIE patients (OIE vs. TLE). We reveal, for the first time, the structural connectivity distribution in OIE. The observed pattern of connectivity in OIE likely reflects a diffuse epileptic network incorporating insular-connected regions and may represent a structural signature and diagnostic biomarker.

There is fast-emerging, cumulative clinical data on coronavirus disease 2019 (COVID-19) in kidney transplant recipients. Although respiratory tract symptoms are often the initial presentation among kidney transplant recipients who contract COVID-19, other clinical features which may indicate underlying SARS-CoV-2-related inflammation, such as gastrointestinal symptoms, are not uncommon. Hyponatremia can develop and may reflect underlying inflammation. Interferon-6 is an important pro-inflammatory cytokine involved in the pathogenesis of severe COVID-19 complications and may play a role in the inappropriately higher secretion of antidiuretic hormone leading to hyponatremia. This pathway is the so-called immuno-neuroendocrine interface. Hyponatremia in COVID-19 has been reported in a few case series of non-kidney transplant patients and only one reported kidney transplant recipient. However, the clinical course and prognostic value of hyponatremia in this population are not described in detail. We report a kidney transplant recipient who was infected with COVID-19 and exhibited severe hyponatremia secondary to the syndrome of inappropriate antidiuretic hormone secretion. Hyponatremia is one of the clinical presentations of COVID-19, although less common, and may occur more frequently in kidney transplant recipients. Thus, the possible underlying immuno-neuroendocrine relationship related to the inflammatory process of COVID-19 leading to hyponatremia and its prognostic value are reviewed.

Objective: Non‐invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure‐day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt. Methods: Seventy‐eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non‐rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high‐frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo‐prospective seizure‐day forecasting, with forecasting horizons of 16‐ and 24‐h to include both diurnal and nocturnal seizures (24‐h) or diurnal seizures only (16‐h). The algorithm's performance was assessed using a nested leave‐one‐patient‐out cross‐validation approach. Results: Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16‐ and 24‐h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16‐h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24‐h horizon. Significance: Smart shirt‐based nocturnal sleep analysis holds promise as a non‐invasive approach for seizure‐day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long‐term recordings, could pave the way for the development of innovative and practical seizure forecasting devices. Plain Language Summary: Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach. [ABSTRACT FROM AUTHOR]

Introduction: Operculo-insular epilepsy (OIE) is a rare condition amenable to surgery in well-selected cases. Despite the high rate of neurological complications associated with OIE surgery, most postoperative deficits recover fully and rapidly. We provide insights into this peculiar pattern of functional recovery by investigating the longitudinal reorganization of structural networks after surgery for OIE in 10 patients. Methods: Structural T1 and diffusion-weighted MRIs were performed before surgery (t0) and at 6  months (t1) and 12  months (t2) postoperatively. These images were processed with an original, comprehensive structural connectivity pipeline. Using our method, we performed comparisons between the t0 and t1 timepoints and between the t1 and t2 timepoints to characterize the progressive structural remodeling. Results: We found a widespread pattern of postoperative changes primarily in the surgical hemisphere, most of which consisted of reductions in connectivity strength (CS) and regional graph theoretic measures (rGTM) that reflect local connectivity. We also observed increases in CS and rGTMs predominantly in regions located near the resection cavity and in the contralateral healthy hemisphere. Finally, most structural changes arose in the first six months following surgery (i.e., between t0 and t1). Discussion: To our knowledge, this study provides the first description of postoperative structural connectivity changes following surgery for OIE. The ipsilateral reductions in connectivity unveiled by our analysis may result from the reversal of seizure-related structural alterations following postoperative seizure control. Moreover, the strengthening of connections in peri-resection areas and in the contralateral hemisphere may be compatible with compensatory structural plasticity, a process that could contribute to the recovery of functions seen following operculo-insular resections for focal epilepsy. [ABSTRACT FROM AUTHOR]

The insula is often considered the fifth lobe of the brain and is increasingly recognized as one of the most connected regions in the brain, with widespread connections to cortical and subcortical structures. As a follow-up to our previous tractography work, we investigated the resting-state functional connectivity (rsFC) profiles of insular subregions and assessed their concordance with structural connectivity. We used the CONN toolbox to analyze the rsFC of the same 19 insular regions of interest (ROIs) we used in our prior tractography work and regrouped them into six subregions based on their connectivity pattern similarity. Our analysis of 50 healthy participants confirms the known broad connectivity of the insula and shows novel and specific whole-brain and intra-connectivity patterns of insular subregions. By examining such subregions, our findings provide a more detailed pattern of connectivity than prior studies that may prove useful for comparison between patients. [ABSTRACT FROM AUTHOR]

Objective/Background: Zingiber monophyllum Gagnep., a member of the Zingiberaceae family, is known for its significant biological activities. The current study aimed to determine the volatile components of the ethyl acetate (EtOAc) fractionated extract found in the rhizomes of this species. This is the first report on the chemical composition and bioactivities of Z. monophyllum rhizomes fractionated extract. Methods: The chemical constituents were analyzed and determined using gas chromatography-mass spectrometry (GC-MS). Antioxidant activities were evaluated by 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging and ferric reducing antioxidant power (FRAP) assays using ascorbic acid as a positive control. Antibacterial and antifungal properties of the EtOAc fractionated extract of Z. monophyllum rhizomes were assessed against Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, Enterococcus faecalis, Staphylococcus aureus, Bacillus cereus, and Candida albicans. Density functional theory (DFT) and molecular docking were also employed to illustrate antioxidant and antimicrobial activities. Results: Nine components were identified by GC-MS analysis from the EtOAc fractionated extract of Z. monophyllum rhizomes. (E)-labda-8(17),12-diene-15,16-dial (9), spathulenol (2), and neointermedeol (5) were the major components (21.8%, 16.8%, and 11.9%, respectively). Moderate antioxidant activities of the EtOAc fractionated extract were observed via both the DPPH assay and the FRAP assay using ascorbic acid as the standard compound. The extract demonstrated remarkable antimicrobial activity against all examined microbial strains, except for P. aeruginosa. The DFT study analyzed the antioxidant potential of each component in the fractionated extract. Molecular docking study chose E. faecalis DNA gyrase B, E. coli DNA gyrase B, S. aureus biotin protein ligase, E. faecalis Alanine racemase, and C. albicans N-myristoyltransferase as potential target proteins for antimicrobial activity. Conclusion: In this study, the chemical composition of the EtOAc fractionated extract of Z monophyllum rhizomes was demonstrated through GC-MS analysis for the first time. Nine components, including alloaromadendrene, spathulenol, globulol, τ-cadinol, neointermedeol, aromadendrene oxide-(2), ambrial, (E) -15,16-dinorlabda-8(17),11-dien-13-one, and (E) -lambda-8 (17),12-diene-15,16-dial along with relative content were identified in this fractionated extract. The bioassays revealed that the fractionated extract showed moderate antioxidant activities and significant antimicrobial activities. The antioxidant and antimicrobial potential of each component was also theoretically examined by the DFT study and molecular docking study, respectively. [ABSTRACT FROM AUTHOR]