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Corsi MC, Troisi Lopez E, Sorrentino P, Cuozzo S, Danieli A, Bonanni P, Duma GM. Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics. Sci Rep 2024; 14:14039. [PMID: 38890363 DOI: 10.1038/s41598-024-64870-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
The epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. We aimed at building an automatable workflow, helping the clinicians in the diagnosis of temporal lobe epilepsy (TLE). We hypothesized that neuronal avalanches (NA) represent a feature better encapsulating the rich brain dynamics compared to classically used functional connectivity measures (Imaginary Coherence; ImCoh). We analyzed large-scale activation bursts (NA) from source estimation of resting-state electroencephalography. Using a support vector machine, we reached a classification accuracy of TLE versus controls of 0.86 ± 0.08 (SD) and an area under the curve of 0.93 ± 0.07. The use of NA features increase by around 16% the accuracy of diagnosis prediction compared to ImCoh. Classification accuracy increased with larger signal duration, reaching a plateau at 5 min of recording. To summarize, NA represents an interpretable feature for an automated epilepsy identification, being related with intrinsic neuronal timescales of pathology-relevant regions.
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Affiliation(s)
- Marie-Constance Corsi
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute -ICM, CNRS, Inria, Inserm, AP-HP, Hopital de la Pitié Salpêtrière, 75013, Paris, France.
| | - Emahnuel Troisi Lopez
- Institute of Applied Sciences and Intelligent Systems of National Research Council, Pozzuoli, Italy
| | - Pierpaolo Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, 13005, Marseille, France.
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro, 07100, Sassari, Italy.
| | - Simone Cuozzo
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Via Costa Alta 37, 31015, Conegliano, Treviso, Italy
| | - Alberto Danieli
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Via Costa Alta 37, 31015, Conegliano, Treviso, Italy
| | - Paolo Bonanni
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Via Costa Alta 37, 31015, Conegliano, Treviso, Italy
| | - Gian Marco Duma
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Via Costa Alta 37, 31015, Conegliano, Treviso, Italy
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Goldenholz DM, Eccleston C, Moss R, Westover MB. Prospective validation of a seizure diary forecasting falls short. Epilepsia 2024; 65:1730-1736. [PMID: 38606580 PMCID: PMC11166505 DOI: 10.1111/epi.17984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 03/13/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
OBJECTIVE Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI. RESULTS The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.
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Affiliation(s)
- Daniel M. Goldenholz
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Celena Eccleston
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | | | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- McCance Center for Brain Health, Boston, Massachusetts, USA
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Goldenholz D, Brinkmann BH, Westover MB. How accurate do self-reported seizures need to be for effective medication management in epilepsy? Epilepsia 2024. [PMID: 38776216 DOI: 10.1111/epi.18019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/05/2024] [Accepted: 05/06/2024] [Indexed: 05/24/2024]
Abstract
Studies suggest that self-reported seizure diaries suffer from 50% under-reporting on average. It is unknown to what extent this impacts medication management. This study used simulation to predict the seizure outcomes of a large heterogeneous clinic population treated with a standardized algorithm based on self-reported seizures. Using CHOCOLATES, a state-of-the-art realistic seizure diary simulator, 100 000 patients were simulated over 10 years. A standard algorithm for medication management was employed at 3 month intervals for all patients. The impact on true seizure rates, expected seizure rates, and time-to-steady-dose were computed for self-reporting sensitivities 0%-100%. Time-to-steady-dose and medication use mostly did not depend on sensitivity. True seizure rate decreased minimally with increasing self-reporting in a non-linear fashion, with the largest decreases at low sensitivity rates (0%-10%). This study suggests that an extremely wide range of sensitivity will have similar seizure outcomes when patients are clinically treated using an algorithm similar to the one presented. Conversely, patients with sensitivity ≤10% would be expected to benefit (via lower seizure rates) from objective devices that provide even small improvements in seizure sensitivity.
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Affiliation(s)
- Daniel Goldenholz
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | | | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- McCace Center, Boston, Massachusetts, USA
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Hong R, Zheng T, Marra V, Yang D, Liu JK. Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration. J Neural Eng 2024; 21:021002. [PMID: 38621378 DOI: 10.1088/1741-2552/ad3eb4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Objective: Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details.Approach: Here, we review various types of computational model of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail.Main results: A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioural observations.Significance: With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.
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Affiliation(s)
- Rongqi Hong
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Tingting Zheng
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | | | - Dongping Yang
- Research Centre for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Jian K Liu
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
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Goldenholz DM, Karoly PJ, Viana PF, Nurse E, Loddenkemper T, Schulze-Bonhage A, Vieluf S, Bruno E, Nasseri M, Richardson MP, Brinkmann BH, Westover MB. Minimum clinical utility standards for wearable seizure detectors: A simulation study. Epilepsia 2024; 65:1017-1028. [PMID: 38366862 PMCID: PMC11018505 DOI: 10.1111/epi.17917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/11/2024] [Accepted: 02/01/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. METHODS Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario. RESULTS The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. SIGNIFICANCE The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.
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Affiliation(s)
- Daniel M Goldenholz
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Philippa J Karoly
- Department of Neurology, University of Melbourne, Melbourne, Victoria, Australia
| | - Pedro F Viana
- School of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Ewan Nurse
- Seer Medical, Melbourne, Victoria, Australia
| | - Tobias Loddenkemper
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center Freiburg-University of Freiburg, Freiburg, Germany
| | - Solveig Vieluf
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Elisa Bruno
- School of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Mona Nasseri
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | | | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- McCace Center, Boston, Massachusetts, USA
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Fountain NB, Quigg M, Murchison CF, Carrazana E, Rabinowicz AL. Analysis of seizure-cluster circadian periodicity from a long-term, open-label safety study of diazepam nasal spray. Epilepsia 2024; 65:920-928. [PMID: 38391291 DOI: 10.1111/epi.17911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 02/24/2024]
Abstract
OBJECTIVE Seizure clusters require prompt medical treatment to minimize possible progression to status epilepticus, increased health care use, and disruptions to daily life. Isolated seizures may exhibit cyclical patterns, including circadian and longer rhythms. However, little is known about the cyclical patterns in seizure clusters. This post hoc analysis of data from a long-term, phase 3, open-label, repeat-dose safety study of diazepam nasal spray modeled the periodicity of treated seizure clusters. METHODS Mixed-effects cosinor analysis evaluated circadian rhythmicity, and single component cosinors using 12 and 24 h were used to calculate cosinor parameters (e.g., midline statistic of rhythm, wave ampitude, and acrophase [peak]). Analysis was completed for the full cohort and a consistent cohort of participants with two or more seizure clusters in each of four, 3-month periods. The influence of epilepsy type on cosinor parameters was also analyzed. RESULTS Seizure-cluster events plotted across 24 h showed a bimodal distribution with acrophases (peaks) at ~06:30 and ~18:30. A 12-h plot showed a single peak at ~06:30. Cosinor analyses of the full and consistent cohort aligned, with acrophases for both models predicting peak seizure activity at ~23:30 on a 24-h scale and ~07:30 on a 12-h scale. The consistent cohort was associated with increases in baseline and peak seizure-cluster activity. Analysis by epilepsy type identified distinct trends. Seizure clusters in the focal epilepsy group peaked in the evening (acrophase 19:19), whereas events in the generalized epilepsy group peaked in the morning (acrophase 04:46). Together they compose the bimodal clustering observed over 24 h. SIGNIFICANCE This analysis of seizure clusters treated with diazepam nasal spray demonstrated that seizure clusters occur cyclically in 12- and 24-h time frames similar to that reported with isolated seizures. Further elucidation of these patterns may provide important information for patient care, ranging from improved patient-centered outcomes to seizure-cluster prediction.
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Affiliation(s)
- Nathan B Fountain
- Department of Neurology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Mark Quigg
- Department of Neurology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Charles F Murchison
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Enrique Carrazana
- Neurelis, Inc., San Diego, California, USA
- John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
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Goldenholz DM, Goldenholz SR, Habib S, Westover MB. Inductive reasoning with large language models: a simulated randomized controlled trial for epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.18.24304493. [PMID: 38562831 PMCID: PMC10984041 DOI: 10.1101/2024.03.18.24304493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Importance The analysis of electronic medical records at scale to learn from clinical experience is currently very challenging. The integration of artificial intelligence (AI), specifically foundational large language models (LLMs), into an analysis pipeline may overcome some of the current limitations of modest input sizes, inaccuracies, biases, and incomplete knowledge bases. Objective To explore the effectiveness of using an LLM for generating realistic clinical data and other LLMs for summarizing and synthesizing information in a model system, simulating a randomized clinical trial (RCT) in epilepsy to demonstrate the potential of inductive reasoning via medical chart review. Design An LLM-generated simulated RCT based on a RCT for treatment with an antiseizure medication, cenobamate, including a placebo arm and a full-strength drug arm, evaluated by an LLM-based pipeline versus a human reader. Setting Simulation based on realistic seizure diaries, treatment effects, reported symptoms and clinical notes generated by LLMs with multiple different neurologist writing styles. Participants Simulated cohort of 240 patients, divided 1:1 into placebo and drug arms. Intervention Utilization of LLMs for the generation of clinical notes and for the synthesis of data from these notes, aiming to evaluate the efficacy and safety of cenobamate in seizure control either with a human evaluator or AI-pipeline. Measures The AI and human analysis focused on identifying the number of seizures, symptom reports, and treatment efficacy, with statistical analysis comparing the 50%-responder rate and median percentage change between the placebo and drug arms, as well as side effect rates in each arm. Results AI closely mirrored human analysis, demonstrating the drug's efficacy with marginal differences (<3%) in identifying both drug efficacy and reported symptoms. Conclusions and Relevance This study showcases the potential of LLMs accurately simulate and analyze clinical trials. Significantly, it highlights the ability of LLMs to reconstruct essential trial elements, identify treatment effects, and recognize reported symptoms, within a realistic clinical framework. The findings underscore the relevance of LLMs in future clinical research, offering a scalable, efficient alternative to traditional data mining methods without the need for specialized medical language training.
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Affiliation(s)
- Daniel M Goldenholz
- Department of Neurology, Harvard Medical School, Boston USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston USA
| | - Shira R Goldenholz
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston USA
| | - Sara Habib
- Department of Neurology, Harvard Medical School, Boston USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston USA
| | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Boston USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston USA
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Alshakhouri M, Sharpe C, Bergin P, Sumner RL. Female sex steroids and epilepsy: Part 2. A practical and human focus on catamenial epilepsy. Epilepsia 2024; 65:569-582. [PMID: 37925609 DOI: 10.1111/epi.17820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 11/06/2023]
Abstract
Catamenial epilepsy is the best described and most researched sex steroid-specific seizure exacerbation. Yet despite this there are no current evidence-based treatments, nor an accepted diagnostic tool. The best tool we currently have is tracking seizures over menstrual cycles; however, the reality of tracking seizures and menstrual cycles is fraught with challenges. In Part 1 of this two-part review, we outlined the often complex and reciprocal relationship between seizures and sex steroids. An adaptable means of tracking is required. In this review, we outline the extent and limitations of current knowledge on catamenial epilepsy. We use sample data to show how seizure exacerbations can be tracked in short/long and even irregular menstrual cycles. We describe how seizure severity, an often overlooked and underresearched form of catamenial seizure exacerbation, can also be tracked. Finally, given the lack of treatment options for females profoundly affected by catamenial epilepsy, Section 3 focuses on current methods and models for researching sex steroids and seizures as well as limitations and future directions. To permit more informative, mechanism-focused research in humans, the need for both a consistent classification of catamenial epilepsy and an objective biomarker is highlighted.
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Affiliation(s)
| | - Cynthia Sharpe
- Department of Paediatric Neurology, Starship Children's Health, Auckland, New Zealand
| | - Peter Bergin
- Neurology Department, Auckland Hospital, Te Whatu Ora, Auckland, New Zealand
| | - Rachael L Sumner
- School of Pharmacy, University of Auckland, Auckland, New Zealand
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Löscher W. On hidden factors and design-associated errors that may lead to data misinterpretation: An example from preclinical research on the potential seasonality of neonatal seizures. Epilepsia 2024; 65:287-292. [PMID: 38037258 DOI: 10.1111/epi.17840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/02/2023]
Abstract
Unintentional misinterpretation of research in published biomedical reports that is not based on statistical flaws is often underrecognized, despite its possible impact on science, clinical practice, and public health. Important causes of such misinterpretation of scientific data, resulting in either false positive or false negative conclusions, include design-associated errors and hidden (or latent) variables that are not easily recognized during data analysis. Furthermore, cognitive biases, such as the inclination to seek patterns in data whether they exist or not, may lead to misinterpretation of data. Here, we give an example of these problems from hypothesis-driven research on the potential seasonality of neonatal seizures in a rat model of birth asphyxia. This commentary aims to raise awareness among the general scientific audience about the issues related to the presence of unintentional misinterpretation in published reports.
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Affiliation(s)
- Wolfgang Löscher
- Translational Neuropharmacology Lab, NIFE, Department of Experimental Otology of the ENT Clinics, Hannover Medical School, Hannover, Germany
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Alsammani A, Stacey WC, Gliske SV. Estimation of Circular Statistics in the Presence of Measurement Bias. IEEE J Biomed Health Inform 2024; 28:1089-1100. [PMID: 38032776 PMCID: PMC10964323 DOI: 10.1109/jbhi.2023.3334684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Circular statistics and Rayleigh tests are important tools for analyzing cyclic events. However, current methods are not robust to significant measurement bias, especially incomplete or otherwise non-uniform sampling. One example is studying 24-cyclicity but having data not recorded uniformly over the full 24-hour cycle. Our objective is to present a robust method to estimate circular statistics and their statistical significance in the presence of incomplete or otherwise non-uniform sampling. Our method is to solve the underlying Fredholm Integral Equation for the more general problem, estimating probability distributions in the context of imperfect measurements, with our circular statistics in the presence of incomplete/non-uniform sampling being one special case. The method is based on linear parameterizations of the underlying distributions. We simulated the estimation error of our approach for several toy examples as well as for a real-world example: analyzing the 24-hour cyclicity of an electrographic biomarker of epileptic tissue controlled for states of vigilance. We also evaluated the accuracy of the Rayleigh test statistic versus the direct simulation of statistical significance. Our method shows a very low estimation error. In the real-world example, the corrected moments had a root mean square error of [Formula: see text]. In contrast, the Rayleigh test statistic overestimated the statistical significance and was thus not reliable. The presented methods thus provide a robust solution to computing circular moments even with incomplete or otherwise non-uniform sampling. Since Rayleigh test statistics cannot be used in this circumstance, direct estimation of significance is the preferable option for estimating statistical significance.
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11
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Goldenholz DM, Eccleston C, Moss R, Westover MB. Prospective validation of a seizure diary forecasting falls short. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.11.24301175. [PMID: 38260666 PMCID: PMC10802655 DOI: 10.1101/2024.01.11.24301175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
OBJECTIVE Recently, a deep learning AI model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSS) compared random forecasts and simple moving average forecasts to the AI. RESULTS The AI had an AUC of 0.82. At the group level, the AI outperformed random forecasting (BSS=0.53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (non-verified) diaries (with presumed under-reporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE The previously developed AI forecasting tool did not outperform a very simple moving average forecasting this prospective cohort, suggesting that the AI model should be replaced.
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Affiliation(s)
- Daniel M Goldenholz
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
| | - Celena Eccleston
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
| | | | - M Brandon Westover
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
- Dept. of Neurology, Massachusetts General Hospital, Boston 02114 MA
- McCance Center for Brain Health, Boston, 02114 MA
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12
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Ecochard R, Leiva R, Bouchard TP, Van Lamsweerde A, Pearson JT, Stanford JB, Gronfier C. The menstrual cycle is influenced by weekly and lunar rhythms. Fertil Steril 2024:S0015-0282(23)02076-9. [PMID: 38206269 DOI: 10.1016/j.fertnstert.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024]
Abstract
OBJECTIVE To study whether the menstrual cycle has a circaseptan (7 days) rhythm and whether it is associated with the lunar cycle (also defined as the synodic month, it is the cycle of the phases of the Moon as seen from Earth, averaging 29.5 days in length). DESIGN Cross-sectional study. SUBJECTS A total of 35,940 European and North American women aged 18-40 years. EXPOSURE Data were collected in real-life conditions. INTERVENTION No intervention was performed. MAIN OUTCOME MEASURE The onset of menstruation was assessed in prospectively measured menstrual cycles (311,064 cycles) over 3 full years (2019-2021). Associations were calculated between the onset of menstruation and the day of the week, and between the onset of menstruation and the lunar phase. RESULTS In this large data set, a circaseptan (7-day) rhythmicity of menstruation was observed, with a peak (acrophase) of menstrual onset on Thursdays and Fridays. This circaseptan rhythm was observed in every age group, in every phase of the lunar cycle, and in all seasons. This feature was most pronounced for cycle durations between 27 and 29 days. In winter, the circaseptan rhythm was found in cycles of 27-29 days, but not in other cycle lengths. A circalunar rhythm was also statistically significant, but not as clearly defined as the circaseptan rhythm. The peak (acrophase) of the circalunar rhythm of menstrual onset varied according to the season. In addition, there was a small but statistically significant interaction between the circaseptan rhythm and the lunar cycle. CONCLUSION Although relatively small in amplitude, the weekly rhythm of menstruation was statistically significant. Menstruation occurs more often on Thursdays and Fridays than on other days of the week. This is particularly true for women whose cycles last between 27 and 29 days. Circalunar rhythmicity was also statistically significant. However, it is less pronounced than the weekly rhythm.
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Affiliation(s)
- René Ecochard
- Pôle de Santé Publique, Service de Biostatistique, Lyon, France; Laboratoire Biostatistique Santé, Université Claude Bernard Lyon I, Lyon, France
| | - Rene Leiva
- Bruyère Research Institute, CT Lamont Primary Health Care Research Centre, Ottawa, Ontario, Canada; Department of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Thomas P Bouchard
- Department of Family Medicine, University of Calgary, Calgary, Alberta, Canada
| | | | | | - Joseph B Stanford
- Office of Cooperative Reproductive Health, Division of Public Health Department of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah
| | - Claude Gronfier
- Centre de Recherche en Neurosciences de Lyon (CRNL), Neurocampus, Université de Lyon, Lyon, France.
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Baud MO, Proix T, Gregg NM, Brinkmann BH, Nurse ES, Cook MJ, Karoly PJ. Seizure forecasting: Bifurcations in the long and winding road. Epilepsia 2023; 64 Suppl 4:S78-S98. [PMID: 35604546 PMCID: PMC9681938 DOI: 10.1111/epi.17311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/28/2022]
Abstract
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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Affiliation(s)
- Maxime O Baud
- Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan S Nurse
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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Viana PF, Attia TP, Nasseri M, Duun-Henriksen J, Biondi A, Winston JS, Martins IP, Nurse ES, Dümpelmann M, Schulze-Bonhage A, Freestone DR, Kjaer TW, Richardson MP, Brinkmann BH. Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models. Epilepsia 2023; 64 Suppl 4:S124-S133. [PMID: 35395101 PMCID: PMC9547037 DOI: 10.1111/epi.17252] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. METHODS We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. RESULTS Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. SIGNIFICANCE This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.
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Affiliation(s)
- Pedro F. Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Tal Pal Attia
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Mona Nasseri
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- School of Engineering, University of North Florida, Jacksonville, Florida, USA
| | | | - Andrea Biondi
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
| | - Joel S. Winston
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
| | | | - Ewan S. Nurse
- Seer Medical, Melbourne, Victoria, Australia
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Matthias Dümpelmann
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Dean R. Freestone
- Seer Medical, Melbourne, Victoria, Australia
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Troels W. Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
- National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust, London, UK
| | - Benjamin H. Brinkmann
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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Andrzejak RG, Zaveri HP, Schulze‐Bonhage A, Leguia MG, Stacey WC, Richardson MP, Kuhlmann L, Lehnertz K. Seizure forecasting: Where do we stand? Epilepsia 2023; 64 Suppl 3:S62-S71. [PMID: 36780237 PMCID: PMC10423299 DOI: 10.1111/epi.17546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 02/14/2023]
Abstract
A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures-ICTALS 2022-convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being.
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Grants
- NIH NS109062 NIH HHS
- MR/N026063/1 Medical Research Council
- R01 NS109062 NINDS NIH HHS
- R01 NS094399 NINDS NIH HHS
- NIH NS094399 NIH HHS
- Medical Research Council Centre for Neurodevelopmental Disorders
- National Health and Medical Research Council
- National Institutes of Health
- University of Bern, the Inselspital, University Hospital Bern, the Alliance for Epilepsy Research, the Swiss National Science Foundation, UCB, FHC, the Wyss Center for bio‐ and neuro‐engineering, the American Epilepsy Society (AES), the CURE epilepsy Foundation, Ripple neuro, Sintetica, DIXI medical, UNEEG medical and NeuroPace.
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Affiliation(s)
- Ralph G. Andrzejak
- Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
| | | | - Andreas Schulze‐Bonhage
- Epilepsy Center, NeurocenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Marc G. Leguia
- Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
| | - William C. Stacey
- Department of Neurology, Department of Biomedical EngineeringBioInterfaces Institute, University of MichiganAnn ArborMichiganUSA
- Division of NeurologyVA Ann Arbor Medical CenterAnn ArborMichiganUSA
| | - Mark P. Richardson
- School of NeuroscienceInstitute of Psychiatry Psychology and Neuroscience, King's College LondonLondonUK
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information TechnologyMonash UniversityClaytonVictoriaAustralia
| | - Klaus Lehnertz
- Department of EpileptologyUniversity of Bonn Medical CentreBonnGermany
- Helmholtz Institute for Radiation and Nuclear PhysicsUniversity of BonnBonnGermany
- Interdisciplinary Center for Complex SystemsUniversity of BonnBonnGermany
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17
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Leguia MG, Rao VR, Tcheng TK, Duun-Henriksen J, Kjaer TW, Proix T, Baud MO. Learning to generalize seizure forecasts. Epilepsia 2023; 64 Suppl 4:S99-S113. [PMID: 36073237 DOI: 10.1111/epi.17406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 09/06/2022] [Accepted: 09/06/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Epilepsy is characterized by spontaneous seizures that recur at unexpected times. Nonetheless, using years-long electroencephalographic (EEG) recordings, we previously found that patient-reported seizures consistently occur when interictal epileptiform activity (IEA) cyclically builds up over days. This multidien (multiday) interictal-ictal relationship, which is shared across patients, may bear phasic information for forecasting seizures, even if individual patterns of seizure timing are unknown. To test this rigorously in a large retrospective dataset, we pretrained algorithms on data recorded from a group of patients, and forecasted seizures in other, previously unseen patients. METHODS We used retrospective long-term data from participants (N = 159) in the RNS System clinical trials, including intracranial EEG recordings (icEEG), and from two participants in the UNEEG Medical clinical trial of a subscalp EEG system (sqEEG). Based on IEA detections, we extracted instantaneous multidien phases and trained generalized linear models (GLMs) and recurrent neural networks (RNNs) to forecast the probability of seizure occurrence at a 24-h horizon. RESULTS With GLMs and RNNs, seizures could be forecasted above chance in 79% and 81% of previously unseen subjects with a median discrimination of area under the curve (AUC) = .70 and .69 and median Brier skill score (BSS) = .07 and .08. In direct comparison, individualized models had similar median performance (AUC = .67, BSS = .08), but for fewer subjects (60%). Moreover, calibration of pretrained models could be maintained to accommodate different seizure rates across subjects. SIGNIFICANCE Our findings suggest that seizure forecasting based on multidien cycles of IEA can generalize across patients, and may drastically reduce the amount of data needed to issue forecasts for individuals who recently started collecting chronic EEG data. In addition, we show that this generalization is independent of the method used to record seizures (patient-reported vs. electrographic) or IEA (icEEG vs. sqEEG).
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Affiliation(s)
- Marc G Leguia
- Wyss Center Fellow, Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, University of California, San Francisco, California, USA
| | | | | | - Troels W Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
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18
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Rao VR, Rolston JD. Unearthing the mechanisms of responsive neurostimulation for epilepsy. COMMUNICATIONS MEDICINE 2023; 3:166. [PMID: 37974025 PMCID: PMC10654422 DOI: 10.1038/s43856-023-00401-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023] Open
Abstract
Responsive neurostimulation (RNS) is an effective therapy for people with drug-resistant focal epilepsy. In clinical trials, RNS therapy results in a meaningful reduction in median seizure frequency, but the response is highly variable across individuals, with many receiving minimal or no benefit. Understanding why this variability occurs will help improve use of RNS therapy. Here we advocate for a reexamination of the assumptions made about how RNS reduces seizures. This is now possible due to large patient cohorts having used this device, some long-term. Two foundational assumptions have been that the device's intracranial leads should target the seizure focus/foci directly, and that stimulation should be triggered only in response to detected epileptiform activity. Recent studies have called into question both hypotheses. Here, we discuss these exciting new studies and suggest future approaches to patient selection, lead placement, and device programming that could improve clinical outcomes.
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Affiliation(s)
- Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
| | - John D Rolston
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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19
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Pracucci E, Graham RT, Alberio L, Nardi G, Cozzolino O, Pillai V, Pasquini G, Saieva L, Walsh D, Landi S, Zhang J, Trevelyan AJ, Ratto GM. Daily rhythm in cortical chloride homeostasis underpins functional changes in visual cortex excitability. Nat Commun 2023; 14:7108. [PMID: 37925453 PMCID: PMC10625537 DOI: 10.1038/s41467-023-42711-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 10/19/2023] [Indexed: 11/06/2023] Open
Abstract
Cortical activity patterns are strongly modulated by fast synaptic inhibition mediated through ionotropic, chloride-conducting receptors. Consequently, chloride homeostasis is ideally placed to regulate activity. We therefore investigated the stability of baseline [Cl-]i in adult mouse neocortex, using in vivo two-photon imaging. We found a two-fold increase in baseline [Cl-]i in layer 2/3 pyramidal neurons, from day to night, with marked effects upon both physiological cortical processing and seizure susceptibility. Importantly, the night-time activity can be converted to the day-time pattern by local inhibition of NKCC1, while inhibition of KCC2 converts day-time [Cl-]i towards night-time levels. Changes in the surface expression and phosphorylation of the cation-chloride cotransporters, NKCC1 and KCC2, matched these pharmacological effects. When we extended the dark period by 4 h, mice remained active, but [Cl-]i was modulated as for animals in normal light cycles. Our data thus demonstrate a daily [Cl-]i modulation with complex effects on cortical excitability.
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Affiliation(s)
- Enrico Pracucci
- National Enterprise for nanoScience and nanoTechnology (NEST), Istituto Nanoscienze, Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127, Pisa, Italy
| | - Robert T Graham
- Newcastle University Biosciences Institute, Medical School, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - Laura Alberio
- Newcastle University Biosciences Institute, Medical School, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - Gabriele Nardi
- National Enterprise for nanoScience and nanoTechnology (NEST), Istituto Nanoscienze, Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127, Pisa, Italy
| | - Olga Cozzolino
- National Enterprise for nanoScience and nanoTechnology (NEST), Istituto Nanoscienze, Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127, Pisa, Italy
| | - Vinoshene Pillai
- National Enterprise for nanoScience and nanoTechnology (NEST), Istituto Nanoscienze, Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127, Pisa, Italy
| | - Giacomo Pasquini
- National Enterprise for nanoScience and nanoTechnology (NEST), Istituto Nanoscienze, Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127, Pisa, Italy
| | - Luciano Saieva
- Newcastle University Biosciences Institute, Medical School, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - Darren Walsh
- Newcastle University Biosciences Institute, Medical School, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - Silvia Landi
- Institute of Neuroscience CNR, Pisa, Italy
- National Enterprise for nanoScience and nanoTechnology (NEST), Istituto Nanoscienze, Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127, Pisa, Italy
| | - Jinwei Zhang
- Institute of Biomedical and Clinical Sciences, Medical School, College of Medicine and Institute of Health, University of Exeter, Hatherly Laboratories, Exeter, EX4 4PS, UK
- State Key Laboratory of Chemical Biology. Research Center of Chemical Kinomics, Shangai. Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Andrew J Trevelyan
- Newcastle University Biosciences Institute, Medical School, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK.
| | - Gian-Michele Ratto
- National Enterprise for nanoScience and nanoTechnology (NEST), Istituto Nanoscienze, Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127, Pisa, Italy.
- Institute of Neuroscience CNR, Pisa, Italy.
- Padova Neuroscience Center, Padova, Italy.
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20
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Ojemann WKS, Scheid BH, Mouchtaris S, Lucas A, LaRocque JJ, Aguila C, Ashourvan A, Caciagli L, Davis KA, Conrad EC, Litt B. Resting-state background features demonstrate multidien cycles in long-term EEG device recordings. Brain Stimul 2023; 16:1709-1718. [PMID: 37979654 DOI: 10.1016/j.brs.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. OBJECTIVE/HYPOTHESIS We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may temporarily interrupt these cycles. METHODS We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. RESULTS Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56-0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). CONCLUSIONS Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may temporarily interrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.
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Affiliation(s)
- William K S Ojemann
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA.
| | - Brittany H Scheid
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Sofia Mouchtaris
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Alfredo Lucas
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; University of Pennsylvania, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Joshua J LaRocque
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
| | - Carlos Aguila
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Arian Ashourvan
- The University of Kansas, Department of Psychology, 1415 Jayhawk Blvd, Lawrence, KS, 66045, USA
| | - Lorenzo Caciagli
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Kathryn A Davis
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
| | - Erin C Conrad
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
| | - Brian Litt
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
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21
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Sides K, Kilungeja G, Tapia M, Kreidl P, Brinkmann BH, Nasseri M. Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1227228. [PMID: 37928057 PMCID: PMC10621043 DOI: 10.3389/fnetp.2023.1227228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/19/2023] [Indexed: 11/07/2023]
Abstract
This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p< 0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p> 0.05). There was a significant difference between ovulating and non-ovulating cycles (p< 0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (μS), respectively.
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Affiliation(s)
- Krystal Sides
- School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Grentina Kilungeja
- School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Matthew Tapia
- School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Patrick Kreidl
- School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Mona Nasseri
- School of Engineering, University of North Florida, Jacksonville, FL, United States
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
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Marinelli I, Walker JJ, Seneviratne U, D’Souza W, Cook MJ, Anderson C, Bagshaw AP, Lightman SL, Woldman W, Terry JR. Circadian distribution of epileptiform discharges in epilepsy: Candidate mechanisms of variability. PLoS Comput Biol 2023; 19:e1010508. [PMID: 37797040 PMCID: PMC10581478 DOI: 10.1371/journal.pcbi.1010508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/17/2023] [Accepted: 09/10/2023] [Indexed: 10/07/2023] Open
Abstract
Epilepsy is a serious neurological disorder characterised by a tendency to have recurrent, spontaneous, seizures. Classically, seizures are assumed to occur at random. However, recent research has uncovered underlying rhythms both in seizures and in key signatures of epilepsy-so-called interictal epileptiform activity-with timescales that vary from hours and days through to months. Understanding the physiological mechanisms that determine these rhythmic patterns of epileptiform discharges remains an open question. Many people with epilepsy identify precipitants of their seizures, the most common of which include stress, sleep deprivation and fatigue. To quantify the impact of these physiological factors, we analysed 24-hour EEG recordings from a cohort of 107 people with idiopathic generalized epilepsy. We found two subgroups with distinct distributions of epileptiform discharges: one with highest incidence during sleep and the other during day-time. We interrogated these data using a mathematical model that describes the transitions between background and epileptiform activity in large-scale brain networks. This model was extended to include a time-dependent forcing term, where the excitability of nodes within the network could be modulated by other factors. We calibrated this forcing term using independently-collected human cortisol (the primary stress-responsive hormone characterised by circadian and ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants. We found that either the dynamics of cortisol or sleep stage transition, or a combination of both, could explain most of the observed distributions of epileptiform discharges. Our findings provide conceptual evidence for the existence of underlying physiological drivers of rhythms of epileptiform discharges. These findings should motivate future research to explore these mechanisms in carefully designed experiments using animal models or people with epilepsy.
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Affiliation(s)
- Isabella Marinelli
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Jamie J. Walker
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
| | - Udaya Seneviratne
- Department of Neurosciences, Monash Health, Clayton, Australia
- Department of Neuroscience, St. Vincent’s Hospital, University of Melbourne, Melbourne, Australia
| | - Wendyl D’Souza
- Department of Medicine, St. Vincent’s Hospital, University of Melbourne, Melbourne, Australia
| | - Mark J. Cook
- Department of Neuroscience, St. Vincent’s Hospital, University of Melbourne, Melbourne, Australia
| | - Clare Anderson
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia
- Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Andrew P. Bagshaw
- Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Stafford L. Lightman
- Bristol Medical School: Translational Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - John R. Terry
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
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23
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Goldenholz DM, Goldenholz EB, Kaptchuk TJ. Quantifying and controlling the impact of regression to the mean on randomized controlled trials in epilepsy. Epilepsia 2023; 64:2635-2643. [PMID: 37505116 PMCID: PMC10592227 DOI: 10.1111/epi.17730] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023]
Abstract
OBJECTIVE Randomized controlled trials (RCTs) in epilepsy for drug treatments are plagued by high costs. One potential remedy is to reduce placebo response via better control over regression to the mean (RTM). Here, RTM represents an initial observed seizure rate higher than the long-term average, which gradually settles closer to the average, resulting in apparent response to treatment. This study used simulation to clarify the relationship between eligibility criteria and RTM. METHODS Using a statistically realistic seizure diary simulator, the impact of RTM on placebo response and trial efficacy was explored by varying eligibility criteria for a traditional treatment phase II/III RCT for drug-resistant epilepsy. RESULTS When the baseline period was included in the eligibility criteria, increasingly larger fractions of RTM were observed (25%-47% vs. 23%-25%). Higher fractions of RTM corresponded with higher expected placebo responses (50% responder rate [RR50]: 2%-9% vs. 0%-8%) and lower statistical efficacy (RR50: 47%-67% vs. 47%-81%). The exclusion of baseline from eligibility criteria was shown to decrease the number of patients needed by roughly 30%. SIGNIFICANCE The manipulation of eligibility criteria for RCTs has a predictable and important impact on RTM, and therefore on placebo response; the difference between drug and placebo was more easily detected. This in turn impacts trial efficacy and therefore cost. This study found dramatic improvements in efficacy and cost when baseline was not included in eligibility.
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Affiliation(s)
| | | | - Ted J Kaptchuk
- Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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24
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Schulze‐Bonhage A, Richardson MP, Brandt A, Zabler N, Dümpelmann M, San Antonio‐Arce V. Cyclical underreporting of seizures in patient-based seizure documentation. Ann Clin Transl Neurol 2023; 10:1863-1872. [PMID: 37608738 PMCID: PMC10578895 DOI: 10.1002/acn3.51880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 07/18/2023] [Indexed: 08/24/2023] Open
Abstract
OBJECTIVE Circadian and multidien cycles of seizure occurrence are increasingly discussed as to their biological underpinnings and in the context of seizure forecasting. This study analyzes if patient reported seizures provide valid data on such cyclical occurrence. METHODS We retrospectively studied if circadian cycles derived from patient-based reporting reflect the objective seizure documentation in 2003 patients undergoing in-patient video-EEG monitoring. RESULTS Only 24.1% of more than 29000 seizures documented were accompanied by patient notifications. There was cyclical underreporting of seizures with a maximum during nighttime, leading to significant deviations in the circadian distribution of seizures. Significant cyclical deviations were found for focal epilepsies originating from both, frontal and temporal lobes, and for different seizure types (in particular, focal unaware and focal to bilateral tonic-clonic seizures). INTERPRETATION Patient seizure diaries may reflect a cyclical reporting bias rather than the true circadian seizure distributions. Cyclical underreporting of seizures derived from patient-based reports alone may lead to suboptimal treatment schemes, to an underestimation of seizure-associated risks, and may pose problems for valid seizure forecasting. This finding strongly supports the use of objective measures to monitor cyclical distributions of seizures and for studies and treatment decisions based thereon.
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Affiliation(s)
- Andreas Schulze‐Bonhage
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
- European Reference Network EpiCARE
| | - Mark P. Richardson
- Division of NeuroscienceInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - Armin Brandt
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Nicolas Zabler
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Matthias Dümpelmann
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Victoria San Antonio‐Arce
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
- European Reference Network EpiCARE
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25
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Dallmer-Zerbe I, Jiruska P, Hlinka J. Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy. Epilepsia 2023; 64:2221-2238. [PMID: 37340565 DOI: 10.1111/epi.17690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 06/22/2023]
Abstract
Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long-term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
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26
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Andrzejak RG, Espinoso A, García-Portugués E, Pewsey A, Epifanio J, Leguia MG, Schindler K. High expectations on phase locking: Better quantifying the concentration of circular data. CHAOS (WOODBURY, N.Y.) 2023; 33:091106. [PMID: 37756609 DOI: 10.1063/5.0166468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023]
Abstract
The degree to which unimodal circular data are concentrated around the mean direction can be quantified using the mean resultant length, a measure known under many alternative names, such as the phase locking value or the Kuramoto order parameter. For maximal concentration, achieved when all of the data take the same value, the mean resultant length attains its upper bound of one. However, for a random sample drawn from the circular uniform distribution, the expected value of the mean resultant length achieves its lower bound of zero only as the sample size tends to infinity. Moreover, as the expected value of the mean resultant length depends on the sample size, bias is induced when comparing the mean resultant lengths of samples of different sizes. In order to ameliorate this problem, here, we introduce a re-normalized version of the mean resultant length. Regardless of the sample size, the re-normalized measure has an expected value that is essentially zero for a random sample from the circular uniform distribution, takes intermediate values for partially concentrated unimodal data, and attains its upper bound of one for maximal concentration. The re-normalized measure retains the simplicity of the original mean resultant length and is, therefore, easy to implement and compute. We illustrate the relevance and effectiveness of the proposed re-normalized measure for mathematical models and electroencephalographic recordings of an epileptic seizure.
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Affiliation(s)
- Ralph G Andrzejak
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
| | - Anaïs Espinoso
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
- Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Carrer Baldiri Reixac 10-12, 08028 Barcelona, Catalonia, Spain
| | - Eduardo García-Portugués
- Department of Statistics, Universidad Carlos III de Madrid, Av. de la Universidad 30, 28911 Leganés, Madrid, Spain
| | - Arthur Pewsey
- Mathematics Department, Escuela Politécnica, Universidad de Extremadura, Av. de la Universidad s/n, 10003 Cáceres, Spain
| | - Jacopo Epifanio
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
| | - Marc G Leguia
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
| | - Kaspar Schindler
- Sleep Wake Epilepsy Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
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27
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Dallmer-Zerbe I, Jajcay N, Chvojka J, Janca R, Jezdik P, Krsek P, Marusic P, Jiruska P, Hlinka J. Computational modeling allows unsupervised classification of epileptic brain states across species. Sci Rep 2023; 13:13436. [PMID: 37596382 PMCID: PMC10439162 DOI: 10.1038/s41598-023-39867-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/01/2023] [Indexed: 08/20/2023] Open
Abstract
Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
| | - Nikola Jajcay
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic
- National Institute of Mental Health, 250 67, Klecany, Czech Republic
| | - Jan Chvojka
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Radek Janca
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Petr Jezdik
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Pavel Krsek
- Department of Paediatric Neurology, Second Faculty of Medicine, Motol University Hospital, Charles University, 150 06, Prague, Czech Republic
| | - Petr Marusic
- Department of Neurology, Second Faculty of Medicine, Motol University Hospital, Charles University, 150 06, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic.
- National Institute of Mental Health, 250 67, Klecany, Czech Republic.
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28
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Næsgaard JAR, Gjerstad L, Heuser K, Taubøll E. Biological rhythms and epilepsy treatment. Front Neurol 2023; 14:1153975. [PMID: 37638185 PMCID: PMC10453794 DOI: 10.3389/fneur.2023.1153975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023] Open
Abstract
Approximately one-third of patients with epilepsy are drug-refractory, necessitating novel treatment approaches. Chronopharmacology, which adjusts pharmacological treatment to physiological variations in seizure susceptibility and drug responsiveness, offers a promising strategy to enhance efficacy and tolerance. This narrative review provides an overview of the biological foundations for rhythms in seizure activity, clinical implications of seizure patterns through case reports, and the potential of chronopharmacological strategies to improve treatment. Biological rhythms, including circadian and infradian rhythms, play an important role in epilepsy. Understanding seizure patterns may help individualize treatment decisions and optimize therapeutic outcomes. Altering drug concentrations based on seizure risk periods, adjusting administration times, and exploring hormone therapy are potential strategies. Large-scale randomized controlled trials are needed to evaluate the efficacy and safety of differential and intermittent treatment approaches. By tailoring treatment to individual seizure patterns and pharmacological properties, chronopharmacology offers a personalized approach to improve outcomes in patients with epilepsy.
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Affiliation(s)
| | - Leif Gjerstad
- Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Division of Clinical Neuroscience, ERGO – Epilepsy Research Group of Oslo, Oslo University Hospital, Oslo, Norway
| | - Kjell Heuser
- Department of Neurology, Division of Clinical Neuroscience, ERGO – Epilepsy Research Group of Oslo, Oslo University Hospital, Oslo, Norway
| | - Erik Taubøll
- Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Division of Clinical Neuroscience, ERGO – Epilepsy Research Group of Oslo, Oslo University Hospital, Oslo, Norway
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29
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Schroeder GM, Karoly PJ, Maturana M, Panagiotopoulou M, Taylor PN, Cook MJ, Wang Y. Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states. Brain Commun 2023; 5:fcad205. [PMID: 37693811 PMCID: PMC10484289 DOI: 10.1093/braincomms/fcad205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/07/2023] [Accepted: 07/18/2023] [Indexed: 09/12/2023] Open
Abstract
Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear. Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterized the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states. We then compared seizure network state occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate. In most patients, the occurrence or duration of at least one seizure network state was associated with the time since implantation. Some patients had one or more seizure network states that were associated with phases of circadian and/or multidien spike rate cycles. A given seizure network state's occurrence and duration were usually not associated with the same timescale. Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a seizure network state's occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures and similar principles likely apply to other neurological conditions.
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Affiliation(s)
- Gabrielle M Schroeder
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Philippa J Karoly
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
- Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Matias Maturana
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
- Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria 3010, Australia
- Research Department, Seer Medical Pty Ltd., Melbourne, Victoria 3000, Australia
| | - Mariella Panagiotopoulou
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
- UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Mark J Cook
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
- UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
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30
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Ojemann WK, Scheid BH, Mouchtaris S, Lucas A, LaRocque JJ, Aguila C, Ashourvan A, Caciagli L, Davis KA, Conrad EC, Litt B. Resting-state background features demonstrate multidien cycles in long-term EEG device recordings. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.05.23291521. [PMID: 37461688 PMCID: PMC10350154 DOI: 10.1101/2023.07.05.23291521] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Background Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. Objective/Hypothesis We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may disrupt these cycles. Methods We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. Results Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56 - 0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). Conclusions Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may disrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.
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Affiliation(s)
- William K.S. Ojemann
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Brittany H. Scheid
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Sofia Mouchtaris
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Alfredo Lucas
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- University of Pennsylvania, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104
| | - Joshua J. LaRocque
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
| | - Carlos Aguila
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Arian Ashourvan
- The University of Kansas, Department of Psychology, 1415 Jayhawk Blvd. Lawrence, KS 66045
| | - Lorenzo Caciagli
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Kathryn A. Davis
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
| | - Erin C. Conrad
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
| | - Brian Litt
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
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31
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Perversi F, Costa C, Labate A, Lattanzi S, Liguori C, Maschio M, Meletti S, Nobili L, Operto FF, Romigi A, Russo E, Di Bonaventura C. The broad-spectrum activity of perampanel: state of the art and future perspective of AMPA antagonism beyond epilepsy. Front Neurol 2023; 14:1182304. [PMID: 37483446 PMCID: PMC10359664 DOI: 10.3389/fneur.2023.1182304] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/07/2023] [Indexed: 07/25/2023] Open
Abstract
Glutamate is the brain's main excitatory neurotransmitter. Glutamatergic neurons primarily compose basic neuronal networks, especially in the cortex. An imbalance of excitatory and inhibitory activities may result in epilepsy or other neurological and psychiatric conditions. Among glutamate receptors, AMPA receptors are the predominant mediator of glutamate-induced excitatory neurotransmission and dictate synaptic efficiency and plasticity by their numbers and/or properties. Therefore, they appear to be a major drug target for modulating several brain functions. Perampanel (PER) is a highly selective, noncompetitive AMPA antagonist approved in several countries worldwide for treating different types of seizures in various epileptic conditions. However, recent data show that PER can potentially address many other conditions within epilepsy and beyond. From this perspective, this review aims to examine the new preclinical and clinical studies-especially those produced from 2017 onwards-on AMPA antagonism and PER in conditions such as mesial temporal lobe epilepsy, idiopathic and genetic generalized epilepsy, brain tumor-related epilepsy, status epilepticus, rare epileptic syndromes, stroke, sleep, epilepsy-related migraine, cognitive impairment, autism, dementia, and other neurodegenerative diseases, as well as provide suggestions on future research agenda aimed at probing the possibility of treating these conditions with PER and/or other AMPA receptor antagonists.
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Affiliation(s)
| | - Cinzia Costa
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
- Neurological Clinic, S. Maria Della Misericordia Hospital, Perugia, Italy
| | - Angelo Labate
- Neurophysiopatology and Movement Disorders Clinic, University of Messina, Messina, Italy
| | - Simona Lattanzi
- Neurological Clinic, Department of Experimental and Clinical Medicine, Marche Polytechnic University, Ancona, Italy
| | - Claudio Liguori
- Department of Systems Medicine, University of Rome ‘Tor Vergata”, Rome, Italy
- Epilepsy Center, Neurology Unit, University Hospital “Tor Vergata”, Rome, Italy
| | - Marta Maschio
- Center for Tumor-Related Epilepsy, UOSD Neuro-Oncology, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Stefano Meletti
- Neurology Department, University Hospital of Modena, Modena, Italy
- Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio-Emilia, Modena, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, IRCCS Istituto G. Gaslini, Genova, Italy
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genova, Genova, Italy
| | - Francesca Felicia Operto
- Child and Adolescent Neuropsychiatry Unit, Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
- Department of Science of Health, School of Medicine, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Andrea Romigi
- Sleep Medicine Center, Neurological Mediterranean Institute IRCCS Neuromed, Pozzilli, Italy
- Psychology Faculty, International Telematic University Uninettuno, Rome, Italy
| | - Emilio Russo
- Department of Science of Health, School of Medicine, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Carlo Di Bonaventura
- Epilepsy Unit, Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
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Stirling RE, Hidajat CM, Grayden DB, D’Souza WJ, Naim-Feil J, Dell KL, Schneider LD, Nurse E, Freestone D, Cook MJ, Karoly PJ. Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration. Brain 2023; 146:2803-2813. [PMID: 36511881 PMCID: PMC10316760 DOI: 10.1093/brain/awac476] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/24/2022] [Accepted: 11/26/2022] [Indexed: 08/21/2023] Open
Abstract
Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.
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Affiliation(s)
- Rachel E Stirling
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Research Department, Seer Medical, Melbourne 3000, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
| | - Cindy M Hidajat
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Wendyl J D’Souza
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Jodie Naim-Feil
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
| | - Katrina L Dell
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | | | - Ewan Nurse
- Research Department, Seer Medical, Melbourne 3000, Australia
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Dean Freestone
- Research Department, Seer Medical, Melbourne 3000, Australia
| | - Mark J Cook
- Research Department, Seer Medical, Melbourne 3000, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Research Department, Seer Medical, Melbourne 3000, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
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Xiong W, Stirling RE, Payne DE, Nurse ES, Kameneva T, Cook MJ, Viana PF, Richardson MP, Brinkmann BH, Freestone DR, Karoly PJ. Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study. EBioMedicine 2023; 93:104656. [PMID: 37331164 PMCID: PMC10300292 DOI: 10.1016/j.ebiom.2023.104656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 05/30/2023] [Accepted: 05/31/2023] [Indexed: 06/20/2023] Open
Abstract
BACKGROUND Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices. METHOD Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed. FINDINGS The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance. INTERPRETATION The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications. FUNDING This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.
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Affiliation(s)
- Wenjuan Xiong
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
| | - Rachel E Stirling
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia; Seer Medical, Melbourne, Australia
| | | | - Ewan S Nurse
- Seer Medical, Melbourne, Australia; Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia
| | - Tatiana Kameneva
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Mark J Cook
- Seer Medical, Melbourne, Australia; Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia; Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Pedro F Viana
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, UK; Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, UK; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, MN, USA
| | | | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia; Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia; Graeme Clark Institute, The University of Melbourne, Melbourne, Australia.
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Gelbard-Sagiv H, Pardo S, Getter N, Guendelman M, Benninger F, Kraus D, Shriki O, Ben-Sasson S. Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5805. [PMID: 37447653 DOI: 10.3390/s23135805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/15/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
Abstract
Epilepsy, a prevalent neurological disorder, profoundly affects patients' quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection.
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Affiliation(s)
| | - Snir Pardo
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
| | - Nir Getter
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Miriam Guendelman
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Felix Benninger
- Department of Neurology, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Dror Kraus
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Pediatric Neurology, Schneider Children's Medical Center of Israel, Petach Tikva 4920235, Israel
| | - Oren Shriki
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
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Wang Y, Zhuo Z, Wang H. Epilepsy, gut microbiota, and circadian rhythm. Front Neurol 2023; 14:1157358. [PMID: 37273718 PMCID: PMC10232836 DOI: 10.3389/fneur.2023.1157358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 04/24/2023] [Indexed: 06/06/2023] Open
Abstract
In recent years, relevant studies have found changes in gut microbiota (GM) in patients with epilepsy. In addition, impaired sleep and circadian patterns are common symptoms of epilepsy. Moreover, the types of seizures have a circadian rhythm. Numerous reports have indicated that the GM and its metabolites have circadian rhythms. This review will describe changes in the GM in clinical and animal studies under epilepsy and circadian rhythm disorder, respectively. The aim is to determine the commonalities and specificities of alterations in GM and their impact on disease occurrence in the context of epilepsy and circadian disruption. Although clinical studies are influenced by many factors, the results suggest that there are some commonalities in the changes of GM. Finally, we discuss the links among epilepsy, gut microbiome, and circadian rhythms, as well as future research that needs to be conducted.
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Affiliation(s)
- Yao Wang
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhihong Zhuo
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Provincial Key Laboratory of Childhood Epilepsy and Immunology, Zhengzhou, China
- Henan Provincial Children's Neurological Disease Clinical Diagnosis and Treatment Center, Zhengzhou, China
| | - Huaili Wang
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Provincial Key Laboratory of Childhood Epilepsy and Immunology, Zhengzhou, China
- Henan Provincial Children's Neurological Disease Clinical Diagnosis and Treatment Center, Zhengzhou, China
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36
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Winder K, Bobinger T, Seifert F, Mrochen A, Haupenthal D, Knott M, Dörfler A, Hilz MJ, Schwab S, Fröhlich K. Incidence, temporal profile and neuroanatomic correlates of poststroke epilepsy. J Neuroimaging 2023. [PMID: 37129978 DOI: 10.1111/jon.13110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/11/2023] [Accepted: 04/21/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND AND PURPOSE The relationship between ischemic stroke site and occurrence of poststroke epilepsy (PSE) is incompletely understood. This study intended to evaluate incidence and temporal profiles of seizures and to correlate ischemic lesion sites with PSE using voxel-based lesion symptom mapping (VLSM). METHODS Patients with imaging-confirmed first-ever ischemic stroke without prior history of epilepsy were prospectively included. Demographic data, cardiovascular risk factors, and National Institute of Health Stroke Scale (NIHSS) scores were assessed. Data on seizures and modified Rankin scale scores were determined within a 90-day period after stroke onset. Ischemic lesion sites were correlated voxel wise with occurrence of PSE using nonparametric permutation test. Age- and sex-matched patients with first-ever ischemic strokes without PSE after 90 days served as controls for the VLSM analysis. RESULTS The stroke database contained 809 patients (mean age: 68.4 ± 14.2 years) with first-ever imaging-confirmed ischemic strokes without history of epilep. Incidence of PSE after 90-day follow-up was 2.8%. Five additional patients were admitted to the emergency department with a seizure after 90-day follow-up. Fifty percent of the seizures occurred in the acute phase after stroke. PSE patients had higher NIHSS scores and infarct volumes compared to controls without PSE (p < .05). PSE patients had infarcts predominantly involving the cerebral cortex. The hemisphere-specific VLSM analysis shows associations between PSE and damaged voxels in the left-hemispheric temporo-occipital transition zone. CONCLUSIONS The data indicate that PSE occurs in a small proportion of patients with rather large ischemic strokes predominantly involving the cerebral cortex. Especially patients with ischemic lesions in the temporo-occipital cortex are vulnerable to develop PSE.
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Affiliation(s)
- Klemens Winder
- Department of Neurology, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
- Department of Neurology and Stroke Center, Klinik Hirslanden, Zürich, Switzerland
| | - Tobias Bobinger
- Department of Neurology, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Frank Seifert
- Department of Neurology, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Anne Mrochen
- Department of Neurology, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - David Haupenthal
- Department of Neurology, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Michael Knott
- Department of Neuroradiology, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Arnd Dörfler
- Department of Neuroradiology, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Max J Hilz
- Department of Neurology, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Stefan Schwab
- Department of Neurology, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Kilian Fröhlich
- Department of Neurology, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
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37
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Nurse ES, Perera T, Hannon T, Wong V, Fernandes KM, Cook MJ. Rates of event capture of home video EEG. Clin Neurophysiol 2023; 149:12-17. [PMID: 36867914 DOI: 10.1016/j.clinph.2023.02.165] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/19/2023] [Accepted: 02/06/2023] [Indexed: 02/25/2023]
Abstract
OBJECTIVE Recording electrographic and behavioral information during epileptic and other paroxysmal events is important during video electroencephalography (EEG) monitoring. This study was undertaken to measure the event capture rate of an home service operating across Australia using a shoulder-worn EEG device and telescopic pole-mounted camera. METHODS Neurologist reports were accessed retrospectively. Studies with confirmed events were identified and assessed for event capture by recording modality, whether events were reported or discovered, and physiological state. RESULTS 6,265 studies were identified, of which 2,788 (44.50%) had events. A total of 15,691 events were captured, of which 77.89% were reported. The EEG amplifier was active for 99.83% of events. The patient was in view of the camera for 94.90% of events. 84.89% of studies had all events on camera, and 2.65% had zero events on camera (mean = 93.66%, median = 100.00%). 84.42% of events from wakefulness were reported, compared to 54.27% from sleep. CONCLUSIONS Event capture was similar to previously reported rates from home studies, with higher capture rates on video. Most patients have all events captured on camera. SIGNIFICANCE Home monitoring is capable of high rates of event capture, and the use of wide-angle cameras allows for all events to be captured in the majority of studies.
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Affiliation(s)
- Ewan S Nurse
- Seer Medical, Melbourne 3000, Australia; Department of Medicine, St. Vincent's Hospital Melbourne, University of Melbourne, Parkville 3052, Australia
| | | | - Timothy Hannon
- Department of Medicine, St. Vincent's Hospital Melbourne, University of Melbourne, Parkville 3052, Australia
| | - Victoria Wong
- Department of Medicine, St. Vincent's Hospital Melbourne, University of Melbourne, Parkville 3052, Australia
| | - Kiran M Fernandes
- Department of Medicine, St. Vincent's Hospital Melbourne, University of Melbourne, Parkville 3052, Australia
| | - Mark J Cook
- Seer Medical, Melbourne 3000, Australia; Department of Medicine, St. Vincent's Hospital Melbourne, University of Melbourne, Parkville 3052, Australia.
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D'Amora M, Galgani A, Marchese M, Tantussi F, Faraguna U, De Angelis F, Giorgi FS. Zebrafish as an Innovative Tool for Epilepsy Modeling: State of the Art and Potential Future Directions. Int J Mol Sci 2023; 24:ijms24097702. [PMID: 37175408 PMCID: PMC10177843 DOI: 10.3390/ijms24097702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
This article discusses the potential of Zebrafish (ZF) (Danio Rerio), as a model for epilepsy research. Epilepsy is a neurological disorder affecting both children and adults, and many aspects of this disease are still poorly understood. In vivo and in vitro models derived from rodents are the most widely used for studying both epilepsy pathophysiology and novel drug treatments. However, researchers have recently obtained several valuable insights into these two fields of investigation by studying ZF. Despite the relatively simple brain structure of these animals, researchers can collect large amounts of data in a much shorter period and at lower costs compared to classical rodent models. This is particularly useful when a large number of candidate antiseizure drugs need to be screened, and ethical issues are minimized. In ZF, seizures have been induced through a variety of chemoconvulsants, primarily pentylenetetrazol (PTZ), kainic acid (KA), and pilocarpine. Furthermore, ZF can be easily genetically modified to test specific aspects of monogenic forms of human epilepsy, as well as to discover potential convulsive phenotypes in monogenic mutants. The article reports on the state-of-the-art and potential new fields of application of ZF research, including its potential role in revealing epileptogenic mechanisms, rather than merely assessing iatrogenic acute seizure modulation.
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Affiliation(s)
- Marta D'Amora
- Istituto Italiano di Tecnologia, 16163 Genova, Italy
- Department of Biology, University of Pisa, 56125 Pisa, Italy
| | - Alessandro Galgani
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, 56126 Pisa, Italy
| | - Maria Marchese
- Molecular Medicine and Neurobiology-ZebraLab, IRCCS Fondazione Stella Maris, 56128 Pisa, Italy
| | | | - Ugo Faraguna
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, 56126 Pisa, Italy
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, 56128 Pisa, Italy
| | | | - Filippo Sean Giorgi
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, 56126 Pisa, Italy
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Brain temperature in healthy and diseased conditions: A review on the special implications of MRS for monitoring brain temperature. Biomed Pharmacother 2023; 160:114287. [PMID: 36709597 DOI: 10.1016/j.biopha.2023.114287] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 01/30/2023] Open
Abstract
Brain temperature determines not only an individual's cognitive functionality but also the prognosis and mortality rates of many brain diseases. More specifically, brain temperature not only changes in response to different physiological events like yawning and stretching, but also plays a significant pathophysiological role in a number of neurological and neuropsychiatric illnesses. Here, we have outlined the function of brain hyperthermia in both diseased and healthy states, focusing particularly on the amyloid beta aggregation in Alzheimer's disease.
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Wang ET, Chiang S, Haneef Z, Rao VR, Moss R, Vannucci M. BAYESIAN NON-HOMOGENEOUS HIDDEN MARKOV MODEL WITH VARIABLE SELECTION FOR INVESTIGATING DRIVERS OF SEIZURE RISK CYCLING. Ann Appl Stat 2023; 17:333-356. [PMID: 38486612 PMCID: PMC10939012 DOI: 10.1214/22-aoas1630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
A major issue in the clinical management of epilepsy is the unpredictability of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies, which are a stochastic measurement of seizure risk. We consider a Bayesian non-homogeneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers significant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data. For inference, we implement an efficient sampler that employs stochastic search and data augmentation techniques. We evaluate model performance on simulated seizure count data. We then demonstrate the clinical utility of the proposed model by analyzing daily seizure count data from 133 patients with Dravet syndrome collected through the Seizure Tracker™ system, a patient-reported electronic seizure diary. We report on the dynamics of seizure risk cycling, including validation of several known pharmacologic relationships. We also uncover novel findings characterizing the presence and volatility of risk states in Dravet syndrome, which may directly inform counseling to reduce the unpredictability of seizures for patients with this devastating cause of epilepsy.
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Sun S, Wang H. Clocking Epilepsies: A Chronomodulated Strategy-Based Therapy for Rhythmic Seizures. Int J Mol Sci 2023; 24:ijms24044223. [PMID: 36835631 PMCID: PMC9962262 DOI: 10.3390/ijms24044223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/08/2023] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
Epilepsy is a neurological disorder characterized by hypersynchronous recurrent neuronal activities and seizures, as well as loss of muscular control and sometimes awareness. Clinically, seizures have been reported to display daily variations. Conversely, circadian misalignment and circadian clock gene variants contribute to epileptic pathogenesis. Elucidation of the genetic bases of epilepsy is of great importance because the genetic variability of the patients affects the efficacies of antiepileptic drugs (AEDs). For this narrative review, we compiled 661 epilepsy-related genes from the PHGKB and OMIM databases and classified them into 3 groups: driver genes, passenger genes, and undetermined genes. We discuss the potential roles of some epilepsy driver genes based on GO and KEGG analyses, the circadian rhythmicity of human and animal epilepsies, and the mutual effects between epilepsy and sleep. We review the advantages and challenges of rodents and zebrafish as animal models for epileptic studies. Finally, we posit chronomodulated strategy-based chronotherapy for rhythmic epilepsies, integrating several lines of investigation for unraveling circadian mechanisms underpinning epileptogenesis, chronopharmacokinetic and chronopharmacodynamic examinations of AEDs, as well as mathematical/computational modeling to help develop time-of-day-specific AED dosing schedules for rhythmic epilepsy patients.
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Affiliation(s)
- Sha Sun
- Center for Circadian Clocks, Soochow University, Suzhou 215123, China
- School of Biology and Basic Medical Sciences, Suzhou Medical College, Soochow University, Suzhou 215123, China
| | - Han Wang
- Center for Circadian Clocks, Soochow University, Suzhou 215123, China
- School of Biology and Basic Medical Sciences, Suzhou Medical College, Soochow University, Suzhou 215123, China
- Correspondence: or ; Tel.: +86-186-0512-8971
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Reynolds A, Vranic-Peters M, Lai A, Grayden DB, Cook MJ, Peterson A. Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review. Epilepsia 2023; 64:1125-1174. [PMID: 36790369 DOI: 10.1111/epi.17548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 02/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.
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Affiliation(s)
- Ashley Reynolds
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Michaela Vranic-Peters
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Alan Lai
- Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Andre Peterson
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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43
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Sun Y, Friedman D, Dugan P, Holmes M, Wu X, Liu A. Machine Learning to Classify Relative Seizure Frequency From Chronic Electrocorticography. J Clin Neurophysiol 2023; 40:151-159. [PMID: 34049367 PMCID: PMC8617083 DOI: 10.1097/wnp.0000000000000858] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Brain responsive neurostimulation (NeuroPace) treats patients with refractory focal epilepsy and provides chronic electrocorticography (ECoG). We explored how machine learning algorithms applied to interictal ECoG could assess clinical response to changes in neurostimulation parameters. METHODS We identified five responsive neurostimulation patients each with ≥200 continuous days of stable medication and detection settings (median, 358 days per patient). For each patient, interictal ECoG segments for each month were labeled as "high" or "low" to represent relatively high or low long-episode (i.e., seizure) count compared with the median monthly long-episode count. Power from six conventional frequency bands from four responsive neurostimulation channels were extracted as features. For each patient, five machine learning algorithms were trained on 80% of ECoG, then tested on the remaining 20%. Classifiers were scored by the area-under-the-receiver-operating-characteristic curve. We explored how individual circadian cycles of seizure activity could inform classifier building. RESULTS Support vector machine or gradient boosting models achieved the best performance, ranging from 0.705 (fair) to 0.892 (excellent) across patients. High gamma power was the most important feature, tending to decrease during low-seizure-frequency epochs. For two subjects, training on ECoG recorded during the circadian ictal peak resulted in comparable model performance, despite less data used. CONCLUSIONS Machine learning analysis on retrospective background ECoG can classify relative seizure frequency for an individual patient. High gamma power was the most informative, whereas individual circadian patterns of seizure activity can guide model building. Machine learning classifiers built on interictal ECoG may guide stimulation programming.
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Affiliation(s)
- Yueqiu Sun
- NYU Center for Data Science, New York, NY 10016
| | - Daniel Friedman
- New York University Comprehensive Epilepsy Center, New York, NY 10016
- Department of Neurology, New York University Langone Health, New York, NY 10016
| | - Patricia Dugan
- New York University Comprehensive Epilepsy Center, New York, NY 10016
- Department of Neurology, New York University Langone Health, New York, NY 10016
| | - Manisha Holmes
- New York University Comprehensive Epilepsy Center, New York, NY 10016
- Department of Neurology, New York University Langone Health, New York, NY 10016
| | - Xiaojing Wu
- New York University Comprehensive Epilepsy Center, New York, NY 10016
- Department of Neurology, New York University Langone Health, New York, NY 10016
| | - Anli Liu
- New York University Comprehensive Epilepsy Center, New York, NY 10016
- Department of Neurology, New York University Langone Health, New York, NY 10016
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44
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Goldenholz DM, Westover MB. Flexible realistic simulation of seizure occurrence recapitulating statistical properties of seizure diaries. Epilepsia 2023; 64:396-405. [PMID: 36401798 PMCID: PMC9905290 DOI: 10.1111/epi.17471] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/12/2022] [Accepted: 11/17/2022] [Indexed: 11/21/2022]
Abstract
OBJECTIVE A realistic seizure diary simulator is currently unavailable for many research needs, including clinical trial analysis and evaluation of seizure detection and seizure-forecasting tools. In recent years, important statistical features of seizure diaries have been characterized. These include (1) heterogeneity of individual seizure frequencies, (2) the relation between average seizure rate and standard deviation, (3) multiple risk cycles, (4) seizure clusters, and (5) limitations on inter-seizure intervals. The present study unifies these features into a single model. METHODS Our approach, Cyclic Heterogeneous Overdispersed Clustered Open-source L-relationship Adjustable Temporally limited E-diary Simulator (CHOCOLATES) is based on a hierarchical model centered on a gamma Poisson generator with several modifiers. This model accounts for the aforementioned statistical properties. The model was validated by simulating 10 000 randomized clinical trials (RCTs) of medication to compare with 23 historical RCTs. Metrics of 50% responder rate (RR50) and median percent change (MPC) were evaluated. We also used CHOCOLATES as input to a seizure-forecasting tool to test the flexibility of the model. We examined the area under the receiver-operating characteristic (ROC) curve (AUC) for test data with and without cycles and clusters. RESULTS The model recapitulated typical findings in 23 historical RCTs without the necessity of introducing an additional "placebo effect." The model produced the following RR50 values: placebo: 17 ± 4%; drug 38 ± 5%; and the following MPC values: placebo: 13 ± 6%; drug 40 ± 4%. These values are similar to historical data: for RR50: placebo, 21 ± 10%, drug: 43 ± 13%; and for MPC: placebo: 17 ± 10%, drug: 41 ± 11%. The seizure forecasts achieved an AUC of 0.68 with cycles and clusters, whereas without them the AUC was 0.51. SIGNIFICANCE CHOCOLATES represents the most realistic seizure occurrence simulator to date, based on observations from thousands of patients in different contexts. This tool is open source and flexible, and can be used for many applications, including clinical trial simulation and testing of seizure-forecasting tools.
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Affiliation(s)
- Daniel M. Goldenholz
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
| | - M. Brandon Westover
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
- Dept. of Neurology, Massachusetts General Hospital, Boston 02114 MA
- McCance Center for Brain Health, Boston, 02114 MA
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45
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Gleichgerrcht E, Dumitru M, Hartmann DA, Munsell BC, Kuzniecky R, Bonilha L, Sameni R. Seizure forecasting using machine learning models trained by seizure diaries. Physiol Meas 2022; 43:10.1088/1361-6579/aca6ca. [PMID: 36541513 PMCID: PMC9940727 DOI: 10.1088/1361-6579/aca6ca] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022]
Abstract
Objectives.People with refractory epilepsy are overwhelmed by the uncertainty of their next seizures. Accurate prediction of future seizures could greatly improve the quality of life for these patients. New evidence suggests that seizure occurrences can have cyclical patterns for some patients. Even though these cyclicalities are not intuitive, they can be identified by machine learning (ML), to identify patients with predictable vs unpredictable seizure patterns.Approach.Self-reported seizure logs of 153 patients from the Human Epilepsy Project with more than three reported seizures (totaling 8337 seizures) were used to obtain inter-seizure interval time-series for training and evaluation of the forecasting models. Two classes of prediction methods were studied: (1) statistical approaches using Bayesian fusion of population-wise and individual-wise seizure patterns; and (2) ML-based algorithms including least squares, least absolute shrinkage and selection operator, support vector machine (SVM) regression, and long short-term memory regression. Leave-one-person-out cross-validation was used for training and evaluation, by training on seizure diaries of all except one subject and testing on the left-out subject.Main results.The leading forecasting models were the SVM regression and a statistical model that combined the median of population-wise seizure time-intervals with a test subject's prior seizure intervals. SVM was able to forecast 50%, 70%, 81%, 84%, and 87% of seizures of unseen subjects within 0, 1, 2, 3 to 4 d of mean absolute forecasting error, respectively. The subject-wise performances show that patients with more frequent seizures were generally better predicted.Significance.ML models can leverage non-random patterns within self-reported seizure diaries to forecast future seizures. While diary-based seizure forecasting alone is only one of many aspects of clinical care of patients with epilepsy, studying the level of predictability across seizures and patients paves the path towards a better understanding of predictable vs unpredictable seizures on individualized and population-wise bases.
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Affiliation(s)
| | - Mircea Dumitru
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA
| | - David A. Hartmann
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Brent C. Munsell
- Department of Computer Science, University of North Carolina, Chapel Hill, NC
| | | | - Leonardo Bonilha
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA
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46
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Xiong W, Kameneva T, Lambert E, Cook MJ, Richardson MP, Nurse ES. Forecasting psychogenic non-epileptic seizure likelihood from ambulatory EEG and ECG. J Neural Eng 2022; 19. [PMID: 36270501 DOI: 10.1088/1741-2552/ac9c97] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/21/2022] [Indexed: 12/24/2022]
Abstract
Objective.Critical slowing features (variance and autocorrelation) of long-term continuous electroencephalography (EEG) and electrocardiography (ECG) data have previously been used to forecast epileptic seizure onset. This study tested the feasibility of forecasting non-epileptic seizures using the same methods. In doing so, we examined if long-term cycles of brain and cardiac activity are present in clinical physiological recordings of psychogenic non-epileptic seizures (PNES).Approach.Retrospectively accessed ambulatory EEG and ECG data from 15 patients with non-epileptic seizures and no background of epilepsy were used for developing the forecasting system. The median period of recordings was 161 h, with a median of 7 non-epileptic seizures per patient. The phases of different cycles (5 min, 1 h, 6 h, 12 h, 24 h) of EEG and RR interval (RRI) critical slowing features were investigated. Forecasters were generated using combinations of the variance and autocorrelation of both EEG and the RRI of the ECG at each of the aforementioned cycle lengths. Optimal forecasters were selected as those with the highest area under the receiver-operator curve (AUC).Main results.It was found that PNES events occurred in the rising phases of EEG feature cycles of 12 and 24 h in duration at a rate significantly above chance. We demonstrated that the proposed forecasters achieved performance significantly better than chance in 8/15 of patients, and the mean AUC of the best forecaster across patients was 0.79.Significance.To our knowledge, this is the first study to retrospectively forecast non-epileptic seizures using both EEG and ECG data. The significance of EEG in the forecasting models suggests that cyclic EEG features of non-epileptic seizures exist. This study opens the potential of seizure forecasting beyond epilepsy, into other disorders of episodic loss of consciousness or dissociation.
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Affiliation(s)
- Wenjuan Xiong
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
| | - Tatiana Kameneva
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia.,Iverson Health Innovation Institute, Swinburne University of Technology, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Elisabeth Lambert
- Iverson Health Innovation Institute, Swinburne University of Technology, Melbourne, Australia.,School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
| | - Mark J Cook
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Ewan S Nurse
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia.,Seer Medical, Melbourne, Australia
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47
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Kerr WT, Brandt C, Ngo LY, Patten A, Cheng JY, Kramer L, French JA. Time to exceed pre-randomization monthly seizure count for perampanel in participants with primary generalized tonic-clonic seizures: A potential clinical end point. Epilepsia 2022; 63:2994-3004. [PMID: 36106379 PMCID: PMC9828687 DOI: 10.1111/epi.17411] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/13/2022] [Accepted: 09/13/2022] [Indexed: 01/12/2023]
Abstract
OBJECTIVE To evaluate the exploratory time to exceed pre-randomization seizure count (T-PSC) in the determination of efficacy of adjunctive perampanel in participants with primary generalized tonic-clonic (PGTC) seizures in generalized-onset epilepsy. METHODS In this multicenter, double-blind study (ClinicalTrials.gov identifier: NCT01393743), participants ≥12 years of age with treatment-resistant idiopathic generalized epilepsy were randomized to receive placebo or adjunctive perampanel (≤8 mg/day) across a 17-week double-blind treatment phase (4-week titration; 13-week maintenance). We evaluated the pre-planned exploratory end point of the T-PSC using a Kaplan-Meier analysis. We also re-evaluated the correspondence of the primary end points of median percent seizure frequency change (MPC) and 50% responder rate (50RR) calculated at T-PSC and at the end of the trial. RESULTS The exploratory end point of median T-PSC on placebo was 43 days and >120 days on perampanel (log-rank p < .001). The primary end points calculated at T-PSC did not differ significantly from the end points at the end of the trial (MPC -31% vs -42% at T-PSC; 50RR 32% vs 51% at T-PSC). After T-PSC was reached, participants had a median (interquartile range) of 5 (3-13) additional seizures on placebo and 5 (2-10) on perampanel. SIGNIFICANCE The exploratory end point of T-PSC demonstrated the effectiveness of perampanel despite a shorter duration of monitoring. The seizures that occurred after T-PSC did not influence the conclusions of the trial; therefore, T-PSC may be a viable alternative to traditional trial end points that reduces the risk to participants.
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Affiliation(s)
- Wesley T. Kerr
- Department of NeurologyUniversity of MichiganAnn ArborMichiganUSA
| | - Christian Brandt
- Bethel Epilepsy CenterUniversity Hospital for EpileptologyBielefeldGermany
| | - Leock Y. Ngo
- Department of NeurologyEisai Inc.NutleyNew JerseyUSA
| | | | | | - Lynn Kramer
- Department of NeurologyEisai Inc.NutleyNew JerseyUSA
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48
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Remvig LS, Duun-Henriksen J, Fürbass F, Hartmann M, Viana PF, Kappel Overby AM, Weisdorf S, Richardson MP, Beniczky S, Kjaer TW. Detecting temporal lobe seizures in ultra long-term subcutaneous EEG using algorithm-based data reduction. Clin Neurophysiol 2022; 142:86-93. [PMID: 35987094 DOI: 10.1016/j.clinph.2022.07.504] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/10/2022] [Accepted: 07/03/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Ultra long-term monitoring with subcutaneous EEG (sqEEG) offers objective outpatient recording of electrographic seizures as an alternative to self-reported epileptic seizure diaries. This methodology requires an algorithm-based automatic seizure detection to indicate periods of potential seizure activity to reduce the time spent on visual review. The objective of this study was to evaluate the performance of a sqEEG-based automatic seizure detection algorithm. METHODS A multicenter cohort of subjects using sqEEG were analyzed, including nine people with epilepsy (PWE) and 12 healthy subjects, recording a total of 965 days. The automatic seizure detections of a deep-neural-network algorithm were compared to annotations from three human experts. RESULTS Data reduction ratios were 99.6% in PWE and 99.9% in the control group. The cross-PWE sensitivity was 86% (median 80%, range 69-100% when PWE were evaluated individually), and the corresponding median false detection rate was 2.4 detections per 24 hours (range: 2.0-13.0). CONCLUSIONS Our findings demonstrated that step one in a sqEEG-based semi-automatic seizure detection/review process can be performed with high sensitivity and clinically applicable specificity. SIGNIFICANCE Ultra long-term sqEEG bears the potential of improving objective seizure quantification.
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Affiliation(s)
- Line S Remvig
- UNEEG Medical A/S, Borupvang 2, DK-3450 Allerød, Denmark.
| | | | - Franz Fürbass
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, Austria
| | - Manfred Hartmann
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, Austria
| | - Pedro F Viana
- Department of Basic & Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, Denmark Hill, London, UK; Faculty of Medicine, University of Lisbon, Av. Prof. Egas Moniz MB, 1649-028 Lisboa, Portugal
| | | | - Sigge Weisdorf
- Center of Neurophysiology, Department Neurology, Zealand University Hospital, Sygehusvej 10, DK-4000 Roskilde, Denmark
| | - Mark P Richardson
- Department of Basic & Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, Denmark Hill, London, UK
| | - Sándor Beniczky
- The Danish Epilepsy Centre, Filadelfia, Kolonivej 1, 4293 Dianalund, Denmark; Aarhus University and Aarhus University Hospital, Palle Juul-Jensens Blvd 99, DK-8200 Aarhus, Denmark
| | - Troels W Kjaer
- Center of Neurophysiology, Department Neurology, Zealand University Hospital, Sygehusvej 10, DK-4000 Roskilde, Denmark; Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3b, DK.2200 Copenhagen, Denmark
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49
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Petito GT, Housekeeper J, Buroker J, Scholle C, Ervin B, Frink C, Greiner HM, Skoch J, Mangano FT, Dye TJ, Hogenesch JB, Glauser TA, Holland KD, Arya R. Diurnal rhythm of spontaneous intracranial high-frequency oscillations. Seizure 2022; 102:105-112. [DOI: 10.1016/j.seizure.2022.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/05/2022] [Accepted: 09/28/2022] [Indexed: 11/27/2022] Open
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50
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Vander T, Stroganova T, Doufish D, Eliashiv D, Gilboa T, Medvedovsky M, Ekstein D. What is the optimal duration of home-video-EEG monitoring for patients with <1 seizure per day? A simulation study. Front Neurol 2022; 13:938294. [PMID: 36071898 PMCID: PMC9441894 DOI: 10.3389/fneur.2022.938294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 07/28/2022] [Indexed: 11/22/2022] Open
Abstract
Ambulatory “at home” video-EEG monitoring (HVEM) may offer a more cost-effective and accessible option as compared to traditional inpatient admissions to epilepsy monitoring units. However, home monitoring may not allow for safe tapering of anti-seizure medications (ASM). As a result, longer periods of monitoring may be necessary to capture a sufficient number of the patients' stereotypic seizures. We aimed to quantitatively estimate the necessary length of HVEM corresponding to various diagnostic scenarios in clinical practice. Using available seizure frequency statistics, we estimated the HVEM duration required to capture one, three, or five seizures on different days, by simulating 100,000 annual time-courses of seizure occurrence in adults and children with more than one and <30 seizures per month (89% of adults and 85% of children). We found that the durations of HVEM needed to record 1, 3, or 5 seizures in 80% of children were 2, 5, and 8 weeks (median 2, 12, and 21 days), respectively, and significantly longer in adults −2, 6, and 10 weeks (median 3, 14, and 26 days; p < 10−10 for all comparisons). Thus, longer HVEM than currently used is needed for expanding its clinical value from diagnosis of nonepileptic or very frequent epileptic events to a presurgical tool for patients with drug-resistant epilepsy. Technical developments and further studies are warranted.
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Affiliation(s)
- Tatiana Vander
- Herzfeld Geriatric Rehabilitation Medical Center, Gedera, Israel
- The Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tatiana Stroganova
- MEG-Center, Moscow State University of Psychology and Education, Moscow, Russia
| | - Diya Doufish
- Department of Neurology and Agnes Ginges Center for Human Neurogenetics, Hadassah Medical Organization, Jerusalem, Israel
| | - Dawn Eliashiv
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Tal Gilboa
- The Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
- The Neuropediatric Unit, Division of Pediatrics, Hadassah Medical Organization, Jerusalem, Israel
| | - Mordekhay Medvedovsky
- Department of Neurology and Agnes Ginges Center for Human Neurogenetics, Hadassah Medical Organization, Jerusalem, Israel
| | - Dana Ekstein
- Department of Neurology and Agnes Ginges Center for Human Neurogenetics, Hadassah Medical Organization, Jerusalem, Israel
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- *Correspondence: Dana Ekstein
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