Version 1.0.0 (05/26/2023)

This is a drug repurposing dataset on epilepsy, compiled by Dr. Baptiste PORTE

It consists in two .CSV files:

1. Liste anticonvu.csv with 6 columns

"Compound CID": Best match PubChem CID for the considered chemical compound
"drug_name": The common drug name
"score": preliminary drug class in {0: unknown effect on epileptic patients, 1: antiepileptic drug, that is, treatment for epileptic patients}
"verification": 1st bibliographic round for drug class in {0: unknown effect on epileptic patients, 1: antiepileptic drug, that is, treatment for epileptic patients}
"verif 2": final assigned drug class in {0: unknown effect on epileptic patients, 1: antiepileptic drug, that is, treatment for epileptic patients}
"details": justification -in French- for the final assigned drug class

2. Liste proconvu.csv

"Compound CID": Best match PubChem CID for the considered chemical compound
"drug_name": The common drug name
"score": preliminary drug class in {0: unknown effect on epileptic patients, -1: proconvulsant drug, that is, seizure-inducing}
"verification": 1st bibliographic round for drug class in {0: unknown effect on epileptic patients, -1: proconvulsant drug, that is, seizure-inducing}
"verif 2": final assigned drug class in {0: unknown effect on epileptic patients, -1: proconvulsant drug, that is, seizure-inducing}
"effet convulsivant demontré": justification -in French- for the final assigned drug class

For any questions, please contact the author at

IntroductionThis study evaluated the accuracy of motion signals extracted from video monitoring data to differentiate epileptic motor seizures in patients with drug-resistant epilepsy. 3D near-infrared video was recorded by the Nelli® seizure monitoring system (Tampere, Finland).Methods10 patients with 130 seizures were included in the training dataset, and 17 different patients with 98 seizures formed the testing dataset. Only seizures with unequivocal hyperkinetic, tonic, and tonic-clonic semiology were included. Motion features from the catch22 feature collection extracted from video were explored to transform the patients' videos into numerical time series for clustering and visualization.ResultsChanges in feature generation provided incremental discrimination power to differentiate between hyperkinetic, tonic, and tonic-clonic seizures. Temporal motion features showed the best results in the unsupervised clustering analysis. Using these features, the system differentiated hyperkinetic, tonic and tonic-clonic seizures with 91, 88, and 45% accuracy after 100 cross-validation runs, respectively. F1-scores were 93, 90, and 37%, respectively. Overall accuracy and f1-score were 74%.ConclusionThe selected features of motion distinguished semiological differences within epileptic seizure types, enabling seizure classification to distinct motor seizure types. Further studies are needed with a larger dataset and additional seizure types. These results indicate the potential of video-based hybrid seizure monitoring systems to facilitate seizure classification improving the algorithmic processing and thus streamlining the clinical workflow for human annotators in hybrid (algorithmic-human) seizure monitoring systems.

The Imaging Database for Epilepsy And Surgery (IDEAS)

Peter N. Taylor, Yujiang Wang, Callum Simpson, Vytene Janiukstyte, Jonathan Horsley, Karoline Leiberg, Beth Little, Harry Clifford, Sophie Adler, Sjoerd B. Vos, Gavin P Winston, Andrew W McEvoy, Anna Miserocchi, Jane de Tisi, John S Duncan

Magnetic resonance imaging (MRI) is a crucial tool to identify brain abnormalities in a wide range of neurological disorders. In focal epilepsy MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence algorithms may improve lesion detection if abnormalities are not evident on visual inspection. The success of this approach depends on the volume and quality of training data. Herein, we release an open-source dataset of preprocessed MRI scans from 442 individuals with drug-refractory focal epilepsy who had neurosurgical resections, and detailed demographic information. The MRI scan data includes the preoperative 3D T1 and where available 3D FLAIR, as well as a manually inspected complete surface reconstruction and volumetric parcellations. Demographic information includes age, sex, age of onset of epilepsy, location of surgery, histopathology of resected specimen, occurrence and frequency of focal seizures with and without impairment of awareness, focal to bilateral tonic-clonic seizures, number of anti-seizure medications (ASMs) at time of surgery, and a total of 1764 patient years of post-surgical follow up. Crucially, we also include resection masks delineated from post-surgical imaging. To demonstrate the veracity of our data, we successfully replicated previous studies showing long-term outcomes of seizure freedom in the range of around 50%. Our imaging data replicates findings of group level atrophy in patients compared to controls. Resection locations in the cohort were predominantly in the temporal and frontal lobes. We envisage our dataset, shared openly with the community, will catalyse the development and application of computational methods in clinical neurology.

https://arxiv.org/abs/2406.06731

This release on OpenNeuro includes only raw T1w and FLAR scans. Fully processed data, including resection masks and other demographic information can be found at the following locations: https://www.cnnp-lab.com/ideas-data

• Bids https://figshare.com/s/07fca72410094bc49506 Raw T1w and FLAIR scans organised in BIDS format. Nifti and json descriptors included

• Masks https://figshare.com/s/31ab43d1829b12ac13e8 Resection masks for IDEAS cohort in native, and freesurfer orig.mgz space

• Freesurfer_brain https://figshare.com/s/39b61a1df5fa8443e3c4 skullstripped brain from freesurfer in nifti format

• Freesurfer_orig https://figshare.com/s/f13391a4161b807ce6b0 freesurfer orig.mgz converted to nifti format

• Freesurfer_zip https://figshare.com/s/b13b8bb41390d3f7a088 freesurfer surface and volumetric reconstructions

• Tables_stats_freesurfer https://figshare.com/s/010142dd51e37ba4e4e2 Freesurfer thickness, volume, and surface areas for the Desikan-Kiliany parcellation.

• Tables_metadata https://figshare.com/s/bab70268afeb1071202b clinical and demographic metadata

• Table_resected https://figshare.com/s/097ba0e254e36f0eee52 table indicating the percentage of each brain region in the Desikan-Kiliany atlas subsequently resected by surgery.

• Tables_zscores https://figshare.com/s/8c086fc295a75f85e628 Freesurfer thickness, volume, and surface areas for the Desikan-Kiliany parcellation, z-scored against normative controls post-combat.

• Tables_group_effect https://figshare.com/s/323db205354788c4d1f0 Group effect size differences to controls

Data on drug seizures relate to all seizures made in each country during the year by all law enforcement agencies (police, customs, national guard, etc.). Caution is required in relation to double-counting that might occur within a country — although it is usually avoided — between various law enforcement agencies.

There are almost 100 statistical tables in this dataset. Each data table may be viewed as an HTML table or downloaded in spreadsheet (Excel format).

The dataset contains year- and state-wise data on the total quantity and value of different material, such as cash, liquor, drugs/narcotics, precious items (gold, silver etc.), and freebies, seized by Election Commission of India (ECI) during Loksabha General Elections of 2014, 2019 and 2024. In addition, the dataset also contains data of number of persons against whom bonds (interim/final) were taken, cases were registered, action was taken, illegal and licensed arms/ammunition seized/deposited, etc., during 2014 general elections

Note: Data for 2024 is from 1 March to 18 May 2024

Introduction: Epilepsy is a widespread disease requiring long-term drug treatment. The aim of this study was to collect information on reported suspected adverse drug reactions (sADRs) of antiseizure medications (ASMs) and study their seriousness and outcomes in various system organ classifications (SOCs). We intended to compare old and new ASMs’ ADRs.Methods: Using EudraVigilance (EV) database, we extracted line listings of reported sADRs with different ASMs over the period from January 2012 to December 2021. The list of ASMs was compiled according to the Anatomical therapeutic chemical classification system. The Medical Dictionary for Regulatory Activities version 24.0 was used for determining the SOCs of individual reported preferred terms (PTs) sADRs. In addition, we calculated the Reporting Odds Ratio (ROR), 95% confidence interval (95% CI), p-value (statistically significant if p< 0.05) and chi-square statistics.Results: A total of 276,694 reports were contained in the exported line listings which included 1,051,142 individual sADRs reported as PTs such as seizure (3.49%), drug ineffective (2.46%), somnolence (1.32%), dizziness (1.29%) and represented four SOCs: nervous system disorders (19.26%), general disorders and administration site conditions (14.39%), psychiatric disorders (11.29%) and injury, poisoning and procedural complications (9.79). Among patients, the age group between 18 and 64 years had the highest percentage (52.40%), followed by those aged over 64 years (18.75%). Of all the reported PTs, 882,706 (83.98%) had reported seriousness. Old ASMs had a significant positive association with “caused/prolonged hospitalisation”, “congenital anomaly”, “disabling”, “life threatening” and “results in death”, while new ASMS with ‘other medically important condition’. There were 386 (0.04%) PTs related to Sudden Unexpected Death in Epilepsy (SUDEP).Conclusion: In our study, we examined 10 years’ reported sADRs of ASMs in the EV international database. The majority of PTs were serious. Old ASMs were generally more commonly associated with undesired outcomes and seriousness. Considering their expected seriousness and outcomes, the safety profile of the different ASMs, can play a cardinal role in the selection of ASMs.

Introduction: Epilepsy is a widespread disease requiring long-term drug treatment. The aim of this study was to collect information on reported suspected adverse drug reactions (sADRs) of antiseizure medications (ASMs) and study their seriousness and outcomes in various system organ classifications (SOCs). We intended to compare old and new ASMs’ ADRs.Methods: Using EudraVigilance (EV) database, we extracted line listings of reported sADRs with different ASMs over the period from January 2012 to December 2021. The list of ASMs was compiled according to the Anatomical therapeutic chemical classification system. The Medical Dictionary for Regulatory Activities version 24.0 was used for determining the SOCs of individual reported preferred terms (PTs) sADRs. In addition, we calculated the Reporting Odds Ratio (ROR), 95% confidence interval (95% CI), p-value (statistically significant if p< 0.05) and chi-square statistics.Results: A total of 276,694 reports were contained in the exported line listings which included 1,051,142 individual sADRs reported as PTs such as seizure (3.49%), drug ineffective (2.46%), somnolence (1.32%), dizziness (1.29%) and represented four SOCs: nervous system disorders (19.26%), general disorders and administration site conditions (14.39%), psychiatric disorders (11.29%) and injury, poisoning and procedural complications (9.79). Among patients, the age group between 18 and 64 years had the highest percentage (52.40%), followed by those aged over 64 years (18.75%). Of all the reported PTs, 882,706 (83.98%) had reported seriousness. Old ASMs had a significant positive association with “caused/prolonged hospitalisation”, “congenital anomaly”, “disabling”, “life threatening” and “results in death”, while new ASMS with ‘other medically important condition’. There were 386 (0.04%) PTs related to Sudden Unexpected Death in Epilepsy (SUDEP).Conclusion: In our study, we examined 10 years’ reported sADRs of ASMs in the EV international database. The majority of PTs were serious. Old ASMs were generally more commonly associated with undesired outcomes and seriousness. Considering their expected seriousness and outcomes, the safety profile of the different ASMs, can play a cardinal role in the selection of ASMs.

This dataset contains the all-India, year and drug law enforcement agency (DLEAs) wise number of drug seizures made. It includes drug seizures by various DLEAs such as Narcotics Control Bureau (NCB), Directorate of Revenue Intelligence (DRI), Custom and Centre Excise, Central Bureau of Narcotics (CBN), State Police, State Excise and Other agencies.

This data is licensed under a Creative Commons license with conditions on ATTRIBUTION, NON-COMMERCIAL use and SHARE-ALIKENON-COMMERCIAL- data use is for private individual/research/teaching training and no other purpose- commercial entities may not use this data for any reason- this data may not be used for patent applications, licensed software, IP or other for-profit useATTRIBUTIONData should be cited as the Melbourne University NeuroVista Seizure Prediction Data (https://doi.org/10.26188/5b6a999fa2316).The following reference should be used:Cook, Mark J., et al. "Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study." The Lancet Neurology 12.6 (2013): 563-571.SHARE-ALIKE- derivative works must be distributed under a Creative Commons license- derivative works may include (but not limited to) neural networks, classifiers, mathematical models, computational models, networks, figures that are derived substantially from the dataSeizures for 12 patients recorded during the NeuroVista trial. Folders contain a number of .mat files (one file per seizure). Each file contains a variable 'data' with dimensions TxN where T is the data length and N is the number of electrode channels (16). T is variable (depending on seizure length. Seizure onset is at 1 minute. Seizure offset is 10s before the end of data. Sampling rate is 400Hz.For more information refer to Cook et al (2013).To use this data in your work please cite the original paper (provided in references) by Cook et al (2013).

This data includes the number of seizures made, by drug type, for all drugs controlled under the Misuse of Drugs Act 1971 and the volume of seizures made on the main drug types. The data is broken down to show each UK nation, for example, England, Wales, Scotland and Northern Ireland. The Home Office publication presents figures for drug seizures made in 2011-12 in England and Wales by police and the former UK Border Agency. From 1 March 2012, Border Force separated from the former UK Border Agency and became an operational command within the Home Office taking responsibility (amongst other things) for drug detection at the border. For 2011-12 onwards, the UK Border Agency/Border Force figures include those seizures made by our fleet of seagoing patrol vessels which operate around the UK coastline. The location of the seizures made by the patrol vessels are reported in line with where the seizures are landed. This data set has been consolidated into that on 'Border Force transparency data' from 2015.

Clinical Database

XLSX clinical database: General characteristics, neuroimaging, and electroclinical features of BESTA patients included in the study

EU Drug Market: Heroin and other opioids describes the European opioids market from production and trafficking to distribution and use. It details the processes, materials and players involved at various stages and levels of the market. This dataset contains most the source data behind the graphics used in the report as well as additional data tables.

Presents figures for drug seizures made by law enforcement agencies in England and Wales. The statistics relate to all drugs controlled under the Misuse of Drugs Act 1971 (MDA), which divides drugs into three categories (classes A, B and C) according to their harmfulness, with class A drugs considered to be the most harmful.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. In this study, researchers used capture-recapture sampling and multiple data sources to gauge the impact of drug trafficking in Quebec, Canada on the United States drug market. The main analyses were based on arrest data that were obtained for Quebec. In addition, analysis of the chemical composition and price assessments of the Quebec synthetic drugs was done. The study includes one SPSS data file (Quebec Arrest Data (Synthetic Drugs Cases, September 2014; n=20261)-ICPSR.sav ; n=20,261 ; 13 variables) and one Excel data file (Chemical composition of seized synthetic drugs.xls ; n=365 ; 14 variables). Spatial analyses of border seizure data was performed by the researchers, but these data are not available at this time. The data used for these analyses concerned synthetic drug seizures at Canadian borders from 2007 to 2012. The dataset was provided by the Canadian Border Services Agency (CBSA). For each seizure, the specific border crossing where the seizure was made was provided, as well as the value of the seizure (except for precursors), the country of origin and the type of drug seized. The types of drugs were classified into five types: (1) Precursors, (2) MDMA, (3) Amphetamine, (4) Methamphetamine and (5) Others. Most of the seizures (86.6 percent) were classified in this last category. The country of origin of the seizure was also provided.

Bang sensitive (BS) Drosophila mutants display characteristic seizure-like phenotypes resembling, in some aspects, those of human seizure disorders such as epilepsy. The BS mutant parabss1, caused by a gain-of-function mutation of the voltage-gated Na+ channel gene, is extremely seizure-sensitive with phenotypes that have proven difficult to ameliorate by anti-epileptic drug feeding or by seizure-suppressor mutation. It has been presented as a model for intractable human epilepsy. Here we show that cacophony (cacTS2), a mutation of the Drosophila presynaptic Ca++ channel α1 subunit gene, is a particularly potent seizure-suppressor mutation, reverting seizure-like phenotypes for parabss1 and other BS mutants. Seizure-like phenotypes for parabss1 may be suppressed by as much as 90% in double mutant combinations with cacTS2. Unexpectedly, we find that parabss1 also reciprocally suppresses cacTS2 seizure-like phenotypes. The cacTS2 mutant displays these seizure-like behaviors and spontaneous high-frequency action potential firing transiently after exposure to high temperature. We find that this seizure-like behavior in cacTS2 is ameliorated by 85% in double mutant combinations with parabss1. Fig 1. Drosophila cacTS2 electrophysiology.Fig 1.xlsFig 2. Suppression of bang sensitive behavioral phenotypes by cacTS2.Fig 2.xlsFig 3. Electrophysiology of cacTS2 suppression.Fig 3.xlsFig 4. Suppression of parabss1 and eas behavioral phenotypes by cacRNAi at room temperature.Fig 4.xlsFig 5. Suppression of cacTS2 temperature-sensitive seizure-like activity by parabss1.Fig 5.xlsS1 Fig. Giant fiber stimulation of cacTS2 and bang sensitive mutants.S1 Fig.xlsS2 Fig. Suppression of eas bang sensitivity by cacRNAi using different GAL4 drivers.S2 Fig.xls

The virtual epileptic patient cohort is derived from a dataset of 30 drug-resistant epilepsy patients from La Timone hospital in Marseille, France. These patients have undergone invasive SEEG electrode implantation to record seizure activity. This dataset contains simulated SEEG recordings of spontaneous seizures, stimulation-induced seizures and interictal activity with spikes. In addition, patient-specific information including whole-brain connectivity, reconstructed SEEG electrode implantation and epileptogenic zone hypothesis are provided. The patient’s epileptogenic zone (EZ) hypothesis is used to parametrize their whole-brain model used for simulating epileptic brain activity. The purpose of this virtual cohort containing ground truth epileptogenic zone information is for facilitating evaluation of diagnostic data analysis methods in clinical epilepsy research. We used The Virtual Brain platform to implement personalized brain models by integrating T1-MRI, diffusion-weighted MRI and CT-scans. For each brain model, the EZ was set using the clinical hypothesis in one scenario and the Virtual Epileptic Patient pipeline’s estimation of EZ in another scenario. We then ran simulations and validated the synthetic SEEG data against the empirical SEEG recordings by comparing for each electrode the seizure’s spatial and temporal propagation, the stimulation response and the interictal spike distribution. This dataset is BIDS-iEEG compatible, and the code used to generate it is available here: https://github.com/BalanceKey/virtual_epilepsy_patient_cohort.

BackgroundSeizures are the main cause of maternal death in women with epilepsy, but there are no tools for predicting seizures in pregnancy. We set out to develop and validate a prognostic model, using information collected during the antenatal booking visit, to predict seizure risk at any time in pregnancy and until 6 weeks postpartum in women with epilepsy on antiepileptic drugs.Methods and findingsWe used datasets of a prospective cohort study (EMPiRE) of 527 pregnant women with epilepsy on medication recruited from 50 hospitals in the UK (4 November 2011–17 August 2014). The model development cohort comprised 399 women whose antiepileptic drug doses were adjusted based on clinical features only; the validation cohort comprised 128 women whose drug dose adjustments were informed by serum drug levels. The outcome was epileptic (non-eclamptic) seizure captured using diary records. We fitted the model using LASSO (least absolute shrinkage and selection operator) regression, and reported the performance using C-statistic (scale 0–1, values > 0.5 show discrimination) and calibration slope (scale 0–1, values near 1 show accuracy) with 95% confidence intervals (CIs). We determined the net benefit (a weighted sum of true positive and false positive classifications) of using the model, with various probability thresholds, to aid clinicians in making individualised decisions regarding, for example, referral to tertiary care, frequency and intensity of monitoring, and changes in antiepileptic medication. Seizures occurred in 183 women (46%, 183/399) in the model development cohort and in 57 women (45%, 57/128) in the validation cohort. The model included age at first seizure, baseline seizure classification, history of mental health disorder or learning difficulty, occurrence of tonic-clonic and non-tonic-clonic seizures in the 3 months before pregnancy, previous admission to hospital for seizures during pregnancy, and baseline dose of lamotrigine and levetiracetam. The C-statistic was 0.79 (95% CI 0.75, 0.84). On external validation, the model showed good performance (C-statistic 0.76, 95% CI 0.66, 0.85; calibration slope 0.93, 95% CI 0.44, 1.41) but with imprecise estimates. The EMPiRE model showed the highest net proportional benefit for predicted probability thresholds between 12% and 99%. Limitations of this study include the varied gestational ages of women at recruitment, retrospective patient recall of seizure history, potential variations in seizure classification, the small number of events in the validation cohort, and the clinical utility restricted to decision-making thresholds above 12%. The model findings may not be generalisable to low- and middle-income countries, or when information on all predictors is not available.ConclusionsThe EMPiRE model showed good performance in predicting the risk of seizures in pregnant women with epilepsy who are prescribed antiepileptic drugs. Integration of the tool within the antenatal booking visit, deployed as a simple nomogram, can help to optimise care in women with epilepsy.

Ultrasonic therapy is an increasingly promising approach for the treatment of seizures and drug-resistant epilepsy (DRE). Therapeutic focused ultrasound (FUS) uses thermal or nonthermal energy to either ablate neural tissue or modulate neural activity through high- or low-intensity FUS (HIFU, LIFU), respectively. Both HIFU and LIFU approaches have been investigated for reducing seizure activity in DRE, and additional FUS applications include disrupting the blood–brain barrier in the presence of microbubbles for targeted-drug delivery to the seizure foci. Here, we review the preclinical and clinical studies that have used FUS to treat seizures. Additionally, we review effective FUS parameters and consider limitations and future directions of FUS with respect to the treatment of DRE. While detailed studies to optimize FUS applications are ongoing, FUS has established itself as a potential noninvasive alternative for the treatment of DRE and other neurological disorders.