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746 results for “Seizures”
Dataset related to article "Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures"
<p>This dataset is related to the article entitled: Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures. This article is published in the Journal GLIA.</p> <p>Bres EE et al.<br> Lipoprotein receptor loss in forebrain radial glia results in<br> neurological deficits and severe seizures. Glia. 2020;1–33.</p>
dataset related to article "Seizure activity and brain damage in a model of focal non-convulsive status epilepticus"
<p>Excell files with the values used on the construction of the graphical illustrations for the publication</p>
Time series of epileptic seizures in a 8yo male on a cannabidiol trial
<p>Time series of epileptic seizures in a 8yo male with idiopathic catastrophic epilepsy (probably of mitochondrial origin) in a cannabidiol (CBD) trial.</p> <p>The data are of <strong>observed seizures </strong>during each day by the patient's parents and caregivers and thus are not exact values for total daily seizure activity; the values in this dataset are best considered a lower bound. However, observation effort was essentially constant over the time series, so the data likely reflect overall trends even though precise daily seizure counts are not available.</p> <p>The time series includes a 21-day baseline period and a 36-day trial period. The CBD was administered via g-tube in an olive oil base. Dosage was approximately 7.5 mg/kg/day for two weeks, and approximately 8 mg/kg/day for three weeks.</p> <p>Each 1ml of oil contained 10.00mg of total CBD and 0.42mg of total THC, for a CBD:THC ratio of approximately 20:1 (analysis by HPLC).</p> <p><strong><em>Data Dictionary</em></strong></p> <ul> <li><strong>Date</strong>: YYYY-MM-DD</li> <li><strong>Tonic</strong>: tonic seizure count (http://www.epilepsy.com/learn/types-seizures/tonic-seizures)</li> <li><strong>Tonic-Clonic</strong>: tonic-clonic (aka "grand mal") seizure count (http://www.epilepsy.com/learn/types-seizures/tonic-clonic-seizures)</li> <li><strong>Spasms</strong>: myoclonic seizure count (http://www.epilepsy.com/learn/types-seizures/myoclonic-seizures)</li> <li><strong>Total</strong>: total number of seizures (sum of above)</li> <li><strong>Notes</strong>: notes by parents</li> </ul>
Acetylcholine receptor based chemogenetics engineered for neuronal inhibition and seizure control assessed in mice
<p>Analysis scripts and underlying data used to produce the figures in our study entitled "Acetylcholine receptor based chemogenetics engineered for neuronal inhibition and seizure control assessed in mice", in Nature Communications. The listed project leaders can be contacted with any questions.</p>
Dataset The Lactate Receptor HCAR1: a Key Modulator of Epileptic Seizure Activity
<p>This dataset is related to the study: </p> <p>The Lactate Receptor HCAR1: a Key Modulator of Epileptic Seizure Activity</p> <p>by Maxime Alessandri (1), Alejandro Osorio-Forero (1), Anita Luthi (1) & Jean-Yves Chatton* (1)</p> <p><span>(1) Department of Fundamental Neurosciences, University of Lausanne, 1005 Lausanne, Vaud, Switzerland.</span></p>
Patient-specific processed data, code and visualisations for "Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions"
<p>Processed data and code for reproducing the main results and figures of the paper "<strong>Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions</strong>".</p> <p>We analysed publicly available data from subjects with drug-resistant focal epilepsy. A total of 2656 hours of long-term intracranial electroencephalography (iEEG) from 18 subjects was obtained using the "The SWEC-ETHZ iEEG Database and Algorithms" (available at <a href="http://ieeg-swez.ethz.ch">http://ieeg-swez.ethz.ch</a>) (Burrello et al., 2019).</p> <p>Reference<br> A. Burrello, L. Cavigelli, K. Schindler, L. Benini, A. Rahimi, <strong>‘‘</strong>Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms<strong>’’</strong> <em>in proceedings of the</em> <em>ACM/IEEE Design, Automation, and Test in Europe Conference (DATE)</em>, Florence, Italy, March 25-29, 2019. </p>
Spontaneous Low-Voltage Fast Onset Seizures in Human Mesial Temporal Lobe Epilepsy Are Caused by Specific Inhibitory/Excitatory Imbalance
<p>Local field potential recordings during low voltage fast seizures recorded from microelectrodes in patients with medically refractory temporal lobe epilepsy</p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-Figure 2. Identified risk factors hierarchy
<p>AED - antiepileptic drug, CP - cerebral palsy, GDD - global developmental delay, GA - gestational age, BW- birth weight, RS - repeated/recurrent seizure, MD - type/mode of delivery, AS1 - Apgar score at 1 minute, AS5 - Apgar score at 5 minute, AS10 - Apgar score at 10 minute, SO - seizure onset, ST_EPI - status epilepticus, UBS - ultrasound brain scan, MSU- maternal substance used, MIS – maternal inflammatory state, PRM- prolonged rupture of membranes, PNN – postnatal neuroimaging, PNS – postnatal seizure. The most frequently identified risk factors were the EEG findings (abnormal / severe electroencephalogram results), seizure characteristics (type, onset, duration, semiology), etiology, birth weight, Apgar score, cerebral ultrasound scan findings (abnormal) (Figure 2).</p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context
<p>R retrospective, P prospective, CC case – control study, MC multicenter controlled trail, PB populational based, HB hospital based, C clinical, CT computed tomographic scan, MRI cerebral magnetic resonance imaging, CUS cranial ultrasonography / cerebral ultrasound, USG ultrasonography, EEG electroencephalogram (standard), CpH cord Ph, BpH blood Ph, HT therapeutic hypothermia It can be noticed that seizure diagnosis was based on clinical grounds and functional explorations naming neuroimaging and/or EEG procedures (conventional EEG, aEEG, vEEG, CUS, MRI). The minimum number of newborns considered in these studies was 55, while the maximum was 403 with a mean of 148 (SD=86.75, median=112, IQR: (98,175)) and a total of 2226 evaluated cases.</p>
Spiking Seizure Classification Dataset
<p><strong>Dataset for event encoded analog EEG signals for detection of Epileptic seizures </strong></p> <p>This dataset contains events that are encoded from the analog signals recorded during pre-surgical evaluations of patients at the Sleep-Wake-Epilepsy-Center (SWEC) of the University Department of Neurology at the Inselspital Bern. The analog signals are sourced from the <a href="http://ieeg-swez.ethz.ch/" target="_blank" rel="noopener">SWEC-ETHZ iEEG Database</a></p> <p>This database contains event streams for 10 seizures recorded from 5 patients and generated by the DYnamic Neuromorphic Asynchronous Processor (DYNAP-SE2) to demonstrate a proof-of-concept of encoding seizures with network synchronization. The pipeline consists of two parts (I) an Analog Front End (AFE) and (II) an SNN termed as"Non-Local Non-Global" (NLNG) network. </p> <p>In the first part of the pipeline, the digitally recorded signals from <a href="http://ieeg-swez.ethz.ch/" target="_blank" rel="noopener">SWEC-ETHZ iEEG Database</a> are converted to analog signals via an 18-bit Digital-to-Analog converter (DAC) and then amplified and encoded into events by an Asynchronous Delta Modulator (ADM). Then in the second part, the encoded event streams are fed into the SNN that extracts the features of the epileptic seizure by extracting the partial synchronous patterns intrinsic to the seizure dynamics. </p> <p>Details about the neuromorphic processing pipeline and the encoding process are included in a manuscript under review. The preprint is available in <a title="Encoding seizures with partial synchronization: A spiking neural network for biosignal monitoring on a mixed signal neuromorphic processor" href="https://doi.org/10.1101/2024.05.22.595225" target="_blank" rel="noopener">bioRxiv</a></p> <p><strong>Installation</strong><br>The installation requires Python>=3.x and conda (or py-venv) package. Users can then install the requirements inside a conda environment using </p> <pre><code>conda env create -f requirements.txt -n sez</code></pre> <p><br>Once created the conda environment can be activated with <strong><em>conda activate sez</em></strong></p> <div> <div>The main files in the database are described in the hierarchy below.</div> <div> </div> <div>EventSezDataset/</div> <div>├─ data/</div> <div>│ ├─ P x S x</div> <div>│ │ ├─ Pat x _Sz_ x _CH x .csv</div> <div>├─ LSVM_Params/</div> <div>│ ├─ opt_svm_params/</div> <div>│ ├─ pat_x_features_SYNCH/</div> <div>├─ fig_gen.py</div> <div>├─ sync_mat_gen.py</div> <div>├─ SeizDetection_FR.py</div> <div>├─ SeizDetection_SYNCH.py</div> <div>├─ support.py</div> <div>├─ run.sh</div> <div>├─ requirements.txt</div> <div> </div> <div>where <strong>x</strong> represents the Patient ID and the Seizure ID respectively.</div> <ul> <li><strong>requirements.txt</strong>: This file lists the requirements for the execution of the Python code.</li> <li><strong>fig_gen.py</strong>: This file plots the analog signals and the associated AFE and NLNG event streams. The execution of the code happens with `<em>python fig_gen.py 1 1 13', </em>where patient 2, seizure 1, and channel 13 of the recording are plotted.</li> <li><strong>sync_mat_gen.py: </strong>This file describes the function for plotting the synchronization matrices emerging from the ADM and the NLNG spikes with either linear or log colorbar. The execution of the code happens with `<em>python sync_mat_gen.py 1 1' or `python sync_mat_gen.py 1 1 log'</em>. This execution generated four figures for pre-seizure, First Half of seizure, Second Half of seizure, and post-seizure time periods, where patient 1 and seizure 1. The third option can either be left blank or input as `lin` or `log`, for respective color bar scales. The time is the signal-time as mentioned in the table below.</li> <li><strong>run.sh:</strong> A simple Linux script to run the above code for all patients and seizures.</li> <li><strong>SeizDetection_FR.py:</strong> This file runs the LSVM on the ADM and NLNG spikes, using the firing rate (FR) as a feature. The code is currently set up with plotting with pre-computed features (in the LSVM_Params/opt_svm_params/ folder). Users can use the code for training the LSVM with different parameters as well.</li> <li><strong>SeizDetection_SYNCH.py:</strong> This file runs the LSVM on the kernelized ADM and NLNG spikes, using the flattened SYNC matrices as a feature. The code is currently set up with plotting with pre-computed features (in the LSVM_Params/pat_x_features_SYNCH/ folder). Users can use the code for training the LSVM with different parameters as well.</li> <li><strong>LSVM_Params:</strong> Folder containing LSVM features with different parameter combinations.</li> <li><strong>support.py:</strong> This file contains the necessary functions.</li> <li><strong>data/P1S1/</strong>: This folder, for example, contains the event streams for all channels for seizure 1 of patient 1.</p> <ul> <li><strong>Pat1_Sz_1_CH1.csv</strong>: This file contains the spikes of the AFE and the NLNG layers with the following tabular format (which can be extracted by the <em>fig_gen.py</em>)</li> </ul> </li> </ul> <div> <h3>## Comments</h3> <p># SStart: 180 <em>//Start of the Seizure in signal time</em><br># SEnd: 276.0 <em>//Start of the Seizure in signal time</em><br># Pid: 2 <em>// The patient ID as per the <a href="http://ieeg-swez.ethz.ch/" target="_blank" rel="noopener">SWEC-ETHZ iEEG Database</a> </em><br># Sid: 1 <em> // The Seizure ID as per the <a href="http://ieeg-swez.ethz.ch/" target="_blank" rel="noopener">SWEC-ETHZ iEEG Database</a> </em> <br># Channel_No: 1 <em>// The channel number</em></p> <div> <table> <tbody> <tr> <td><strong>SYS_time</strong></td> <td><strong>signal_time</strong></td> <td><strong>dac_value</strong></td> <td><strong>ADMspikes</strong></td> <td><strong>NLNGspikes</strong></td> </tr> <tr> <td>The time from the interface FPGA</td> <td>The time of the signal as per the SWEC ETHZ Database</td> <td>The value of the analog signals as recorded in the SWEC ETHZ Database</td> <td>The event-steam is the output of the AFE in boolean format. True represents a spike</td> <td>The spike-steam is the output of the SNN in boolean format. True represents a spike</td> </tr> </tbody> </table> </div> </div> </div>
A Placebo-controlled Study of Efficacy & Safety of 2 Trough-ranges of Everolimus as Adjunctive Therapy in Patients With Tuberous Sclerosis Complex (TSC) & Refractory Partial-onset Seizures
ClinicalTrials.gov study NCT01713946. IPD Sharing: YES. Countries: 25. Publications: 3.
Data from: Restoring failed inhibition in the substantia nigra pars reticulata suppresses absence seizures in rats
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Raw data of hypothermia and seizure intensity scores
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Slow and fast cortical cholinergic arousal is reduced in a mouse model of focal seizures with impaired consciousness
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Data from: Thalamus and focal to bilateral seizures: a multi-scale cognitive imaging study
<p><span><span><span><span><span><span><span><span><span><span><span><b><i>Objective</i></b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>To investigate the functional correlates of recurrent secondarily generalized seizures in temporal lobe epilepsy (TLE), using task-based fMRI as a framework to test for epilepsy-specific network rearrangements. As the thalamus modulates propagation of temporal-lobe onset seizures and promotes cortical synchronization during cognition, we hypothesized that occurrence of secondarily generalized, i.e. focal to bilateral tonic-clonic seizures (FBTCS), would relate to thalamic dysfunction, altered connectivity and whole-brain network centrality.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b><i>Methods</i></b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>FBTCS occur in a third of patients with TLE and are a major determinant of disease severity. In this cross-sectional study, we analyzed 113 patients with drug-resistant TLE (55 left/58 right), who performed a verbal fluency fMRI task that elicited robust thalamic activation. Thirty-three patients (29%) had experienced at least one FBTCS in the year preceding the investigation. We compared patients with TLE-FBTCS to those without FBTCS via a multi-scale approach, entailing analysis of SPM12-derived measures of activation, task-modulated thalamic functional connectivity (psychophysiological interaction), and graph-theoretical metrics of centrality. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b><i>Results</i></b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Individuals with TLE-FBTCS had less task-related activation of bilateral thalamus, with left-sided emphasis, and left hippocampus than those without FBTCS. In TLE-FBTCS, we also found greater task-related thalamotemporal and thalamo-motor connectivity, and higher thalamic degree and betweenness centrality. Receiver operating characteristic curves, based on a combined thalamic functional marker, accurately discriminated individuals with and without FBTCS.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b><i>Conclusions</i></b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>In TLE-FBTCS, impaired task-related thalamic recruitment coexists with enhanced thalamotemporal connectivity and whole-brain thalamic network embedding. Altered thalamic functional profiles are proposed as imaging biomarkers of active secondary generalization.</span></span></span></span></span></span></span></span></span></span></span></p>
Data: Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy
<p>We make available all the brain network data, and metadata of 83 patients and 29 healthy controls included in our study.</p> <p>Nishant Sinha, Natalie Peternell, Gabrielle M. Schroeder, Jane de Tisi, Sjoerd B. Vos, Gavin P. Winston, John S. Duncan, Yujiang Wang, and Peter N. Taylor "<em>Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy.</em>" Epilepsia 2021 <em>doi:10.1111/epi.16819</em>.</p> <p>Methodological details on MRI acquisition and data processing are provided in our manuscript. We request users to kindly cite our article and data appropriately.</p>
Zebrafish 2-photon imaging data during seizures; Original data
<p>This is imaging data in .tiff format from the publication "Seizures initiate in zones of relative hyperexcitation in a zebrafish epilepsy model"</p>
Raw Data for the article: Functional Gastrointestinal Disorders in Patients With Epilepsy: Reciprocal Influence and Impact on Seizure Occurrence
<p><strong>Introduction:</strong> The complex relationship between the microbiota-gut-brain axis (MGBA) and epilepsy has been increasingly investigated in preclinical studies. Conversely, evidence from clinical studies is still scarce. In recent years, the pivotal role of MGBA dysregulation in the pathophysiology of functional gastrointestinal disorders (FGID) has been recognized. With this background, we aimed to investigate the prevalence of FGID in patients with epilepsy (PWE) and the possible impact of bowel movement abnormalities on seizure recurrence. <strong>Methods:</strong> A total of 120 PWE and 113 age-, sex-, and BMI-matched healthy subjects (HS) were consecutively enrolled. A questionnaire to evaluate the presence of FGID (according to Rome III diagnostic criteria) was administrated to all participants. In a subgroup of drug-resistant patients, we administered an <em>ad-hoc</em> questionnaire combining Bristol stool charts and seizure diaries to evaluate seizure trends and bowel movement changes. <strong>Results:</strong> A higher prevalence of FGID in PWE (62.5%) than in HS (39.8%) was found (<em>p</em> < 0.001). The most frequently observed disorder was constipation, which was significantly higher in PWE than in HS (43.3 vs. 21.2%, <em>p</em> < 0.001), and was not associated with anti-seizure medication intake according to multivariable analysis. In drug-resistant patients, most seizures occurred during periods of altered bowel movements, especially constipation. A significant weak negative correlation between the number of days with seizures and the number of days with normal bowel movements was observed (<em>p</em> = 0.04). According to multivariable logistic regression analysis, FGID was significantly associated with temporal lobe epilepsy as compared with other lobar localization (<em>p</em> = 0.03). <strong>Conclusions:</strong> Our clinical findings shed new light on the complex relationship between epilepsy and the MGBA, suggesting a bidirectional link between bowel movement abnormalities and seizure occurrence. However, larger studies are required to better address this important topic.</p>
Data from: Billeci et al. "Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis"
<p>Dataset of electrocardiogram and electroencephalogram signals (.edf) acquired in epileptic patients (N=15).</p> <p>All the patients were long-term monitored with a Video-EEG, with electrodes arranged on the basis of the international 10-20 system, and with ECG. ECG was measured simultaneously with a sampling rate of 512 Hz.</p> <p>Each data include a descriptor file (.txt) containing all the information related to the acquisition: data, registration start (time), registration end (time), seizure/s start, seizure/s end and the electrodes involved at the seizure onset.</p> <p> </p>
How Absence Seizures Impair Sensory Perception: Insights from Awake fMRI and Simulation Studies in Rats
<p>This repository contains measured and simulated data for the manuscript titled "How Absence Seizures Impair Sensory Perception: Insights from Awake fMRI and Simulation Studies in Rats" by Stenroos P. et al. Electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data was recorded simultaneously from awake GAERS; a rat model of absence epilepsy. Visual and whisker stimulation was experimentally applied, and visual stimulation was simulated during interictal and ictal states and whole brain hemodynamic and neural responsiveness was compared between states.</p>
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