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461 results for “Electrophysiology”
Four-Chamber Human Heart Model for the Simulation of Cardiac Electrophysiology and Cardiac Mechanics
<p><strong>Changes in version 1.1 compared to version 1.0:</strong></p> <ul> <li>Ventricular fiber orientation changed to 66° on the endocardial and −41° on the epicardial surface</li> <li>Electrophysiology mesh was resampled</li> <li>Updated material tags in EP mesh</li> <li>Further details can be found in the <a href="https://github.com/KIT-IBT/CardioMechanics/tree/main">CardioMechanics GitHub repository</a> new reference paper:</li> </ul> <blockquote> <p>Gerach, T.; Loewe, A. Differential effects of mechano-electric feedback mechanisms on whole-heart activation, repolarization, and tension. <em>The Journal of Physiology</em> <strong>2024. </strong>https://doi.org/10.1113/JP285022 </p> </blockquote> <p>This repository contains a four-chamber model of the human heart which is ready to use for simulations of cardiac electrophysiology and cardiac mechanics problems. When using this dataset, please also cite the accompanying paper</p> <blockquote> <p>Gerach, T.; Schuler, S.; Fröhlich, J.; Lindner, L.; Kovacheva, E.; Moss, R.; Wülfers, E.M.; Seemann, G.; Wieners, C.; Loewe, A. Electro-Mechanical Whole-Heart Digital Twins: A Fully Coupled Multi-Physics Approach. <em>Mathematics</em> <strong>2021</strong>, <em>9</em>, 1247. https://doi.org/10.3390/math9111247</p> </blockquote> <p>The cardiac anatomy was manually segmented from magnetic resonance imaging (MRI) data of a 33 year old male volunteer. The volunteer provided informed consent and the study was approved by the IRB of Heidelberg University Hospital (Fritz et al., 2014).<br>The MRI data were acquired using a 1.5 T MR tomography system and consist of a static whole heart image stack taken during diastasis as well as time-resolved images in several long and short axis slices. Based on the segmentation, we first labeled the atria and the ventricles. The geometry was extended by a representation of the mitral valve, the tricuspid valve, the aortic valve and the pulmonary valve. Additionally, we closed the endo- and epicardial surfaces of the atria and added truncated pulmonary veins, vena cavae as well as the ascending aorta and pulmonary artery. Furthermore, we added a concentric layer of tissue around the entire heart which phenomenologically represents the influence of the pericardium and the surrounding tissue.</p> <p>Two tetrahedral meshes were created using Gmsh (Geuzaine et al., 2009): (1) the mechanical reference domain (<strong>M.vtu</strong>) with 128,976 elements (on average 3.17 mm edge length) and (2) the electrophysiological reference domain (<strong>EP.vtu</strong>) as a subset of M with 50,058,295 elements (on average 0.4 mm edge length).<br>We used rule-based methods to assign the myofiber orientation on EP: Wachter et al. (2015) was used for the atria and Bayer et al. (2012) for the ventricles. The fiber angle in the ventricles was chosen as +60° and -60° on the endocardial and epicardial surface, respectively. The sheet angle was set to -65° on the endocardium and 25° on the epicardium. Github repositories to these fiber generation tools are given in the sidebar. All geometry files are given in millimeter (mm).</p> <ul> <li><strong>Data:</strong><br>We provide time resolved data evaluated from cine MRI data, which can be used for model calibration. <ul> <li>Wall thickening (<strong>17AHA_WT.txt</strong>) / fractional wall thickening (<strong>17AHA_fractionalWT.txt</strong>) in the 17 AHA segments of the left ventricle</li> <li>Atrioventricular plane displacement (<strong>AVPD.txt</strong>) and velocity (<strong>AVPV.txt</strong>) as well as the displacement of all tracked points used for AVPD calculation (<strong>AVPD_trackedPoints.txt</strong>)</li> <li>Left and right ventricular volume (<strong>Volume_LV_RV.txt</strong>). RV volume is only available for end-diastole and end systole.</li> </ul> </li> <li><strong>Surfaces:</strong><br>This directory contains *.stl files with triangulated surfaces on which boundary conditions can be applied. <ul> <li><strong>cavityXX.stl</strong>: blood volume of the LV, RV, LA, RA</li> <li><strong>epicard.stl</strong>: the whole epicardium</li> <li><strong>outerPeri.stl</strong> and <strong>outerTrunks.stl</strong>: surfaces for Dirichlet boundary conditions</li> <li><strong>master.stl </strong>and <strong>slave.stl</strong>: surfaces used for the frictionless contact problem described in Fritz et al. (2014)</li> </ul> </li> <li><strong>TetGen:</strong><br>Contains the geometry <strong>M.vtu</strong> in the TetGen file format. T4 mesh with 4 node tetrahedrons and 3 node triangles. <ul> <li>.bases: fiber, sheet, and normal orientation at quadrature points of all elements</li> <li>.node: vertex coordinates</li> <li>.ele: list of tetrahedra</li> <li>.sur: list of triangles</li> </ul> </li> <li><strong>EP.vtu:</strong><br>Contains the cell arrays Fiber and Material.</li> <li><strong>M.vtu:</strong><br>Contains the cell arrays Fiber, Sheet, Sheetnormal, Material, and Label.</li> <li><strong>LabelIDs.txt:</strong><br>List of Label and Material identification numbers and corresponding anatomical structures.</li> </ul> <p> </p>
Integrated analysis of anatomical and electrophysiological human intracranial data
<p>The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated and readily adjusted to iEEG data sets with other characteristics. The protocol can be implemented by a graduate student or post-doctoral fellow with minimal MATLAB experience and takes approximately an hour, excluding the automated cortical surface extraction.</p> <p>This collection contains the data described in the protocol and that can be used to replicate all results.</p>
EEG: Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms
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Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. <em>NeuroImage</em>, <em>226</em>, 117569.</strong></p> <p>Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces <em>evolve</em> over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two different neutral faces on different background colors which serve as CS+ and CS-, respectively. Thereby, this design provides sufficient trials for EEG analyses while tripling the relative amount of trials that tap into more transient neurobiological processes. Consistent with prior studies on ERP components, data-driven topographic EEG analyses revealed that ERP amplitudes were potentiated during time periods from 33–60 ms, 108–200 ms, and 468–820 ms indicating that fear conditioning prioritizes early sensory processing in the brain, but also facilitates neural responding during later attentional and evaluative stages. Importantly, averaging across the three CS+/CS- sets allowed us to probe the temporal evolution of neural processes: Responses during each of the three time windows gradually increased from early to late fear conditioning, while long-latency (460–730 ms) electrocortical responses diminished throughout fear extinction. Our novel paradigm demonstrates how short-, mid-, and long-latency EEG responses change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.</p>
Electrophysiological characterization and functionality of neurons in murine cerebral organoids.
<p>Dataset includes patch-clamp recordings aim to characterize functionality of neurons belonging to murine cerebral organoids.</p> <p>Patch-clamp recordings were performed by University of Modena and Reggio Emilia unit (PI Prof. Curia Giulia).</p> <p>Organoids were generated by University of Verona unit (PI Prof. Decimo Ilaria) and transferred to Modena for electrophysiology experiments.</p>
Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology
<p><strong>General Description.</strong> This dataset consists of:</p> <ol> <li>The threshold crossing times of extracellularly and simultaneously recorded spikes, sorted into units (up to five, including a "hash" unit), along with sorted waveform snippets, and,</li> <li>The x,y position of the fingertip of the reaching hand and the x,y position of reaching targets (both sampled at 250 Hz).</li> </ol> <p>The behavioral task was to make self-paced reaches to targets arranged in a grid (e.g. 8x8) without gaps or pre-movement delay intervals. One monkey reached with the right arm (recordings made in the left hemisphere); The other reached with the left arm (right hemisphere). In some sessions recordings were made from both M1 and S1 arrays (192 channels); in most sessions M1 recordings were made alone (96 channels).</p> <p>Data from two primate subjects are included: 37 sessions from monkey 1 ("Indy", spanning about 10 months) and 10 sessions from monkey 2 ("Loco", spanning about 1 month), for a total of ~ 20,000 reaches and 6,500 reaches from monkeys 1 and 2, respectively.</p> <p><strong>Possible uses. </strong>These data are ideal for training BCI decoders, in particular because they are not segmented into trials. We expect that the dataset will be valuable for researchers who wish to design improved models of sensorimotor cortical spiking or provide an equal footing for comparing different BCI decoders. Other uses could include analyses of the statistics of arm kinematics, spike noise-correlations or signal-correlations, or for exploring the stability or variability of extracellular recording over sessions.</p> <p><strong>Variable names. </strong>Each file contains data in the following format. In the below, <em>n</em> refers to the number of recording channels, <em>u</em> refers to the number of sorted units, and <em>k</em> refers to the number of samples.</p> <ul> <li>chan_names - n x 1 <ul> <li>A cell array of channel identifier strings, e.g. "<em>M1 001</em>".</li> </ul> </li> <li>cursor_pos - k x 2 <ul> <li>The position of the cursor in Cartesian coordinates (x, y), mm.</li> </ul> </li> <li>finger_pos - k x 3 <em>or </em>k x 6 <ul> <li>The position of the working fingertip in Cartesian coordinates (z, -x, -y), as reported by the hand tracker in cm. Thus the cursor position is an affine transformation of fingertip position using the following matrix:<br>\(\begin{pmatrix} 0 & 0 \\ -10 & 0 \\ 0 & -10 \end{pmatrix}\)<br>Note that for some sessions finger_pos includes the orientation of the sensor as well; the full state is thus: (z, -x, -y, azimuth, elevation, roll).</li> </ul> </li> <li>target_pos - k x 2 <ul> <li>The position of the target in Cartesian coordinates (x, y), mm.</li> </ul> </li> <li>t - k x 1 <ul> <li>The timestamp corresponding to each sample of the cursor_pos, finger_pos, and target_pos, seconds.</li> </ul> </li> <li>spikes - n x u <ul> <li>A cell array of spike event vectors. Each element in the cell array is a vector of spike event timestamps, in seconds. The first unit (<em>u</em>1) is the "unsorted" unit, meaning it contains the threshold crossings which remained after the spikes on that channel were sorted into other units (<em>u</em>2, <em>u</em>3, etc.) For some sessions spikes were sorted into up to 2 units (i.e. <em>u</em>=3); for others, 4 units (<em>u</em>=5).</li> </ul> </li> <li>wf - n x u <ul> <li>A cell array of spike event waveform "snippets". Each element in the cell array is a matrix of spike event waveforms. Each waveform corresponds to a timestamp in "spikes". Waveform samples are in microvolts.</li> </ul> </li> </ul> <p><strong>Decoder Results.</strong> These data were used to fit decoder models, as reported in Makin, et al [1]. To aid comparisons to other decoders, we include performance summaries (for each session, decoder, bin-width, etc.) in the file <em>refh_results.csv</em>, containing the following columns:</p> <ul> <li>session - a session identifier, e.g. "indy_20160407_02"</li> <li>monkey - one of, "indy" or "loco"</li> <li>num_neurons - total number of features used in the decoder</li> <li>num_training_samples - number of samples (at the specified bin-width) used to train the decoder (sequential, from file start)</li> <li>num_testing_samples - number of samples used to evaluate the decoder (sequential, until file end)</li> <li>kinematic_axis - one of, "posx", "posy", "velx", "vely", "accx" or "accy"</li> <li>bin_width - one of, "16", "32", "64" or "128"</li> <li>decoder - one of, "regression", "KF_observed", "KF_static", "KF_dynamic", "UKF", "rEFH_static" or "rEFH_dynamic"</li> <li>rsq - coefficient of determination, R2</li> <li>snr - Signal to noise ratio, SNR := -10 log10(1 - R2)</li> </ul> <p><strong>Videos. </strong>For some sessions, we recorded screencasts of the stimulus presentation display using a dedicated hardware video grabber. These screencasts are thus a faithful representation of the stimuli and feedback presented to the monkey and are available for the following sessions:</p> <ul> <li><a href="https://youtu.be/bPkpdpm03z8">indy_20160921_01</a></li> <li><a href="https://youtu.be/B02z6w4c3yk">indy_20160930_02</a></li> <li><a href="https://youtu.be/S640zzIKJs8">indy_20160930_05</a></li> <li><a href="https://youtu.be/tRoe84E0AzA">indy_20161005_06</a></li> <li><a href="https://youtu.be/hNZlBa516jM">indy_20161006_02</a></li> <li><a href="https://youtu.be/L6GKwI2u1Es">indy_20161007_02</a></li> <li><a href="https://youtu.be/eV1joYU5vt0">indy_20161011_03</a></li> <li><a href="https://youtu.be/4LM_gKt2cYg">indy_20161013_03</a></li> <li><a href="https://youtu.be/GLGrKHgf-zw">indy_20161014_04</a></li> <li><a href="https://youtu.be/6aPrv8HEPGQ">indy_20161017_02</a></li> </ul> <p><strong>Supplements. </strong>The raw broadband neural recordings that the spike trains in this dataset were extracted from are available for the following sessions:</p> <ul> <li>indy_20160622_01: <a href="https://doi.org/10.5281/zenodo.1488440">doi:10.5281/zenodo.1488440</a></li> <li>indy_20160624_03: <a href="https://doi.org/10.5281/zenodo.1486147">doi:10.5281/zenodo.1486147</a></li> <li>indy_20160627_01: <a href="https://doi.org/10.5281/zenodo.1484824">doi:10.5281/zenodo.1484824</a></li> <li>indy_20160630_01: <a href="https://doi.org/10.5281/zenodo.1473703">doi:10.5281/zenodo.1473703</a></li> <li>indy_20160915_01: <a href="https://doi.org/10.5281/zenodo.1467953">doi:10.5281/zenodo.1467953</a></li> <li>indy_20160916_01: <a href="https://doi.org/10.5281/zenodo.1467050">doi:10.5281/zenodo.1467050</a></li> <li>indy_20160921_01: <a href="https://doi.org/10.5281/zenodo.1451793">doi:10.5281/zenodo.1451793</a></li> <li>indy_20160927_04: <a href="https://doi.org/10.5281/zenodo.1433942">doi:10.5281/zenodo.1433942</a></li> <li>indy_20160927_06: <a href="https://doi.org/10.5281/zenodo.1432818">doi:10.5281/zenodo.1432818</a></li> <li>indy_20160930_02: <a href="https://doi.org/10.5281/zenodo.1421880">doi:10.5281/zenodo.1421880</a></li> <li>indy_20160930_05: <a href="https://doi.org/10.5281/zenodo.1421310">doi:10.5281/zenodo.1421310</a></li> <li>indy_20161005_06: <a href="https://doi.org/10.5281/zenodo.1419774">doi:10.5281/zenodo.1419774</a></li> <li>indy_20161006_02: <a href="https://doi.org/10.5281/zenodo.1419172">doi:10.5281/zenodo.1419172</a></li> <li>indy_20161007_02: <a href="https://doi.org/10.5281/zenodo.1413592">doi:10.5281/zenodo.1413592</a></li> <li>indy_20161011_03: <a href="https://doi.org/10.5281/zenodo.1412635">doi:10.5281/zenodo.1412635</a></li> <li>indy_20161013_03: <a href="https://doi.org/10.5281/zenodo.1412094">doi:10.5281/zenodo.1412094</a></li> <li>indy_20161014_04: <a href="https://doi.org/10.5281/zenodo.1411978">doi:10.5281/zenodo.1411978</a></li> <li>indy_20161017_02: <a href="https://doi.org/10.5281/zenodo.1411882">doi:10.5281/zenodo.1411882</a></li> <li>indy_20161024_03: <a href="https://doi.org/10.5281/zenodo.1411474">doi:10.5281/zenodo.1411474</a></li> <li>indy_20161025_04: <a href="https://doi.org/10.5281/zenodo.1410423">doi:10.5281/zenodo.1410423</a></li> <li>indy_20161026_03: <a href="https://doi.org/10.5281/zenodo.1321264">doi:10.5281/zenodo.1321264</a></li> <li>indy_20161027_03: <a href="https://doi.org/10.5281/zenodo.1321256">doi:10.5281/zenodo.1321256</a></li> <li>indy_20161206_02: <a href="https://doi.org/10.5281/zenodo.1303720">doi:10.5281/zenodo.1303720</a></li> <li>indy_20161207_02: <a href="https://doi.org/10.5281/zenodo.1302866">doi:10.5281/zenodo.1302866</a></li> <li>indy_20161212_02: <a href="https://doi.org/10.5281/zenodo.1302832">doi:10.5281/zenodo.1302832</a></li> <li>indy_20161220_02: <a href="https://doi.org/10.5281/zenodo.1301045">doi:10.5281/zenodo.1301045</a></li> <li>indy_20170123_02: <a href="https://doi.org/10.5281/zenodo.1167965">doi:10.5281/zenodo.1167965</a></li> <li>indy_20170124_01: <a href="https://doi.org/10.5281/zenodo.1163026">doi:10.5281/zenodo.1163026</a></li> <li>indy_20170127_03: <a href="https://doi.org/10.5281/zenodo.1161225">doi:10.5281/zenodo.1161225</a></li> <li>indy_20170131_02: <a href="https://doi.org/10.5281/zenodo.854733">doi:10.5281/zenodo.854733</a></li> </ul> <p><strong>Contact Information.</strong> We would be delighted to hear from you if you find this dataset valuable, especially if it leads to publication. Corresponding author: J. E. O'Doherty <joeyo@neuroengineer.com>.</p> <p><strong>Citation.</strong></p> <p>@misc{ODoherty:2017, author = {O'{D}oherty, Joseph E. and Cardoso, Mariana M. B. and Makin, Joseph G. and Sabes, Philip N.}, title = {Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex electrophysiology}, doi = {10.5281/zenodo.788569}, url = {https://doi.org/10.5281/zenodo.788569}, month = may, year = {2017} }</p> <p><strong>Publications making use of this dataset.</strong></p> <ol> <li>Makin, J. G., O'Doherty, J. E., Cardoso, M. M. B. & Sabes, P. N. (2018). Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm. <em>J Neural Eng.</em> 15(2): 026010. <a href="https://doi.org/10.1088/1741-2552/aa9e95">doi:10.1088/1741-2552/aa9e95</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2018). Spike Rate Estimation Using Bayesian Adaptive Kernel Smoother (BAKS) and Its Application to Brain Machine Interfaces. <em>2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</em>, Honolulu, HI, USA, 2018, pp. 2547-2550. <a href="https://doi.org/10.1109/EMBC.2018.8512830">doi:10.1109/EMBC.2018.8512830</a></li> <li>Balasubramanian, M., Ruiz, T., Cook, B., Bhattacharyya, S., Prabhat, Shrivastava, A. & Bouchard K. (2018). Optimizing the Union of Intersections LASSO (UoILASSO) and Vector Autoregressive (UoIVAR) Algorithms for Improved Statistical Estimation at Scale. <em>arXiv Preprint.</em> <a href="https://arxiv.org/abs/1808.06992">arXiv:1808.06992</a></li> <li>Sachdeva, P. S., Bhattacharyya, S., & Bouchard, K. E. (2019). Sparse, Predictive, and Interpretable Functional Connectomics with UoILasso, <em>41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</em>, Berlin, Germany, pp. 1965-1968. <a href="https://doi.org/10.1109/EMBC.2019.8856316">doi:10.1109/EMBC.2019.8856316</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2019). End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network. <em>2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), </em>Nara, Japan, pp. 1-4. <a href="https://doi.org/10.1109/biocas.2019.8919131">doi:10.1109/biocas.2019.8919131</a></li> <li>Bose, S. K., Acharya, J., & Basu, A. (2019). Is my Neural Network Neuromorphic? Taxonomy, Recent Trends and Future Directions in Neuromorphic Engineering. <em>2019 53rd Asilomar Conference on Signals, Systems, and Computers</em>, Pacific Grove, CA, USA, pp. 1522-1527. <a href="https://doi.org/10.1109/IEEECONF44664.2019.9048891">doi:10.1109/IEEECONF44664.2019.9048891</a></li> <li>Shaikh, S., So, R., Sibindi, T., Libedinsky, C., & Basu, A. (2019). Towards Intelligent Intra-cortical BMI (i2BMI): Low-power Neuromorphic Decoders that outperform Kalman Filters. <em>bioRxiv Preprint.</em> 772988. <a href="https://doi.org/10.1101/772988">doi:10.1101/772988</a></li> <li>Keshtkaran, M. R., & Pandarinath, C. (2019). <a href="https://papers.nips.cc/paper/9722-enabling-hyperparameter-optimization-in-sequential-autoencoders-for-spiking-neural-data">Enabling hyperparameter optimization in sequential autoencoders for spiking neural data.</a> <em>Advances in Neural Information Processing Systems (NeurIPS) 32.</em></li> <li>Clark, D. G., Livezey, J. A., & Bouchard, K. E. (2019). Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis. <em>arXiv Preprint.</em> <a href="https://arxiv.org/abs/1905.09944">arXiv:1905.09944</a></li> <li>Shaikh, S., So, R., Sibindi, T., Libedinsky, C., & Basu, A. (2019). Towards Intelligent Intracortical BMI (i2BMI): Low-Power Neuromorphic Decoders That Outperform Kalman Filters. <em>IEEE Transactions on Biomedical Circuits and Systems</em>. 13(6): 1615-1624. <a href="https://doi.org/10.1109/TBCAS.2019.2944486">doi:10.1109/TBCAS.2019.2944486</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2019). Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network. <em>arXiv Preprint.</em> <a href="https://arxiv.org/abs/1901.00708">arXiv:1901.00708</a></li> <li>Balasubramanian, M., Ruiz, T., Cook, B., Prabhat, Bhattacharyya, S., Shrivastava, A. & Bouchard K. (2020). Scaling of Union of Intersections for Inference of Granger Causal Networks from Observational Data. <em>Proceeding of the 34th IEEE International Parallel & Distributed Processing Symposium (IPDPS). </em>New Orleans, LA, USA, pp. 264-273. <a href="https://doi.org/10.1109/IPDPS47924.2020.00036">doi: 10.1109/IPDPS47924.2020.00036</a></li> <li>Bose, S. K., Acharya, J. & Basu, A. (2020). Is my neural network neuromorphic? Taxonomy, recent trends and future directions in neuromorphic engineering. <em>arXiv Preprint</em>. <a href="https://arxiv.org/abs/2002.11945">arXiv:2002.11945</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2020). Inferring entire spiking activity from local field potentials with deep learning. <em>bioRxiv Preprint.</em> 2020.05.02.074104. <a href="https://doi.org/10.1101/2020.05.02.074104">doi:10.1101/2020.05.02.074104</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2020). Improved Spike-based Brain-Machine Interface Using Bayesian Adaptive Kernel Smoother and Deep Learning. <em>TechRxiv Preprint.</em> <a href="https://doi.org/10.36227/techrxiv.12383600.v1">doi:10.36227/techrxiv.12383600.v1</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2020). Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning. <em>bioRxiv Preprint.</em> 2020.05.07.083063 <a href="https://doi.org/10.1101/2020.05.07.083063">doi:10.1101/2020.05.07.083063</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis. C.-S. (2020). Impact of referencing scheme on decoding performance of LFP-based brain-machine interface. <em>bioRxiv Preprint. </em>2020.05.03.075218 <a href="https://doi.org/10.1101/2020.05.03.075218">doi:10.1101/2020.05.03.075218</a></li> <li>Ahmadi, N., Constandinou, T. & Bouganis, C. (2020). 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Effects of packet loss on neural decoding effectiveness in wireless transmission. <em>Brain Sciences.</em> 15(3): 221. doi:10.3390/brainsci15030221</li> </ol> <p><strong>History.</strong></p> <ul> <li>Version 2 - added CSV of results from Makin et al.</li> <li>Version 1 - initial release.</li> </ul>
Raw data for PIP2 interaction with TRPC3, explored through computation and electrophysiology
<p>The transient receptor potential canonical type 3 (TRPC3) channel plays a pivotal role in regulating neuronal excitability within the brain via its constitutive activity. The channel is intricately regulated by lipids and has previously been demonstrated to be positively modulated by PIP2. Using molecular dynamics simulations and patch clamp techniques, we reveal that PIP2 predominantly interacts with TRPC3 at the L3 lipid binding site, located at the intersection of pre-S1 and S1 helices. We propose a novel signal transduction pathway from the L3 through the re-entrant loop to a salt bridge between the TRP helix and S4-S5 linker. Notably, we find that both stimulated and constitutive TRPC3 activity require PIP2. These structural insights into the function of TRPC3 are invaluable for understanding the role of the TRPC subfamily in health and disease in native tissue.</p>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Electrophysiology Data
<p>In vivo electrophysiological data, slice electrophysiological data, and HTP vs tyrosine hydroxylase immunohistochemistry data. These data were used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Figure 1, panels c-f</li> <li>Ext. Fig 1, all panels</li> <li>Ext. Fig 2, all panels</li> </ul> <p>Specifics of the data:</p> <ul> <li>Slice_Ephys .csv files contain the summarized in vitro electrophysiology data.</li> <li>HTP_Mouse_*.7z and ddHTP_Mouse_*.7z are zipped folders with the raw data, sorted data, and extracted cells for each recording, grouped by mouse the recordings were obtained from.</li> <li>HTP final_grouped spiking analysis_n39.mat is the MATLAB data file with all the extracted firing metrics for all 39 +HTP cells.</li> <li>ddHTP final_grouped spiking analysis_n18.mat is the MATLAB data file with all the extracted firing metrics for all 18 ddHTP cells.</li> <li> Ephys_Summary_Data .csv files contain the summarized in vivo electrophysiology data.</li> <li>VHist4_HTP and TH.7z contains the raw histological images, drawn ROIs, and ilastik cell counting performed to compare tyrosine hydroxylase and HTP expression.</li> </ul>
Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20170131_02
<p>This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> .<br> <br> <strong>General description.</strong> These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20170131_02".</p> <p><strong>Filtering. </strong>The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate.</p> <p><strong>File format.</strong> The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification.</p> <p><strong>Datasets. </strong>A few of the relevant dataset paths are listed here for convenience. In the below, <em>n</em> refers to the number of recording channels and <em>k</em> refers to the number of samples.</p> <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> <p>Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.</p> <p><strong>History</strong></p> <ul> <li>Version 2 - corrects a error with the electrode mapping.</li> <li>Version 1 - initial release.</li> </ul>
in vivo electrophysiological data of DRN serotonin neurons
<p>This repository contains in vivo electrophysiological data of DRN serotonin neurons of freely behaving mice.</p> <p>The data is related to the following research article: Li, Y., Zhong, W., Wang, D. et al. Serotonin neurons in the dorsal raphe nucleus encode reward signals. Nat Commun 7, 10503 (2016). https://doi.org/10.1038/ncomms10503 Please refer to the original publication for details.</p> <p>Please refer to the Readme.txt in the files for details.</p> <p> </p>
openSAHE: Open Source Statistical Anatomical Atlas of the Human head for Electrophysiology Applications (precomputed atlases)
<p>Computed anatomical atlases of the human head at 100Hz, 1kHz 10kHz 100kHz and 1MHz. Electrical properties: resistivity, conductivity and relative permittivity in SI units. This dataset is part of the article 'Anatomical atlas of the upper part of the human head for electroencephalography and bioimpedance applications' by Moura, F, Beraldo R, Ferreira, L and Siltanen S, Physiological Measurement, Volume 42, Number 10, 2021. If you use any of these files, please add a reference to this <a href="https://iopscience.iop.org/article/10.1088/1361-6579/ac3218">article</a>.</p> <p>Source code available at https://github.com/fsmMLK/openSAHE</p>
DS3_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_neurons electrophysiology
<p>Whole-cell current clamp recordings of the electrical activity of neurons plated on P3HT flat and pillars</p>
Ion permeation through a narrow cavity constriction in KCNQ1 channels, scours files of MD simulations and analysis of electrophysiological experiments.
<p>Source files of Molecular Dynamic (MD) simulations and analysis files of electrophysiology data in Igor pro software format. KCNQ1 channel pore region (G245-K354) was embedded in a lipid bilayer consisting of phosphatidylcholine phospholipids (POPC) and ion permission mechanism was analized by MD simulations using the computational electrophysiology (compEL) method implemented in GROMACS v2022.4. Ion imbalance between compartments of double-membrane system created a membrane potential of abour 300 mV which drives ion movment.</p>
Molecular and electrophysiological features of GABAergic neurons in the dentate gyrus reveal limited homology with cortical interneurons
<p>GABAergic interneurons tend to diversify into similar classes across telencephalic regions. However, it remains unclear whether the electrophysiological and molecular properties commonly used to define these classes are discriminant in the hilus of the dentate gyrus. Here, using patch-clamp combined with single cell RT-PCR, we compare the relevance of commonly used electrophysiological and molecular features for the clustering of GABAergic interneurons sampled from the mouse hilus and primary sensory cortex. While unsupervised clustering groups cortical interneurons into well-established classes, it fails to provide a convincing partition of hilar interneurons. Statistical analysis based on resampling indicates that hilar and cortical GABAergic interneurons share limited homology. While our results do not invalidate the use of classical molecular marker in the hilus, they indicate that classes of hilar interneurons defined by the expression of molecular markers do not exhibit strongly discriminating electrophysiological properties.</p>
Electrophysiological data of the paper 'Serotonergic and dopaminergic neurons in the dorsal raphe are differentially altered in a mouse model for parkinsonism'
<p>This excel data set contains the electrophysiological data presented in the paper including figure 1I, 1J, figure 3, figure 5, suppl. figure 2A, suppl. figure 3, suppl. figure 6E, 6G, 6L & 6N.</p> <p> </p> <p>More information about how the data was extracted can be found in the materials and methods section of the paper. </p>
Dataset: experimental electrophysiological dynamics data
<p> </p> <p>msSEI_exportTrials.zip contains raw iEEG data, the structure of which is described in msSEI_exportTrials_readme.docx (same as in v1).</p>
Fig. 4 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 4. The occurrence of 3 different antennal trichoid sensilla in relation to their widths (A, B and C) in male and female diamondback moth, Plutella xylostella. D shows the widths of the sensilla plotted against their lengths for the 3 trichoid sensilla types (Tr I, Tr II and Tr III). Data obtained from 3 females and 5 males.
Fig. 3 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 3. The number of each of 4 types of sensilla along various antennal segments (1–10 = proximal end, 11–20 = middle, 21–30 = distal end) of female (A, C) and male (B, D) diamondback moths, Plutella xylostella. The estimated number of sensilla (C, D) was calculated based on the surface area of each segment section, and by assuming that sensilla were present on 2/3 of the surface (mean ± S.E., n = 5–15 in females and 9–18 in males).
Fig. 7 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 7. Sensilla styloconica identified on the antennae of female (A, C and D) and male (B, E and F) Plutela xylostella. One sensillum styloconicum is located near the anterio-ventral end in each flagella subsegment (A, B). Usually 1 terminal sensory cone is present at the distal end in each sensillum styloconicum (C, D, E, F).
Fig. 2 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)
Fig. 2. Gross antennal morphology of female (A, B) and male (C) Plutella xylostella, and a part of a male antenna showing the presence of different morphological types of sensilla (D). Ac (sensilla auricillica); Cc (sensilla coeloconica); Ch (sensilla chaetica); Tr I (sensilla trichodea Type I); Tr II (sensilla trichodea Type II); Tr III (sensilla trichodea Type III).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.