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Raw data for "Development and characterization of a non-human primate model of disseminated synucleinopathy"
<p><span>In this study, the performance and biodistribution of the retrogradely-spreading AAV9-SynA53T vector was evaluated in the NHP brain. Conducted intraparenchymal deliveries of viral suspensions in the left putamen gave rise to a disseminated synucleinopathy in a circuit-specific basis.</span></p>
3D Reconstruction of Shoulder Muscles in Hominoid Primates: Correlating Scapular Attachment Areas with Muscle Volume
<h2><strong>How To Cite:</strong></h2> <p>If you use this data or code in your research, please cite the associated open-access <strong>manuscript, </strong>which you can find here: <a href="https://doi.org/10.1111/joa.14199">https://doi.org/10.1111/joa.14199</a><br>and this <strong>zenodo repository</strong>.</p> <h2><strong>Online Visualization:</strong></h2> <p>You can access an interactive, web-based view of the notebooks and analyses <a title="Shoulder Muscle Reconstruction Code" href="https://juliavanbeesel.github.io/ShoulderMuscleReconstructions/intro.html" target="_blank" rel="noopener">here</a>.</p> <h2><strong>Repository Description:</strong></h2> <p>This repository contains two zip files related to the analysis and visualization of 3D reconstructed muscle volumes and lengths from various hominoid specimens.</p> <ol> <li> <p><strong>MeshFiles.zip:</strong></p> <ul> <li><strong>Contents:</strong> This zip file includes all <code>.obj</code> files for 3D reconstructed muscle volumes and associated anatomical structures. Specifically, it contains: <ul> <li><strong>Muscles:</strong> Supraspinatus, Infraspinatus, Subscapularis, Teres Major, Teres Minor</li> <li><strong>Bones:</strong> Scapula and Humerus</li> <li><strong>Attachment Sites</strong></li> </ul> </li> <li><strong>Organization:</strong> The files are organized into folders by specimen. There are 9 hominoid specimens from the following species: <ul> <li><em>Hylobates lar</em></li> <li><em>Symphalangus syndactylus</em></li> <li><em>Pongo pygmaeus</em></li> <li><em>Pongo abelii</em></li> <li><em>Gorilla gorilla</em></li> <li><em>Pan troglodytes</em></li> <li><em>Homo sapiens</em></li> </ul> </li> <li><strong>Surface Scans of Muscle Geometry: </strong>The specimens <em>Pongo</em> (ID 3) and <em>Symphalangus </em>(ID 122) also contain surface scans that depict the muscle geometry of the listed muscles. These surface scans can be used for training with the iterative polygonal modelling approach. The scans are stored as <code>.obj</code>, <code>.mtl</code> and <code>.png</code> files. To view textures on these meshes, keep all three files together in the same folder.</li> <li><strong>Additional Details:</strong> Muscle reconstructions were performed for different arm positions. Each folder contains multiple humerus files, with each file representing a humerus in a specific position aligned with the corresponding muscles. The humerus file names indicate the muscles the humerus is aligned with.<br><br></li> </ul> </li> <li> <p><strong>DataAndCode.zip:</strong></p> <ul> <li><strong>Contents:</strong> <ul> <li><strong>Excel File:</strong> The original data used for analysis, presented in Table 2 of the manuscript.</li> <li><strong>Jupyter Notebook Files: </strong>These notebooks provide the analyses and figures as described in the manuscript: <ul> <li><em>Accuracy_Muscle_Length_Reconstruction:</em> Analysis of muscle length measurement comparisons, detailed in Supplementary Information Section 3: <em>Accuracy of estimating Muscle Length from 3D reconstructions</em>.</li> <li><em>Accuracy_Muscle_Volume_Reconstruction:</em> Analysis of muscle volume measurement comparisons, detailed in Results Section 3.2: <em>Accuracy of Muscle Volume and Length Reconstruction</em>.</li> <li><em>Correlation_Analysis_SIS:</em> Correlation analysis of muscle origin area to volume for the supraspinatus, infraspinatus, and subscapularis muscles, detailed in Results Section 3.3:<em> Correlation Analysis</em>.</li> <li><em>Correlation_Analysis_TT:</em> Correlation analysis of muscle origin area to volume for the teres major and minor muscles, detailed in Supplementary Information Section 1: <em>Correlation results of teres major and minor</em>.</li> </ul> </li> <li><strong>Requirements.txt:</strong> A file listing the necessary packages required to run the Jupyter notebooks.</li> </ul> </li> <li><strong>Purpose:</strong> The Python files include code for performing statistical analyses and generating figures as described in the manuscript.</li> </ul> </li> </ol> <h2><strong>Usage Instructions:</strong></h2> <ul> <li>For analyzing muscle volumes and lengths, refer to the Jupyter notebooks included in the <code>DataAndCode.zip</code>. Ensure all dependencies listed in the <code>requirements.txt</code> file are installed.</li> <li>The <code>MeshFiles.zip</code> contains the 3D models necessary for visualizing muscle and bone reconstructions, organized by specimen and arm position.</li> </ul>
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). Inferring entire spiking activity from local field potentials. <em>Scientific reports.</em> 11. <a href="https://doi.org/10.1038/s41598-021-98021-9">doi:10.1038/s41598-021-98021-9</a></li> <li>Sachdeva, P. S, Livezey, J. A, Dougherty, M. E., Gu, B.-M., Berke, J. D, & Bouchard, K. E. (2020). Accurate Inference in Parametric Models Reshapes Neuroscientific Interpretation and Improves Data-driven Discovery. <em>bioRxiv Preprint.</em> 2020.04.10.036244. <a href="https://doi.org/10.1101/2020.04.10.036244">doi:10.1101/2020.04.10.036244</a></li> <li>Pei, F., Ye, J., Zoltowski, D., Wu, A., Chowdhury, R. H., Sohn, H., O'Doherty, J. E., Shenoy, K. V., Kaufman, M. T., Churchland, M., Jazayeri, M., Miller, L. E., Pillow, J., Park, I. M., Dyer, E. L., & Pandarinath, C. (2021). Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. <em>arXiv Preprint.</em> <a href="http://arxiv.org/abs/2109.04463">arXiv:2109.04463</a></li> <li>Jensen, K. T., Kao, T.-C., Stone, J. T., & Hennequin, G. (2021). Scalable Bayesian GPFA with automatic relevance determination and discrete noise models. <em>bioRxiv Preprint.</em> 2021.06.03.446788. <a href="https://doi.org/10.1101/2021.06.03.446788">doi:10.1101/2021.06.03.44678</a></li> <li>Savolainen, O.W. (2021). The significance of neural inter-frequency power correlations. <em>Sci. Rep.</em> 11, 23190. <a href="https://doi.org/10.1038/s41598-021-02277-0">doi:10.1038/s41598-021-02277-0</a></li> <li>Schimel, M., Kao, T.-C., Jensen, K.T., & Hennequin, G. (2021). iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data. <em>bioRxiv Preprint.</em> 2021.10.07.463540. <a href="https://doi.org/10.1101/2021.10.07.463540">doi:10.1101/2021.10.07.463540</a></li> <li>Li, Y., Qi, Y., Wang, Y., Wang, Y., Xu, K., & Pan, G. (2021). Robust neural decoding by kernel regression with Siamese representation learning. <em>J Neural Eng. </em>18(5): 056062. <a href="http://doi.org/10.1088/1741-2552/ac2c4e">doi:10.1088/1741-2552/ac2c4e</a></li> <li>Savolainen, O. W. (2021). The Significance of Neural Inter-Frequency Correlations. <em>Research Square Preprint (v1)</em>. <a href="https://doi.org/10.21203/rs.3.rs-329644/v1">doi:10.21203/rs.3.rs-329644/v1</a></li> <li>Sani, O. G., Pesaran, B., & Shanechi., M. M. (2021). Where is all the nonlinearity: flexible nonlinear modeling of behaviorally relevant neural dynamics using recurrent neural networks. <em>bioRxiv Preprint.</em> 2021.09.03.458628. <a href="https://doi.org/10.1101/2021.09.03.458628">doi:10.1101/2021.09.03.458628</a></li> <li>Yang, S.-H., Huang, J.-W., Huang, C.-J., Chiu, P.-H., Lai, H.-Y., & Chen, Y.-Y. (2021). 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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>
Selection against admixture and gene regulatory divergence in a long-term primate field study
<p><strong>Selection against admixture and gene regulatory divergence in a long-term primate field study</strong><br> <em>Vilgalys & Fogel et al. (bioRxiv)</em></p> <ul> <li><a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/Panubis1.0_to_hg38.chain.gz">Panubis1.0_to_hg38.chain.gz</a>; <a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/hg38_to_Panubis1.0.chain.gz">hg38_to_Panubis1.0.chain.gz</a>: Liftover chain files between Panubis1.0 and hg38. </li> <li><a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/amboseli_LCLAE_tracts.txt.gz">amboseli_LCLAE_tracts.txt.gz</a>: Local ancestry calls for 442 wild, hybrid baboons studied as part of the Amboseli Baboon Research Project. Local ancestry was called using LCLAE and is represented by a 0 for homozygous yellow ancestry, 2 for homozygous anubis ancestry, and 1 for heterozygous ancestry. Each row in the file has a genomic position (chromosome, start, and end), local ancestry call, and the individual for whom the call was made. </li> <li><a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/masked_yellow_and_anubis.vcf.gz?versionId=25e26878-bca3-4667-9668-9e19424bc23e">masked_yellow_and_anubis.vcf.gz</a>: Genotype calls for non-Amboseli yellow and anubis baboons, after masking to remove putative introgressed ancestry. </li> <li>A time-stamped version of the code is included here, and also available on GitHub at <a href="http://github.com/TaurVil/VilgalysFogel_Amboseli_admixture">github.com/TaurVil/VilgalysFogel_Amboseli_admixture</a>. </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>
Field data for: Enterovirus sequence data obtained from primate samples in Central Africa suggest a high prevalence of enteroviruses with possible zoonotic potential
<p>Enteroviruses infect humans and animals, can cause disease, and some may be transmitted across species barriers. We collected different types of samples from various species of Central African wildlife, including data on sampling location and tested the samples for the presence of Enterovirus RNA using a family level PCR. Specimen collection was approved by an Institutional Animal Care and Use Committee (IACUC) of the University of California Davis, and the Governments of Cameroon and the Democratic Republic of the Congo. Enterovirus RNA was detected in samples from 17 primates and 2 rodents. Some sequences were very similar while others were dissimilar to known species, highlighting the unexplored enterovirus diversity in wildlife.</p> <p>The samples and filed data were collected by field ecologists as part of the USAID funded PREDICT project (https://ohi.vetmed.ucdavis.edu/programs-projects/predict-project) and screened for enterovirus RNA using consensus PCR. Maps were generated using basic maps from Paintmaps (http://www.paintmaps.com), a free tool for educational and academic use. The dataset contains the metadata on enterovirus screening among wildlife in Cameroon and the Democratic Republic of the Congo from 2003-2014 as part of the USAID funded PREDICT project. Please refer to the article for more information on methods and references.</p>
Multilevel atlas comparisons reveal divergent evolution of the primate brain
<p>Nifti files of 20 mammalian atlases modified into a Common Multilevel Segmentation.</p> <p>(See Figure 1 in Multilevel atlas comparisons reveal divergent evolution of the primate brain; https://www.pnas.org/doi/full/10.1073/pnas.2202491119#sec-3)</p> <p>These nifti files are based on the brain atlases from 18 mammalian species, that were published between the years 2013 and 2021 (see list).</p> <p>The Python script to re-segment the "original" atlases into the modified version (that is shared here) is also available:</p> <p>see Modify_atlases.py</p> <p>Each species folder contains 5 nifti files: 1 for each level of segmentation and 1 for the brain segmentation.</p> <p>The other txt files are the volumetric output extracted using AFNI on each nifti file.</p> <p>Please read the Readme.txt file to credit and cite accordingly all the authors.</p> <p> </p> <p> </p> <p> </p> <p> </p>
MRI Brain Template and Atlas of the Mouse Lemur Primate Microcebus murinus
<p>MRI template and 120-region atlas for the mouse lemur primate Microcebus murinus.<br> <br> Generated from 34 animals aged 15-58 months old scanned at 7T using a T2-weighted sequence, resolution 115 × 115 × 230 µm. The code developed to create and manipulate the template has been refined into general procedures for registering small mammal brain MR images, available within a python module sammba-mri (SmAll-maMMals BrAin MRI; <a href="https://sammba-mri.github.io/">https://sammba-mri.github.io/</a>). The template was up-sampled to 91 µm isotropic for hand-segmentation of structures, and also used to create probability maps of grey matter, white matter and cerebro-spinal fluid.</p> <p>if used for publication please cite: </p> <p><strong>A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy</strong><br> Nachiket A Nadkarni, Salma Bougacha, Clément Garin, Marc Dhenain, Jean-Luc Picq<br> Jan 2019<br> <strong>NeuroImage</strong> 185, 85-95<br> DOI: 10.1016/J.NEUROIMAGE.2018.10.010<br> <a href="https://www.sciencedirect.com/science/article/pii/S1053811918319694">https://www.sciencedirect.com/science/article/pii/S1053811918319694</a></p>
Functional organization of the mouse lemur Primate Microcebus murinus : from multilevel validation to comparison with humans
<p>Brain network organization in the mouse lemur (Microcebus murinus) Primate.<br> (comparison with humans)<br> Archives contain:<br> <br> - Dictionary learning analysis in mouse lemurs and humans showing networks identified in these two species.<br> <br> - Cerebral templates from mouse lemurs and humans (MNI template). They can be used to localize networks.<br> <br> - A functional atlas of the mouse lemur brain issued from resting fMRI. Resting-state functional MR images were recorded from 14 mouse lemurs at 11.7 Tesla (2 time point per animal).<br> - An atlas from human brain (issued from <a href="http://www.gin.cnrs.fr/fr/outils/aal-aal2/">http://www.gin.cnrs.fr/fr/outils/aal-aal...</a>) that can be used to attribute human cerebral networks.<br> <br> - Templates, atlases and networks can be easily observed together using ITK-SNAP (<a href="http://www.itksnap.org/">http://www.itksnap.org/</a>).</p> <p>if used for publication please cite: </p> <p><strong>Resting state functional atlas and cerebral networks in mouse lemur primates at 11.7 Tesla</strong><br> <strong>Clément M Garin</strong>, Nachiket A Nadkarni, Brigitte Landeau, Gaël Chételat, Jean-Luc Picq, Salma Bougacha, Marc Dhenain<br> Feb 2021<br> <strong>NeuroImage</strong> 226, 117589<br> DOI: 10.1016/J.NEUROIMAGE.2020.117589<br> <a href="https://www.sciencedirect.com/science/article/pii/S1053811920310740">https://www.sciencedirect.com/science/article/pii/S1053811920310740</a></p>
Figure 5 in The type specimens and type localities of the orangutans, genus Pongo Lacépède, 1799 (Primates: Hominidae)
Figure 5. The nomenclature and geographic distribution of the subspecies of Pongo pygmaeus, (A) following Groves (2001), and (B) as revised in this paper
Maternal and genetic correlations between morphology and physical performance traits in a small captive primate, Microcebus murinus
<p>Physical performance traits are key components of fitness and direct targets of selection. Maternal effects are important components of integrated phenotypes in a variety of species. Yet their contribution to variation in performance, and phenotypes closely associated with performance, remains poorly understood. We used an animal model approach to quantify the contribution of maternal effects to performance trait variation (in bite force and pull strength) and the relationships between performance and the relevant underlying morphology in <i>Microcebus murinus</i>. We show that bite force is heritable (h<sup>2</sup>~0.23), and that maternal effects are also important source of variation, resulting in a medium inclusive heritability (IH<sup>2</sup>~0.47). Grip strength presented a rather low and non-significant narrow-sense heritability suggesting a higher selective pressure on this trait. Genetic correlations between performance traits and their associated morphometric traits were significant and high (0.47 bite force-head width; 0.48 grip strength-radius length), as was the maternal correlation for bite force-head width (0.75). Further studies evaluating the heritability of performance for other taxa and the role of maternal effects are badly needed to better understand the drivers of variation in performance ultimately allowing for a better understanding of the importance of these types of traits in an evolutionary context.</p>
Data from: Differential impact of severe drought on infant mortality in two sympatric neotropical primates
<p>Extreme climate events can have important consequences for the dynamics of natural populations, and severe droughts are predicted to become more common and intense due to climate change. We analysed infant mortality in relation to drought in two primate species (white-faced capuchins, <i>Cebus capucinus imitator,</i> and Geoffroy's spider monkeys, <i>Ateles geoffroyi</i>) in a tropical dry forest in north-western Costa Rica. Our survival analyses combine several rare and valuable long-term data sets, including long-term primate life-history, landscape-scale fruit abundance, food-tree mortality, and climate conditions. Infant capuchins showed a threshold mortality response to drought, with exceptionally high mortality during a period of intense drought, but not during periods of moderate water shortage. In contrast, spider monkey females stopped reproducing during severe drought, and the mortality of infant spider monkeys peaked later during a period of low fruit abundance and high food-tree mortality linked to the drought. These divergent patterns implicate differing physiology, behaviour, or associated factors in shaping species-specific drought responses. Our findings link predictions about the Earth's changing climate to environmental influences on primate mortality risk and thereby improve our understanding of how the increasing severity and frequency of droughts will affect the dynamics and conservation of wild primates.</p>
Data from: Convergent rates of protein evolution identify novel targets of sexual selection in primates
<p>Sexual selection is the differential reproductive success of individuals, resulting from competition for mates, mate choice, or success in fertilization. In primates, this selective pressure often leads to the development of exaggerated traits which play a role in sexual competition and successful reproduction. In order to gain insight into the mechanisms driving the development of sexually selected traits, we used an unbiased genome-wide approach across 21 primate species to correlate individual rates of protein evolution to relative testes size and sexual dimorphism in body size, two anatomical hallmarks of sexual selection in mammals. Among species with presumed high levels of sperm competition, we detected strong conservation of testes-specific proteins responsible for spermatogenesis and ciliary form and function. In contrast, we identified accelerated evolution of female reproductive proteins expressed in the vagina, cervix, and fallopian tubes in these same species. Additionally, we found accelerated protein evolution in lymphoid tissue, indicating that adaptive immune functions may also be influenced by sexual selection. This study demonstrates the distinct complexity of sexual selection in primates revealing contrasting patterns of protein evolution between male and female reproductive tissues.</p>
Fig. 3 in Nasalis larvatus (Primates: Colobini)
Fig. 3.—Nasalis larvatus is endemic to the island of Borneo in Brunei, the Malaysian states of Sarawak and Sabah and the Indonesian states of Daerah Tingkat I Kalimantan Barat, Kalimantan Tengah, Kalimantan Selatan, and Kalimantan Timur, collectively shown as Kalimantan. Some place names mentioned in the text are shown. Based on Meijaard and Nijman (2000a) and Sha et al. (2008).
Fig. 2 in Nasalis larvatus (Primates: Colobini)
Fig. 2.—Dorsal, ventral, and lateral views of skull and lateral view of mandible of an adult male Nasalis larvatus. Occipitonasal length is 133 mm. Photographed by Phil Myers, Museum of Zoology, University of Michigan; used with permission of the photographer. First published by http://animaldiversity.org.
Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine
<p>Supplementary Table Captions:</p> <p>Supplementary Table S9. Evaluation and parameter determination of host and microbiome RNA read classification using simulation datasets</p> <p>Supplementary Table S10. 40 pathways significantly upregulated in the cecum as compared to the transverse colon</p> <p>Supplementary Table S11. Host-microbiome gene co-expression network edges</p> <p>Supplementary Table S12. Host-host gene co-expression network edges</p> <p>Supplementary Table S13. Microbiome-microbiome gene co-expression network edges</p> <p>Supplementary Table S14. List of genes included in each gene module identified from the gene co-expression network</p> <p>Supplementary Table S15. Results of enrichment analysis for each gene module identified from the gene co-expression network</p> <p>Supplementary Table S16. The top 32 bacterial species in terms of expression abundance based on metatranscriptome profiles</p> <p>Supplementary Table S17. Number of microbiome RNA reads annotated by the KEGG database</p> <p>Supplementary Table S18. Results of enrichment analysis of gene modules for each parameter</p> <p>Supplementary Table S19. Evaluation of modules in each parameter of Newman algorithm</p> <p>Supplementary Table S20. Evaluation of modules in each parameter of Louvain algorithm</p> <p>Supplementary Table S21. Evaluation of modules in each parameter of Leiden algorithm</p> <p>Supplementary Table S22. Evaluation of modules in each parameter of WGCNA</p>
Data From: what mandrills leave behind: using fecal samples to characterize the major histocompatibility complex in a threatened primate
<p>The major histocompatibility complex (MHC) can be useful in guiding conservation planning because of its influence on immunity, fitness, and reproductive ecology in vertebrates. The mandrill (<em>Mandrillus sphinx</em>) is a threatened primate endemic to central Africa. Considerable research in this species has shown that the MHC is important for disease resistance, mate choice, and reproductive success. However, all previous MHC research in mandrills has focused on an inbred semi-captive population, so their genetic diversity may have been underestimated. Here we expand our current knowledge of mandrill MHC variation by performing next-generation sequencing of non-invasively collected fecal samples from a large wild horde in central Gabon. We observe MHC lineages and alleles shared with other primates, and we uncover 45 putative new class II MHC DRB alleles, including representatives of the DRB9 pseudogene, which has not previously been identified in mandrills. We also document methodological challenges associated with fecal samples in NGS-based MHC research. Even with high read depth, the replicability of alleles from fecal samples was lower than that of tissue samples, and allele assignments are inconsistent between sample types. Further, the common assumption that variants with very high read depth should represent true alleles does not appear to be reliable for fecal samples. Nevertheless, the use of degraded DNA in the present study still enabled significant progress in quantifying immunogenetic diversity and its evolution in wild primates.</p>
Fig. 5. Trypanoxyuris kotudoi n in Pinworms of the red howler monkey (Alouatta seniculus) in Colombia: Gathering the pieces of the pinworm-primate puzzle
Fig. 5. Trypanoxyuris kotudoi n. sp. (A) Male full body, lateral view. (B) Male cephalic end, apical view. (C) Male posterior end, ventral view; (D) Male posterior end, lateral view, (E) Female full body, lateral view; (F) Female cephalic end, apical view; (G) Female cross section showing lateral alae; (H) Egg.
Fig. 6 in Pinworms of the red howler monkey (Alouatta seniculus) in Colombia: Gathering the pieces of the pinworm-primate puzzle
Fig. 6. SEM of buccal structures of males of Trypanoxyuris species found in howler monkeys (A) Trypanoxyuris seunimii n. sp. (B) T. keumimae n. sp. (C) T. kotudoi n. sp. (D) T. minutus from Alouatta seniculus. (E) T. pigrae. (F) T. minutus from Mesoamerican howler monkeys. R: right ventral lip; L: left ventral lip. White arrow pointing at the sharp protuberances formed as a result of the notches in the lips.
Fig. 4 in Pinworms of the red howler monkey (Alouatta seniculus) in Colombia: Gathering the pieces of the pinworm-primate puzzle
Fig. 4. SEM of buccal structures of females of Trypanoxyuris species found in howler monkeys (A) Trypanoxyuris seunimii n. sp. (B) T. keumimae n. sp. (C) T. kotudoi n. sp. (D) T. minutus from Alouatta seniculus. (E) T. pigrae. (F) T. minutus from Mesoamerican howler monkeys. R: right ventral lip; L: left ventral lip. White arrow pointing at the lateral indentations. Black arrow pointing at the square-shaped edge.
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.