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175 results for “Somatosensory”
Data from: Effects of arousal and movement on secondary somatosensory and visual thalamus
<p>Neocortical sensory areas have associated primary and secondary thalamic nuclei. While primary nuclei transmit sensory information to cortex, secondary nuclei remain poorly understood. We recorded juxtasomally from secondary somatosensory (POm) and visual (LP) nuclei of awake mice while tracking whisking and pupil size. POm activity correlated with whisking, but not precise whisker kinematics. This coarse movement modulation persisted after facial paralysis and thus was not due to sensory reafference. This phenomenon also continued during optogenetic silencing of somatosensory and motor cortex and after lesion of superior colliculus, ruling out a motor efference copy mechanism. Whisking and pupil dilation were strongly correlated, possibly reflecting arousal. Indeed LP, which is not part of the whisker system, tracked whisking equally well, further indicating that POm activity does not encode whisker movement <em>per se.</em> The semblance of movement-related activity is likely instead a global effect of arousal on both nuclei. We conclude that secondary thalamus monitors behavioral state, rather than movement, and may exist to alter cortical activity accordingly.</p>
Data from: Emerging experience-dependent dynamics in primary somatosensory cortex reflect behavioral adaptation
<p><span><span>Behavioral experience and flexibility are crucial for survival in a constantly changing environment. Despite evolutionary pressures to develop adaptive behavioral strategies in a dynamically changing sensory landscape, the underlying neural correlates have not been well explored. Here, we use genetically encoded voltage imaging to measure signals in primary somatosensory cortex (S1) during sensory learning and behavioral adaptation in the mouse. In response to changing stimulus statistics, mice adopt a strategy that modifies their detection behavior in a context dependent manner as to maintain reward expectation. Surprisingly, neuronal activity in S1 shifts from simply representing stimulus properties to transducing signals necessary for adaptive behavior in an experience dependent manner. Our results suggest that neuronal signals in S1 are part of an adaptive framework that facilitates flexible behavior as individuals gain experience, which could be part of a general scheme that dynamically distributes the neural correlates of behavior during learning. </span></span></p>
Ipsilateral stimulus encoding in primary and secondary somatosensory cortex of awake mice
<p>Data and code to accompany "Ipsilateral stimulus encoding in primary and secondary somatosensory cortex of awake mice" by Pala and Stanley, in press, Journal of Neuroscience, 2022.</p>
High frequency somatosensory MEG: raw data
<p>This dataset contains somatosensory evoked responses recorded with Elekta TRIUX magnetoencephalography (MEG) system. The purpose of the measurements was to examine high-frequency (HF) somatosensory responses. To this end, a large number of responses (couple of thousand) were recorded with a short interstimulus interval (randomized between 300-350 ms). A constant-current electric stimulator was used, with the electrodes placed around the median nerve at the right wrist. The magnitude of the current was individually determined so that the stimulation was slightly below motor threshold; it was approximately 7 mA. The length of the current pulse was set at 200 microseconds.</p> <p>Measurements from two subjects are included. This dataset contains raw MEG data only. For evoked data and structural (MRI) data, see https://doi.org/10.5281/zenodo.889234</p>
Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.
<p><strong>If you use the data or the analysis pipeline, please refer to:</strong></p> <p>Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00006">https://doi.org/10.3389/fnins.2018.00006</a></p> <p><strong>and/or</strong></p> <p>Andersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00261">https://doi.org/10.3389/fnins.2018.00261</a></p> <p><strong>IMPORTANT</strong><br> Version 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p> <p><br> </p> <p>Dataset with tactile expectations to be analysed with pipelines for either <a href="https://mne.tools/stable/index.html">MNE-Python</a> or <a href="http://www.fieldtriptoolbox.org/">FieldTrip</a>, aiming to follow the MEG-BIDS structure</p> <p><br> <strong>Unzipping the data</strong></p> <p>Data is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called "analyses", which has a sub-directory called "omission_frontiers_BIDS-FieldTrip", which has a sub-directory called "data".<br> Thus, as an example, in my case, I should have the path: /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/data</p> <p><strong>Path:</strong><br> on a Linux system the path would be /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a macOS system the path would be /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a Windows system the path would be C:\Users\your_name\analyses\omission_frontiers_BIDS-FieldTrip\data</p> <p><strong>Steps for unzipping:</strong></p> <p>1. Set up the folder above according to your operating system, following the examples above and substitute "your_name" for your user name.<br> 2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/</p> <p>Now you are ready to run the analyses.</p> <p><br> <strong>The MEG data</strong></p> <p>Raw fif files are contained in the data folder, ordered by subject (n=20)<br> There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recording</p> <p><strong>Processed MRI data </strong></p> <p>For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects' identity</p> <p>For Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG data<br> For MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.</p> <p><br> <strong>Metadata</strong></p> <p>Each subject has a number of tsv-files:<br> *channel.tsv contain information about the channels in that recording<br> *events.tsv contain information about the events in that recording<br> removed_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)<br> ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)<br> *scans_tsv contain information about the scans conducted</p> <p><br> <strong>Scripts </strong></p> <p>Please see Github for the updated scripts at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p>
Primary somatosensory cortical processing in tactile communication
<p>Touch is an essential form of non-verbal communication. While language and its neural basis are widely studied, tactile communication is less well understood. We used fMRI and multivariate pattern analyses in pairs of emotionally close adults to examine the neural basis of human-to-human tactile communication. In each pair, a participant was designated either as sender or as receiver. The sender was instructed to communicate specific messages by touching only the arm of the receiver, who was inside the scanner. The receiver then identified the message based on the touch expression alone. We designed two multivariate decoder algorithms – one based on the sender’s intent (sender-decoder), and another based on the receiver’s response (receiver-decoder). We identified several brain areas that significantly predicted behavioral accuracy of the receiver. Regarding our a priori region of interest, the receiver’s primary somatosensory cortex (S1), both decoders were able to accurately differentiate the messages based on neural activity patterns here. The receiver-decoder, which relied on the receivers’ interpretations of the touch expressions, outperformed the sender-decoder, which relied on the sender’s intent. Our results identified a network of brain areas involved in human-to-human tactile communication and supported the notion of non-sensory factors being represented in S1.</p> <p>Log files per subject and run (end of file name: subID_run_log.csv)</p> <p>response mat file per subject receiver_only and sender_only</p> <p>readresponsemat_SVM_S1...m = reads in response mat files and performs SVM classification</p> <p>ECOC_S1.m = decoding code</p> <p>Normalized brain scan data can be found here: https://zenodo.org/records/4925648</p>
The causal role of the somatosensory cortex in prosocial behavior - Pain Localizer
<p>Participants with no reported neurological, psychiatric, or other medical problems or any contraindication to fMRI, underwent a total of 40 electrical and 40 mechanical stimulations, split in 8 runs (4 electrical and 4 mechanical) of 10 (5 high intensity and 5 low intensity) stimulations each on their right hand. For more information about task please refer to the pubblication. See the associated readme file for more details.</p> <p> </p>
The causal role of the somatosensory cortex in prosocial behavior - EEG dataset
<p>Participants performed a costly helping paradigm while their brain activity was recorded. For more information about the paradigm see the associate pubblication. For more infomation about the data see README.txt</p>
A Model of Rat Non-barrel Somatosensory Cortex Anatomy
<p><strong>A full description of the model is available in the two companion manuscripts: </strong></p> <p><a href="https://www.biorxiv.org/content/10.1101/2022.08.11.503144v3.abstract">Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy</a></p> <p><a href="https://www.biorxiv.org/content/10.1101/2023.05.17.541168v5">Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation</a></p> <p><em>We kindly ask that you cite these papers, as well as the Zenodo repository, in any articles or presentations using the model or any of its constituent components.</em></p> <p>---</p> <p>We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat. The modeling process is based on a previously established workflow for a single cortical column, but is extended here to build a much larger circuit in an atlas-based geometry. Neurons in the model belong to 60 different morphological types and are connected by synapses placed by two established algorithms, one modeling local connectivity determined by axo-dendritic overlap, and one for long-range connectivity between sub-regions. Long-range connectivity is defined with topographic mapping and laminar connectivity profiles, providing intrinsic feed-forward and feedback pathways. Additionally, we incorporate core- and matrix-type thalamocortical projection systems, associated with VPM and POm thalamic nuclei respectively, that enable extrinsic input.</p> <p>The model comprises 211712 neurons in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas, scaled down to juvenile size. It is available in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity associated with all systems described above. Modeled synapses are associated with their exact location in the dendritic tree, and additional anatomical parameters, such as spine length (where biologically plausible). Extrinsic synaptic connections from neurons in the remainder of non-barrel somatosensory cortex and thalamic inputs are also contained.</p> <p>Note that this is an <em>anatomical</em> model: Parameters and files related to neuronal and synaptic <em>physiology</em> can be found in our <a href="../record/7930276">release of the <em>physiological </em>model</a>.</p> <p><strong>[UPDATE 23/07/17]: </strong>Added a zip archive containing the voxel atlas data used. This comprises the region atlas (brain_regions, hierarchy) and generated voxelized densities for each neuron type ([cell_density]*). All atlas files are in the .nrrd format, best loaded using the python package <a href="https://github.com/BlueBrain/voxcell">voxcell</a>. The atlas files cover the entire S1 regions, with the location of the part of the model released here indicated by <em>published_volume.nrrd. </em><strong>All other files remained unchanged!</strong></p> <p>Please refer to the documentation of the SONATA format for information how to load and analyze the model. A jupyter notebook has been included with basic examples of how to load the data using our open-source packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a> and <a href="https://bluebrainsnap.readthedocs.io/en/stable/">Blue Brain SNAP</a>.</p> <p><em>This study was supported by funding to the Blue Brain Project, a research center of the Ecole polytechnique federale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology. RL, JPS and JL were supported by EPSRC under grant number EP/P025072/1. RL was supported by a collaboration grant from EPFL.</em></p>
Somatosensory Training Versus Exercise Therapy in Awake Bruxism
ClinicalTrials.gov study NCT07336082. IPD Sharing: NO. Countries: 1. Publications: 9.
Somatosensory Modulation of Salivary Gene Expression and Oral Feeding in Preterm Infants
ClinicalTrials.gov study NCT02696343. IPD Sharing: YES. Countries: 1. Publications: 1.
Reversing Synchronized Brain Circuits With Targeted Auditory-Somatosensory Stimulation to Treat Phantom Percepts
ClinicalTrials.gov study NCT03621735. IPD Sharing: NO. Countries: 1. Publications: 1.
Can rTMS Enhance Somatosensory Recovery After Stroke?
ClinicalTrials.gov study NCT02811913. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Effects of arousal and movement on secondary somatosensory and visual thalamus
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Data from: Attentional modulation of secondary somatosensory and visual thalamus of mice
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Two photon data from: Functional and structural properties of highly responsive somatosensory neurons in mouse barrel cortex
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Data from: Emerging experience-dependent dynamics in primary somatosensory cortex reflect behavioral adaptation
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Data from: Area 2 of primary somatosensory cortex encodes kinematics of the whole arm
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Data from: Repetitive somatosensory stimulation shrinks the body image
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Data from: The effects of aging on neuropil structure in mouse somatosensory cortex—A 3D electron microscopy analysis of layer 1
This study has used dense reconstructions from serial EM images to compare the neuropil ultrastructure and connectivity of aged and adult mice. The analysis used models of axons, dendrites, and their synaptic connections, reconstructed from volumes of neuropil imaged in layer 1 of the somatosensory cortex. This shows the changes to neuropil structure that accompany a general loss of synapses in a well-defined brain region. The loss of excitatory synapses was balanced by an increase in their size such that the total amount of synaptic surface, per unit length of axon, and per unit volume of neuropil, stayed the same. There was also a greater reduction of inhibitory synapses than excitatory, particularly those found on dendritic spines, resulting in an increase in the excitatory/inhibitory balance. The close correlations, that exist in young and adult neurons, between spine volume, bouton volume, synaptic size, and docked vesicle numbers are all preserved during aging. These comparisons display features that indicate a reduced plasticity of cortical circuits, with fewer, more transient, connections, but nevertheless an enhancement of the remaining connectivity that compensates for a generalized synapse loss.
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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.