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opencc-by-4.0May 2020View details →

A VTA to basal amygdala dopamine projection contributes to signal salient somatosensory events during fear learning

This dataset represents the raw data that gave rise to the study by Tang et al., J. Neuroscience 2020 (DOI: 10.1523/JNEUROSCI.1796-19.2020). Please refer to the original publication regarding experimental design and methodological details of data acquisition and analysis.  Below we supply information on the provided metadata files (which, in turn, refer to individual raw data files), and essential details on the specific data formats. <ol> <li>raw data is organized in the datasets related to the Figs. 1H-L, 1M, 3B-C, 2, 4G-H, 5D-E, 5H-I of the paper (Tang et al., 2020);</li> <li>the metadata for each individual dataset, which lists individual data filenames from the respective dataset, are stored in separate “.csv” files, one per each dataset. Field separator: comma;</li> <li>individual metadata files for each dataset are described in the master metadata file “metadata_master.csv”. Field separator: comma;</li> <li>widefield fluorescent images of single coronal slices containing VTA (Fig. 1H-M) were converted from the proprietary format of Olympus slide scanning microscope into composite TIFF format readable by FIJI/ImageJ (<a href="https://fiji.sc/">https://fiji.sc/</a> or <a href="https://imagej.net/Fiji/Downloads">https://imagej.net/Fiji/Downloads</a>). Image stacks covering the injection area of CTB into BA are provided in single multi-plane TIFF files, one per animal.  Unless specified in the metadata file, the information on pixel resolution is embedded inside the individual image files as TIFF metadata. Attribution of fluorescent probes to the color channels is given in the corresponding metadata files; AP positions refer to Franklin KB, Paxinos G (2016) The mouse brain in stereotaxic coordinates, Ed 4. San Diego: Elsevier/Academic;</li> <li>confocal fluorescent image stacks acquired from single coronal slices containing VTA (Fig. 3B-C) are provided in the original format “.lsm” written by a confocal software Zen (Carl Zeiss). Besides the original Zen software, this format can be readily imported into FIJI/ImageJ using built-in converters “LSM…” or “Bio-Formats”. All the metadata containing imaging parameters is embedded in this format and can be accessed from FIJI/ImageJ after importing the stack. Attribution of fluorescent probes to the color channels is given in the corresponding “.csv” metadata file; AP positions refer to Franklin and Paxinos (2016);</li> <li>video recordings of animal behavior are provided as “.wmv” files, unmodified from the original version created by the acquisition software VideoFreeze (MedAssociates Inc). Video stream parameters: wmv3 codec, color space yuv420p, 320x240 pixels, bitrate 300 kb/s, 30 fps;</li> <li>timing patterns of the sound (CS) and of the footshock (US) applications during each day of fear conditioning protocol are provided in the respective “.csv” files: “day1_CS_timing.csv”, “day2_CS_timing.csv”, “day2_US_timing.csv”, “day3_CS_timing.csv”. The timestamps in these files are expressed in seconds relative to the start of the VideoFreeze video recordings (see p. 4 above). These patterns are in common for all the video recordings done on respective training days in every data subset (Figs. 2, 4-5);</li> <li>extracellular optrode recording data (Fig. 2) were converted from the original proprietary “.mcd” format of the MC_Rack software (Multichannel Systems) into the open HDF5 format “.h5” using the Multi Channel DataManager software (Multichannel Systems). We provide both the continuous recording data acquired during behavior sessions on days 1-3 of the fear learning protocol, as well as recordings of light-evoked spiking acquired during the opto-tagging sessions on each experimental day. In the latter, each of 8-10 consequently recorded data files contains individual triggered sweeps (from -50 to +50 ms), each centered around a single laser pulse (t=0 ms);</li> <li>the raw unfiltered electrode data is stored as a 32-bit integer matrix 16xN (16 - number of electrodes, N - number of sampling points @ 40 kHz) in the container Data->Recording_0->AnalogStream->Stream_1->ChannelData of the HDF5 files. Conversion factor to the units of volts for the raw values is 1.25e-6. Correspondence of the rows of the data matrix to the electrodes “E1”-“E16” is indexed by the string array Data->Recording_0->AnalogStream->Stream_1->I_Label. The electrodes were physically arranged into four tetrodes in following groups: E1-E4, E5-E8, E9-E12, E13-E16;</li> <li>timestamps for the continuous recordings, or for each of the triggered sweeps in case of opto-tagging, are stored in the 2D floating point array Data->Recording_0->AnalogStream->Stream_1->ChannelDataTimeStamps; the timestamp values are in microseconds;</li> <li>the timing of CS and US stimuli produced by the VideoFreeze software (see p. 5 above) was sampled as input digital triggers by the amplifier for extracellular recordings for precise synchronization between the optrode- and video recordings. These signals are stored as single bit changes in the 32-bits integer N-samples array Data->Recording_0->AnalogStream->Stream_0->ChannelData of the corresponding HDF5 files.</li> </ol>

opencc-zeroSep 2020View details →

nNOS-expressing interneurons control basal and behaviorally evoked arterial dilation in somatosensory cortex of mice

Cortical neural activity is coupled to local arterial diameter and blood flow. However, which neurons control the dynamics of cerebral arteries is not well understood. We dissected the cellular mechanisms controlling the basal diameter and evoked dilation in cortical arteries in awake, head-fixed mice. Locomotion drove robust arterial dilation, increases in gamma band power in the local field potential (LFP), and increases calcium signals in pyramidal and neuronal nitric oxide synthase (nNOS)-expressing neurons. Chemogenetic or pharmocological modulation of overall neural activity up or down caused corresponding increases or decreases in basal arterial diameter. Modulation of pyramidal neuron activity alone had little effect on basal or evoked arterial dilation, despite pronounced changes in the LFP. Modulation of the activity of nNOS-expressing neurons drove changes in the basal and evoked arterial diameter without corresponding changes in population neural activity.

opencc-by-4.0Jul 2017View details →

Data + Analyses: "Gaze-dependent Coding of Somatosensory Reach Targets after Effector Movement: Testing the Impact of Online Information, Movement Timing, and Target Distance"

This upload contains the experiment scripts (written in Presentation), data, and analyses (performed with MATLAB and SPSS) underlying the publication by Mueller & Fiehler (2017). PloS one. doi:10.1371/journal.pone.0180782

opencc-by-4.0May 2024View details →

Bilateral integration in somatosensory cortex is controlled by behavioral relevance

Sensory perception naturally requires processing stimuli from both sides of the body. Yet, how neurons bind stimulus features across the hemispheres to create a unified perceptual experience remains unknown. To address this question, we performed large-scale recordings from neurons in both somatosensory cortices (S1) while mice shared information between their hemispheres and discriminated between two categories of bilateral stimuli. When expert mice touched stimuli associated with reward, they moved their whiskers with greater bilateral symmetry. During this period, synchronous spiking and enhanced spike-field coupling emerged between the hemispheres. This coordinated activity was absent in stimulus-matched naïve animals, indicating that interhemispheric (IH) binding was controlled by a goal-directed, internal process. In S1 neurons, the addition of ipsilateral touch primarily facilitated the contralateral, principal whisker response. This facilitation primarily emerged for reward-associated stimuli and was lost on trials where expert mice failed to respond. Taken together, these results reveal a novel state-dependent logic underlying bilateral integration in S1, where stimulus binding and facilitation are controlled by behavioral relevance.

opencc-by-4.0Sep 2017View details →

High frequency somatosensory MEG: evoked responses, FreeSurfer reconstruction

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. Measurements from two subjects are included. The dataset also contains anatomical MRIs and FreeSurfer reconstructions. Only evoked (averaged) MEG data is included; raw MEG data is in a separate dataset at https://doi.org/10.5281/zenodo.889295

opencc-by-4.0Aug 2024View details →

Dataset: Stimulus selection drives value-modulated somatosensory processing in superior colliculus

Pluta Lab .mat data files used in the following paper Stimulus selection drives value-modulated somatosensory processing in superior colliculus  Details can be found in readme.txt

opencc-by-4.0Nov 2022View details →

Bayesian Surprise Shapes Neural Responses in Somatosensory Cortical Circuit

Numerous psychophysical studies demonstrate that Bayesian inference governs sensory decision-making, however the specific neural circuitry underlying this probabilistic mechanism remains unknown. We record extracellular neural activity along the somatosensory pathway of mice while delivering sensory stimulation paradigms designed to isolate the response to the surprise generated by Bayesian inference. Our results demonstrate that laminar cortical circuits in early sensory areas encode Bayesian surprise. Systematic sensitivity to surprise is not identified in the somatosensory thalamus, rather emerging in the primary (S1) and secondary (S2) somatosensory cortices. Multiunit spiking activity and evoked potentials in layer 6 of these regions exhibit the highest sensitivity to surprise. Gamma power in S1 layer 2/3 exhibits an NMDAR-dependent scaling with surprise, as does alpha power in layers 2/3 and 6 of S2. These results demonstrate a precise spatiotemporal neural representation of Bayesian surprise and suggest that Bayesian inference is a fundamental component of cortical proc

opencc-zeroOct 2023View details →

Data from: Scn2a insufficiency alters spontaneous neuronal Ca2+ activity in somatosensory cortex during wakefulness

SCN2A protein-truncating variants (PTV) can result in neurological disorders such as autism spectrum disorder and intellectual disability, but they are less likely to cause epilepsy in comparison to missense variants. While in vitro studies showed PTV reduce action potential firing, consequences at in vivo network level remain elusive. Here, we generated a mouse model of Scn2a insufficiency using antisense oligonucleotides (Scn2a ASO mice), which recapitulated key clinical feature of SCN2A PTV disorders. Simultaneous two-photon Ca2+ imaging and electrocorticography (ECoG) in awake mice showed that spontaneous Ca2+ transients in somatosensory cortical neurons, as well as their pairwise co-activities were generally decreased in Scn2a ASO mice during spontaneous awake state and induced seizure state. The reduction of neuronal activities and paired co-activity are mechanisms associated with motor, social and cognitive deficits observed in our mouse model of severe Scn2a insufficiency, indicating these are likely mechanisms driving SCN2A PTV pathology.

publicOct 2023View details →

Data from: Scn2a insufficiency alters spontaneous neuronal Ca2+ activity in somatosensory cortex during wakefulness

Open the record for dataset details and reuse information.

publicAug 2023View details →

nNOS-expressing interneurons control basal and behaviorally evoked arterial dilation in somatosensory cortex of mice

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2020View details →

Data set for "Anatomically and functionally distinct thalamocortical inputs to primary and secondary mouse whisker somatosensory cortices"

Data set for: El-Boustani S, Sermet BS, Foustoukos G, Oram TB, Yizhar O, Petersen CCH (2020) Anatomically and functionally distinct thalamocortical inputs to primary and secondary mouse whisker somatosensory cortices. Nature Communications 11: 3342. doi: 10.1038/s41467-020-17087-7 There are 4 files in this upload: 1. The file named "2020_El-Boustani_NCOMMS.pdf" is the Open Access pdf file of the manuscript published in Nature Communications. 2. The file named "2020_El-Boustani_NCOMMS_SupMovie1.avi" is Supplementary Movie 1 in .avi format, accompanying the Nature Communications publication. 2. The file named "2020_El-Boustani_NCOMMS_SupMovie2.avi" is Supplementary Movie 2 in .avi format, accompanying the Nature Communications publication. 4. The file named "El-Boustani_data_code.zip" (~36 GB) is a zipped version of a folder "El-Boustani_data_code" (~45 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. In the main folder, data for all experiments are stored within folders starting by the prefix “SB”. The code for generating population data and plotting figures from the paper are in the folder “Matlab_code”. In this folder, several Matlab scripts are named after the panels or figures they will plot such as “Plot_Fig3d_Axon_GCaMP6s_traces_example.m”. After opening each file, executing the script will automatically plot the panels and name them accordingly. In some files, the type of data to plot should be specified at the very beginning of the script: “VPM” for VPM data, “POMf” for POm-FO data and “Layer1” for POm-HO data in layer 1. For figure 2, the code is located in a dedicated folder where a Matlab file “Plot_Fig2d_g_Populatin_Plot_POm_FO_HO.m” is used to generate the figures. Finally, other Matlab files are included that are used to create population .mat files or for additional analysis related to the manuscript.

opencc-by-4.0Jul 2020View details →

Data set for "Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task"

Data set for: Vavladeli A, Daigle T, Zeng H, Crochet S, Petersen CCH (2020) Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task. FUNCTION 1: zqaa008. doi: 10.1093/function/zqaa008 There are 2 files in this upload: 1. The file named "2020_Vavladeli_FUNCTION.pdf" is the Open Access pdf file of the manuscript published in FUNCTION. 2. The file named "Vavladeli_data_code.zip" (~2 GB) is a zipped version of a folder named "Vavladeli_data_code" (~2 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and two '.mat' data files. In order to run the analysis of the data set, you need to execute the '.m' file with the corresponding figure name.

dryad36/100

Data from: Area 2 of primary somatosensory cortex encodes kinematics of the whole arm

Proprioception, the sense of body position, movement, and associated forces, remains poorly understood, despite its critical role in movement. Most studies of area 2, a proprioceptive area of somatosensory cortex, have simply compared neurons' activities to the movement of the hand through space. By using motion tracking, we sought to elaborate this relationship by characterizing how area 2 activity relates to whole arm movements. We found that a whole-arm model, unlike classic models, successfully predicted how features of neural activity changed as monkeys reached to targets in two workspaces. However, when we then evaluated this whole-arm model across active and passive movements, we found that many neurons did not consistently represent the whole arm over both conditions. These results suggest that 1) neural activity in area 2 includes representation of the whole arm during reaching and 2) many of these neurons represented limb state differently during active and passive movements.

opencc-zeroJan 2020View details →

opencc-by-4.0Dec 2016View details →

Gaze-dependent Coding of Somatosensory Reach Targets after Effector Movement: Testing the Impact of Online Information, Movement Timing, and Target Distance

The uploaded files contain data as SPSS data files (.sav) and tab delimited (.csv) textfiles as well as the respective variable desriptions as images (.png). In particular, I uploaded 1) the raw data, 2) the means of reach errors which we analyzed by repeated-measures ANOVA, and 3) the means of ellipse sizes on which repeated-measures ANOVA of precision were based. After publication of the article, I also uploaded the reported analyses and corresponding datafiles (plus some more material) here: https://doi.org/10.5281/zenodo.821307

opencc-by-4.0Feb 2017View details →

Dataset supporting "Reach-relevant somatosensory signals modulate tactile suppression"

Here we provide the psychophysical and kinematic data reported in: Gertz, H., Voudouris, D., & Fiehler, K. (2017). Reach-relevant somatosensory signals modulate tactile suppression. Journal of Neurophysiology, jn.00052.2017. http://doi.org/10.1152/jn.00052.2017 Tactile stimuli on moving limbs are typically attenuated during reach planning and execution. This phenomenon has been related to internal forward models that predict the sensory consequences of a movement. Tactile suppression is considered to occur due to a match between the actual and predicted sensory consequences of a movement, which might free capacities to process novel or task-relevant sensory signals. Here we examined whether and how tactile suppression depends on the relevance of somatosensory information for reaching. Participants reached with their left or right index finger to the unseen index finger of their other hand (body target) or an unseen pad on a screen (external target). In the body target condition, somatosensory signals from the static hand were available for localizing the reach target. Vibrotactile stimuli were presented on the moving index finger before or during reaching, or in a separate no-movement baseline block, and participants indicated whether they detected a stimulus. As expected, detection thresholds before or during reaching were higher compared to baseline. Tactile suppression was also stronger for reaches to body targets than external targets, as reflected by higher detection thresholds and lower precision of detectability. Moreover, detection thresholds were higher when reaching with the left than with the right hand. Our results suggest that tactile suppression is modulated by position signals from the target limb that are required to successfully reach to the own body. Moreover, limb dominance seems to affect tactile suppression presumably due to disparate uncertainty of feedback signals from the moving limb.