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Data from Churan et al. Action-dependent processing of self-motion in parietal cortex of macaque monkeys
<p><strong>Animals</strong></p> <p>Two adult male monkeys (macaca mulatta) participated in the study. Single-unit recordings were done using standard tungsten microelectrodes (FHC, Bowdoin, USA) with an impedance of ~2 MΩ at 1 kHz that were positioned by an hydraulic micromanipulator (MO-95, Narishige, Tokyo, Japan). A stainless-steel guiding tube was used for transdural penetration and support of the electrode. The neuronal signal was processed using a commercial system (Alpha Omega, Nof HaGalil, Israel). It was band-pass filtered (cut-off frequencies at 500 Hz and 8000 Hz) and sampled at 44 kHz.</p> <p><strong>Apparatus</strong></p> <p>During recordings, the monkeys were sitting head-fixed in a primate chair in a dark room, and their eye-position was monitored at 1000 Hz using a video-based eye tracker (EyeLink 1000, SR Research, Ottawa, Canada). The chair was positioned at a distance of 97 cm from a semi-transparent screen (size 160 cm x 90 cm, subtending the central 79 deg x 50 deg of the visual field) on which the visual stimuli were back-projected using a PROPixx-projector (VPixx Technologies, St-Bruno de Montarville, Canada) running at a resolution of 1920 x 1080 pixels and at a frame rate of 100 Hz. A custom-made touch sensor (length 10 cm, diameter 1 cm) was integrated into the monkey chair in front of the monkey and its status was monitored online at a sampling rate of 1 kHz.</p> <p><strong>Data processing</strong></p> <p>Single units were isolated using a semi-manual spike sorter (Plexon Inc, Dallas, Texas). To this end we used a threshold on the electrode signal that was set manually to separate the action potentials from noise. The samples that exceeded the threshold were further analyzed using principal components as well as other features that were derived from the signal (like local maxima and minima). Then clusters of samples with similar properties were identified visually and each defined as representing a single unit. For a detailed description of the sorting process see the offline User Guide (Plexon, 2020).</p> <p>Further description of the Methods, see: Churan et al. 2021, doi: 10.1152/jn.00049.2021</p> <p><strong>Data:</strong></p> <p>The file '<strong>data_active_passive.mat</strong>' contains following variables:</p> <p>monkey: code for the tested monkey (1=monkey S, 2=monkey O)</p> <p>baseline: Mean and standard deviation of the activity in a time window of 150 ms to 20 ms before the press of the button.</p> <p>reaction: Mean time between the switch of the color of the fixation point from red to green and the time of the button press.</p> <p>anti_p: Significance of a one sided t-test between the baseline activity and activity 200 ms to 0 ms prior to the onset of stimulus motion.</p> <p>p_win (a (1-3),b (1-3),c (1-3),n(1-110)): 4D matrix containing p-values of t-tests</p> <p>a:</p> <p>1: Was preparatory activity significantly higher in the passive relative to the active condition?</p> <p>2: Was preparatory activity significantly lower in the passive relative to the active condition?</p> <p>3: Was the tonic motion response (200 ms to 500 ms after motion onset) significantly different between the active and the passive conditions?</p> <p>b:</p> <p>1: Calculation was made based on all motion directions</p> <p>2: Calculation was made based on the preferred motion direction</p> <p>3: Calculation was made based on the flanking motion directions</p> <p>c:</p> <p>1: Calculation was made based on all presented delays</p> <p>2: Calculation was made based on the shorter set of delays (500 ms to 700 ms)</p> <p>3: Calculation was made based on the longer set of delays (701 ms to 1000 ms)</p> <p>n: number of the investigated neuron</p> <p>psth_alldir: cell array containing the PSTHs (obtained by convolving each spike with a Gaussian as described in the manuscript) in a time window between 1000 ms before and 800 ms after the onset of motion (in 1 ms steps). PSTHs were calculated based on data from all tested directions. Each cell array consists of 4 elements containing different conditions:</p> <p>1: active condition</p> <p>2: passive condition shorter set of delays (500 ms to 700 ms)</p> <p>3: passive condition longer set of delays (701 ms to 1000 ms)</p> <p>4: passive condition all delays</p> <p>psth_bestdir: same as above - using only the preferred direction</p> <p>psth_nbestdir: same as above - using only the flanking directions</p> <p>d_alldir: cell array containing the continuous d-prime (as described in the manuscript) in a time window between 1000 ms before and 800 ms after the onset of motion (in 1 ms steps). d' were calculated based on data from all tested directions. Each cell array consists of 4 elements containing different conditions:</p> <p>1: active condition</p> <p>2: passive condition shorter set of delays (500 ms to 700 ms)</p> <p>3: passive condition longer set of delays (701 ms to 1000 ms)</p> <p>4: passive condition all delays</p> <p>d_bestdir: same as above - using only the preferred direction</p> <p>d_nbestdir: same as above - using only the flanking directions</p> <p>The file '<strong>timecourse_preparatory.mat</strong>' contains the cell array 'd_alldir_preparatory' that consists of 201 elements. Each of the elements contains PSTHs of 23 neurons that have exhibited significant preparatory activity in the passive condition in a time window 1000 ms to 0 ms before the motion onset. Each of the 201 elements describes a specific range of delays between button press and motion onset. This delay range is always a 100 ms wide sliding window, e.g. the element 1 represents delays between 500 and 600 ms, in element 2, the delays are between 501 and 601 ms and so on with the last element (201) representing delays between 700 and 800 ms.</p> <p>Some example code that re-creates most of the figures from the manuscript and that may serve as a starting point for further exploration of the data is available on request from the corresponding author.</p>
Dataset accompanying 'The Representation of Priors and Decisions in The Human Parietal Cortex'
<p>This dataset accompanies the manuscript 'The Representation of Priors and Decisions in The Human Parietal Cortex', published in PLoS Biology.</p><p>This dataset consists of:</p><p>1) Biophysical model simulations of a neural mass model performing a 2-alternative perceptual decision making task. </p><p>2) MEG data from 26 human volunteers performing the same task (see fig1 of the manuscript). MEG data were combined with anatomical MRI scans (not shared) to source localise brain activity in parietal cortex. The neural source time courses are shared. Full details can be found in the methods section of the manuscript.</p><p>3) Analysis code to reproduce all main data figures in the manuscript. Details of how to run the analyses are included in the analysis code documentation.</p><p>4) .txt files including the summary data necessary to reproduce figures in the manuscript. This is to allow users who do not have access to matlab to access the data underlying the figures without running the entire analysis pipeline.</p>
Training and test data, plus saved models for the upcoming paper `Top-down perceptual inference shaping the activity of early visual cortex'
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. These are 40px images that contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. These are 40px images that contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>The .zip file contains a saved model snapshot and various intermediate evaluative data. Details on these are coming soon.</p>
Data from: Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior
<p>Animals rely on their experience to guide their next choice. In foraging-type tasks guided by history-dependent value, these experiences are typically integrated such that the weights of past events initially decay quickly over time but show a longer tail than expected by exponential decay. Rather, such integration is better described by a hyperbolic function. Hyperbolic integration affords sensitivity to both recent environmental dynamics and long-term trends, however the mechanism by which the brain implements this hyperbolic integration is unknown. We trained mice on a history-dependent, value-based decision task and found that the mice indeed showed hyperbolic decay on their weighting of past experience. However, the activity of history-encoding cortical neurons showed weighting with exponential decay. In resolving this apparent mismatch, we observed that cortical neurons encode history information heterogeneously across a wide variety of exponential time-constants, with the retrosplenial cortex (RSC) overrepresenting longer time-constants compared to other areas. A model that combines these diverse timescales of exponential history integration can recreate the heavy-tailed, hyperbolic history integration observed in behavior. In particular, time-constants of RSC neurons best matched the behavior, and optogenetic inactivation of RSC uniquely reduced the use of history information. These results indicate that behavior-relevant history information is maintained in neurons across multiple timescales in parallel, and suggest that the neural population in RSC is a critical reservoir of this information guiding decision-making.</p>
Spike-induced cytoarchitectonic changes in epileptic human cortex are reduced via MAP2K inhibition
<p>Interictal spikes are electroencephalographic discharges that occur at or near brain regions that produce epileptic seizures. While their role in generating seizures is not well understood, spikes have profound effects on cognition and behavior, depending on where and when they occur. We previously demonstrated that spiking areas of the human neocortex show sustained MAPK activation in superficial cortical layers I-III and are associated with microlesions in deeper cortical areas characterized by reduced neuronal nuclear protein (NeuN) staining and increased microglial infiltration. Based on these findings, we chose to investigate additional neuronal populations within microlesions, specifically inhibitory interneurons. Additionally, we hypothesized that spiking would be sufficient to induce similar cytoarchitectonic changes within the rat cortex and that inhibition of MAPK signaling, using a MAP2K inhibitor, would not only inhibit spike formation but also reduce these cytoarchitectonic changes and improve behavioral outcomes. To test these hypotheses, we analyzed tissue samples from 16 patients with intractable epilepsy who required cortical resections. We also utilized a tetanus toxin-induced animal model of interictal spiking, designed to produce spikes without seizures in male Sprague-Dawley rats. Rats were fitted with epidural electrodes, to permit EEG recording for the duration of the study, and automated algorithms were implemented to quantify spikes. After 6 months, animals were sacrificed to assess the effects of chronic spiking on cortical cytoarchitecture. Here, we show that microlesions may promote excitability due to a significant reduction of inhibitory neurons that could be responsible for promoting interictal spikes in superficial layers. Similarly, we found that the induction of epileptic spikes in the rat model produced analogous changes, including reduced NeuN, calbindin, and parvalbumin-positive neurons and increased microglia, suggesting that spikes are sufficient for inducing these cytoarchitectonic changes in humans. Finally, we implicated MAPK signaling as a driving force producing these pathological changes. Using CI-1040 to inhibit MAP2K, both acutely and after spikes developed, resulted in fewer interictal spikes, reduced microglial activation, and less inhibitory neuron loss. Treated animals had significantly fewer high-amplitude, short-duration spikes, which correlated with improved spatial memory performance on the Barnes maze. Together, our results provide evidence for a cytoarchitectonic pathogenesis underlying the epileptic cortex, which can be ameliorated through both early and delayed MAP2K inhibition. These findings highlight the potential role of CI-1040 as a pharmacological treatment that could prevent the development of epileptic activity and reduce cognitive impairment in both patients with epilepsy and those with non-epileptic spike-associated neurobehavioral disorders.</p>
Bilateral integration in somatosensory cortex is controlled by behavioral relevance
<p><span><span>Sensory</span> <span>p</span><span>ercep</span><span>tion</span><span> naturally </span><span>requires</span> <span>processing</span> <span>stimuli </span><span>from</span> <span>both sides of the body</span><span>.</span> <span>Yet</span><span>, </span><span>how</span> <span>neurons</span> <span>bind stimulus</span> <span>features</span><span> across the hemispheres to </span><span>create</span><span> a unified </span><span>percept</span><span>ual</span><span> experience</span> <span>remains</span> <span>unknown.</span> <span>To </span><span>address this </span><span>question</span><span>, w</span><span>e </span><span>performed</span><span> large-scale</span> <span>recordings</span><span> from</span> <span>neurons in</span> <span>both</span><span> somatosensory cort</span><span>ices</span><span> (S1)</span> <span>while</span> <span>mice</span> <span>shared information between </span><span>their </span><span>hemispheres</span> <span>and</span><span> discriminate</span><span>d</span><span> between two categories of bilateral </span><span>stimuli</span><span>. </span><span>When </span><span>expert </span><span>mice </span><span>touched</span> <span>stimuli</span> <span>associated with reward</span><span>,</span> <span>they</span> <span>moved their whiskers</span><span> with greater bilateral symmetry</span><span>.</span> <span>During this period,</span> <span>synchronous spiking</span><span> and </span><span>enhanced </span><span>spike-field coupling</span> <span>emerged</span> <span>between</span> <span>the hemispheres</span><span>.</span> <span>This coordinated activity </span><span>was </span><span>absent</span><span> in</span> <span>stimulus</span><span>-matched</span><span> naïve animals</span><span>,</span> <span>indicating</span><span> that </span><span>interhemispheric </span><span>(IH)</span> <span>binding</span> <span>was</span> <span>controlled</span> <span>by</span> <span>a</span><span> goal-directed</span><span>,</span> <span>internal </span><span>process</span><span>.</span> <span>I</span><span>n</span> <span>S1 neurons,</span> <span>the addition of ipsilateral touch</span><span> primarily </span><span>facilitate</span><span>d</span> <span>the </span><span>contralateral</span><span>, principal whisker</span><span> response. </span><span>Th</span><span>is</span> <span>facilitation</span> <span>primarily </span><span>emerged</span><span> for</span><span> reward-associated </span><span>stimuli</span> <span>and </span><span>was lost on trials </span><span>where</span> <span>expert </span><span>mice </span><span>failed to</span><span> re</span><span>spond</span><span>.</span> <span>Taken together</span><span>, t</span><span>hese </span><span>results</span><span> reveal </span><span>a</span> <span>novel</span> <span>state-dependent l</span><span>ogic</span> <span>underlying</span> <span>bilateral </span><span>integration</span><span> in S1</span><span>,</span><span> where</span> <span>stimulus</span> <span>binding</span><span> and</span><span> facilitation are controlled by </span><span>behavioral relevance</span><span>. </span></span></p>
The entorhinal cortex modulates trace fear memory formation and neuroplasticity in the lateral amygdala via cholecystokinin
<p>Although the neural circuitry underlying fear memory formation is important in fear-related mental disorders, it is incompletely understood. Here, we utilized trace fear conditioning to study the formation of trace fear memory. We identified the entorhinal cortex (EC) as a critical component of sensory signaling to the amygdala. Moreover, we used the loss of function and rescue experiments to demonstrate that release of the neuropeptide cholecystokinin (CCK) from the EC is required for trace fear memory formation. We discovered that CCK-positive neurons extend from the EC to the lateral nuclei of the amygdala (LA), and inhibition of CCK-dependent signaling in the EC prevented long-term potentiation of sensory signals to the LA and formation of trace fear memory. Altogether, we suggest a model where sensory stimuli trigger the release of CCK from EC neurons, which potentiates sensory signals to the LA, ultimately influencing neural plasticity and trace fear memory formation.</p>
Dataset: Measuring stimulus-evoked neurophysiological differentiation in distinct populations of neurons in mouse visual cortex
<p>This dataset contains the calcium imaging and behavioral data analyzed in our paper, "Measuring stimulus-evoked neurophysiological differentiation in distinct populations of neurons in mouse visual cortex".</p> <p>These data were obtained at the Allen Brain Observatory as part of the <em>OpenScope</em> project, which is operated by the Allen Institute.</p> <p>Analysis code is available at <a href="https://github.com/wmayner/openscope-differentiation">https://github.com/wmayner/openscope-differentiation</a>.</p>
Normal Retinotopy in Primary Visual Cortex in a Congenital Complete Unilateral Lesion of Lateral Geniculate Nucleus in Human: A Case Study
<p>The data set contains .nii files for each condition of retinotopic mapping in fMRI. (Meridians, Wedges and concentric rings). It also contains DTI data files with .bvec and .bval files. Psychophysics data is in two excel files for motion and orientation discrimination. </p>
Neuropixels recordings from mouse visual cortex for Jia et al (2022)
<p>Neuropixels recordings from mouse visual cortex. The dataset was used in the paper: <strong>Multi-regional module-based signal transmission in mouse visual cortex,</strong> Jia et al. (2022) Neuron.</p> <p>For information about experimental procedures, see Siegle, Jia et al. (2021) Nature 592, 86-92 (https://www.nature.com/articles/s41586-020-03171-x).</p> <p>For information about file contents, see https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html</p> <p>The NWB 1.0 files can be opened with HDF5 and HDFview. </p> <table> <tbody> <tr> <td><strong>Mouse ID</strong></td> <td><strong>Genotype</strong></td> <td> </td> </tr> <tr> <td>306046</td> <td>['Sst-IRES-Cre/wt;Ai32/wt']</td> <td> </td> </tr> <tr> <td>388523</td> <td>['Pvalb-Cre',]</td> <td> </td> </tr> <tr> <td>389262</td> <td>['Vip-Cre']</td> <td> </td> </tr> <tr> <td>408153</td> <td>['Sst-IRES-Cre/wt;Ai32/wt']</td> <td> </td> </tr> <tr> <td>410344</td> <td>['Vip-Cre']</td> <td> </td> </tr> <tr> <td>415149</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>412809</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>412804</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>416856</td> <td>['Sst-IRES-Cre/wt;Ai32/wt']</td> <td> </td> </tr> <tr> <td>419114</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>419117</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>419118</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>419119</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>424445</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>415148</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>416356</td> <td>['Sst-IRES-Cre/wt;Ai32/wt']</td> <td> </td> </tr> <tr> <td>416861</td> <td>['Sst-IRES-Cre/wt;Ai32/wt']</td> <td> </td> </tr> <tr> <td>419112</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>419116</td> <td>['wt/wt']</td> <td> </td> </tr> </tbody> </table> <p> </p>
Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex
<p><span>Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, c</span>urrent approaches restrict recordings to few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here, we describe a new probe variant and set of techniques which enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread, and modulation by LFP events such as inter-ictal discharges and burst suppression. While some challenges remain in creating a turn-key recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution. </p>
Data and Codes from: Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex
<p>These datasets and codes were used for analysis in a manuscript titled "Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex." For more details on the experimental and analysis methods, please refer to that manuscript. The raw data was obtained by recording signals from a 64-channel ECoG array implanted on the PFC of monkeys performing a behavioral task using custom software (NS computer service, Japan) running on LabVIEW Real-Time (National Instruments, TX, USA). The recorded raw data were converted into time-series data of power in six frequency bands using FieldTrip. The total file size of all the raw data is over 50GB, so it is not included here. It is available upon request.</p>
FSS7B - Inhibiting human aversive memory by transcranial theta-burst stimulation to primary sensory cortex: Supplementary fMRI data
<p>Functional magnetic resonance imaging (fMRI) data supplementing a publication on inhibiting somatosensory fear memory in humans with transcranial magnetic stimulation (TMS). Contains 1) individual regions-of-interest (ROIs) masks in the bilateral primary somatosensory cortex (S1) to target with TMS, 2) S1 masks for left and right hemisphere used in restricting the ROIs to a priori expected area, 3) sum and probability maps of the ROIs over participants, and 4) summary group level fMRI NIFTI images including beta images and T-maps. Individual SPMs/beta images can be requested from the authors for academic research purposes (k.ojala@uke.de). Details on the methods are found in the Supplement of the publication (see linked DOI). </p>
A role for myosin II cluster and membrane energy in cortex rupture for Dictyostelium discoideum cells
<p>Blebs, pressure driven protrusions of the cell membrane, facilitate the movement of eukaryotic cells such as the soil amoeba <em>Dictyostelium discoideum</em>, white blood cells and cancer cells. Blebs initiate when the cell membrane separates from the underlying cortex. A local rupture of the cortex, has been suggested as a mechanism by which blebs are initiated. However, much clarity is still needed about how cells inherently regulate rupture of the cortex in locations where blebs are expected to form. In this work, we examine the role of membrane energy and the motor protein myosin II (myosin) in facilitating the cell driven rupture of the cortex. We perform under-agarose chemotaxis experiments, using <em>Dictyostelium discoideum</em> cells, to visualize the dynamics of myosin and calculate changes in membrane energy in the blebbing region. To facilitate a rapid detection of blebs and analysis of the energy and myosin distribution at the cell front, we introduce an autonomous bleb detection algorithm that takes in discrete cell boundaries and returns the coordinate location of blebs with its shape characteristics. We are able to identify by microscopy naturally occurring gaps in the cortex prior to membrane detachment at sites of bleb nucleation. These gaps form at positions calculated to have high membrane energy, and are associated with areas of myosin enrichment. Myosin is also shown to accumulate in the cortex prior to bleb initiation and just before the complete disassembly of the cortex. Together our findings provide direct spatial and temporal evidence to support cortex rupture as an intrinsic bleb initiation mechanism and suggests that myosin clusters are associated with regions of high membrane energy where its contractile activity leads to a rupture of the cortex at points of maximal energy.</p>
Data set of Reversing anterior insular cortex neuronal hypoexcitability attenuates compulsive behavior in juvenile rats
<p>Development of self-regulatory competencies during adolescence is partially dependent on normative brain maturation. Here we report that adolescent rats as compared to adults exhibit impulsive and compulsive-like behavioral traits, the latter being associated with lower expression of mRNA levels of the immediate early gene zif268 in the anterior insula cortex (AIC). This suggests that underdeveloped AIC function in adolescent rats could contribute to an immature pattern of interoceptive cue integration in decision-making and a compulsive phenotype. In support of this, we report that layer 5 pyramidal neurons in the adolescent rat AIC are hypoexcitable and receive fewer glutamatergic synaptic inputs compared to adults. Chemogenetic activation of the AIC attenuated compulsive traits in adolescent rats supporting the idea that in early stages of AIC maturity there exists a suboptimal integration of sensory and cognitive information that contributes to inflexible behaviors in specific conditions of reward availability.</p>
Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex
<p>Local field potential (LFP) recordings reflect the dynamics of the current source density (CSD) in brain tissue. The synaptic, cellular and circuit contributions to current sinks and sources are ill-understood. We investigated these in mouse primary visual cortex using public Neuropixels recordings and a detailed circuit model based on simulating the Hodgkin-Huxley dynamics of >50,000 neurons belonging to 17 cell types. The model simultaneously captured spiking and CSD responses and demonstrated a two-way dissociation: Firing rates are altered with minor effects on the CSD pattern by adjusting synaptic weights, and CSD is altered with minor effects on firing rates by adjusting synaptic placement on the dendrites. We describe how thalamocortical inputs and recurrent connections sculpt specific sinks and sources early in the visual response, whereas cortical feedback crucially alters them in later stages. These results establish quantitative links between macroscopic brain measurements (LFP/CSD) and microscopic biophysics-based understanding of neuron dynamics and show that CSD analysis provides powerful constraints for modeling beyond those from considering spikes.</p>
Neural coding in barrel cortex during whisker-guided locomotion
<p>Data accompanying publication at <a href="https://doi.org/10.7554/eLife.12559">https://doi.org/10.7554/eLife.12559</a> and code at <a href="https://doi.org/10.5281/zenodo.2949955">https://doi.org/10.5281/zenodo.2949955</a>. For example usage see the notebooks in the repository.</p> <p>The data is organized according to animal id, `00` - `18`.</p> <p>Animals `00` - `12` are electrophysiology data. Each electrophysiology animal data contains the timestamps of the extracted spikes and various processed tabular data. For usage see the `ephys-traces.ipynb` and `ephys-table.ipynb`. Raw voltage traces are not provided.</p> <p>Animals `13` - `18` are imaging data. Each imaging animal data contains timeseries of extracted calcium transients, pixel-wise regression maps of the field of view and various processed tabular data. For usage see the `imaging-raw.ipynb`, `imaging-traces.ipynb`, and `imaging-traces.ipynb`. Raw imaging movies are not provided.</p>
parallel-fibered bone; A5, osteocyte lacunae with well-preserved canaliculi; B3, osteocyte lacunae lacking canaliculi; B4, B5, growth pattern with preserved residuals of the thick annuli and zones (zo I–III) and thin annuli and zones (zo IV–VII); A6, growth pattern with preserved thin annuli and thick zones (zo I–IV), the dotted line marks the border between the perimedullary region and the cortex. Arrows in A5 and B3 indicate osteocyte lacunae; in B4, B5, and A6 indicate the annuli. Growth pattern in B4 figured on the lateral section side, in B5 and A5 on the ventral side; note the cortex thickness variation between B4 and B5. A1, A3, A4, A6, B1, B4, B5 in polarized light and A2, A5, B2, B3 in normal transmitted light. Abbreviations: an, annulus; ec, erosion cavity; pmr, perimedullary region; pos, primary osteon; sos, secondary osteon; zo, zone. in Palaeohistology helps reveal taxonomic variability in exceptionally large temnospondyl humeri from the Upper Triassic of Krasiejów, SW Poland
parallel-fibered bone; A5, osteocyte lacunae with well-preserved canaliculi; B3, osteocyte lacunae lacking canaliculi; B4, B5, growth pattern with preserved residuals of the thick annuli and zones (zo I–III) and thin annuli and zones (zo IV–VII); A6, growth pattern with preserved thin annuli and thick zones (zo I–IV), the dotted line marks the border between the perimedullary region and the cortex. Arrows in A5 and B3 indicate osteocyte lacunae; in B4, B5, and A6 indicate the annuli. Growth pattern in B4 figured on the lateral section side, in B5 and A5 on the ventral side; note the cortex thickness variation between B4 and B5. A1, A3, A4, A6, B1, B4, B5 in polarized light and A2, A5, B2, B3 in normal transmitted light. Abbreviations: an, annulus; ec, erosion cavity; pmr, perimedullary region; pos, primary osteon; sos, secondary osteon; zo, zone.
Yoga Asana Increases Pre-Frontal Cortex Activity and Reduces Resting State Functional Connectivity
<p>This dataset characterizes changes in the prefrontal cortex (PFC) before, during and after Yoga Asana (physical postures) with the mobile neuroimaging technique of functional near-infrared spectroscopy (fNIRS). Measurements were conducted with twenty-seven healthy adults executing four basic Asanas for 23 minutes with each Asana maintained for 25 -30 seconds. All postures significantly increased PFC activity versus baseline and resting state functional connectivity showed a significant decrease post Yoga Asana.</p> <p>Files 8, 15 and 24 were removed due to poor signal quality.</p> <p>During the measurement process of Asana the following stim marks were used to distinguish between postures:</p> <p>Posture A (Tadasana): A</p> <p>Posture B (Uttanasana): B</p> <p>Posture C (Adho Mukah Svasana): C</p> <p>Posture D (Urdhva Muka Svasana): D</p> <p>Results of the repeated measures ANOVA are presented for each combination of Asana. Those showing a significant difference are highlighted in green in the second to last tab of the file (Final Table). Demographics of volunteers are outlined in the last tab of the excel file (Demographics Volunteers). </p>
Data from: Neural interactions in the human frontal cortex dissociate reward and punishment learning
<p>How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to reward and punishment. The non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.</p>
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.