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133 results for “visual cortex”
The Interplay between Hebbian and homeostatic plasticity in the Adult Visual cortex
<p>Data linked to the article "The interplay between Hebbian and homeostatic plasticity in the adult visual cortex", Journal of Physiology, DOI: <a href="https://doi.org/10.1113/JP287665">https://doi.org/10.1113/JP287665</a></p> <p>Data from binocular rivalry measurements and processed data from EEG Visual Evoked Potentials (VEP) are separated in different files.</p> <p>The ocular dominance index (ODI) files are split in two: the "ODI_values" file contains the raw measurements from participants, and the "change_from_baseline file" contains the same data normalized to baseline for each measurement.</p> <p>In both files, each column refers to a different measurement and condition:</p> <p>noHFS: data measured with the 17Hz HFS block before monocular deprivation<br>HFS: data measured with the 8.6Hz HFS block before monocular deprivation</p> <p>Baseline: Ocular dominance index measured at the start of the session, before any manipulation<br>Post_MD_1: first measurement after 60 minutes monocular deprivation (starting immediately after the end of deprivation)<br>Post_MD_2: second measurement after 60 minutes monocular deprivation (starting 11 minutes after the end of deprivation)<br>Post_MD_3: third measurement after 60 minutes monocular deprivation (starting 22 minutes after the end of deprivation)</p> <p>In VEP files, each column refers to a different condition:</p> <p>HFS: VEP recorded in the high-frequency stimulation condition, no monocular deprivation<br>HFS_MD: VEP recorded in the high-frequency stimulation condition with monocular deprivation<br>noHFS: VEP recorded in the condition where the HFS block was withheld, as a control for its role in our effect</p> <p>pre: first 500 measurements, before the High-Frequency Stimulation (HFS) block<br>post: last 500 measurements, after the HFS block (or after the break in the noHFS condition).</p>
Decoding of multisensory semantics and memories in low-level visual cortex
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Effects of Phase Regression on High-Resolution Functional MRI of the Primary Visual Cortex
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Data for Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex
<p>Data for the paper: Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex, Nature 640, 2025</p> <p>In brief, this data archive includes information about the skeleton morphology and synaptic features of neurons whose cell bodies fell within a 100 micron by 100 micron column spanning all layers of mouse visual cortex. See <a href="https://www.microns-explorer.org/cortical-mm3">MICrONs-Explorer</a> for a full description of the broader volume and how it was collected.</p> <p>The data here include both data tables of cell locations, neuronal features, synapse lists, and more, as well as files containing morphological descriptions of all neurons used for the analysis in the initial version of the preprint. See the README.md file for more complete information about the individual files.</p> <p>Note: Data has been updated with post-publication files.</p>
A mesial-to-lateral dissociation for orthographic processing in the visual cortex
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Dataset: Feedback contribution to surface motion perception in the human early visual cortex
<p><strong>Dataset</strong></p> <p>Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (<a href="https://doi.org/10.1101/653626">biorxiv</a>).</p> <p><strong>Description</strong></p> <p>fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.</p> <p>├── sub-01<br> │ ├── anat<br> │ │ └── ...<br> │ ├── func<br> │ │ └── ...<br> │ ├── func_se<br> │ │ └── ...<br> │ └── func_se_op<br> │ └── ...</p> <p>The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.</p> <p>Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.</p> <p>The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on <a href="https://github.com/ingo-m/pyprf">github</a>.</p> <p>For example videos of the main experimental stimuli, see <a href="https://doi.org/10.5281/zenodo.2583017">zenodo.2583017</a>. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on <a href="https://github.com/ingo-m/PacMan/tree/master/stimuli/experiment">github</a>.</p> <p>The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on <a href="https://github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata">github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata</a>.</p> <p><strong>Analysis</strong></p> <p>The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on <a href="https://github.com/ingo-m/PacMan/tree/master/docker">github</a>.</p> <p>If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_PacMan_Image_Jessie.txt">here</a> and <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_CBS_Image.txt">here</a>.</p> <p>Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. <a href="http://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_01.sh">~/analysis/20180118/metascript_01.sh</a> for subject 20180118) and a separate script for the depth sampling (e.g. <a href="https://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_03.sh">~/analysis/20180118/metascript_03.sh</a>). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).</p> <p>Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">py_depthsampling</a>.</p> <p><strong>Further resources</strong></p> <p>Please refer to the research paper for more details: <a href="https://doi.org/10.1101/653626">https://doi.org/10.1101/653626</a></p> <p>The analysis pipeline can be found on <a href="https://github.com/ingo-m/PacMan">https://github.com/ingo-m/PacMan</a></p> <p>A separate repository contains the code used for visualisation of depth-sampling results: <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">https://github.com/ingo-m/py_depthsampling/tree/PacMan</a></p> <p>Free & open source software package for population receptive field mapping: <a href="https://github.com/ingo-m/pyprf">https://github.com/ingo-m/pyprf</a></p> <p> </p>
MERFISH data of the developing mouse visual cortex under normal- and dark-rearing
<p>MERFISH data for the manuscript "Spatial profiling of the interplay between cell type- and vision-dependent transcriptomic programs in the visual cortex"</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>
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>
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>
Source code and experimental data of human brain tissue (visual cortex, corona radiata) for poro-viscoelastic parameter identification
<p>Computer code and experimental data that we used for our inverse parameter identification of poro-viscoelastic material parameters for two different brain regions: visual cortex (gray matter) and corona radiata (white matter). The experimental data comprises large-strain cyclic loading and compression/tension relaxation. For details see the corresponding publication: "Model-driven exploration of poro-viscoelasticity in human brain tissue: Be careful with the parameters!".</p> <p>Further explanation regarding the specimen preparation, experimental setup, as well as the assignment of regions and governing regions can be found in Hinrichsen, J., Reiter, N., Bräuer, L. et al. Inverse identification of region-specific hyperelastic material parameters for human brain tissue. Biomech Model Mechanobiol (2023). <a href="https://doi.org/10.1007/s10237-023-01739-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10237-023-01739-w</a>.</p> <p>The file "<span>nonlinear-poro-viscoelasticity.cc</span>" contains our C++ Finite Element code based on the open source library deal.II. It is accompanied by an exemplary parameter file.</p> <p><strong>Funding:</strong> The support from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the grants BU 3728/1-1, BU 3728/3-1, STE 544/70-1 as well as through project number 460333672 CRC1540 Exploring Brain Mechanics is gratefully acknowledged.</p>
Representations of color and form in mouse visual cortex
<p>Spatial transitions in color can aid any visual perception task, and its neural representation – an "integration of color and form" – is thought to begin at primary visual cortex (V1). Color and form integration is untested in mouse V1, yet studies show that the ventral retina provides the necessary substrate from green-sensitive rods and UV-sensitive cones. Here, we used two-photon imaging in V1 to measure spatial frequency (SF) tuning along four axes of rod and cone contrast space, including luminance and color. We first reveal that V1 has similar responsiveness to luminance and color, yet average SF tuning is significantly shifted lowpass for color. Next, guided by linear models, we used SF tuning along all four color axes to estimate the proportion of neurons that fall into classic models of color opponency – "single-", "double-", and "non-opponent". Few neurons (~6%) fit the criteria for double-opponency, which are uniquely tuned for chromatic borders. Most of the population can be described as a unimodal distribution ranging from strongly single-opponent to non-opponent. Consistent with recent studies of the rodent and primate retina, our V1 data is well-described by a simple model in which ON and OFF channels to V1 sample the photoreceptor mosaic randomly.</p>
Sound improves neuronal encoding of visual stimuli in mouse primary visual cortex
<p class="MsoNormal"><span>In everyday life, we integrate visual and auditory information in routine tasks such as navigation and communication. While concurrent sound can improve visual perception, the neuronal <span>correlates of audiovisual integration are not fully understood. Specifically, it remains unclear whether neuronal firing patters in the primary visual cortex (V1) of awake animals demonstrate similar sound-induced improvement in visual discriminability. Furthermore, presentation of sound is associated with movement in the subjects, but little is understood about whether and how sound-associated movement affects audiovisual integration in V1. Here, we investigated how sound and movement interact </span>to modulate V1 visual responses in awake, head-fixed mice and whether this interaction improves neuronal encoding of the visual stimulus. We presented visual drifting gratings with and without simultaneous auditory white noise to awake mice while recording mouse movement and V1 neuronal activity. Sound modulated activity of 80% of light-responsive neurons, with 95% of neurons increasing activity when the auditory stimulus was present. A generalized linear model revealed that sound and movement had distinct and complementary effects of the neuronal visual responses. Furthermore, decoding of the visual stimulus from the neuronal activity was improved with sound, an effect that persisted even when controlling for movement. These results demonstrate that sound and movement modulate visual responses in complementary ways, improving neuronal representation of the visual stimulus. This study clarifies the role of movement as a potential confound in neuronal audiovisual responses and expands our knowledge of how multimodal processing is mediated at a neuronal level in the awake brain.</span></p> <p></p>
Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses
<p>Raw data and code to reproduce figures in the manuscript "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses"</p> <p># README</p> <p>## Introduction</p> <p>This README provides essential information about the codebase for the manuscript titled "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses." The code in this repository is self-contained and is expected to run smoothly given the appropriate versions of the required libraries/packages.</p> <p>## Directory structure and execution details</p> <p>### R code</p> <p>- Main Figures 2A-2D, 3A-3C, and 4A-4E, as well as supplemental figures S2A-S2H, S3A-S3E, S4L, and S5A-S5I, were generated using R. Execute the `R_figs_master.r` script located in the `r_code` directory.<br> - All figures will be saved within the `r_code/code_generated_figures` directory.<br> - Note: Exact UMAP representations might vary across different hardware and operating systems, likely due to an issue with the UWOT package ([Reference Issue](https://github.com/satijalab/seurat/issues/5514)). If figures appear outside their designated plot ranges, set "FixAxes" to 'FALSE' in the `single_cell_variables.r` script.</p> <p>### MATLAB code</p> <p>- Main figures 1B, 1D-1F, and 6A-6H, as well as supplemental figures S1A-S1J and S6A-S6I, were generated using MATLAB (version 9.11.0.1809720 (R2021b) Update 1). Execute the `get_the_figs_matlab.m` script located in the `matlab_code` directory.<br> - All figures will be saved within the `matlab_code/code_generated_figures` directory.<br> - Required: [fca_readfcs, version 2020.06.22](https://ch.mathworks.com/matlabcentral/fileexchange/9608-fca_readfcs).</p> <p>### Python code</p> <p>- Figures 5B-5F panels were generated using Python (version 3.6.8). Run the `fig_5_analysis_code.py` script located in the `python_code` directory.<br> - All figures will be saved within the `python_code/code_generated_figures` directory.<br> - The preprocessed images located in `python_code/data_repository/Adamts2_processed`, `python_code/data_repository/Agmat_processed`, and `python_code/data_repository/Baz1a_processed` were generated using the ImageJ macro `python_code/cropped_to_processed_macro.ijm` from the raw images in `python_code/data_repository/Adamts2_cropped`, `python_code/data_repository/Agmat_cropped`, and `python_code/data_repository/Baz1a_cropped`.</p> <p>## Supplementary code (for reference only as raw data is not included)</p> <p>### Mapping code and genome construction code</p> <p>- Initial processing of Single-cell RNA-sequencing was performed with Cell Ranger, coordinated by the Python script:<br> `python_code/mapping_and_genome_construction/single_cell_mapping_pipeline.py`. Some components of this script are deprecated and were primarily used to pass .fastq files to Cell Ranger and organize the outputs.<br> - A custom genome was constructed to account for the expression of CaMPARI2 in the single-cell RNA-sequencing dataset:<br> `python_code/mapping_and_genome_construction/campari2_genome_construction.py`.<br> - Processing of Bulk RNA-sequencing, either single or paired-end, was executed through Python:<br> `python_code/mapping_and_genome_construction/bulk_single_end_mapping.py` and `python_code/mapping_and_genome_construction/bulk_paired_end_mapping.py`.<br> - A custom genome was constructed to account for the expression of various artificial promoter viruses:<br> `python_code/mapping_and_genome_construction/bulk_seq_genome_construction.py`.</p>
Dataset for Task-dependent spatial processing in the visual cortex
<p>The dataset contains the mean ERPs values of each participant after the audiovidual stimulus (S2 for the spatial bisection and S for the spatial localization), divided as follows:</p> <p>- condition (i.e., 1sc: short distance between S1 and S2, 1sl: long distance between S1 and S2),</p> <p>- task (i.e., spatial bisection or spatial localization),</p> <p>- time window (i.e., 50-90 ms or 110-160 ms post stimulus), </p> <p>- roi (i.e., O1, O2, C1, C2, T7 or T8 electrodes). </p>
Stimulus-dependent representational drift in primary visual cortex
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A three-photon head-mounted microscope for imaging all layers of visual cortex in freely moving mice
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Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex
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ScienceDex guides
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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.