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opencc-by-4.0Nov 2024View details →

Multimodal mismatch responses in mouse auditory cortex

<div>All raw data and Matlab code necessary to produce the figures of https://elifesciences.org/reviewed-preprints/95398</div> <div> </div> <div> </div>

opencc-by-4.0Feb 2023View details →

Hodgkin-Huxley simulation summarizing statistics for mouse motor cortex

Synthetic data set of model paramater vectors and electrophysiological features derived from a Hodgkin-Huxley-based model reproducing electrophysiological data in mouse motor and visual cortex.

opencc-by-4.0May 2022View details →

Visual stimuli elicit feedforward and feedback waves in mouse cortex (data and code)

See the readme file for details of the information contained therein. There are also separate readme files for publicly available github repositories from Lyle Muller and the circular statistics toolbox (both for matlab).  This dataset includes both the raw data and the analysis code used to process them in Aggarwal et al, Nature Communications, 2022.

openmit-licenseJun 2024View details →

Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model

data.tar.gz contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:   ./marvel: - This directory contains rds and Rdata objects that were created using the MARVEL R package cell_type_goresults.rds - This is the go results split by cell type marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting normalized_sj_expression.Rds - This object is the normalized splice junction expression Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition   ./seurat: - This directory contains all intermediate and final Seurat single-cell gene expression objects annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses. clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers. filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA. filtered_brain_samples.rds - This is the filtered Seurat object, before PCA. integrated_brain_samples.rds - This the integrated Seurat object, before other steps.   ./star: - All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space. J1/ - This directory contains outputs for brain sample J1 J13/ - This directory contains outputs for brain sample J13 J15/ - This directory contains outputs for brain sample J15 J2/ - This directory contains outputs for brain sample J2 J3/ - This directory contains outputs for brain sample J3 J4/ - This directory contains outputs for brain sample J4 K1/ - This directory contains outputs for kidney sample K1 K2/ - This directory contains outputs for kidney sample K2 K3/ - This directory contains outputs for kidney sample K3 K4/ - This directory contains outputs for kidney sample K4 K5/ - This directory contains outputs for kidney sample K5 K6/ - This directory contains outputs for kidney sample K6   ./star/genome: - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from<a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"> https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</a> chrLength.txt chrNameLength.txt chrName.txt chrStart.txt exonGeTrInfo.tab exonInfo.tab geneInfo.tab Genome genomeParameters.txt Log.out SA SAindex sjdbInfo.txt sjdbList.fromGTF.out.tab sjdbList.out.tab transcriptInfo.tab   ./star/J1: - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation<a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"> https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</a>  Log.final.out Log.out Log.progress.out SJ.out.tab Solo.out/ STARgenome/   ./star/J1/Solo.out:- This directory contains the outputs used for downstream analysis Barcodes.stats GeneFull_Ex50pAS/ SJ/   ./star/J1/Solo.out/GeneFull_Ex50pAS: - This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns) Features.stats filtered/ raw/ Summary.csv UMIperCellSorted.txt   ./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: - This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages) barcodes.tsv.gz - This file contains filtered cell barcodes features.tsv.gz - This file contains filtered features (genes) matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix   ./star/J1/Solo.out/GeneFull_Ex50pAS/raw: - This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered. barcodes.tsv features.tsv matrix.mtx   ./star/J1/Solo.out/SJ: - This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression Features.stats raw/ Summary.csv   ./star/J1/Solo.out/SJ/raw: - This directory contains the raw barcodes, features, and matrix files for splice junction expression barcodes.tsv - This file contains filtered cell barcodes features.tsv - This file contains filtered features (splice junctions) matrix.mtx - This file contains the filtered cell by gene expression count matrix   ./star/J1/_STARgenome: - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from<a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"> https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</a>  exonGeTrInfo.tab exonInfo.tab geneInfo.tab sjdbInfo.txt sjdbList.fromGTF.out.tab sjdbList.out.tab transcriptInfo.tab

opencc-by-4.0Jul 2024View details →

Cholinergic input to mouse visual cortex signals a movement state and acutely enhances layer 5 responsiveness

<div>All raw data and Matlab code necessary to produce the figures of <a href="https://elifesciences.org/reviewed-preprints/89986">https://elifesciences.org/reviewed-preprints/89986</a></div> <div> <div> </div> </div>

opencc-by-4.0Sep 2024View details →

Dataset from Makino H. and Suhaimi A. Distributed representations of temporally accumulated reward prediction errors in the mouse cortex.

Dataset from the paper: Makino H. and Suhaimi A. Distributed representations of temporally accumulated reward prediction errors in the mouse cortex. Each variable is described in Description.pdf. Analysis code is available at <a href="https://github.com/HiroshiMakinoLaboratory/RPEAccumulation" target="_blank" rel="noopener">https://github.com/HiroshiMakinoLaboratory/RewardPredictionErrorAccumulation</a>.

opencc-by-4.0Nov 2022View details →

Data for Nat Comm paper "Complexity of cortical wave patterns of the wake mouse cortex"

Dataset for Nat. Comm. paper "Complexity of cortical wave patterns of the wake mouse cortex"

opencc-zeroMar 2023View details →

Denervated mouse CA1 pyramidal neurons express homeostatic synaptic plasticity following entorhinal cortex lesion

Structural, functional, and molecular reorganization of denervated neural networks is often observed in neurological conditions. The loss of input is accompanied by homeostatic synaptic adaptations, which can affect the reorganization process. A major challenge of denervation-induced homeostatic plasticity operating in complex neural networks is the specialization of neuronal inputs. It remains unclear whether neurons respond similarly to the loss of distinct inputs. Here, we used in vitro entorhinal cortex lesion (ECL) and Schaffer collateral lesion (SCL) in mouse organotypic entorhino-hippocampal tissue cultures to study denervation-induced plasticity of CA1 pyramidal neurons. We observed microglia accumulation, presynaptic bouton degeneration, and a reduction in dendritic spine numbers in the denervated layers three days after SCL and ECL. Transcriptome analysis of the CA1 region revealed complex changes in differential gene expression following SCL and ECL compared to non-lesioned controls with a specific enrichment of differentially expressed synapse-related genes observed after ECL. Consistent with this finding, denervation-induced homeostatic plasticity of excitatory synapses was observed three days after ECL but not after SCL. Chemogenetic silencing of the EC but not CA3 confirmed the pathway-specific induction of homeostatic synaptic plasticity in CA1. Additionally, increased RNA oxidation was observed after SCL and ECL. These results reveal important commonalities and differences between distinct pathway lesions and demonstrate a pathway-specific induction of denervation-induced homeostatic synaptic plasticity.

openmit-licenseJul 2023View details →

PANDA cell-type-specific networks for S858R and WT mouse cerebral cortex

The below files are from the data/results directory of this associated project and include the following: <ul> <li>PANDA networks : cell-type-specific TF-gene regulatory networks constructed using multi-omic inputs (snRNA-seq, TF-motif, and PPI) for all cell types in S858R and WT mouse cerebral cortex tissues (n = 16). All the files included for PANDA networks indicate the condition (heterozygous or control), tissue (cerebral cortex) followed by _PANDA.Rdata. (example: astrocytes_heterozygouscortexexpression_PANDA.Rdata)</li> </ul>

publicNov 2019View details →

Data from: Stability of spontaneous, correlated activity in mouse auditory cortex

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publicApr 2021View details →

Two photon data from: Functional and structural properties of highly responsive somatosensory neurons in mouse barrel cortex

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publicJul 2024View details →

Data from: Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky

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publicMar 2023View details →

Denervated mouse CA1 pyramidal neurons express homeostatic synaptic plasticity following entorhinal cortex lesion

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dryad32/100

Differential activation of c-Fos and Egr1 during development of the mouse visual cortex

Critical periods (CP) in brain development are associated with profound changes in gene expression cascades. Here we examine the expression of the immediate early genes c-Fos and Egr1 at different stages of mouse visual cortex (VC) development. Mice 11, 25, and 50 days of age were maintained under standard light-dark conditions, deprived of light for 5 days, or deprived of light for 5 days and then exposed to light for 90 min. Their brains were analyzed at PND16 (before the onset of the CP), PND30 (during the CP) and PND55 (after the CP) to determine the changes in the number of cells expressing c-Fos and Egr1 in the binocular primary visual and primary somatosensory cortices. We found highly specific induction of c-Fos expression in the primary VC in response to light. We also observed transient cross-modal activation of c-Fos in the barrel field of the primary somatosensory cortex in response to light before and during the CP; such activation disappeared after the CP. Expression of Egr1 was not induced by light in the VC before the CP, but was evident during and after the CP, although the induction was much less pronounced than that of c-Fos. Dynamic changes in c-Fos and Egr1 expression may reflect their contribution to the VC plasticity during the CPs of postnatal brain development.

opencc-zeroJan 2021View details →

dryad32/100

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.

opencc-zeroDec 2017View details →

dryad32/100

Nanostring mRNA expression profiling of P7 Arx(GCG)10+7 neonatal cortex using nCounter® mouse Neuropathology Plus Panel

X-linked infantile spasms syndrome (ISSX) is a clinically devastating developmental epileptic encephalopathy with life-long impact. Arx(GCG)10+7, a mouse model of the most common triplet-repeat expansion mutation of ARX, exhibits neonatal spasms, electrographic phenotypes and abnormal migration of GABAergic interneuron subtypes. Neonatal presymptomatic treatment with 17β-estradiol (E2) in Arx(GCG)10+7 reduces spasms and modifies progression of epilepsy. Cortical pathology during this period, a crucial point for clinical intervention in ISSX, has largely been unexplored, and the pathogenic cellular defects that are targeted by early interventions are unknown. In the first postnatal week, we identified a transient wave of elevated apoptosis in Arx(GCG)10+7 mouse cortex that is non-Arx cell autonomous, since mutant Arx-immunoreactive (Arx+) cells are not preferentially impacted by cell death. NeuN+ (also known as Rbfox3) survival was also not impacted, suggesting a vulnerable subpopulation in the immature Arx(GCG)10+7 cortex. Inflammatory processes during this period might explain this transient elevation in apoptosis; however, transcriptomic and immunohistochemical profiling of several markers of inflammation revealed no innate immune activation in Arx(GCG)10+7 cortex. Neither neonatal E2 hormone therapy, nor ACTH(1-24), the frontline clinical therapy for ISSX, diminished the augmented apoptosis in Arx(GCG)10+7, but both rescued neocortical Arx+ cell density. Since early E2 treatment effectively prevents seizures in this model, enhanced apoptosis does not solely account for the seizure phenotype, but may contribute to other aberrant brain function in ISSX. However, since both hormone therapies, E2 and ACTH(1-24), elevate the density of cortical Arx+-interneurons, their early therapeutic role in other neurological disorders hallmarked by interneuronopathy should be explored.

opencc-zeroMar 2020View details →

opencc-by-4.0Nov 2023View details →

Stage-specific expression patterns and co-targeting relationships among miRNAs in the developing mouse cortex

Analysis of expression patterns and co-targeting relationships between miRNAs in the embryonic mouse cortex. The following supplementary data are uploaded:Supplementary_table_1.xlsx: Differentially expressed miRNAs between E14, E17 and P0 cortical samples as well as in NPCs isolated from the mouse cortex and differentiated into neurons in vitro.Supplementary_table_2.xlsx: Weighted co-expression gene network analysis of miRNAs in the embryonic mouse cortex.Supplementary_table_3.xlsx: Gene ontology terms of miRNA targets of the black and green modules from the WCGNA analysis.Supplementary_table_4.xlsx: Significant co-targeting relationships between miRNAs in the embryonic mouse cortex.

opencc-by-4.0Mar 2024View details →

Dataset and Software code _ Localized and global representation of prior value, sensory evidence, and choice in male mouse cerebral cortex

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openSep 2023View details →

EMI-Meshing: High-quality extracellular-membrane-intracellular meshes of the mouse visual cortex

This repository features a family of tetrahedral meshes of a dense reconstruction of the mouse visual cortex at extreme resolution. Both the extracellular space (ECS) and the main cellular structures are explicitly represented and labelled. The dataset is based on the <a href="https://www.microns-explorer.org/cortical-mm3">Cortical MM^3 dataset</a> centred at position <a href="https://ngl.microns-explorer.org/#!%7B%22dimensions%22:%7B%22x%22:%5B4e-9%2C%22m%22%5D%2C%22y%22:%5B4e-9%2C%22m%22%5D%2C%22z%22:%5B4e-8%2C%22m%22%5D%7D%2C%22position%22:%5B225182.5%2C107314.5%2C22000.5%5D%2C%22crossSectionScale%22:11.406101410482504%2C%22projectionOrientation%22:%5B0.1528419554233551%2C0.49656152725219727%2C0.39075320959091187%2C0.7598538994789124%5D%2C%22projectionScale%22:40961.499900183306%2C%22layers%22:%5B%7B%22type%22:%22image%22%2C%22source%22:%7B%22url%22:%22precomputed://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/em%22%2C%22subsources%22:%7B%22default%22:true%7D%2C%22enableDefaultSubsources%22:false%7D%2C%22tab%22:%22source%22%2C%22annotationColor%22:%22#7d7d7d%22%2C%22shaderControls%22:%7B%22normalized%22:%7B%22range%22:%5B86%2C172%5D%7D%7D%2C%22name%22:%22img%22%7D%2C%7B%22type%22:%22segmentation%22%2C%22source%22:%7B%22url%22:%22precomputed://gs://iarpa_microns/minnie/minnie65/seg%22%2C%22subsources%22:%7B%22default%22:true%2C%22mesh%22:true%7D%2C%22enableDefaultSubsources%22:false%7D%2C%22tab%22:%22segments%22%2C%22annotationColor%22:%22#949494%22%2C%22selectedAlpha%22:0.3%2C%22segments%22:%5B%22864691134947427836%22%2C%22864691135337771494%22%2C%22864691135393949941%22%2C%22864691135462270365%22%2C%22864691135474669888%22%2C%22864691135617729935%22%2C%22864691135718476593%22%2C%22864691136024102713%22%2C%22864691136390364287%22%2C%22864691136436690846%22%5D%2C%22segmentQuery%22:%22864691136194301772%2C%20864691136814938734%22%2C%22colorSeed%22:3728349837%2C%22name%22:%22seg%22%7D%5D%2C%22showAxisLines%22:false%2C%22showSlices%22:false%2C%22selectedLayer%22:%7B%22visible%22:true%2C%22layer%22:%22seg%22%7D%2C%22layout%22:%7B%22type%22:%224panel%22%2C%22orthographicProjection%22:true%7D%2C%22selection%22:%7B%22layers%22:%7B%22seg%22:%7B%22annotationId%22:%22data-bounds%22%2C%22annotationSource%22:0%2C%22annotationSubsource%22:%22bounds%22%7D%7D%7D%7D">225182-107314-22000</a> with a resolution of 32 x 32 x 40 nm^3. It contains a total of 20 meshes. The meshed domains are cubes with side lengths 5000, 10000, 20000 and 40000 nm and include the largest 5, 10, 50, 100 and 200 cells in the respective tissue volume, respectively.  Data The dataset has the following content: <ul> <li> surface meshes: The surfaces of the extracted cells in `.ply` format, suitable for visualization with ParaView or usage in other meshing or simulation software </li> <li> volume meshes: The generated volumetric meshes in `.xdmf` format, containing labels for the extracellular space (label 1) and increasing integer values (2,..., N) for all cells. The file `facet.xdmf`contains facet marker, where the label *l* corresponds to the boundary between ECS and cell *l*. The outer boundaries are marked as `l + offset`, where `offset` is the next higher power of ten of the number of cells (`offset=int(10 ** np.ceil(np.log10(N_cells)))`). </li> </ul> Usage The meshes are intended for usage with FEniCS (see code below), but can equally be read and used with other Software. <pre><code>from fenics import * import numpy as np mesh = Mesh() infile = XDMFFile("mesh.xdmf") infile.read(mesh) gdim = mesh.geometric_dimension() labels = MeshFunction("size_t", mesh, gdim) infile.read(labels, "label") infile.close() # get all local labels np.unique(labels.array()) # array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype=uint64) infile = XDMFFile("facets.xdmf") infile.read(mesh) gdim = mesh.geometric_dimension() boundary_marker = MeshFunction("size_t", mesh, gdim - 1) infile.read(boundary_marker, "boundaries") infile.close() # get all local facet labels np.unique(boundary_marker.array()) # array([ 0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 101, 102, # 103, 104, 105, 106, 107, 108, 109, 110, 111], dtype=uint64)</code></pre>