Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,448
datasets available to search
ShareScore release 0.9.0
Dataset results
5,448 results for “Neuron”
Sun compass neurons are tuned to migratory orientation in monarch butterflies
Every autumn, monarch butterflies migrate from North America to their overwintering sites in Central Mexico. To maintain their southward direction, these butterflies rely on celestial cues as orientation references. The position of the sun combined with additional skylight cues are integrated in the central complex, a region in the butterfly's brain that acts as an internal compass. However, the central complex does not solely guide the butterflies on their migration but helps monarchs in their non-migratory form manoeuvre on foraging trips through their habitat. By comparing the activity of input neurons of the central complex between migratory and non-migratory butterflies, we investigated how a different lifestyle affects the coding of orientation information in the brain. During recording, we presented the animals with different simulated celestial cues and found that the encoding of the sun was narrower in migratory compared to non-migratory butterflies. This feature might reflect the need of the migratory monarchs to rely on a precise sun compass to keep their direction during their journey. Taken together, our study sheds light on the neural coding of celestial cues and provides insights into how a compass is adapted in migratory animals to successfully steer them to their destination.
Example Datasets for Iliski - Neuronal calcium, RBC velocities and fUS responses to odorant stimuli in the mouse olfactory bulb
<p>Dataset containing 2 HDF5 files, one per mouse. It is intended to be used to test Iliski, a Transfer Function computation software. Iliski is available on GitLab (<a href="https://gitlab.com/AliK_A/iliski">https://gitlab.com/AliK_A/iliski</a>) along with the User Manual. Refer to the User Manual and to the ReadMe file for more details on Iliski. Data were already published on Zenodo (<a href="https://doi.org/10.5281/zenodo.3773863">https://doi.org/10.5281/zenodo.3773863</a>), but along an old version of the software. This upload is made for clarity purposes.</p> <p>Each file contains acquisitions of responses to odorant stimuli in the olfactory bulb made with :</p> <ul> <li>two-photon linescan microscopy (for Ca2+ and RBC velocity);</li> <li>functional ultrafast ultrasound, acquired from a coronal plane.</li> </ul> <p>HDF5 files tree is as follows :</p> <ul> <li>Data type <ul> <li>Raw : straight out of our extraction software, no specific treatment applied;</li> <li>Aligned : every acquisition has been aligned so that the odor delivery matches the 10 s mark. Acquisitions have also been interpolated to be meaned;</li> <li>Delta : aligned acquisitions are subtracted with the baseline value (between 5 and 10 s);</li> <li>DetaOverBSL : aligned acquisitions are subtracted and then divided with the baseline value.</li> </ul> </li> <li>Data source <ul> <li>Ca : calcium data from GCamP6f expressed in the mitral cells dendritic tufts;</li> <li>RBC : RBC velocities in a capillary near the calcium recording site, simultaneously acquired;</li> <li>FUS : fUS data, coronal plane. Only in FUS folder is two different folders then : High and Lowspeed, corresponding to different filter for fUS treatment, > 80Hz and 10-30Hz respectively.</li> </ul> </li> <li>Stimulation type : Odorant_Quantity_Duration <ul> <li>Odorant type, either Iso Amyl Acetate (AA) or Ethyl tiglate (ET);</li> <li>Odor quantity : measured and calibrated in volt with a photo-ionizator;</li> <li>Odor duration : from 5 s down to 120 ms, a single sniff for a mouse.</li> </ul> </li> </ul> <p>Ca2+ : Calcium</p> <p>fUS : functional ultrafast ultrasound</p> <p>RBC : Red Blood Cell</p>
Raw dataset and additional data for article "Nonmotor symptoms associated with progressive loss of dopaminergic neurons in a mouse model of Parkinson's disease"
<p>Dataset from the project investigating the presence of nonmotor symptoms of Parkinson's disease in a mouse model of progressive loss of dopaminergic neurons (namely,TIF-IADATCreERT2 strain). Mice were tested for executive and cognitive functions (males: Operant Sensation Seeking test, OSS; females: Probabilistic Reversal Learning Task in Intellicages), olfactory acuity (males: buried food test), saccharin preference (males and females), and motor performance (males and females: test using CatWalk apparatus).</p><p>The dataset includes files used to perform statistical analyses but their names may vary from the ones used in the scripts. For the purpose of recreating our analyses, please refer to the GitHub page, where both scripts and input data file names (in 'Raw data files' section) are compliant: https://github.com/annaradli/tif-pd-behavior.</p><p><strong>Description of files:</strong></p><p><i>Raw data files:</i></p><ul><li>animals_info.csv - animals data: genotype, sex, age, Intellicage tag identifier</li><li>catwalk_run_statistics_all_females.csv - data recorded in CatWalk apparatus for females</li><li>catwalk_run_statistics_all_males.csv - data recorded in CatWalk apparatus for males</li><li>females_weight_raw_data_revised.csv - females' body weight (revised for containing Polish words)</li><li>intellicage_raw_data.csv - data recorded in IntelliCage exported to .csv format</li><li>intellicage_raw_data_R.RData - data recorded in IntelliCage in .RData format</li><li>males_weight_raw_data.csv - males' body weight</li><li>olfactory_time_digging_raw_data.csv - time to start digging at the right place in the buried food test</li><li>olfactory_time_retrieve_raw_data.csv- time to retrieve cracker in the buried food test</li><li>oss_raw_data.csv - data recorded in the OSS test</li><li>saccharin_preference_males_raw_data.csv - saccharin preference test results for males</li><li>snvta_cells_count.csv - number of TH+ cells in SN and VTA in male mice (3+3) 14 weeks after tamoxifen treatment</li></ul><p><i>Additional data files:</i></p><ul><li>all_anova.xlsx - summary of two-way ANOVAs of all behavioral tests and weight measurements for males and females</li><li>catwalk_complete.xlsx - CatWalk complete dataset with datapoints</li><li>catwalk_correlation_between_paws.xlsx - correlation coefficients of CatWalk parameters between the left and right paws</li><li>catwalk_reduced.xlsx - CatWalk parameters used in linear regression model reduction of data</li><li>intelli.xlsx - IntelliCage data summarized in bins</li><li>oss.xlsx - operant sensation-seeking data</li></ul><p>v2 contains the corrected 'animals_info.csv' file without an unnecessary column.</p><p>v3 has a revised version of file containing females' weight measurements and also added a file with midbrain cell counts</p><p>v4 has a whole section of 'Additional data files' added</p>
Data for Altered Glia-Neuron Communication in Alzheimer's Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq
<p><strong>data.tar.gz contains all files from the data directory associated with the 230313_TS_CCCinHumanAD GitHub project and includes the following:</strong></p><ul><li><strong>CellRangerCounts/</strong><ul><li><strong>GSE157827/</strong><ul><li><strong>post_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>matrix.mtx</strong></li></ul></li></ul></li><li><strong>pre_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>filtered_feature_ bc_matrix.h5</strong></li><li><strong>Raw_feature_bc_matrix.h5</strong></li></ul></li></ul></li></ul></li><li><strong>GSE174367/ : </strong>contains 19 directories for 19 samples, which contain 3 files each from Cell Ranger alignment of fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN19128610_S1_CTRL/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>Matrix.mtx</strong></li></ul></li></ul></li></ul></li><li><strong>ccc/</strong><ul><li><strong>nichenet_grn/</strong><ul><li><strong>gr_network_human_21122021.rds : </strong>accessed in October 2023, gene regulation network – gene regulatory information from MultiNicheNet</li><li><strong>ligand_tf_matrix_nsga2r_final.rds: </strong>accessed in October 2023, ligand tf matrix for signaling path determination from MultiNicheNet</li><li><strong>signaling_network_human_21122021.rds : </strong>accessed in October 2023, signaling network – protein-protein interaction information from MultiNicheNet</li><li><strong>weighted_networks_nsga2r_final.rds : </strong>accessed in October 2023, networks weighted by literature evidence from MultiNicheNet</li></ul></li><li><strong>nichenet_prior/</strong><ul><li><strong>ligand_target_matrix.rds : </strong>accessed in April 2023, ligand to target matrix from NicheNet</li><li><strong>lr_network.rds : </strong>accessed in April 2023, ligand-receptor matrix from NicheNet</li></ul></li><li><strong>nichenet_v2_prior/</strong><ul><li><strong>ligand_target_matrix_nsga2r_final.rds : </strong>accessed in June 2023, ligand to target matrix from MultiNicheNet used to predict target genes.</li><li><strong>lr_network_human_21122021.rds : </strong>accessed in June 2023, ligand-receptor matrix from MultiNicheNet used to predict ligand-receptor pairs.</li></ul></li><li><strong>geo_multinichenet_output.rds </strong>: MultiNicheNet output for Morabito et al., 2021 data</li><li><strong>geo_signaling_igraph_objects.rds </strong>: list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Morabito et al., 2021 dataset. </li><li><strong>gse_multinichenet_output.rds</strong> : MultiNicheNet output for Lau et al., 2020 data</li><li><strong>gse_signaling_igraph_objects.rds</strong> : list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Lau et al., 2020 dataset </li></ul></li><li><strong>seurat_preprocessing/</strong><ul><li><strong>geo_filtered_seurat.rds : </strong>merged and filtered seurat object of Morabito et al., 2021 data</li><li><strong>geo_integrated_seurat.rds :</strong> seurat object integrated using harmony of Morabito et al., 2021 data</li><li><strong>geo_clustered_seurat.rds : </strong>clustered seurat object of Morabito et al., 2021 data</li><li><strong>geo_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Morabito et al., 2021 data</li><li><strong>gse_filtered_seurat.rds : </strong>merged and filtered seurat object of Lau et al., 2020 data</li><li><strong>gse_integrated_seurat.rds : </strong>seurat object integrated using harmony of Lau et al., 2020 data</li><li><strong>gse_clustered_seurat.rds :</strong> clustered seurat object of Lau et al., 2020 data</li><li><strong>gse_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Lau et al., 2020 data </li></ul></li></ul>
Dataset for monkeys A and B from AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity
<p>ECoG data from two monkeys, affi (A) and beignet (B) used in AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity. Jingyuan Li, Leo Scholl, Trung Le, Pavithra Rajeswaran, Amy L Orsborn, and Eli Shlizerman. NeurIPS. 2023. https://openreview.net/forum?id=7ntI4kcoqG</p><p>See also https://github.com/shlizee/AMAG</p>
Characterization of a loss-of-function NAPB mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected iPSC lines. Author names and affiliations
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from induced pluripoent stem cells (iPSC). There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz) and reverse read (R2_001.fastq.gz).</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01: Proband sample</p> <p>NDD_04: Proband sample</p> <p>NDD_05: Proband sample</p>
Acetylcholine receptor based chemogenetics engineered for neuronal inhibition and seizure control assessed in mice
<p>Analysis scripts and underlying data used to produce the figures in our study entitled "Acetylcholine receptor based chemogenetics engineered for neuronal inhibition and seizure control assessed in mice", in Nature Communications. The listed project leaders can be contacted with any questions.</p>
Chronic exposure to glucocorticoids amplifies inhibitory neuron cell fate during human neurodevelopment in organoids
<p><strong>Abstract: </strong>Disruptions in the tightly regulated process of human brain development have been linked to increased risk for brain and mental illnesses. While the genetic contribution to these diseases is well established, important environmental factors have been less studied at molecular and cellular levels. In this study, we used single-cell and cell-type-specific techniques to investigate the effect of glucocorticoid (GC) exposure, a mediator of antenatal environmental risk, on gene regulation and lineage specification in unguided human neural organoids. We characterized the transcriptional response to chronic GC exposure during neural differentiation and studied the underlying gene regulatory networks by integrating single-cell transcriptomics- with chromatin accessibility data. We found lasting cell type-specific changes that included autism risk genes and several transcription factors associated with neurodevelopment. Chronic GCs influenced lineage specification primarily by priming the inhibitory neuron lineage through key transcription factors like PBX3. We provide evidence for convergence of genetic and environmental risk factors through a common mechanism of altering lineage specification.</p>
Data from: Whole-brain spatial organization of hippocampal single-neuron projectomes
<p>Mapping hippocampal single-neuron projections is essential for understanding brain-wide circuit organization and diverse functions of the hippocampus, a brain structure underlying episodic memory and cognition. Here, we reconstructed 10,100 single-neuron projectomes of the mouse hippocampus, identified rostral and caudal axon pathways that preferentially innervated cortical vs. subcortical areas, and classified 43 projectome subtypes with distinct axon targeting patterns. Notably, the soma locations along hippocampal longitudinal and transverse axes determined the number of their target areas and the spatial distribution and complexity of their axon arbors within the targets. We defined selective hippocampal subdomains based on spatial transcriptomic profiles and found that many projectome subtypes were enriched in specific subdomains. Next, we defined the wiring diagram for hippocampal neurons exclusively projecting to hippocampal formation (HPF) and those projecting to both intra- and extra-HPF targets with coordinated projection strengths. Furthermore, bi-hemispheric projecting hippocampal neurons generally projected to one pair of homologous targets with ipsilateral preference. These organization principles of single-neuron projectomes provide a structural basis for understanding diverse but coordinated functions of hippocampal neurons.</p>
Data Sets "Proteinoids--Polyaniline Interaction with Stimulated Neurons on Living and Plastic Surfaces"
<p>Data sets for the paper Proteinoids--Polyaniline Interaction with Stimulated Neurons on Living and Plastic Surfaces.</p>
Dataset of "Hysteresis, impedance and transients effects in halide perovskite solar cells and memory devices analysis by neuron-style models"
<p>This dataset supports the article published<em> </em>in the Advanced Energy Materials:</p> <p>"Hysteresis, impedance and transients effects in halide perovskite solar cells and memory devices analysis by neuron-style models"</p> <p> </p> <p>Raw data for the article "Hysteresis, impedance and transients effects in halide perovskite solar cells and memory devices analysis by neuron-style models". For further details see the readme.txt file.</p>
The impulse response of optic flow sensitive descending neurons to roll m-sequences
<p>When animals move through the world, their own movements generate widefield optic flow across their eyes. In insects, such widefield motion is encoded by optic lobe neurons. These lobula plate tangential cells (LPTCs) synapse with optic flow sensitive descending neurons, which in turn project to areas that control neck, wing and leg movements. As the descending neurons play a role in sensori-motor transformation, it is important to understand their spatio-temporal response properties. Recent work shows that a relatively fast and efficient way to quantify such response properties is to use m-sequences or other white noise techniques. We therefore here used m-sequences to quantify the impulse responses of optic flow sensitive descending neurons in male <i>Eristalis tenax </i>hoverflies. We focused on roll impulse responses as hoverflies perform exquisite head roll stabilizing reflexes, and the descending neurons respond particularly well to roll. We found that the roll impulse responses were fast, peaking after 16.5-18.0 ms. This is similar to the impulse response time-to-peak (18.3 ms) to widefield horizontal motion recorded in hoverfly LPTCs. We found that the roll impulse response amplitude scaled with the size of the stimulus impulse, and that its shape could be affected by the addition of constant velocity roll or lift. For example, the roll impulse response became faster and stronger with the addition of excitatory stimuli, and vice versa. We also found that the roll impulse response had a long return to baseline, which was significantly and substantially reduced by the addition of either roll or lift.</p>
Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations
<p>Neuronal oscillations, their inter-areal synchronization, and scale-free dynamics constitute fundamental mechanisms for cognition by regulating communication in neuronal networks. These oscillatory dynamics have large inter-individual variability that is partly heritable. We hypothesized that this variability could be partially explained by genetic polymorphism in neuromodulatory genes. We recorded resting-state magnetoencephalography (MEG) from 82 healthy participants and investigated whether oscillation dynamics were influenced by genetic polymorphisms in Catechol-O-methyltransferase (COMT) Val<sup>158</sup>Met and brain-derived neurotrophic factor (BDNF) Val<sup>66</sup>Met. Both COMT and BDNF polymorphisms influenced local oscillation amplitudes and their long-range temporal correlations (LRTCs), while only BDNF polymorphism affected the strength of large-scale synchronization. Our findings demonstrate that COMT and BDNF genetic polymorphisms contribute to inter-individual variability in neuronal oscillation dynamics. Comparison of these results to computational modeling of near-critical synchronization dynamics further suggested that COMT and BDNF polymorphisms influenced local oscillations by modulating the excitation-inhibition balance according to the brain criticality framework.</p>
AP-4-mediated axonal transport controls endocannabinoid production in neurons - Imaging data 3
<p>Imaging data associated with Fig. 6c,e,f, 7e-h, S5a,b, S6b-d and S7 from the article "AP-4-mediated axonal transport controls endocannabinoid production in neurons", published in Nature Communications by Davies et al.</p> <p>DAGLB_Axon_iPSC_derived_neurons: High-throughput confocal imaging was used to assay the density of DAGLB puncta in axons of iPSC neurons from a patient with AP-4 deficiency syndrome (patient 1) and their matched control, labelled with anti-DAGLB, the axonal marker antibody cocktail SMI312 and DAPI.</p> <p>DAGLB_Soma_iPSC_derived_neurons: High-throughput confocal imaging was used to assay the distribution of DAGLB in iPSC-derived neurons from two patients with AP-4 deficiency syndrome (LoF/LoF) and their matched unaffected controls (WT/LoF). Neurons in 96-well plates were labelled with antibodies against DAGLB, GOLGA1 (a TGN marker) and TUJ1, and DAPI. The ratio between the area of high intensity (HI; overlaps with TGN) and low intensity (LI) DAGLB labelling was quantified from three differentiations per cell line.</p> <p>iPSC_derived_neurons_neurite_outgrowth: Neurite outgrowth was assayed in iPSC-derived cortical neurons from two patients with AP4B1-associated AP-4 deficiency syndrome (SPG47) and their unaffected same sex heterozygous parents (control), using automated live cell imaging. Neurons were cultured in the presence of DMSO (vehicle control) or the MGLL inhibitor ABX-1431 at 10, 50, 100 or 500 nM (the highest two doses were administered only to the patient neurons). Neurons were monitored from 4 h post-plating, with images captured every 3 h until 25 h post-plating.</p> <p>See article for methods and further detail.</p>
AP-4-mediated axonal transport controls endocannabinoid production in neurons - Imaging data 2
<p>Imaging data from the article "AP-4-mediated axonal transport controls endocannabinoid production in neurons", published in Nature Communications by Davies et al., from Fig. 5 and S4.</p> <p>Super resolution structured illumination microscopy (SR-SIM) was used to image HeLa cells expressing endogenously-tagged SERINC1-Clover or SERINC3-Clover, treated with siRNA to knock down AP-4 or non-targeting control siRNA, and labelled with anti-GFP and either anti-ATG9A or anti-DAGLB.</p> <p>SR-SIM was performed on a Zeiss Elyra PS.1 microscope equipped with a 63x/1.46 oil objective (alpha Plan-Apochromat 63x/1.46 Oil Korr M27) and a PCO pco.edge 4.2 sCMOS Camera, and controlled with Zeiss ZEN 2 software with the SR-SIM module. Imaging was performed sequentially, with SERINC1 or SERINC3 (labelled with Alexa Fluor Plus 488) imaged first and DAGLB or ATG9A (labelled with Alexa Fluor 568) imaged second, using 3 rotations of the grid pattern and 5 phases for each rotation. Experiments were performed in biological duplicate, from separate dishes of cells, and with immunofluorescence and microscopy performed independently for each replicate. 20 cells were imaged per condition per replicate, and cell selection and manual focus were performed on the first acquired channel only, without viewing the second channel. For channel alignment, slides with multi-coloured fluorescent beads were imaged before and after each experiment using the same acquisition settings. The data were processed using the SIM module in Zen 2 software in manual mode, using default settings and a theoretical PSF model, except for the following settings: Noise filter -1; SR frequency weighting 2; Max.Isotrop on; Baseline shifted on; Raw scale on. Channel alignment fit parameters were calculated in Zen 2 software using a multi-coloured bead image as input, in affine mode. The resulting parameters were tested on further bead images and then applied to the experimental images. The channel-aligned images were used for colocalisation analysis (see methods of article for details).</p> <p>This dataset includes the raw unprocessed data and the processed and channel aligned input data used for the colocalisation analysis.</p> <p>See article for methods and further detail.</p>
AP-4-mediated axonal transport controls endocannabinoid production in neurons - Imaging data 1
<p>Imaging data from the article "AP-4-mediated axonal transport controls endocannabinoid production in neurons", published in Nature Communications by Davies et al., from Fig. 2, 3, 4, 6 and S2.</p> <p>Widefield images were captured on a Leica DMi8 inverted microscope equipped with an iTK LMT200 motorised stage, a 63x/1.47 oil objective (HC PL APO 63x/1.47 OIL) and a Leica DFC9000 GTC Camera, and controlled with LAS X (Leica Application Software X).</p> <p>HeLa_HADAGL_RUSC2_Fig_4: Widefield imaging of wild-type HeLa and HeLa cells stably expressing GFP-RUSC2, transiently expressing HA-DAGLB or HA-DAGLA, and labelled with anti-HA (Alexa Fluor 680), anti-ATG9A (Alexa Fluor 568) and DAPI.</p> <p>HeLa_KD_DAGLB_TGN_Fig_S2a,b: Widefield imaging of immunofluorescence double labelling of DAGLB (Alexa Fluor 568) and TGN46 (Alexa Fluor 680) in HeLa cells transfected with a non-targeting siRNA (control) or with siRNA to knock down AP-4. DAPI labelling of the nucleus is also shown. Note, the antibody used to label AP4E1 (Alexa Fluor 488) in replicate 1 had high non-specific background and so was not used in the analysis.</p> <p>HeLa_KO_DAGLB_TGN_Fig_2a,b: Widefield imaging of immunofluorescence double labelling of DAGLB (Alexa Fluor 568) and TGN46 (Alexa Fluor 680) in wild-type, AP4B1 knockout, and AP4B1 knockout HeLa cells stably expressing AP4B1 (functional rescue). DAPI labelling of the nucleus is also shown.</p> <p>HeLa_RUSC2_DAGLB_Fig_3: Widefield imaging of DAGLB (Alexa Fluor 568) labelling in wild-type and AP-4 knockout (AP4B1 KO or AP4E1 KO) HeLa cells, stably expressing GFP-tagged RUSC2. AP4B1 knockout HeLa expressing RUSC2-GFP were also transiently transfected with AP4B1 (rescue) or mock transfected without DNA as a negative control. DAPI labelling of the nucleus is also shown.</p> <p>iPSC_Neurons_DAGLB_TGN_Fig_6a,b: Widefield imaging of immunofluorescence triple labelling of DAGLB (Alexa Fluor 568), TGN46 (Alexa Fluor 680) and TUJ1 (Alexa Fluor 488; a marker to distinguish neurons from co-cultured astrocytes) in iPSC neurons from a patient with AP-4 deficiency syndrome (patient 1) and their matched control.</p> <p>SHSY5Y_DAGLB_TGN_Fig_2c,d_Fig_S2c,d: Widefield imaging of immunofluorescence double labelling of DAGLB (Alexa Fluor 568) and TGN46 (Alexa Fluor 680) in control (parental Cas9-expressing), AP4B1-depleted (BKO) and AP4E1-depleted (EKO) undifferentiated and neuronally-differentiated SH-SY5Y cells. DAPI labelling of the nucleus is also shown.</p> <p>See article for methods and further detail.</p>
Working memory capacity of crows and monkeys arises from similar neuronal computations
<p>Complex cognition relies on flexible working memory, which is severely limited in its capacity. The neuronal computations underlying these capacity limits have been extensively studied in humans and in monkeys, resulting in competing theoretical models. We probed the working memory capacity of crows (<em>Corvus corone</em>) in a change detection task, developed for monkeys (<em>Macaca mulatta</em>), while we performed extracellular recordings of the prefrontal-like area nidopallium caudolaterale. We found that neuronal encoding and maintenance of information were affected by item load, in a way that is virtually identical to results obtained from monkey prefrontal cortex. Contemporary neurophysiological models of working memory employ divisive normalization as an important mechanism that may result in the capacity limitation. As these models are usually conceptualized and tested in an exclusively mammalian context, it remains unclear if they fully capture a general concept of working memory or if they are restricted to the mammalian neocortex. Here we report that carrion crows and macaque monkeys share divisive normalization as a neuronal computation that is in line with mammalian models. This indicates that computational models of working memory developed in the mammalian cortex can also apply to non-cortical associative brain regions of birds.</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>
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>
raw data for Optogenetic Stimulation of Prelimbic Pyramidal Neurons Maintains Fear Memories and Modulates Amygdala Pyramidal Neuron Transcriptome
<p>Figure Legend </p> <p>Modulation of cellular excitability of Prelimbic (PrL) pyramidal neurons by optogenetic stimulation. (<strong>A</strong>) Representative traces in current-clamp configuration reporting evoked firing activity triggered by a series of depolarizing current steps (0 to 400 pA) applied to PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups. The cumulative plot shows the changes in firing activity. (<strong>B</strong>) Representative traces of PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups showing the firing activity triggered by linear depolarization from 0 to 800 pA. Graph (on the right) reports the effects of optogenetic stimulation on rheobase value. Namely, PrL pyramidal neurons of OPTO FEAR and No-EX groups recorded after optogenetic stimulation showed a clear reduction in the rheobase value in comparison to neurons of SHAM FEAR group (* at least <em>p</em>= 0.01). (<strong>C</strong>) Representative traces of Excitatory Post-Synaptic Currents (EPSC) of PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups. Graph plot (in the middle) and cumulative curve (on the right) depict the clear increase in firing frequency in PrL pyramidal neurons of OPTO FEAR and No-EX groups (* at least <em>p</em> = 0.01). (<strong>D</strong>) Graphs and cumulative curves report no significant differences in cellular excitability in PrL pyramidal neurons of SHAM NOT FEAR (black) and OPTO NOT FEAR (blue) groups. Data are reported as median with interquartile range.</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.