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10 results for “mouse brain imaging”
ISS Mouse brain embryo - MIPPED images , all rounds all channels
<p>Repository containing the stitched, mipped and aligned images of all the cycles and channels used in the Mouse embryo ISS characterization from La Manno et al 2020 The repository contains:</p> <ul> <li>Stitched aligned and mipped images of all round and cycles for different samples (2A,2D, 6B,10B)</li> <li>A codebook with the code of every expected gene detecoded is included</li> <li>A preliminary decoding of the 4 samples included in the folder "decoded_spots"</li> <li>Information about channel order in a .txt</li> </ul>
Data set for "Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas"
<p>Data set for: Liu Y, Foustoukos G, Crochet S and Petersen CCH (2022) Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas. Front Neuroanat 15: 791015. https://doi.org/10.3389/fnana.2021.791015</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2022_Liu_FrontNeuroanat.pdf</strong>" is the Open Access pdf of the online publication in Frontiers in Neuroanatomy.</p> <p>2. The file named "<strong>Liu_data_code.zip</strong>" (~1 GB) is a zipped version of a folder ‘<em>Liu_data_code</em>’, which contains the data analyzed in the study along with the Python codes used to generate the published figures. The original high resolution image stacks obtained through whole-brain two-photon serial tomography are unfortunately too large for Zenodo, and only highly-downsampled data are included in this upload, which were used for registration with the Allen CCFv3. Instructions on how to view and analyse the anatomical data are provided in the 'README.docx' file, which you will find upon unzipping the folder.</p> <p> </p>
Small-angle X-ray scattering datasets for imaging crossing fibers in mouse, pig, monkey, and human brain
<p>Small-angle X-ray scattering datasets for resolving crossing fibers (myelinated neuronal axon bundles), as described in</p> <p>"<strong><em>Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering</em></strong>"</p> <p>deposited in bioRxiv:</p> <p>https://doi.org/10.1101/2022.09.30.510198</p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 4. Mouse trajectory – images taken from http://iographica.com 4 hours in Photoshop (left) vs. 4 hours in Eclipse (right)
<p>Studies from different fields (Arroyo & Wei, 2006), (Mäkiaho & Poranen, 2012), (Lockton, Harrison, Cain, Stanton, & Jennings, 2013), (Seelye, et al., 2015), (Hehman, Stolier, & Freeman, 2014) have shown that mouse trajectory and clicks are environment (application, device) and user specific, especially if mapped over a long working session (Figure 4). </p>
Raw data for "Deep mouse brain two-photon near-infrared fluorescence imaging using a superconducting nanowire single-photon detector array"
<p>Two-photon microscopy (2PM) has become an important tool in biology to study the structure and function of intact tissues in-vivo. However, adult mammalian tissues such as the mouse brain are highly scattering, thereby putting fundamental limits on the achievable imaging depth, which typically resides around 600-800um. In principle, shifting both the excitation as well as (fluorescence) emission light to the shortwave near-infrared (SWIR, 1000-1700 nm) region promises substantially deeper imaging in 2PM, yet has proven challenging in the past due to the limited availability of detectors and probes in this wavelength region. To overcome these limitations and fully capitalize on the SWIR region, in this work we introduce a novel array of superconducting nanowire single-photon detectors (SNSPDs) and associated custom detection electronics for the use in near-infrared 2PM. The SNSPD array exhibits high efficiency and dynamic range, as well as low dark-count rates over a wide wavelength range. Additionally, the electronics and software permit seamless integration into typical 2PM systems. Together with a fluorescent dye emitting at 1105 nm, we report imaging depth of > 1.1mm in the in-vivo mouse brain, limited only by available labeling density and laser power. Our work further establishes SWIR 2PM approaches and SNSPDs as promising technologies for deep tissue biological imaging. </p>
Ultrafast two-photon fluorescence imaging of cerebral blood circulation in the mouse brain in vivo
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Data from: Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
<p>We have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.</p>
3D ground truth annotations of cleared whole mouse brain nuclei imaged with a mesoSPIM system
<p>Please see Achard et al. 2024 for Data Card and other information.</p>
A Multi-Scale Neuron Morphometry Dataset from Peta-voxel Mouse Whole-Brain Images
<p><span>Neuron morphology and sub-neuronal patterns offer vital insights into cell typing and the structural organization of brain networks. The community-collaborative BRAIN Initiative Cell Census Network (BICCN) project has yielded a vast amount of whole-brain imaging data. However, reconstructing multi-scale neuron morphometry at a whole-brain scale requires not only the integration of diverse hardware devices, tools, and algorithms but also a dedicated production workflow. To address these challenges, we developed a cloud-based, collaborative platform capable of handling peta-scale imaging data. Using this platform, we generated the largest multi-scale morphometry dataset from hundreds of sparsely labeled mouse brains. The morphometry dataset comprises 182,497 annotated cell bodies, 15,441 locally traced morphologies, and 1,876 fully reconstructed morphologies. We also identified sub-neuronal arborizations for both axons and dendrites, along with the primary axonal tracts connecting them. In addition, we identified 2.63 million putative boutons. All morphometric data were registered to the Allen Common Coordinate Framework (CCF) atlas. The morphometry dataset has proven to be an invaluable resource for whole-brain cross-scale morphological studies in mouse.</span></p>
Data from: Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
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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.