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http://www.ini.uzh.ch/~acardona/trakem2.html

An ImageJ plugin for morphological data mining, three-dimensional modeling and image stitching, registration, editing and annotation. Two independent modalities exist: either XML-based projects, working directly with the file system, or database-based projects, working on top of a local or remote PostgreSQL database. What can you do with it? * Semantic segmentation editor: order segmentations in tree hierarchies, whose template is exportable for reuse in other, comparable projects. * Model, visualize and export 3D. * Work from your laptop on your huge, remote image storage. * Work with an endless number of images, limited only by the hard drive capacity. Dozens of formats supported thanks to LOCI Bioformats and ImageJ. * Import stacks and even entire grids (montages) of images, automatically stitch them together and homogenize their histograms for best montaging quality. * Add layers conveniently. A layer represents, for example, one 50 nm section (for TEM) or a confocal section. Each layer has its own Z coordinate and thickness, and contains images, labels, areas, nodes of 3d skeletons, profiles... * Insert layer sets into layers: so your electron microscopy serial sections can live inside your optical microscopy sections. * Run any ImageJ plugin on any image. * Measure everything: areas, volumes, pixel intensities, etc. using both built-in data structures and segmentation types, and standard ImageJ ROIs. And with double dissectors! * Visualize RGB color channels changing the opacity of each on the fly, non-destructively. * Annotate images non-destructively with floating text labels, which you can rotate/scale on the fly and display in any color. * Montage/register/stitch/blend images manually with transparencies, semiautomatically, or fully automatically within and across sections, with translation, rigid, similarity and affine models with automatically extracted SIFT features. * Correct the lens distortion present in the images, like those generated in transmission electron microscopy. * Add alpha masks to images using ROIs, for example to split images in two or more parts, or to remove the borders of an image or collection of images. * Model neuronal arbors with 3D skeletons (with areas or radiuses), and synapses with connectors. * Undo all steps. And much more...

Proper citation: TrakEM2 (RRID:SCR_008954) Copy   

•   Source: SciCrunch Registry

https://www.bci2000.org/

BCI2000 is a general-purpose system for brain-computer interface (BCI) and adaptive neurotechnology research. It can also be used for data acquisition, stimulus presentation, and brain monitoring applications. The mission of the BCI2000 project is to facilitate research and applications in the areas described. Their vision is that BCI2000 will become a widely used software tool for diverse areas of real-time biosignal processing. In order to achieve this vision, BCI2000 system is available for free for non-profit research and educational purposes. BCI2000 supports a variety of data acquisition systems, brain signals, and study/feedback paradigms. During operation, BCI2000 stores data in a common format (BCI2000 native or GDF), along with all relevant event markers and information about system configuration. BCI2000 also includes several tools for data import/conversion (e.g., a routine to load BCI2000 data files directly into Matlab) and export facilities into ASCII. BCI2000 also facilitates interactions with other software. For example, Matlab scripts can be executed in real-time from within BCI2000, or BCI2000 filters can be compiled to execute as stand-alone programs. Furthermore, a simple network-based interface allows for interactions with external programs written in any programming language. For example, a robotic arm application that is external to BCI2000 may be controlled in real time based on brain signals processed by BCI2000, or BCI2000 may use and store along with brain signals behavioral-based inputs such as eye-tracker coordinates. Because it is based on a framework whose services can support any BCI implementation, the use of BCI2000 provides maximum benefit to comprehensive research programs that operate multiple BCI2000 installations to collect data for a variety of studies. The most important benefits of the system in such situations are: - A Proven Solution - Facilitates Operation of Research Programs - Facilitates Deployment in Multiple Sites - Cross-Platform and Cross-Compiler Compatibility - Open Resource Sponsors: BCI2000 development is sponsored by NIH/NIBIB R01 and NIH/NINDS U24 grants. Keywords: General, Purpose, Systems, Brain, Computer, Interface, Research, Application, Brain, Diverse, Educational, Laboratory, Software, Network, Signals, Behavioral, Eye, Tracker,

Proper citation: Brain Computer Interface 2000 Software Package (RRID:SCR_007346) Copy   

•   Source: SciCrunch Registry

http://ccr.coriell.org/Sections/Collections/NINDS/?SsId=10

Open resource of biological samples (DNA, cell lines, and other biospecimens) and corresponding phenotypic data to promote neurological research. Samples from more than 34,000 unique individuals with cerebrovascular disease, dystonia, epilepsy, Huntington's Disease, motor neuron disease, Parkinsonism, and Tourette Syndrome, as well as controls (population control and unaffected relatives) have been collected. The mission of the NINDS Repository is to provide 1) genetics support for scientists investigating pathogenesis in the central and peripheral nervous systems through submissions and distribution; 2) information support for patients, families, and advocates concerned with the living-side of neurological disease and stroke.

Proper citation: NINDS Repository (RRID:SCR_004520) Copy   

•   Source: SciCrunch Registry

http://surfer.nmr.mgh.harvard.edu/fswiki/Tracula

Software tool developed for automatically reconstructing a set of major white matter pathways in the brain from diffusion weighted images using probabilistic tractography. This method utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual intervention with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. The trac-all script is used to preprocess raw diffusion data (correcting for eddy current distortion and B0 field inhomogenities), register them to common spaces, model and reconstruct major white matter pathways (included in the atlas) without any manual intervention. trac-all may be used to execute all the above steps or parts of it depending on the dataset and user''''s preference for analyzing diffusion data. Alternatively, scripts exist to execute chunks of each processing pipeline, and individual commands may be run to execute a single processing step. To explore all the options in running trac-all please refer to the trac-all wiki. In order to use this script to reconstruct tracts in Diffusion images, all the subjects in the dataset must have Freesurfer Recons.

Proper citation: TRACULA (RRID:SCR_013152) Copy   

•   Source: SciCrunch Registry

http://becs.aalto.fi/en/research/bayes/drifter/

Model based Bayesian method for eliminating physiological noise from fMRI data. This algorithm uses image voxel analysis to isolate the cardiac and respiratory noise from the relevant data.

Proper citation: DRIFTER (RRID:SCR_014937) Copy   

•   Source: SciCrunch Registry

https://github.com/flatironinstitute/mountainsort

Neurophysiological spike sorting software.

Proper citation: MountainSort (RRID:SCR_017446) Copy   

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https://cloudreg.neurodata.io/

Software automated, terascale, cloud based image analysis pipeline for preprocessing and cross modal, nonlinear registration between volumetric datasets with artifacts. Automatic terabyte scale cross modal brain volume registration.

Proper citation: CloudReg (RRID:SCR_022795) Copy   

•   Source: SciCrunch Registry

http://rkscope.sourceforge.net/

Two photon microscope control software with multi area capabilities.

Proper citation: Scope (RRID:SCR_017454) Copy   

•   Source: SciCrunch Registry

https://github.com/lambdaloop/anipose

Software package for 3D pose estimation. Uses DeepLabCut for 2D tracking and uses triangulation methods to project pose estimations into three dimensions.Toolkit for robust markerless 3D pose estimation.

Proper citation: Anipose (RRID:SCR_023041) Copy   

•   Source: SciCrunch Registry

https://datascience.uth.edu/medcis

NIH funded center to provide system for sharing multimodal epilepsy data for Sudden Unexpected Death in Epilepsy. Modality Epilepsy Data Capture and Integration System (MEDCIS) is cross cohort query interface for SUDEP (Sudden Unexpected Death in EPilepsy) research.

Proper citation: University of Texas Health Science at Houston Center for SUDEP Research (RRID:SCR_024700) Copy   

•   Source: SciCrunch Registry

https://github.com/danbider/lightning-pose

Software video centric package for direct video manipulation. Semi supervised animal pose estimation algorithm, Bayesian post processing approach and deep learning package. Improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools.

Proper citation: Lightning Pose (RRID:SCR_024480) Copy   

•   Source: SciCrunch Registry

http://proteomics.ucsd.edu/Software/NeuroPedia/index.html

A neuropeptide encyclopedia of peptide sequences (including genomic and taxonomic information) and spectral libraries of identified MS/MS spectra of homolog neuropeptides from multiple species.

Proper citation: NeuroPedia (RRID:SCR_001551) Copy   

•   Source: SciCrunch Registry

http://ibvd.virtualbrain.org/

A database of brain neuroanatomic volumetric observations spanning various species, diagnoses, and structures for both individual and group results. A major thrust effort is to enable electronic access to the results that exist in the published literature. Currently, there is quite limited electronic or searchable methods for the data observations that are contained in publications. This effort will facilitate the dissemination of volumetric observations by making a more complete corpus of volumetric observations findable to the neuroscience researcher. This also enhances the ability to perform comparative and integrative studies, as well as metaanalysis. Extensions that permit pre-published, non-published and other representation are planned, again to facilitate comparative analyses. Design strategy: The principle organizing data structure is the "publication". Publications report on "groups" of subjects. These groups have "demographic" information as well as "volume" information for the group as a whole. Groups are comprised of "individuals", which also have demographic and volume information for each of the individuals. The finest-grained data structure is the "individual volume record" which contains a volume observation, the units for the observation, and a pointer to the demographic record for individual upon which the observation is derived. A collection of individual volumes can be grouped into a "group volume" observation; the group can be demographically characterized by the distribution of individual demographic observations for the members of the group.

Proper citation: Internet Brain Volume Database (RRID:SCR_002060) Copy   

•   Source: SciCrunch Registry

http://kimlab.io/brain-map/atlas/

Website to visualize and share anatomical labels. Franklin and Paxinos (FP) based anatomical labels in Allen Common Coordinate Framework (CCF). Cell type specific transgenic mice and MRI atlas were used to adjust and further segment labels. New segmentations were created in dorsal striatum using cortico-striatal connectivity data. Anatomical labels were digitized based on Allen ontology, and web-interface was created for easy visualization. These labels provide resource to isolate and identify mouse brain anatomical structures. Open source data sharing will facilitate further refinement of anatomical labels and integration of data interpretation within single anatomical platform.

Proper citation: Enhanced and Unified Anatomical Labeling for Common Mouse Brain Atlas (RRID:SCR_019267) Copy   

•   Source: SciCrunch Registry

https://neurophysics.ucsd.edu/software.php

Matlab-based routines for the detection and clustering of putative single units from a multi-unit time series, along with quality metrics. This sofwtare was developed by the David Kleinfeld Laboratory at UC San Diego.

Proper citation: UltraMegaSort 2000 (RRID:SCR_015857) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/ibsr

Data set of manually-guided expert segmentation results along with magnetic resonance brain image data. Its purpose is to encourage the development and evaluation of segmentation methods by providing raw test and image data, human expert segmentation results, and methods for comparing segmentation results. Please see the MediaWiki for more information. This repository is meant to contain standard test image data sets which will permit a standardized mechanism for evaluation of the sensitivity of a given analysis method to signal to noise ratio, contrast to noise ratio, shape complexity, degree of partial volume effect, etc. This capability is felt to be essential to further development in the field since many published algorithms tend to only operate successfully under a narrow range of conditions which may not extend to those experienced under the typical clinical imaging setting. This repository is also meant to describe and discuss methods for the comparison of results.

Proper citation: Internet Brain Segmentation Repository (RRID:SCR_001994) Copy   

•   Source: SciCrunch Registry

http://krasnow1.gmu.edu/cn3/L-Neuron/database/

A database of virtually generated anatomically plausible neurons for several morphological classes, including cerebellar Purkinje cells, hippocampal pyramidal and granule cells, and spinal cord motoneurons. It presently contains 542 cells. In the trade neurons collection the database contains an amaral cell archive, neuron morpho reconstructions, and mouse alpha motoneurons. Their collection of generated neurons include motoneurons, Purkinje cells, and hippocampal pyramidal cells.

Proper citation: Virtual NeuroMorphology Electronic Database (RRID:SCR_007118) Copy   

•   Source: SciCrunch Registry

https://neurojson.io/

Web application for human-readable, searchable neuroimaging datasets using universally accessible JSON format and URL-based RESTful APIs. NeuroJSON.io is built upon highly scalable document-store NoSQL database technologies, specifically, open-source Apache CouchDB engine, that can handle millions of datasets without major performance penalties. Provides fine-grained data search capabilities to allow users to find, preview and re-combine complex data records from public datasets before download.

Proper citation: NeuroJSON.io (RRID:SCR_027793) Copy   

•   Source: SciCrunch Registry

https://github.com/ReproBrainChart

Open data resource for mapping brain development and its associations with mental health. Integrates data from 5 large studies of brain development in youth from three continents (N = 6,346). Bifactor models were used to create harmonized psychiatric phenotypes, capturing major dimensions of psychopathology. Neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner.

Proper citation: Reproducible Brain Charts (RRID:SCR_027837) Copy   

•   Source: SciCrunch Registry

https://doi.org/10.17605/OSF.IO/WDR78

Open source resource of manually curated and expert reviewed infant brain segmentations hosted on OpenNeuro.org. and OSF.io. Anatomical MRI data was segmented from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Images showed dramatic differences in myelination and intensities across 1–9 months, emphasizing the need for densely sampled gold-standard segmentations across early life. This dataset provides a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this dataset provides a vitally needed foundation for early-life large-scale studies such as HBCD.

Proper citation: Baby Open Brains (RRID:SCR_027836) Copy   

•   Source: SciCrunch Registry