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http://neuroviisas.med.uni-rostock.de/neuroviisas.html

An open framework for integrative data analysis, visualization and population simulations for the exploration of network dynamics on multiple levels. This generic platform allows the integration of neuroontologies, mapping functions for brain atlas development, and connectivity data administration; all of which are required for the analysis of structurally and neurobiologically realistic simulations of networks. What makes neuroVIISAS unique is the ability to integrate neuroontologies, image stacks, mappings, visualizations, analyzes and simulations to use them for modelling and simulations. Based on the analysis of over 2020 tracing studies, atlas terminologies and registered histological stacks of images, neuroVIISAS permits the definition of neurobiologically realistic networks that are transferred to the simulation engine NEST. The analysis on a local and global level, the visualization of connectivity data and the results of simulations offer new possibilities to study structural and functional relationships of neural networks. neuroVIISAS provide answers to questions like: # How can we assemble data of tracing studies? (Metastudy) # Is it possible to integrate tracing and brainmapping data? (Data Integration) # How does the network of analyzed tracing studies looks like? (Visualization) # Which graph theoretical properties posses such a network? (Analysis) # Can we perform population simulations of a tracing study based network? (Simulation and higher level data integration) neuroVIISAS can be used to organize mapping and connectivity data of central nervous systems of any species. The rat brain project of neuroVIISAS contains 450237 ipsi- and 175654 contralateral connections. A list of evaluated tracing studies are available. PyNEST script generation does work using WINDOWS OS, however, the script must be transferred to a UNIX OS with installed NEST. The results file of the NEST simulation can be visualized and analyzed by neuroVIISAS on a WINDOWS OS.

Proper citation: neuroVIISAS (RRID:SCR_006010) Copy   

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http://www.rad.upenn.edu/sbia/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 2, 2023. Software package used to simulate brain images with local growth / atrophy within a prescribed spherical region. Specifically, given an input image and its segmented image, the location of the center of the spherical region, and the radius of that sphere, it simulates new images that have tissue growth or shrinkage within that pre-specified brain region according to given rates (atrophy for rates less than one and growth for rates greater than one). The algorithm uses an iterative procedure that tries to achieve the given level of volumetric change for brain tissues within the region, by seeking a smooth deformation field, whose Jacobian determinants match the prescribed volume change rate within the region. Note that in the current software, the simulation of growth or atrophy for brain tissue requires that the input spherical region has to cover some CSF or background regions.

Proper citation: Atrophy Simulation Package (RRID:SCR_006046) Copy   

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http://www.nitrc.org/projects/toads-cruise/

A collection of software plug-ins developed for the automatic segmentation of magnetic resonance brain images. The tools include multiple published algorithms developed at Johns Hopkins University. The SPECTRE algorithm performs brain extraction. The TOADS algorithm generates a topology-preserving tissue classification into cortical, subcortical, and cerebellar structures. The CRUISE algorithm produces inner, central, and outer cortical surfaces suitable for computing thickness and other geometric measures. Tools are also included for performing gyral labeling, lesion segmentation, thickness computation, surface visualization, and surface file conversion. All tools are released as plug-ins for the MIPAV software package and were developed using the Java Image Science Toolkit (both available at NITRC: http://nitrc.org). They are therefore cross-platform and compatible with a wide variety of file formats.

Proper citation: TOADS-CRUISE Brain Segmentation Tools (RRID:SCR_005977) Copy   

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http://songbirdtranscriptome.net/

Database containing cDNA clone information of the brains of songbirds. These clones are annotated with behavioral information, as well as links to information of homologous genes of other species. The database includes over 91,000 zebra finch brain cDNAs (2009) sequenced by Duke, ESTIMA, and Rockefeller research groups. The project is a collaborative effort of the Jarvis Laboratory of Duke University, Duke Bioinformatics, and The Genomics group of RIKEN, with Erich D. Jarvis as P.I. and Kazuhiro Wada as Co-P.I. Microarrays with the cDNAs in this database are available at Duke http://mgm.duke.edu/genome/dna_micro/core/spotted.htm and through the NIH Neurosciences Microarray Consortium http://arrayconsortium.tgen.org/np2/public/overview.jsp

Proper citation: Songbird Brain Transcriptome Database (RRID:SCR_006182) Copy   

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https://www.braintest.org/brain_test/BrainTest

A portal of online studies that encourage community participation to tackle the most challenging problems in neuropsychiatry, including attention-deficit / hyperactivity disorder, schizophrenia, and bipolar disorder. Our approach is to engage the community and try to recruit tens of thousands of people to spend an hour of their time on our site. You folks will provide data in both brain tests and questionnaires, as well as DNA, and in return, we will provide some information about your brain and behavior. You will also be entered to win amazon.com gift cards. While large collaborative efforts were made in genetics in order to discover the secrets of the human genome, there are still many mysteries about the behaviors that are seen in complex neuropsychiatric syndromes and the underlying biology that gives rise to these behaviors. We know that it will require studying tens of thousands of people to begin to answer these questions. Having you, the public, as a research partner is the only way to achieve that kind of investment. This site will try to reach that goal, by combining high-throughput behavioral assessment using questionnaires and game-like cognitive tests. You provide the data and then we will provide information and feedback about why you should help us achieve our goals and how it benefits everyone in the world. We believe that through this online study, we can better understand memory and attention behaviors in the general population and their genetic basis, which will in turn allow us to better characterize how these behaviors go awry in people who suffer from mental illness. In the end, we hope this will provide better, more personalized treatment options, and ultimately prevention of these widespread and extremely debilitating brain diseases. We will use the data we collect to try to identify the genetic basis for memory and impulse control, for example. If we can achieve this goal, maybe we can then do more targeted research to understand how the biology goes awry in people who have problems with cognition, including memory and impulse control, like those diagnosed with ADHD, Schizophrenia, Bipolar Disorder, and Autism Spectrum Disorders. By participating in our research, you can learn about mental illness and health and help researchers tackle these complex problems. We can''t do it without your help.

Proper citation: Brain Test (RRID:SCR_006212) Copy   

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http://www.callisto-science.org/NSI/Neuroscience_Image_Database/Images%20of%20the%20Human%20Nervous%20System%20-%20Neuroanatomy.html

Data set of images of the human nervous system focusing on neuroanatomy.

Proper citation: Human Nervous System Neuroanatomy (RRID:SCR_006371) Copy   

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http://vinovia.ncl.ac.uk/emagewebapp/pages/eadhb_home.jsf

Database of a set of standard 3D virtual models at different stages of development from Carnegie Stages (CS) 12-23 (approximately 26-56 days post conception) in which various anatomical regions have been defined with a set of anatomical terms at various stages of development (known as an ontology). Experimental data is captured and converted to digital format and then mapped to the appropriate 3D model. The ontology is used to define sites of gene expression using a set of standard descriptions and to link the expression data to an ''''anatomical tree''''. Human data from stages CS12 to CS23 can be submitted to the HUDSEN Gene Expression Database. The anatomy ontology currently being used is based on the Edinburgh Human Developmental Anatomy Database which encompasses all developing structures from CS1 to CS20 but is not detailed for developing brain structures. The ontology is being extended and refined (by Prof Luis Puelles, University of Murcia, Spain) and will be incorporated into the HUDSEN database as it is developed. Expression data is annotated using two methods to denote sites of expression in the embryo: spatial annotation and text annotation. Additionally, many aspects of the detection reagent and specimen are also annotated during this process (assignment of IDs, nucleotide sequences for probes etc). There are currently two main ways to search HUDSEN - using a gene/protein name or a named anatomical structure as the query term. The entire contents of the database can be browsed using the data browser. Results may be saved. The data in HUDSEN is generated from both from researchers within the HUDSEN project, and from the wider scientific community. The HUDSEN human gene expression spatial database is a collaboration between the Institute of Human Genetics in Newcastle, UK, and the MRC Human Genetics Unit in Edinburgh, UK, and was developed as part of the Electronic Atlas of the Developing Human Brain (EADHB) project (funded by the NIH Human Brain Project). The database is based on the Edinburgh Mouse Atlas gene expression database (EMAGE), and is designed to be an openly available resource to the research community holding gene expression patterns during early human development.

Proper citation: HUDSEN Human Gene Expression Spatial Database (RRID:SCR_006325) Copy   

•   Source: SciCrunch Registry

http://users.loni.ucla.edu/~shattuck/brainsuite/

Suite of image analysis tools designed to process magnetic resonance images (MRI) of the human head. BrainSuite provides an automatic sequence to extract genus-zero cortical surface mesh models from the MRI. It also provides a set of viewing tools for exploring image and surface data. The latest release includes graphical user interface and command line versions of the tools. BrainSuite was specifically designed to guide its users through the process of cortical surface extraction. NITRC has written the software to require minimal user interaction and with the goal of completing the entire process of extracting a topologically spherical cortical surface from a raw MR volume within several minutes on a modern workstation. The individual components of BrainSuite may also be used for soft tissue, skull and scalp segmentation and for surface analysis and visualization. BrainSuite was written in Microsoft Visual C using the Microsoft Foundation Classes for its graphical user interface and the OpenGL library for rendering. BrainSuite runs under the Windows 2000 and Windows XP Professional operating systems. BrainSuite features include: * Sophisticated visualization tools, such as MRI visualization in 3 orthogonal views (either separately or in 3D view), and overlayed surface visualization of cortex, skull, and scalp * Cortical surface extraction, using a multi-stage user friendly approach. * Tools including brain surface extraction, bias field correction, voxel classification, cerebellum removal, and surface generation * Topological correction of cortical surfaces, which uses a graph-based approach to remove topological defects (handles and holes) and ensure a tessellation with spherical topology * Parameterization of generated cortical surfaces, minimizing a harmonic energy functional in the p-norm * Skull and scalp surface extraction

Proper citation: BrainSuite (RRID:SCR_006623) Copy   

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http://www.medschool.lsuhsc.edu/epilepsy_center/

The LSU Epilepsy Center of Excellence is dedicated to providing state-of-the-art, comprehensive epilepsy treatment, enhancing access to epilepsy education for patients and physicians, and promoting multidisciplinary epilepsy research in pharmacology, neuroelectrophysiology, neuroimaging, neurosurgery, neuropsychology, biomedical engineering and public health. The center''s team of professionals offers diagnostic and presurgical monitoring, the strategic use of antiepileptic medications, specialized epilepsy neuroimaging, vagus nerve stimulator implantation, ketogenic diet management, neuropsychological testing, psychiatric support and epilepsy surgery for adults and children. The Center also hosts several clinical research trials each year for investigational medications and devices. The following are the treatment methods currently available at this center: - Epilepsy Brain Implants - Responsive Neurostimulator (RNS) - Medications - Medication blood level monitoring - Vagus Nerve Stimulators (VNS) - Epilepsy Surgery - Ketogenic Diet - Psychiatric Services - Radiosurgery Epilepsy Center Sections: *Electrophysiology *Neuroimaging *Neuropsychology *Neuroscience *Neurosurgery *Pharmacology *Psychiatry *Research

Proper citation: Louisiana State University School of Medicine, Health Sciences Center: Epilepsy Center (RRID:SCR_006519) Copy   

•   Source: SciCrunch Registry

http://www.mitre.org/news/digest/archives/2002/neuroinformatics.html

This resource''s long-term goal is to develop informatics methodologies and tools that will increase the creativity and productivity of neuroscience investigators, as they work together to use shared human brain mapping data to generate and test ideas far beyond those pursued by the data''s originators. This resource currently has four major projects supporting this goal: * Database tools: The goal of the NeuroServ project is to provide neuroscience researchers with automated information management tools that reduce the effort required to manage, analyze, query, view, and share their imaging data. It currently manages both structural magnetic resonance image (MRI) datasets and diffusion tensor image (DTI) datasets. NeuroServ is fully web-enabled: data entry, query, processing, reporting, and administrative functions are performed by qualified users through a web browser. It can be used as a local laboratory repository, to share data on the web, or to support a large distributed consortium. NeuroServ is based on an industrial-quality query middleware engine MRALD. NeuroServ includes a specialized neuroimaging schema and over 40 custom Java Server Pages supporting data entry, query, and reporting to help manage and explore stored images. NeuroServ is written in Java for platform independence; it also utilizes several open source components * Data sharing: DataQuest is a collaborative forum to facilitate the sharing of neuroimaging data within the neuroscience community. By publishing summaries of existing datasets, DataQuest enables researchers to: # Discover what data is available for collaborative research # Advertise your data to other researchers for potential collaborations # Discover which researchers may have the data you need # Discover which researchers are interested in your data. * Image quality: The approach to assessing the inherent quality of an image is to measure how distorted the image is. Using what are referred to as no-reference or blind metrics, one can measure the degree to which an image is distorted. * Content-based image retrieval: NIRV (NeuroImagery Retrieval & Visualization) is a work environment for advanced querying over imagery. NIRV will have a Java-based front-end for users to issue queries, run processing algorithms, review results, visualize imagery and assess image quality. NIRV interacts with an image repository such as NeuroServ. Users can also register images and will soon be able to filter searches based on image quality.

Proper citation: MITRE Neuroinformatics (RRID:SCR_006508) Copy   

•   Source: SciCrunch Registry

https://github.com/markmikkelsen/Gannet

Free, open-source MATLAB-based software toolkit for analyzing edited 1H magnetic resonance spectroscopy (MRS) data.

Proper citation: Gannet (RRID:SCR_016049) Copy   

•   Source: SciCrunch Registry

https://www.biomax.com/neuroxm

Software toolkit for semantic integration of multi-modal brain data. It is used to collect, structure, connect, analyze and re-use brain data.

Proper citation: NeuroXM Brain Science Suite (RRID:SCR_016372) Copy   

•   Source: SciCrunch Registry

https://github.com/CPernet/brain_colours

Software tool as a perceptually uniform color maps. Used in scientific literature to reflect data of brain imaging.

Proper citation: Colour maps for brain imaging (RRID:SCR_016715) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/anocva/index.html

Software R package as a nonparametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). Used for analysis of cluster variability in the diagnosis of neuropsychological disorders.

Proper citation: ANOCVA (RRID:SCR_016719) Copy   

•   Source: SciCrunch Registry

https://www.janelia.org/project-team/mouselight

Software imaging platform to generate datasets of whole mouse brains imaged at submicron resolution that allow reconstructions of complete axonal arbors of individual neurons across the entire mouse brain.

Proper citation: MouseLight Project (RRID:SCR_016668) Copy   

•   Source: SciCrunch Registry

https://github.com/vaklip/rsfmri_fconn

Software program for preprocessing resting state functional magnetic resonance imaging (rsfMRI) measurements and calculating region of interest based whole brain functional connectivity.

Proper citation: rsfMRI_fconn calculation (RRID:SCR_016591) Copy   

•   Source: SciCrunch Registry

https://github.com/afids

Open framework for evaluating correspondence between brain images and teaching neuroanatomy.

Proper citation: AFIDs (RRID:SCR_016623) Copy   

•   Source: SciCrunch Registry

http://portal.brain-map.org/

Portal provides access to data and web based applications created for benefit of global research community by Allen Institute for Brain Science. Projects to ombine genomics with neuroanatomy by creating gene expression maps for mouse and human brain. Mouse Brain Atlas, Human Brain Atlas, Developing Mouse Brain Atlas, Developing Human Brain Atlas, Mouse Connectivity Atlas, Non-Human Primate Atlas, and Mouse Spinal Cord Atlas and three related projects Glioblastoma, Mouse Diversity, and Sleep data banks, are used to advance various fields of science especially in neurobiological diseases.

Proper citation: Allen Brain Atlas (RRID:SCR_017001) Copy   

•   Source: SciCrunch Registry

http://brainethics.org/?page_id=849

How do the recent developments in neuroscience affect psychology and society? Today, many new findings challenge the way we think about ourselves and others. These changes impact on how we should think about issues such as the self, mentality, psychology, free will, morale, law and society. Through interviews of prominent scholars in the fields of neuroscience and beyond, the BrainEthics Podcast provides updated, novel and comprehensive news and views from the world of neuroscience, and how the world responds to it. We provide a full list of all podcast episodes. Although we are hosting all episodes, you can also subscribe to the podcast on all major podcasting directories.

Proper citation: BrainEthics Podcast (RRID:SCR_005533) Copy   

•   Source: SciCrunch Registry

https://github.com/SciCrunch/NIF-Ontology

The NIF Standard Ontology (NIFSTD) is a collection of modular ontologies that provides an extensive set of terms and concepts important for the domains of neuroscience and biology, as well as the data and resources relevant for the life sciences. It is a core component of the Neuroscience Information Framework (NIF) project, a semantically enhanced portal for accessing and integrating neuroscience data, tools and information.

Proper citation: NIFSTD (RRID:SCR_005414) Copy   

•   Source: SciCrunch Registry