Tools   Select Another Resource Report Type

http://brain-development.org/ixi-dataset/

Data set of nearly 600 MR images from normal, healthy subjects, along with demographic characteristics, collected as part of the Information eXtraction from Images (IXI) project available for download. Tar files containing T1, T2, PD, MRA and DTI (15 directions) scans from these subjects are available. The data has been collected at three different hospitals in London: * Hammersmith Hospital using a Philips 3T system * Guy''s Hospital using a Philips 1.5T system * Institute of Psychiatry using a GE 1.5T system

Proper citation: IXI dataset (RRID:SCR_005839) Copy   

•   Source: SciCrunch Registry

http://riodb.ibase.aist.go.jp/brain/index.php?LANG=ENG

Atlas of magnetic resonance images and histological sections of a Japanese monkey brain, Rhesus monkey and human. The Brain Explorer allows for display, magnification, and comparison these images. Other formats include a collection of .jpg images, Quicktime VR (allow user to zoom in), and EmonV, a voxel viewer for MacOS X.

Proper citation: Brain Atlas Database of Japanese Monkey for WWW (RRID:SCR_006104) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.mouseimaging.ca/

A unique resource and comprehensive imaging facility combining the latest state-of-the-art digital medical imaging technologies for the characterization of mouse functional genomics. The goals of the Mouse Imaging Centre are: * To provide a variety of medical imaging technologies adapted to studying genetically modified mice. These technologies include magnetic resonance (MR) imaging, micro computed tomography (micro-CT), ultrasound biomicroscopy (UBM), and optical projection tomography (OPT). * To screen large numbers of mice for models of human diseases. * To image an individual mouse over time to observe development, disease progression and responses to experimental treatment. * To develop an exciting team of investigators with expertise in imaging techniques, computer science, engineering, imaging processing, developmental biology and mouse pathology. * To work by collaboration with researchers throughout the world. When we look for human diseases in the human population, we make extensive use of medical imaging. Therefore, it makes sense to have available the same imaging capabilities as we investigate mice for models of human disease. The Mouse Imaging Centre (MICe) has developed high field magnetic resonance imaging microscopy, ultrasound biomicroscopy, micro computed tomography, and optical techniques. With these imaging tools, MICe is screening randomly mutagenized mice to look for phenotypes that represent human diseases and is taking established human disease models in mice and using imaging to follow the progression of disease and response to treatment over time. It is clear that imaging has a major contribution to make to phenotyping genetic variants and to characterizing mouse models. MICe is staffed by an exciting new team of about 30 investigators with expertise in imaging techniques, computer science, engineering, imaging processing, developmental biology and mouse pathology. The Mouse Imaging Centre (MICe) is not a fee-for-service facility but works through collaborations. Services include: * Projects involving MicroCT are available as a fee for service. * We will eventually move to the same model above with MRI. * Ultrasound Biomicroscopy is used for cardiac, embryo and cancer studies and is available as fee for service at $100 per study or in some cases on a collaborative basis. * Optical Projection Tomography has only limited availability on a collaborative basis. Mouse Atlas As our images are inherently three-dimensional, we will be able to make quantitative measures of size and volume. With this in mind, we are developing a mouse atlas showing the normal deviation of organ sizes. This atlas is an important resource for biologists as it has the potential to eliminate the need to sacrifice as many controls when making comparisons with mutants. Mouse Atlas Examples: * Variational Mouse Brain Atlas * Cerebral Vascular Atlas of the CBA Mouse * Neuroanatomy Atlas of the C57Bl/6j Mouse * Vascular Atlas of the Developing Mouse Embryo * Micro-CT E15.5 Mouse Embryo Atlas

Proper citation: MICe - Mouse Imaging Centre (RRID:SCR_006145) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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://learn.genetics.utah.edu/content/addiction/drugs/mouse.html

Mouse Party is an interactive website that teaches how various drugs disrupt the synapse by taking a look inside the brains of mice on drugs! Every drug of abuse has its own unique molecular mechanism. Where applicable, this presentation primarily depicts how drugs interact with dopamine neurotransmitters because this website focuses on the brain''s reward pathway. Mouse Party is designed to provide a small glimpse into the chemical interactions at the synaptic level that cause the drug user to feel ''high''. The simplified mechanisms of drug action presented here are just a small part of the story. When drugs enter the body they elicit very complex effects in many different regions of the brain. Often they interact with many different types of neurotransmitters and may bind with a variety of receptor types in a variety of different locations. For example, THC in marijuana can bind with cannabinoid receptors located on the presynaptic and/or postsynaptic cell in a synapse.

Proper citation: Mouse Party (RRID:SCR_006438) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/functionalconnectivitytoolbox/

MATLAB toolbox for performing functional connectivity analyses includes many of the most commonly-used approaches researchers have utilized to date for the identification of condition-dependent functional interactions between fMRI time-series obtained from two or more brain regions. The approaches are either bivariate or multivariate methods defined in time or frequency domains that emphasize distinct features of relationships among the time-series.

Proper citation: Functional Connectivity Toolbox (RRID:SCR_006394) Copy   

•   Source: SciCrunch Registry

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

A fast, scalable tool developed at the Johns Hopkins University to automatically segment the major anatomical fiber tracts within the human brain from clinical quality diffusion tensor MR imaging. With an atlas-based Markov Random Field representation, DOTS directly estimates the tract probabilities, bypassing tractography and associated issues. Overlapping and crossing fibers are modeled and DOTS can also handle white matter lesions. DOTS is released as a plug-in for the MIPAV software package and as a module for the JIST pipeline environment. They are therefore cross-platform and compatible with a wide variety of file formats.

Proper citation: DOTS WM tract segmentation (RRID:SCR_009459) Copy   

•   Source: SciCrunch Registry

http://alzheimer.ucdavis.edu/research/resources.php#tissue

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Support research in Alzheimer's disease (AD) offering pilot grants, recruitment of research subjects, access to database, tissue samples, and statistical and research study design consultation for investigators. The scientific effort of the program seeks to: promote research directed at understanding factors that influence the expression and progression of Alzheimer's disease; develop and maintain cohorts of carefully diagnosed and well characterized research subjects available for research studies on Alzheimer's disease and dementia; provide support to investigators in subject recruitment, clinical research, experimental design, and statistical analysis of data; and maintain a variety of samples (brain, DNA, serum) and an extensive electronic database suitable for developing new research and supporting existing programs.

Proper citation: UC Davis Alzheimers Disease Center - Resources (RRID:SCR_010699) Copy   

•   Source: SciCrunch Registry

https://github.com/jefferis/elmr

Software tool as support for working with light and electron microscopy fly brain data. Part of suite of R packages based on NeuroAnatomy Toolbox. Provides tools to move between adult brain EM and light level data, emphasising interaction between CATMAID web application and R Neuroanatomy Toolbox package.

Proper citation: elmr (RRID:SCR_017249) Copy   

•   Source: SciCrunch Registry

https://www.mbfbioscience.com/neurolucida-explorer

Companion analytical software for Neurolucida and Neurolucida 360, designed to perform extensive morphometric analysis on neuron reconstructions, serial section reconstructions, and brain maps.

Proper citation: Neurolucida Explorer (RRID:SCR_017348) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/bva-io/

Software package for interfacing the Brain Vision Analyser data files (load/save) for ongoing development of Matlab routines . This package is also compatible with the EEGLAB software, and may be uncompressed in the plugin folder of this software.

Proper citation: BVA import/export EEGLAB plugin (RRID:SCR_016333) Copy   

•   Source: SciCrunch Registry

https://github.com/DiedrichsenLab/DCBC/tree/v1.0.0

Software Python toolbox for brain parcellation evaluation.

Proper citation: DCBC toolbox (RRID:SCR_022176) Copy   

•   Source: SciCrunch Registry

https://www.nitrc.org/projects/rshrf

Software toolbox for resting state HRF estimation and deconvolution analysis. Matlab and Python toolbox that implements HRF estimation and deconvolution from resting state BOLD signal. Used to retrieve optimal lag between events and HRF onset, as well as HRF shape. Once that HRF has been retrieved for each voxel/vertex, it can be deconvolved from time series or one can map shape parameters everywhere in brain and use it as pathophysiological indicator. Input can be 2D GIfTI, 3D or 4D NIfTI images, but also on time series matrices/vectors. Output are three HRF shape parameters for each voxel/vertex, plus deconvolved time series, and number of retrieved pseudo events. All can be written back to GIfTI or NIfTI images.

Proper citation: Resting State Hemodynamic Response Function Retrieval and Deconvolution (RRID:SCR_023663) Copy   

•   Source: SciCrunch Registry

http://mindhacks.com/

Mind Hacks: Neuroscience and psychology tricks to find out what's going on inside your brain. Mind Hacks is also a book by Tom Stafford and Matt Webb.

Proper citation: Mind Hacks (RRID:SCR_000170) Copy   

•   Source: SciCrunch Registry

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT

Software toolbox for analysis of diffusion weighted images.

Proper citation: FMRIB's Diffusion Toolbox (RRID:SCR_024931) Copy   

•   Source: SciCrunch Registry

http://www.cognitorium.com/

THIS RESOURCE IS NO LONGER IN SERVICE, documented October 28, 2015. Interactive, informative and educational community platform dedicated to cognitive science or the multidisciplinary exploration of the mind. This online platform, will help gather and link information providing a thorough and reliable source of information for students and professionals in the field, as well as help bridge the gap between academia and the society. Due to the multidisciplinary nature of cognitive science, the work is becoming increasingly specialized. Therefore to keep an eye on the bigger picture, it seems necessary to bring the discoveries of various disciplines together in one place, look at their similarities and differences and discuss them for future directions.

Proper citation: Cognitorium (RRID:SCR_000098) Copy   

•   Source: SciCrunch Registry