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Software designed to automatically realign brain images for easier cross patient examination regardless of age, disease or head position. It positions and aligns anatomy-related sagittal, coronal and axial slices using anatomical landmarks.
Proper citation: AutoAlign Head (RRID:SCR_014245) Copy
https://cfn.upenn.edu/~zewang/BENtbx.php
A toolkit for mapping brain entropy using fMRI. It uses several functions from ASLtbx. At its core, the toolkit is a collection of batch scripts that implement a pipeline for processing fMRI data in order to get Sample entropy for each voxel.
Proper citation: Brain Entropy Mapping Toolbox (RRID:SCR_014470) Copy
http://www.brainsimagebank.ac.uk
A searchable collection of anonymised images and associated clinical data. It includes normal individuals at all ages (from prenatal to old age). The image bank contains integrated data sets already collected as part of research studies which include control subjects. New data is added as they become available.
Proper citation: BRAINS Imagebank (RRID:SCR_014576) Copy
http://www.nitrc.org/projects/voxbo
Software package for brain image manipulation and analysis, focusing on fMRI and lesion analysis. VoxBo can be used independently or in conjunction with other packages. It provides GLM-based statistical tools, an architecture for interoperability with other tools (they encourage users to incorporate SPM and FSL into their processing pipelines), an automation system, a system for parallel distributed computing, numerous stand-alone tools, decent wiki-based documentation, and lots more.
Proper citation: VoxBo (RRID:SCR_002166) Copy
http://www.nitrc.org/projects/msseg
Training material for the MS lesion segmentation challenge 2008 to compare different algorithms to segment the MS lesions from brain MRI scans. Data used for the workshop is composed of 54 brain MRI images and represents a range of patients and pathology which was acquired from Children's Hospital Boston and University of North Carolian. Data has initially been randomized into three groups: 20 training MRI images, 24 testing images for the qualifying and 8 for the onsite contest at the 2008 workshop. The downloadable online database consists now of the training images (including reference segmentations) and all the 32 combined testing images (without segmentations). The naming has not been changed in comparison to the workshop compeition in order to allow easy comparison between the workshop papers and the online database papers. One dataset has been removed (UNC_test1_Case02) due to considerable motion present only in its T2 image (without motion artifacts in T1 and FLAIR). Such a dataset unfairly penalizes methods that use T2 images versus methods that don't use the T2 image. Currently all cases have been segmented by expert raters at each institution. They have significant intersite variablility in segmentation. MS lesion MRI image data for this competition was acquired seperately by Children's Hospital Boston and University of North Carolina. UNC cases were acquired on Siemens 3T Allegra MRI scanner with slice thickness of 1mm and in-plane resolution of 0.5mm. To ease the segmentation process all data has been rigidly registered to a common reference frame and resliced to isotrophic voxel spacing using b-spline based interpolation. Pre-processed data is stored in NRRD format containing an ASCII readable header and a separate uncompressed raw image data file. This format is ITK compatible. If you want to join the competition, you can download data set from links here, and submit your segmentation results at http://www.ia.unc.edu/MSseg after registering your team. They require team name, password, and email address for future contact. Once experiment is completed, you can submit the segmentation data in a zip file format. Please refer submission page for uploading data format.
Proper citation: MS lesion segmentation challenge 2008 (RRID:SCR_002425) Copy
http://millette.med.sc.edu/Lab%209%2610/histology_of_nervous_tissue.htm
A website for a neuroscience lab class from the University of South Carolina that contains images of different parts of the nervous system and allows students to identify each part and answer questions about it. You should be able to (a) recognize nervous tissue in routine histological sections; (b) distinguish peripheral nerves from dense CT and smooth muscle; (c) recognize the morphological differences between myelinated and unmyelinated nerves at both the light microscopic and electron microscopic levels; (d) recognize nerve cell bodies and their component parts; (e) identify and differentiate dendrites and axons; (f) understand and identify various types of neuroglia, including Schwann cells; (g) understand and identify the structural relationship of the Schwann cell cytoplasm and plasma membrane enveloping axons; (h) understand the general features of nerve synapses. You should be able to draw nerves, cell bodies, Nodes of Ranvier, synapses etc. as they would appear under both the electron and light microscopes.
Proper citation: Histology of Nervous Tissue Laboratory Course (RRID:SCR_002367) Copy
A toolbox with graphical user interfaces for processing infant brain MR images. Longitudinal (or single-time-point) multimodality (including T1, T2, and FA) (or single-modality) data can be processed using the toolbox. Main functions of the software (step by step) include image preprocessing, brain extraction, tissue segmentation and brain labeling. Linux operating system (64 bit) is required. A workstation or server with memory >8G is recommended for processing many images simutaneously. The graphical user interfaces and overall framework of the software are implemented in MATLAB. The image processing functions are implemented with the combination of C/C++, MATLAB, Perl and Shell languages. Parallelization technologies are used in the software to speed up image processing.
Proper citation: iBEAT (RRID:SCR_002470) Copy
http://learn.genetics.utah.edu/content/addiction/
A physiologic and molecular look at drug addiction involving many factors including: basic neurobiology, a scientific examination of drug action in the brain, the role of genetics in addiction, and ethical considerations. Designed to be used by students, teachers and members of the public, the materials meet selected US education standards for science and health. Drug addiction is a chronic disease characterized by changes in the brain which result in a compulsive desire to use a drug. A combination of many factors including genetics, environment and behavior influence a person's addiction risk, making it an incredibly complicated disease. The new science of addiction considers all of these factors - from biology to family - to unravel the complexities of the addicted brain. * Natural Reward Pathways Exist in the Brain: The reward pathway is responsible for driving our feelings of motivation, reward and behavior. * Drugs Alter the Brain's Reward Pathway: Drugs work over time to change the reward pathway and affect the entire brain, resulting in addiction. * Genetics Is An Important Factor In Addiction: Genetic susceptibility to addiction is the result of the interaction of many genes. * Timing and Circumstances Influence Addiction: If you use drugs when you are an adolescent, you are more likely to develop lifetime addiction. An individual's social environment also influences addiction risk. * Challenges and Issues in Addiction: Addiction impacts society with many ethical, legal and social issues.
Proper citation: New Science of Addiction: Genetics and the Brain (RRID:SCR_002770) Copy
The long range goal of this laboratory is to understand the computational resources of brains from the biophysical to the systems levels. The central issues being addressed are how dendrites integrate synaptic signals in neurons, how networks of neurons generate dynamical patterns of activity, how sensory information is represented in the cerebral cortex, how memory representations are formed and consolidated during sleep, and how visuo-motor transformations are adaptively organized. Additionally, new techniques have been developed for modeling cell signaling using Monte Carlo methods (MCell) and the blind separation of brain imaging data into functionally independent components (ICA).
Proper citation: Computational Neurobiology Laboratory at the Salk Institute (RRID:SCR_002809) Copy
The Rodent Brain WorkBench is the portal to atlases, databases and tools developed by the Neural Systems and Graphics Computing Laboratory (NeSys) at the Centre for Molecular Biology and Neuroscience (CMBN), University of Oslo, Oslo, Norway. The Rodent Brain WorkBench presents a collection of brain mapping and atlasing oriented database applications and tools. The main category of available data is high resolution mosaic images covering complete histological sections through the rat and mouse brain. A highly structured relational database system for archiving, retrieving, viewing, and analysing microscopy and imaging data, aiming at presentation in standardized brain atlas space, is used to present a series of web applications for individual research projects. * Brain Connectivity * Atlases of Mouse Brain Promoter Gene Expression * General Brain Atlas and Navigation Systems * Downloadable tools for 3-DVisualization Open Access: * Atlas 3D * Cerebro-Cerebellar I * Cerebro-Cerebellar II * Neurotransporter Atlas * Rat Hippocampus * Tet-Off Atlas I (PrP) * Tet-Off Atlas II (PrP/CamKII) * Whole Brain Connectivity Atlas The data presented have been produced in collaboration with a large number of laboratories in Europe and the United States.
Proper citation: Rodent Brain WorkBench (RRID:SCR_002727) Copy
THIS RESOURCE IS NO LONGER IN SERVICE, documented on May 11, 2016. Repository of brain-mapping data (surfaces and volumes; structural and functional data) derived from studies including fMRI and MRI from many laboratories, providing convenient access to a growing body of neuroimaging and related data. WebCaret is an online visualization tool for viewing SumsDB datasets. SumsDB includes: * data on cerebral cortex and cerebellar cortex * individual subject data and population data mapped to atlases * data from FreeSurfer and other brainmapping software besides Caret SumsDB provides multiple levels of data access and security: * Free (public) access (e.g., for data associated with published studies) * Data access restricted to collaborators in different laboratories * Owner-only access for work in progress Data can be downloaded from SumsDB as individual files or as bundles archived for offline visualization and analysis in Caret WebCaret provides online Caret-style visualization while circumventing software and data downloads. It is a server-side application running on a linux cluster at Washington University. WebCaret "scenes" facilitate rapid visualization of complex combinations of data Bi-directional links between online publications and WebCaret/SumsDB provide: * Links from figures in online journal article to corresponding scenes in WebCaret * Links from metadata in WebCaret directly to relevant online publications and figures
Proper citation: SumsDB (RRID:SCR_002759) Copy
http://sncid.stanleyresearch.org/
A database of 1749 neuropathological markers measured in 12 different brain regions from 60 brains in the Consortium Collection from the Stanley Medical Research Institute combined with microarray data and statistical tools. Fifteen brains each are from patients diagnosed with schizophrenia, bipolar disorder, or major depression, and unaffected controls. The four groups are matched by age, sex, race, postmortem interval, pH, side of brain, and mRNA quality. A Repository of raw data is also included. Users must register for access.
Proper citation: Stanley Neuropathology Consortium Integrative Database (RRID:SCR_002749) Copy
An MRI data repository that holds a set of 7 Tesla images and behavioral metadata. Multi-faceted brain image archive with behavioral measurements. For each participant a number of different scans and auxiliary recordings have been obtained. In addition, several types of minimally preprocessed data are also provided. The full description of the data release is available in a dedicated publication. This project invites anyone to participate in a decentralized effort to explore the opportunities of open science in neuroimaging by documenting how much (scientific) value can be generated out of a single data release by publication of scientific findings derived from a dataset, algorithms and methods evaluated on this dataset, and/or extensions of this dataset by acquisition and integration of new data.
Proper citation: studyforrest.org (RRID:SCR_003112) Copy
Flytrap is an interactive database for displaying gene expression patterns, in particular P(GAL4) patterns, via an intuitive WWW based interface. This development consists of two components, the first being the HTML interface to the database and the second, a tool-kit for constructing and maintaining the database. The browser component of the project is entirely platform independent; based on javascript and HTML and therefore only requires a "standard" browser. This is to facilitate CD-ROM distribution and off-line browsing. Whether on-line or on CD, the basic browser structure does not reply on any server based scripts. Basic searching is now available. The search page uses javascript and will work off-line (i.e. from a CD-ROM copy). The construction tool-kit is UNIX based and requires an on-line web server. The tool-kit is used to compile the HTML browser interface from a simple database. The tool-kit part comprises a forms based HTML interface to the datasets allowing new information to b e added and updated very simply. We are also developing a java interface for the tool-kit that will enable us to edit and annotate images on-line. The basic browser interface is complete and a demonstration version can be accessed via the website. The first working version of the tool-kit is now on-line and is available for use.
Proper citation: flytrap (RRID:SCR_003075) Copy
Biomedical technology resource center specializing in novel approaches and tools for neuroimaging. It develops novel strategies to investigate brain structure and function in their full multidimensional complexity. There is a rapidly growing need for brain models comprehensive enough to represent brain structure and function as they change across time in large populations, in different disease states, across imaging modalities, across age and sex, and even across species. International networks of collaborators are provided with a diverse array of tools to create, analyze, visualize, and interact with models of the brain. A major focus of these collaborations is to develop four-dimensional brain models that track and analyze complex patterns of dynamically changing brain structure in development and disease, expanding investigations of brain structure-function relations to four dimensions.
Proper citation: Laboratory of Neuro Imaging (RRID:SCR_001922) Copy
http://www.brain-connectivity-toolbox.net
A large selection of complex network measures in Matlab that are increasingly used to characterize structural and functional brain connectivity datasets. Several people have contributed to the toolbox, and if you wish to contribute with a new function or set of functions, please contact Olaf Sporns. All efforts have been made to avoid errors, but users are strongly urged to independently verify the accuracy and suitability of toolbox functions for the chosen application. Please report bugs or substantial improvements.
Proper citation: Brain Connectivity Toolbox (RRID:SCR_004841) Copy
http://www.fmri.wfubmc.edu/cms/software
Research group based in the Department of Radiology of Wake Forest University School of Medicine devoted to the application of novel image analysis methods to research studies. The ANSIR lab also maintains a fully-automated functional and structural image processing pipeline supporting the image storage and analysis needs of a variety of scientists and imaging studies at Wake Forest. Software packages and toolkits are currently available for download from the ANSIR Laboratory, including: WFU Biological Parametric Mapping Toolbox, WFU_PickAtlas, and Adaptive Staircase Procedure for E-Prime.
Proper citation: Advanced Neuroscience Imaging Research Laboratory Software Packages (RRID:SCR_002926) Copy
http://www.thomaskoenig.ch/Lester/ibaspm.htm
The aim of this work is to present a toolbox for structure segmentation of structural MRI images. All programs were developed in MATLAB based on a widely used fMRI, MRI software package, SPM99, SPM2, SPM5 (Wellcome Department of Cognitive Neurology, London, UK). Other previous works have developed a similar strategy for obtaining the segmentation of individual MRI image into different anatomical structures using a standardized Atlas. Have to be mentioned the one introduced by Montreal Neurological Institute (MNI) that merges the information coming from ANIMAL (algorithm that deforms one image (nonlinear registration) to match previously labelled) and INSECT (Cerebral Tissue Classification) programs for obtaining a suitable gross cortical structure segmentation (Collins et al, 1999). Here both, nonlinear registration and gray matter segmentation processes have been performed through SPM99, SPM2, SPM5 subroutines. Three principal elements for the labeling process are used: gray matter segmentation, normalization transform matrix (that maps voxels from individual space to standardized one) and MaxPro MNI Atlas. All three are combined to yield a good performance in segmenting gross cortical structures. The programs here can be used in general for any standardized Atlas and any MRI image modality. System Requirements: 1. The IBASPM graphical user interface (GUI) runs only under MATLAB 7.0 or higher. The non-graphical version runs under MATLAB 6.5 or higher. 2. Statistical Parametrical Mapping Software SPM2, SPM5 Main Functions: * Atlasing: Main function ( This file contains spm_select script from SPM5 toolbox and uigetdir script from MATLAB 7.0 ). * Auto_Labeling : Computes individual atlas. * Create_SPAMs : Constructs Statistical Probability Anatomy Maps (SPAMs). * Create_MaxProb : Creates Maximum Probability Atlas (MaxPro) using the SPAMs previously computed. * All_Brain_Vol : Computes whole brain volume masking the brain using the segmentation files (if the segmentation files does not exist it segments). * Struct_Vol : Computes the volume for different structures based on individual Atlas previously obtained by the atlasing process. * Vols_Stats : Computes mean and standard deviation for each structure in a group of individual atlases.
Proper citation: IBASPM: Individual Brain Atlases using Statistical Parametric Mapping Software (RRID:SCR_007110) Copy
https://support.inscopix.com/search/site/Mosaic
Mosaic software features apps designed to help you derive deeper insights from videos of large scale circuit dynamics by Inscopix Inc.
Proper citation: Mosaic (RRID:SCR_017408) Copy
https://github.com/dorianps/LESYMAP
Software R package to conduct lesion-to-symptom mapping from human MRI data.Takes lesion maps and cognitive performance scores from patients with stroke, and maps brain areas responsible for cognitive deficit.
Proper citation: LESYMAP (RRID:SCR_017967) Copy
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