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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   

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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   

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

A software application developed to support computational anatomy and shape analysis. The capabilities of CAWorks include: interactive landmark placement to create segmentation (mask) of desired region of interest; specialized landmark placement plugins for subcortical structures such as hippocampus and amygdala; support for multiple Medical Imaging data formats, such as Nifti, Analyze, Freesurfer, DICOM and landmark data; Quadra Planar view visualization; and shape analysis plugin modules, such as Large Deformation Diffeomorphic Metric Mapping (LDDMM). Specific plugins are available for landmark placement of the hippocampus, amygdala and entorhinal cortex regions, as well as a browser plugin module for the Extensible Neuroimaging Archive Toolkit.

Proper citation: CAWorks (RRID:SCR_014185) Copy   

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https://github.com/nebneuron/neural-ideal

Software package for extracting neural activity codes.

Proper citation: Neural Ideal (RRID:SCR_017448) Copy   

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https://github.com/Nevermore520/NeuronTools

Software tools for converting data files into persistence diagrams and distance matrices.

Proper citation: Neuron Tools (RRID:SCR_017450) Copy   

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http://www.cmrr.umn.edu/

Biomedical technology research center that focuses on development of unique magnetic resonance (MR) imaging and spectroscopy methodologies and instrumentation for the acquisition of structural, functional, and biochemical information non-invasively in humans, and utilizing this capability to investigate organ function in health and disease. The distinctive feature of this resource is the emphasis on ultrahigh magnetic fields (7 Tesla and above), which was pioneered by this BTRC. This emphasis is based on the premise that there exists significant advantages to extracting biomedical information using ultrahigh magnetic fields, provided difficulties encountered by working at high frequencies corresponding to such high field strengths can be overcome by methodological and engineering solutions. This BTRC is home to some of the most advanced MR instrumentation in the world, complemented by human resources that provide unique expertise in imaging physics, engineering, and signal processing. No single group of scientists can successfully carry out all aspects of this type of interdisciplinary biomedical research; by bringing together these multi-disciplinary capabilities in a synergistic fashion, facilitating these interdisciplinary interactions, and providing adequate and centralized support for them under a central umbrella, this BTRC amplifies the contributions of each of these groups of scientists to basic and clinical biomedical research. Collectively, the approaches and instrumentation developed in this BTRC constitute some of the most important tools used today to study system level organ function and physiology in humans for basic and translational research, and are increasingly applied world-wide. CMRR Faculty conducts research in a variety of areas including: * High field functional brain mapping in humans; methodological developments, mechanistic studies, and neuroscience applications * Metabolism, bioenergetics, and perfusion studies of human pathological states (tumors, obesity, diabetes, hepatic encephalopathy, cystic fibrosis, and psychiatric disorders) * Cardiac bioenergetics under normal and pathological conditions * Automated magnetic field shimming methods that are critical for spectroscopy and ultrafast imaging at high magnetic fields * Development of high field magnetic resonance imaging and spectroscopy techniques for anatomic, physiologic, metabolic, and functional studies in humans and animal models * Radiofrequency (RF) pulse design based on adiabatic principles * Development of magnetic resonance hardware for high fields (e.g. RF coils, pre-amplifiers, digital receivers, phased arrays, etc.) * Development of software for data analysis and display for functional brain mapping.

Proper citation: Center for Magnetic Resonance Research (RRID:SCR_003148) Copy   

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http://www.mcw.edu/EPRCenter.htm

Biomedical technology research center focusing on technological innovation and application of new techniques to biological problems. The main areas of research are free radicals, spin labeling, metal complexes, and metallo proteins. Spectrometers are available for S-, X-, L-, Q- , and W-band EPR, many with ENDOR, ELDOR, saturation-transfer, saturation-recovery, and multiquantum capabilities. * Development of multiquantum Q- and W-band spectrometers, including multiquantum ELDOR, development of time-locked sub-sampling (TLSS) for broadband detection of periodically modulated signals * Development of loop-gap resonators using finite element modeling of Maxwell''s equations * Application of multifrequency (1 to 100 GHz) electron paramagnetic resonance (EPR) to characterize paramagnetic centers * Study of relaxation processes using multifrequency pulse saturation recovery * Use of nitroxide radical spin labels to measure translational and rotational diffusion in biological systems, site-directed spin labeling (SDSL), and use of EPR for the detection of nitric oxide and oxy radicals

Proper citation: National Biomedical Electron Paramagnetic Resonance Center (RRID:SCR_006601) Copy   

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http://mialab.mrn.org/data/index.html

An MRI data set that demonstrates the utility of a mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12-71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described, provide a useful baseline for future investigations of brain networks in health and disease.

Proper citation: MIALAB - Resting State Data (RRID:SCR_008914) Copy   

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http://nac.spl.harvard.edu/

Biomedical Technology Resource Center that develops image processing and analysis techniques for basic and clinical neurosciences. The NAC research approach emphasizes both specific core technologies and collaborative application projects. The core activity of the center is the development of algorithms and techniques for postprocessing of imaging data. New segmentation techniques aid identification of brain structures and disease. Registration methods are used for relating image data to specific patient anatomy or one set of images to another. Visualization tools allow the display of complex anatomical and quantitative information. High-performance computing hardware and associated software techniques further accelerate algorithms and methods. Digital anatomy atlases are developed for the support of both interactive and algorithmic computational tools. Although the emphasis of the NAC is on the dissemination of concepts and techniques, specific elements of the core software technologies have been made available to outside researchers or the community at large. The NAC's core technologies serve the following major collaborative projects: Alzheimer's disease and the aging brain, morphometric measures in schizophrenia and schizotypal disorder, quantitative analysis of multiple sclerosis, and interactive image-based planning and guidance in neurosurgery. One or more NAC researchers have been designated as responsible for each of the core technologies and the collaborative projects.

Proper citation: Neuroimage Analysis Center (RRID:SCR_008998) Copy   

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http://mimic.physionet.org/

Collection of comprising deidentified health related data associated with patients who stayed in critical care units of Beth Israel Deaconess Medical Center between 2001 and 2012. Database includes information such as demographics, vital sign measurements made at bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (both in and out of hospital).

Proper citation: Medical Information Mart for Intensive Care-III (RRID:SCR_017384) Copy   

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http://code.google.com/p/annotare/

A software tool for annotating biomedical investigations and the resulting data, then producing a MAGE-TAB file. This software is a standalone desktop which features: an editor function, an annotation modifier, incorporation of terms from biomedical ontologies, standard templates for common experiment types, a design aid to help create a new document, and a validator that checks for syntactic and semantic violations.

Proper citation: Annotare (RRID:SCR_000319) Copy   

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http://bmsr.usc.edu/software/targetgene/

MATLAB tool to effectively identify potential therapeutic targets and drugs in cancer using genetic network-based approaches. It can rapidly extract genetic interactions from a precompiled database stored as a MATLAB MAT-file without the need to interrogate remote SQL databases. Millions of interactions involving thousands of candidate genes can be mapped to the genetic network within minutes. While TARGETgene is currently based on the gene network reported in (Wu et al.,Bioinformatics 26:807-813, 2010), it can be easily extended to allow the optional use of other developed gene networks. The simple graphical user interface also enables rapid, intuitive mapping and analysis of therapeutic targets at the systems level. By mapping predictions to drug-target information, TARGETgene may be used as an initial drug screening tool that identifies compounds for further evaluation. In addition, TARGETgene is expected to be applicable to identify potential therapeutic targets for any type or subtype of cancers, even those rare cancers that are not genetically recognized. Identification of Potential Therapeutic Targets * Prioritize potential therapeutic targets from thousands of candidate genes generated from high-throughput experiments using network-based metrics * Validate predictions (prioritization) using user-defined benchmark genes and curated cancer genes * Explore biologic information of selected targets through external databases (e.g., NCBI Entrez Gene) and gene function enrichment analysis Initial Drug Screening * Identify for further evaluation existing drugs and compounds that may act on the potential therapeutic targets identified by TARGETgene * Explore general information on identified drugs of interest through several external links Operating System: Windows XP / Vista / 7

Proper citation: TARGETgene (RRID:SCR_001392) Copy   

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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   

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http://imaging.indyrad.iupui.edu/projects/SPHARM/

A matlab-based 3D shape modeling and analysis toolkit, and is designed to aid statistical shape analysis for identifying morphometric changes in 3D structures of interest related to different conditions. SPHARM-MAT is implemented based on a powerful 3D Fourier surface representation method called SPHARM, which creates parametric surface models using spherical harmonics.

Proper citation: SPHARM-MAT (RRID:SCR_002545) Copy   

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http://www.med.unc.edu/bric/ideagroup/free-softwares/unc-infant-0-1-2-atlases

3 atlases dedicated for neonates, 1-year-olds, and 2-year-olds. Each atlas comprises a set of 3D images made up of the intensity model, tissue probability maps, and anatomical parcellation map. These atlases are constructed with the help of state-of-the-art infant MR segmentation and groupwise registration methods, on a set of longitudinal images acquired from 95 normal infants (56 males and 39 females) at neonate, 1-year-old, and 2-year-old.

Proper citation: UNC Infant 0-1-2 Atlases (RRID:SCR_002569) Copy   

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https://github.com/scidash/neuronunit

Software toolkit for data-driven validation of neuron and ion channel models using SciUnit. NeuronUnit implements an interface to several simulators and model description languages, handles test calculations according to domain standards, and enables automated construction of tests based on data from several major public data repositories.

Proper citation: NeuronUnit (RRID:SCR_015634) Copy   

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http://www.civm.duhs.duke.edu/neuro2012ratatlas/

Multidimensional atlas of the adult Wistar rat brain based on magnetic resonance histology (MRH). The atlas has been carefully aligned with the widely used Paxinos-Watson atlas based on optical sections to allow comparisons between histochemical and immuno-marker data, and the use of the Paxinos-Watson abbreviation set. Our MR atlas attempts to make a seamless connection with the advantageous features of the Paxinos-Watson atlas, and to extend the utility of the data through the unique capabilities of MR histology: a) ability to view the brain in the skull with limited distortion from shrinkage or sectioning; b) isotropic spatial resolution, which permits sectioning along any arbitrary axis without loss of detail; c) three-dimensional (3D) images preserving spatial relationships; and d) widely varied contrast dependent on the unique properties of water protons. 3D diffusion tensor images (DTI) at what we believe to be the highest resolution ever attained in the rat provide unique insight into white matter structures and connectivity. The 3D isotropic data allow registration of multiple data sets into a common reference space to provide average atlases not possible with conventional histology. The resulting multidimensional atlas that combines Paxinos-Watson with multidimensional MRH images from multiple specimens provides a new, comprehensive view of the neuroanatomy of the rat and offers a collaborative platform for future rat brain studies. To access the atlas, click view supplementary materials in CIVMSpace at the bottom of the following webpage.

Proper citation: Adult Wistar Rat Atlas (RRID:SCR_006288) Copy   

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https://github.com/QTIM-Lab/DeepNeuro

Software Python package for neuroimaging data. Framework to design and train neural network architectures. Used in medical imaging community to ensure consistent performance of networks across variable users, institutions, and scanners.

Proper citation: DeepNeuro (RRID:SCR_016911) Copy   

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https://yeatmanlab.github.io/pyAFQ/

Software package focused on automated delineation of major fiber tracts in individual human brains, and quantification of tissue properties within the tracts.Software for automated processing and analysis of diffusion MRI data. Automates tractometry.

Proper citation: Automated Fiber Quantification in Python (RRID:SCR_023366) Copy   

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http://web.mit.edu/spectroscopy/facilities/lbrc.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. Biomedical technology research center that develops basic scientific understanding and new techniques required for advancing clinical applications of lasers and spectroscopy. LBRC merges optical spectroscopy, imaging, scattering, and interferometry techniques to study biophysics and biochemistry of healthy and diseased biological structures from subcellular to entire-organ scale.

Proper citation: Laser Biomedical Research Center (RRID:SCR_000106) Copy   

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