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

Modeling platform to study the basic interactions between synaptic elements that allows the user to study qualitatively, and also quantitatively the relative contributions of diverse mechanisms underlying synaptic efficacy: the relevance of each and every element that comprises a synapse, the interactions between these components and their subcellular distribution, as well as the influence of synaptic geometry (presynaptic terminal, cleft and postsynaptic density). This platform consists of a graphical interface in which elements that comprise a single glutamatergic synapse (both pre- and post-synaptically), their behavior as well as the underlying synaptic geometry can be modified. For example, EONS offers the ability to study the effect of voltage-gated calcium channels density and distribution, the number and location of receptors and more. EONS is a parametric model of a generic glutamatergic synapse that takes into account pre-synaptic mechanisms, such as calcium buffering and diffusion, neurotransmitter release, diffusion and uptake in the cleft, and postsynaptic elements, such as ionotropic AMPA and NMDA receptors, their distribution and synaptic geometry, as well as metabotropic glutamate receptors. There are no complicated equations to write: all the models are predefined. This version is a great tool for first time users and students interested in learning about synapses, as well as for studying geometry and distribution hypotheses in a 2D rectangular geometry. System Requirements: EONS V1.2 is a Windows program but can be also successfully installed and run on Mac and Linux.

Proper citation: EONS (RRID:SCR_002979) Copy   

•   Source: SciCrunch Registry

http://adni-info.org/

Database of the results of the ADNI study. ADNI is an initiative to develop biomarker-based methods to detect and track the progression of Alzheimer's disease (AD) that provides access to qualified scientists to their database of imaging, clinical, genomic, and biomarker data.

Proper citation: ADNI - Alzheimer's Disease Neuroimaging Initiative (RRID:SCR_003007) Copy   

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http://www.xnat.org

Software platform designed to facilitate common management and productivity tasks for neuroimaging and associated data.

Proper citation: XNAT - The Extensible Neuroimaging Archive Toolkit (RRID:SCR_003048) Copy   

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https://bioimagesuiteweb.github.io/webapp/index.html

Web applications for analysis of multimodal/multispecies neuroimaging data. Image analysis software package. Has facilities for DTI and fMRI processing. Capabilities for both neuro/cardiac and abdominal image analysis and visualization. Many packages are extensible, and provide functionality for image visualization and registration, surface editing, cardiac 4D multi-slice editing, diffusion tensor image processing, mouse segmentation and registration, and much more. Can be intergrated with other biomedical image processing software, such as FSL, AFNI, and SPM.

Proper citation: BioImage Suite (RRID:SCR_002986) Copy   

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http://www.pediatricmri.nih.gov/

Data sets of clinical / behavioral and image data are available for download by qualified researchers from a seven year, multi-site, longitudinal study using magnetic resonance technologies to study brain maturation in healthy, typically-developing infants, children, and adolescents and to correlate brain development with cognitive and behavioral development. The information obtained in this study is expected to provide essential data for understanding the course of normal brain development as a basis for understanding atypical brain development associated with a variety of developmental, neurological, and neuropsychiatric disorders affecting children and adults. This study enrolled over 500 children, ranging from infancy to young adulthood. The goal was to study each participant at least three times over the course of the project at one of six Pediatric Centers across the United States. Brain MR and clinical/behavioral data have been compiled and analyzed at a Data Coordinating Center and Clinical Coordinating Center. Additionally, MR spectroscopy and DTI data are being analyzed. The study was organized around two objectives corresponding to two age ranges at the time of enrollment, each with its own protocols. * Objective 1 enrolled children ages 4 years, 6 months through 18 years (total N = 433). This sample was recruited across the six Pediatric Study Centers using community based sampling to reflect the demographics of the United States in terms of income, race, and ethnicity. The subjects were studied with both imaging and clinical/behavioral measures at two year intervals for three time points. * Objective 2 enrolled newborns, infants, toddlers, and preschoolers from birth through 4 years, 5 months, who were studied three or more times at two Pediatric Study Centers at intervals ranging from three months for the youngest subjects to one year as the children approach the Objective 1 age range. Both imaging and clinical/behavioral measures were collected at each time point. Participant recruitment used community based sampling that included hospital venues (e.g., maternity wards and nurseries, satellite physician offices, and well-child clinics), community organizations (e.g., day-care centers, schools, and churches), and siblings of children participating in other research at the Pediatric Study Centers. At timepoint 1, of those enrolled, 114 children had T1 scans that passed quality control checks. Staged data release plan: The first data release included structural MR images and clinical/behavioral data from the first assessments, Visit 1, for Objective 1. A second data release included structural MRI and clinical/behavioral data from the second visit for Objective 1. A third data release included structural MRI data for both Objective 1 and 2 and all time points, as well as preliminary spectroscopy data. A fourth data release added cortical thickness, gyrification and cortical surface data. Yet to be released are longitudinally registered anatomic MRI data and diffusion tensor data. A collaborative effort among the participating centers and NIH resulted in age-appropriate MR protocols and clinical/behavioral batteries of instruments. A summary of this protocol is available as a Protocol release document. Details of the project, such as study design, rationale, recruitment, instrument battery, MRI acquisition details, and quality controls can be found in the study protocol. Also available are the MRI procedure manual and Clinical/Behavioral procedure manuals for Objective 1 and Objective 2.

Proper citation: NIH MRI Study of Normal Brain Development (RRID:SCR_003394) Copy   

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http://signalml.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023.An XML-based language designed for metadescription of formats, used for digital storage of biomedical time series. Using SignalML, information on the structure of binary data files can be simply and efficiently coded. Once written, this information can be used by any software, which - owing to this metadescription - can read data files in the original format. This eliminates the need for conversions and duplication of data. signalml.org provides the following resources for interchange of relevant information and ideas: * SignalML wiki * Newsgroup / mailing list The main current software project is Svarog - a SignalML-compliant signal viewer, annotator, analyzer and (future) recorder. Svarog is written in Java and is currently best fitted for display of EEG and MEG signals. Also open platform for implementing advanced signal processing methods in user-friendly environment, at the moment interfacs for Java code, standalone executables and Matlab code via Matlab Builder for Java.

Proper citation: signalml.org (RRID:SCR_003383) Copy   

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http://bio3d.colorado.edu/imod

A free, cross-platform set of image processing, modeling and display programs used for tomographic reconstruction and for 3D reconstruction of EM serial sections and optical sections. The package contains tools for assembling and aligning data within multiple types and sizes of image stacks, viewing 3-D data from any orientation, and modeling and display of the image files. IMOD 4.1.8 Is Now Available for Linux, Windows, and Mac OS X

Proper citation: IMOD (RRID:SCR_003297) Copy   

•   Source: SciCrunch Registry

http://www.humanconnectomeproject.org/

A multi-center project comprising two distinct consortia (Mass. Gen. Hosp. and USC; and Wash. U. and the U. of Minn.) seeking to map white matter fiber pathways in the human brain using leading edge neuroimaging methods, genomics, architectonics, mathematical approaches, informatics, and interactive visualization. The mapping of the complete structural and functional neural connections in vivo within and across individuals provides unparalleled compilation of neural data, an interface to graphically navigate this data and the opportunity to achieve conclusions about the living human brain. The HCP is being developed to employ advanced neuroimaging methods, and to construct an extensive informatics infrastructure to link these data and connectivity models to detailed phenomic and genomic data, building upon existing multidisciplinary and collaborative efforts currently underway. Working with other HCP partners based at Washington University in St. Louis they will provide rich data, essential imaging protocols, and sophisticated connectivity analysis tools for the neuroscience community. This project is working to achieve the following: 1) develop sophisticated tools to process high-angular diffusion (HARDI) and diffusion spectrum imaging (DSI) from normal individuals to provide the foundation for the detailed mapping of the human connectome; 2) optimize advanced high-field imaging technologies and neurocognitive tests to map the human connectome; 3) collect connectomic, behavioral, and genotype data using optimized methods in a representative sample of normal subjects; 4) design and deploy a robust, web-based informatics infrastructure, 5) develop and disseminate data acquisition and analysis, educational, and training outreach materials.

Proper citation: MGH-USC Human Connectome Project (RRID:SCR_003490) Copy   

•   Source: SciCrunch Registry

http://neuromorpho.org/index.jsp

Centrally curated inventory of digitally reconstructed neurons associated with peer-reviewed publications that contains some of the most complete axonal arborizations digitally available in the community. Each neuron is represented by a unique identifier, general information (metadata), the original and standardized ASCII files of the digital morphological reconstruction, and a set of morphometric features. It contains contributions from over 100 laboratories worldwide and is continuously updated as new morphological reconstructions are collected, published, and shared. Users may browse by species, brain region, cell type or lab name. Users can also download morphological reconstructions for research and analysis. Deposition and distribution of reconstruction files ultimately prevents data loss. Centralized curation and annotation aims at minimizing the effort required by data owners while ensuring a unified format. It also provides a one-stop entry point for all available reconstructions, thus maximizing data visibility and impact.

Proper citation: NeuroMorpho.Org (RRID:SCR_002145) Copy   

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

Based on JIST and MIPAV, this pipeline combines structural magnetic resonance data with diffusion tensor imaging to estimate a connectome, which is a comprehensive description of the wiring diagram of the brain.

Proper citation: MR Connectome Automated Pipeline (RRID:SCR_002252) Copy   

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http://www.incf.org/

Independent international facilitator catalyzing and coordinating global development of neuroinformatics aiming to advance data reuse and reproducibility in global brain research. Integrates and analyzes diverse data across scales, techniques, and species to understand brain function and positively impact the health and well being of society.

Proper citation: International Neuroinformatics Coordinating Facility (RRID:SCR_002282) Copy   

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http://cogpo.org

Ontology used to describe the experimental conditions within cognitive and behavioral experiments, primarily in humans for application and use in the functional neuroimaging community. CogPO has been developed through the integration of the Functional Imaging Biomedical Informatics Research Network (FBIRN) Human Imaging Database (HID) and the BrainMap Database. The design of CogPO concentrates on what can be observed directly: categorization of each paradigm in terms of (1) the stimulus presented to the subjects, (2) the requested instructions, and (3) the returned response.

Proper citation: Cognitive Paradigm Ontology (RRID:SCR_002235) Copy   

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

An EEGLAB plugin which adaptively estimates and removes sinusoidal artifacts from independent component analysis (ICA) components or scalp channels using a frequency-domain (multi-taper) regression technique with a Thompson F-statistic for identifying significant sinusoidal artifacts. This approach has been advocated by Partha Mitra and Hemant Bokil (Observed Brain Dynamics, Chapter 7.3.4., 2007) and CleanLine utilizes modified routines from the Mitra Lab's Chronux Toolbox (www.chronux.org). Sinusoidal noise can be a prominent artifact in recorded electrophysiological data. This can stem from AC power line fluctuations (e.g. 50/60 Hz line noise + harmonics), power suppliers (e.g. in medical equipment), fluorescent lights, etc. Notch filtering is generally undesirable due to creation of band-holes, and significant distortion of frequencies around the notch frequency (as well as phase distortion at other frequencies and Gibbs rippling in the time-domain).

Proper citation: CleanLine (RRID:SCR_002233) Copy   

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https://code.google.com/p/niak/

Software library of modules and pipelines for fMRI processing with Octave or Matlab(r) that can run in parallel either locally or in a supercomputing environment. Linux OS and MINC file format are supported. NIAK currently includes a preprocessing, a region growing and a connectome pipelines. NIAK features powerful pipeline management capabilities, including parallel computing, generation of detailed logs and automatic handling of pipeline failures or updates.

Proper citation: NeuroImaging Analysis Kit (NIAK) (RRID:SCR_002497) Copy   

•   Source: SciCrunch Registry

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

Software tool that provides a convenient environment to simulate NMR diffusion in closed pores. It builds on the eigenfunction expansion of the magnetization.

Proper citation: Multiple Correlation Function Tool (RRID:SCR_002321) Copy   

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

A powerful and modular medical image viewer/editor. It should be particularly useful to Undergraduates, Postdocs and Researchers in Medical Imaging to visualize data and to easily make attractive figures (for papers or presentations). It will also in a near future offer a number of advanced algorithms for medical image processing. Look at the video to get an idea ! http://www.benoitscherrer.com/MisterI/videos.html

Proper citation: MisterI (RRID:SCR_002317) Copy   

•   Source: SciCrunch Registry

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

A Matlab-based software library to perform independent component analysis on group white matter skeleton generated by FSL TBSS. The script produces stable estimates of the white matter tract or tract segments that resemble highly correlated variation profiles across a group of subjects.

Proper citation: Microstructural correlation toolbox (RRID:SCR_002316) Copy   

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

Software toolkit that provides the following libraries and functions on linux platform: # Multi-resolution registration of MR images include T1, multimodality, and DTI images. # The registration model is B-spline, and users can custermize their own image similarity measures by writing a plugin function and recompile the program.

Proper citation: MIAS Registration Toolkit (RRID:SCR_002312) Copy   

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http://www.pstnet.com/hardware.cfm?ID=92

MRI Simulator that provides a realistic approximation of an actual MRI scanner to allow habituation and training of participants in an environment less daunting than a real scanner. Special populations such as children, the elderly, and psychiatric patients, are often prone to claustrophobia and anxiety in the bore of a magnet, and consequently have a much higher rate of terminating the experiment or scan session before its completion. Some centers that have dealt with these populations estimate a 50%-80% failure rate. With the use of the MRI Simulator this failure rate can often be reduced below 5%, improving cost effectiveness.

Proper citation: PSTNet: MRI Simulator (RRID:SCR_002460) Copy   

•   Source: SciCrunch Registry

http://www.brain.org.au/software/

A collection of tools that generate numerical fiber structures with the complexity of human white matter and simulate Diffusion-Weighted MR images that would arise from them. Its primary use is to enable the testing of tracking algorithms

Proper citation: Numerical Fibre Generator (RRID:SCR_002457) Copy   

•   Source: SciCrunch Registry