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http://sccn.ucsd.edu/fmrlab/index.html

A Matlab toolbox for fMRI data analysis using Independent Component Analysis (ICA). It provides an integrated environment to manage, process and analyze fMRI data in a single framework so that users can complete the analysis without switching between software. In addition, it provides an interactive Matlab graphic user interface (GUI). All the necessary processes to apply ICA to fMRI data and review its results can be run from the graphic interface. The FMRLAB processing flow is straightforward. Custom analyses can be performed with Matlab scripts using the FMRLAB functions and data structure. Since fMRI data analysis is a complex enterprise, including digital image processing, statistical analysis and data visualization, an integrated framework combining processing elements is desired eagerly by users in the neuroimaging community. Recently, large number of software tools for data analysis and visualization have been developed for this purpose. However, most of these tools use model-based statistical methods which assume that the users know the hemodynamic response (HR) for their paradigm in advance and can specify a reasonable HR model. Often, however, accurate or reasonable response HR models are unavailable. An alternative data-driven method, infomax ICA (McKeown et al., 1998), does not require that an a priori HR model, instead deriving HRs of spatially independent components of the entire data set from the higher-order statistics of the data themselves. FMRLAB is a toolbox running under Matlab containing necessary components for data-driven fMRI data analysis using the highly reliable infomax ICA algorithm (Bell & Sejnowski, 1995), normalized (Amari, 1999), extended (Lee, Girolami and Sejnowski, 1999) and automated by Makeig et al. FMRLAB has been developed under Matlab 6.1 running on Red Hat Linux. FMRLAB Features * Graphic user interface * Flexible data importing * Interactive data plotting * Computationally efficient * Defined FMRI data structure * Independent component browser * Smooth, transparent component exporting and spatial normalization process * Interface with other software for further analysis or visualization. * SPM-style component plots (MIP, 2-D slice overlay and 3-D)

Proper citation: FMRLAB (RRID:SCR_005164) Copy   

•   Source: SciCrunch Registry

http://neurosynth.org

Platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. It''s a website wrapped around a set of open-source Python and JavaScript packages. Neurosynth lets you run crude but useful analyses of fMRI data on a very large scale. You can: * Interactively visualize the results of over 3,000 term-based meta-analyses * Select specific locations in the human brain and view associated terms * Browse through the nearly 10,000 studies in the database Their ultimate goal is to enable dynamic real-time analysis, so that you''ll be able to select foci, tables, or entire studies for analysis and run a full-blown meta-analysis without leaving your browser. You''ll also be able to do things like upload entirely new images and obtain probabilistic estimates of the cognitive states most likely to be associated with the image.

Proper citation: NeuroSynth (RRID:SCR_006798) Copy   

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http://fcon_1000.projects.nitrc.org/indi/pro/eNKI_RS_TRT/FrontPage.html

A test-retest dataset to assess the reliability of multiband resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) scans prior to launch of the Enhanced Nathan Kline Institute - Rockland Sample (NKI-RS). The dataset is primarily composed of individuals from the initial NKI-RS - for these individuals psychiatric assessment information is available and included (participants were not excluded due to history of illness. In addition to R-fMRI and DTI, they included: 1) simple visual checkerboard stimulation fMRI scans to allow for assessment of traditional fMRI data quality metrics (e.g., contrast-to-noise ratio), 2) breath holding data to enable assessment of regional differences in vascular responsiveness, and 3) eye movement calibration scans to enable the assessment of eye-movement related artifacts which may be particularly troublesome for multiband sequences since several slices are acquired simultaneously.

Proper citation: NKI-RS Multiband Imaging Test-Retest Pilot Dataset (RRID:SCR_010460) Copy   

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http://fcon_1000.projects.nitrc.org/indi/enhanced/

Dataset of 1000 characterized community-ascertained participants using state-of-the-art multiband imaging-based resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI), genetics, and a deep phenotyping protocol from a large cross-sectional sample of brain development, maturation and aging (ages 6 - 85 yrs). The Center for Magnetic Resonance Research (CMRR), University of Minnesota, provided the NKI-RS effort with the latest version of the Multiband EPI sequence (Xu et al. 2012) and associated image reconstruction algorithms, enabling the acquisition of state-of-the-art imaging datasets for this large-scale imaging effort. The enhanced NKI-RS expands upon the phenotypic protocol of the original NKI-RS and captures a broad range of behavioral and cognitive phenomenology relevant to psychiatric health and illness. The validity and value of assessments were evaluated by consulting leaders in the field of psychiatric phenotyping.

Proper citation: NKI-RS Enhanced Sample (RRID:SCR_010461) Copy   

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http://mus.well.ox.ac.uk/mouse/INBREDS/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2025. Data set of genotypes available for 480 strains and 13370 successful SNP assays that are mapped to build34 of the mouse genome, including 107 SNPs that are mapped to random unanchored sequence 13374 SNPs are mapped onto Build 33 of the mouse genome. You can access the data relative to Build 33 or Build 34.

Proper citation: Wellcome-CTC Mouse Strain SNP Genotype Set (RRID:SCR_003216) Copy   

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http://nif.nimh.nih.gov/

Neurophysiology imaging core facility that provides anatomical and functional MRI scanning for researchers in the National Institute of Mental Health (NIMH), the National Eye Institute (NEI), and the National Institute for Neurological Disorders and Stroke (NINDS). The shared intramural resource centers on a cutting-edge 4.7T vertical bore scanner dedicated to imaging of nonhuman primates.

Proper citation: Neurophysiology Imaging Facility (RRID:SCR_004080) Copy   

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http://fcon_1000.projects.nitrc.org/indi/pro/nki.html

A phenotypically rich neuroimaging sample, consisting of data obtained from individuals between the ages of 4 and 85 years-old. All individuals included in the sample undergo semi-structured diagnostic psychiatric interviews, and complete a battery of psychiatric, cognitive and behavioral assessments in order to provide comprehensive phenotypic information for the purpose of exploring brain / behavior relationships.

Proper citation: NKI/Rockland Sample (RRID:SCR_009435) Copy   

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http://casestudies.brain-map.org/celltax

Cellular Taxonomy of Mouse Visual Cortex by analyzing gene expression patterns at single cell level. Construction of cellular taxonomy of one cortical region, primary visual cortex, in adult mice done on basis of single cell RNA sequencing.

Proper citation: CellTax vignette (RRID:SCR_017000) Copy   

•   Source: SciCrunch Registry

http://myriads.webs.uvigo.es/

Software package for p value based multiple testing that also implements dependence test and p-value simulation.

Proper citation: Myriads (RRID:SCR_017447) Copy   

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https://github.com/namboodirilab/B-CALM

Open source system for behavioral control based on Arduino Mega microcontroller and MATLAB based graphical interface and analysis code. Behavior controller optimized and customized for associative learning and memory tasks. Provided software should be able to control many different types of hardware for different task configurations.

Proper citation: B-CALM (RRID:SCR_023884) Copy   

•   Source: SciCrunch Registry

http://web.stanford.edu/group/barres_lab/brain_rnaseq.html

Database containing RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of cerebral cortex. Collection of RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of mouse cerebral cortex. RNA-Seq of cell types isolated from mouse and human brain.

Proper citation: Brain RNA-Seq (RRID:SCR_013736) Copy   

•   Source: SciCrunch Registry

https://balsa.wustl.edu/

Database for hosting and sharing neuroimaging and neuroanatomical datasets for human and primate species. Includes 1) curated, user created Study datasets, extensively analyzed neuroimaging data associated with published figures/manuscripts, 2) Reference datasets mapped to brain atlas surfaces and volumes in human and nonhuman primates for use as general resources (e.g., published cortical parcellations), and 3) ConnectomeDB powered by BALSA for distributing HCP-Young Adult and related HCP-style processed imaging and phenotypic datasets. Datasets in BALSA may include PMID and/or DOI that links them directly to relevant publications.

Proper citation: BALSA (RRID:SCR_022960) Copy   

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http://wwwchg.duhs.duke.edu/research/osa.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2025. Software application that allows the researcher to evaluate evidence for linkage even when heterogeneity is present in a data set. This is not an unusual occurrence when studying diseases of complex origin. Families are ranked by covariate values in order to test evidence for linkage among homogeneous subsets of families. Because families are ranked, a priori covariate cutpoints are not necessary. Covariates may include linkage evidence at other genes, environmental exposures, or biological trait values such as cholesterol, age at onset, and so on.

Proper citation: OSA (RRID:SCR_002016) Copy   

•   Source: SciCrunch Registry

https://github.com/SciKnowEngine/kefed.io

Knowledge engineering software for reasoning with scientific observations and interpretations. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a "neural connection matrix" interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. The KEfED model is designed to provide a lightweight representation for scientific knowledge that is (a) generalizable, (b) a suitable target for text-mining approaches, (c) relatively semantically simple, and (d) is based on the way that scientist plan experiments and should therefore be intuitively understandable to non-computational bench scientists. The basic idea of the KEfED model is that scientific observations tend to have a common design: there is a significant difference between measurements of some dependent variable under conditions specified by two (or more) values of some independent variable.

Proper citation: Knowledge Engineering from Experimental Design (RRID:SCR_001238) Copy   

•   Source: SciCrunch Registry

http://labs.nri.ucsb.edu/reese/benjamin/SA3D.html

A user-friendly, graphical user interface (GUI) that allows statistical and visual manipulations of real and simulated three-dimensional spatial point patterns. The analyses use files containing sets of X, Y, Z coordinates. These point patterns are frequently coordinates of cells of specific cell classes within in volumes of tissue derived from microscopy analyses. The analyses are scale independent so spatial analyses of coordinates from larger and smaller scale distributions are possible. The software can also generate sample sets of X, Y, Z coordinates for program exploration and modeling purposes.

Proper citation: Spatial Analysis 3D (RRID:SCR_002563) Copy   

•   Source: SciCrunch Registry

http://www.pymvpa.org

A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.

Proper citation: PyMVPA (RRID:SCR_006099) Copy   

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http://mind.loni.usc.edu

The MiND: Metadata in NIfTI for DWI framework enables data sharing and software interoperability for diffusion-weighted MRI. This site provides specification details, tools, and examples of the MiND mechanism for representing important metadata for DWI data sets at various stages of post-processing. MiND framework provides a practical solution to the problem of interoperability between DWI analysis tools, and it effectively expands the analysis options available to end users. To assist both users and developers in working with MiND-formatted files, we provide a number of software tools for download. * MiNDHeader A utility for inspecting MiND-extended files. * I/O Libraries Programming libraries to simplify writing and parsing MiND-formatted data. * Sample Files Example files for each MiND schema. * DIRAC LONI''s Diffusion Imaging Reconstruction and Analysis Collection is a DWI processing suite which utilizes the MiND framework.

Proper citation: LONI MiND (RRID:SCR_004820) Copy   

•   Source: SciCrunch Registry

http://www.nimh.nih.gov/about/director/index.shtml

Blog by the NIMH Director, Thomas R. Insel, M.D. Users may sort posts by topic and/or subsribe to the RSS Feed, http://www.nimh.nih.gov/site-info/feed-directors-blog.atom

Proper citation: NIMH Director's Blog (RRID:SCR_008841) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000674.v1.p1

Human genetics data from an immense (78,000) and ethnically diverse population available for secondary analysis to qualified researchers through the database of Genotypes and Phenotypes (dbGaP). It offers the opportunity to identify potential genetic risks and influences on a broad range of health conditions, particularly those related to aging. The GERA cohort is part of the Research Program on Genes, Environment, and Health (RPGEH), which includes more than 430,000 adult members of the Kaiser Permanente Northern California system. Data from this larger cohort include electronic medical records, behavioral and demographic information from surveys, and saliva samples from 200,000 participants obtained with informed consent for genomic and other analyses. The RPGEH database was made possible largely through early support from the Robert Wood Johnson Foundation to accelerate such health research. The genetic information in the GERA cohort translates into more than 55 billion bits of genetic data. Using newly developed techniques, the researchers conducted genome-wide scans to rapidly identify single nucleotide polymorphisms (SNPs) in the genomes of the people in the GERA cohort. These data will form the basis of genome-wide association studies (GWAS) that can look at hundreds of thousands to millions of SNPs at the same time. The RPGEH then combined the genetic data with information derived from Kaiser Permanente''s comprehensive longitudinal electronic medical records, as well as extensive survey data on participants'' health habits and backgrounds, providing researchers with an unparalleled research resource. As information is added to the Kaiser-UCSF database, the dbGaP database will also be updated.

Proper citation: Resource for Genetic Epidemiology Research on Adult Health and Aging (RRID:SCR_010472) Copy   

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

Software model based segmentation and registration tool. Used for segmentation of sub-cortical structures. Introduces basic segmentation and vertex analysis for detecting group differences.

Proper citation: FMRIB’s Integrated Registration and Segmentation Tool (RRID:SCR_024921) Copy   

•   Source: SciCrunch Registry