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http://ki.se/meb/star

Large, ongoing, multifactorial study based on nation-wide ascertainment of patients with schizophrenia and bipolar disorder through the Swedish Twin Registry to include both neuroimaging data, neurocognitive function, molecular genetic data and early adverse environmental factors in the same model in a genetic sensitive design. Swedish schizophrenia research will benefit from this large study database of in total 240 affected and healthy twin pairs collected over a 5 year period. The specific aims are: * To elucidate neural endophenotypes for schizophrenia and bipolar disorder and to clarify the extent of overlap in these features between the two syndromes. * To investigate candidate genes and genomic regions for linkage and association with neural endophenotypes for schizophrenia and bipolar disease. * To determine the contributions of adverse prenatal and perinatal conditions to neural changes associated with schizophrenia and bipolar disease. Types of samples * EDTA whole blood * DNA * RNA Number of sample donors: 251 (June 2010)

Proper citation: KI Biobank - STAR (RRID:SCR_005923) Copy   

•   Source: SciCrunch Registry

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

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

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://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

https://zenodo.org/records/11095105

Software label transfer tool for single-cell RNA sequencing analysis. Scalable, Interpretable Modeling for Single-cell RNA-seq data classification.

Proper citation: SIMS (RRID:SCR_025787) Copy   

•   Source: SciCrunch Registry

https://portal.brain-map.org/atlases-and-data/bkp/abc-atlas

Provides platform for visualizing multimodal single cell data across mammalian brain and aims to empower researchers to explore and analyze multiple whole brain datasets simultaneously. Allen Institute and its collaborators continue to add new modalities, species, and insights to the ABC Atlas. Atlas as part of Brain Knowledge Platform will enable neuroscience community to identify more cell types in brain; Investigate spatial location of cell types; Investigate gene expression and co-expression patterns in cell types; Refine boundaries and knowledge of brain regions defined by gene expression.

Proper citation: Allen Brain Cell Atlas (RRID:SCR_024440) Copy   

•   Source: SciCrunch Registry

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

Software for the analysis of multiple diffusion properties along fiber bundle as functions in an infinite dimensional space and their association with a set of covariates of interest, such as age, diagnostic status and gender, in real applications. The resulting analysis pipeline can be used for understanding normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles.

Proper citation: Functional Regression Analysis of DTI Tract Statistics (RRID:SCR_002293) Copy   

•   Source: SciCrunch Registry

https://palamaralab.github.io/software/argon/

Software tool as fast simulator of genetic data that samples from Discrete Time Wright Fisher process backwards in time. Used to simulate long chromosomes and large samples under DTWF, with computational time comparable to recent coalescent simulators.

Proper citation: ARGON (RRID:SCR_021635) Copy   

•   Source: SciCrunch Registry

https://github.com/Cai-Lab-at-University-of-Michigan/nTracer

Software tool as plug-in for ImageJ software. Used for tracing microscopic images.

Proper citation: nTracer (RRID:SCR_023032) Copy   

•   Source: SciCrunch Registry

https://fmriprep.org

Software tool as robust preprocessing pipeline for functional MRI.Used for preprocessing of diverse fMRI data.

Proper citation: fMRIPrep (RRID:SCR_016216) Copy   

•   Source: SciCrunch Registry

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.loni.usc.edu/Software/Pipeline

A free workflow application primarily aimed at neuroimaging researchers that allows users to easily describe their executables in a graphical user interface (ie. create a module) and connect them together to create complex analyses all without having to code a single line in a scripting language. The Pipeline Client runs on your PC/Mac/Linux computer upon which you can create sophisticated processing workflows using a variety of commonly available executable tools (e.g. FSL, AIR, FreeSurfer, AFNI, Diffusion Toolkit, etc). The Distributed Pipeline Server can be installed on your Linux cluster and you can submit processing jobs directly to your own compute systems. Once you����??ve created a module for use in the LONI Pipeline, you can save it into your personal library and reuse it in other workflows you create by simply dragging and dropping it in. Because the LONI Pipeline is written in Java, you can work in whatever operating system suits you best. If there are tools that you need that can only work on another operating system, you can install a Pipeline server on that computer and connect from your client to do processing and analysis remotely.

Proper citation: LONI Pipeline Processing Environment (RRID:SCR_001161) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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

https://nda.nih.gov/

The National Institute of Mental Health Data Archive (NDA) makes available human subjects data collected from hundreds of research projects across many scientific domains. Research data repository for data sharing and collaboration among investigators. Used to accelerate scientific discovery through data sharing across all of mental health and other research communities, data harmonization and reporting of research results. Infrastructure created by National Database for Autism Research (NDAR), Research Domain Criteria Database (RDoCdb), National Database for Clinical Trials related to Mental Illness (NDCT), and NIH Pediatric MRI Repository (PedsMRI).

Proper citation: NIMH Data Archive (RRID:SCR_004434) Copy   

•   Source: SciCrunch Registry

http://trackvis.org/

TrackVis is software tool that can visualize and analyze fiber track data from diffusion MR imaging (DTI/DSI/HARDI/Q-Ball) tractography. It does NOT perform actual fiber tracking. Diffusion Toolkit is a set of tools that reconstruct diffusion imaging data and generate fiber track data for TrackVis to visualize. Because these two sets of tools were developed and maintained separately and each has distinguished funtionalities, they decided to distribute them as two separate programs for the ease of maintenance and upgrade. You do need both of them to perform complete diffusion data processing and analysis. Features of TrackVis include: * Cross-platform. Works on Windows, Mac OS X and Linux with native look and feel. * A variety of track filters (track selecting methods) allowing users to explore and locate specific bundles with ease. * Multiple rendering modes with customizable scalar-driven color codes. * Real-time parameter adjustment and 3D render. * Open format of the track data file allowing users to integrate customized scalar data into the track file and visualize and analyze it. Save and restore scenes in XML style scene file. * Statistical scalar analysis of tracks and ROIs. * Synchronized real-time multiple dataset analysis and display allowing time-point and/or subject comparison. Synchronized analysis and display on same dataset can also be performed in real-time remotely over the network. * Upfront in-line parameter adjustment in real-time. No tedious pop-up dialogs. TrackVis works with Track File created by Diffusion Toolkit. Diffusion Toolkit processes raw DICOM, Nifti format and ANALYZE images. TrackVis and Diffusion Toolkit are cross-platform software. They can run on Windows XP, Mac OS X as well as Linux.

Proper citation: TrackVis (RRID:SCR_004817) Copy   

•   Source: SciCrunch Registry