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

Software tool allows to assess dMRI data both at single subject and group levels.Calculates average SNR across all voxels within brain mask to give summary measure of overall quality of dataset. Used to generate single subject and study wise reports and databases.

Proper citation: eddyqc (RRID:SCR_024936) Copy   

•   Source: SciCrunch Registry

https://github.com/xinhe-lab/GSFA

Software R package that performs sparse factor analysis and differential gene expression discovery simultaneously on single cell CRISPR screening data.

Proper citation: Guided Sparse Factor Analysis (RRID:SCR_025023) Copy   

•   Source: SciCrunch Registry

https://kimlab.io/brain-map/DevCCF/

Open access multimodal 3D atlases of developing mouse brain that can be used to integrate mouse brain imaging data for visualization, education, cell census mapping, and more. Atlas ages include E11.5, E13.5, E15.5, E18.5, P4, P14, and P56. Web platform can be utilized to visualize and explore the atlas in 3D. Downloadable atlas can be used to align multimodal mouse brain data. Morphologically averaged symmetric template brains serve as the basis reference space and coordinate system. Anatomical labels are manually drawn in 3D based on the prosomeric model. For additional references, the P56 template includes templates and annotations from the aligned Allen Mouse Brain Common Coordinate Framework (Allen CCFv3) and aligned Molecular Atlas of the Adult Mouse Brain.

Proper citation: 3D Developmental Mouse Brain Common Coordinate Framework (RRID:SCR_025544) Copy   

•   Source: SciCrunch Registry

http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/

Software tool for ultra fast eQTL analysis via large matrix operations.

Proper citation: MatrixEQTL (RRID:SCR_025513) Copy   

•   Source: SciCrunch Registry

https://beadl.org

Universal framework for describing behavioral tasks. Language to abstract and standardize behavioral task descriptions on two layers. Graphical layer specifies elements to describe behavioral tasks as state machine in formal flow diagram and how task controlling system interacts with subject. This graphical layer has been designed to be easy to understand while retaining all aspects of behavioral task. The second layer is corresponding, XML-based description of task. This layer forms rigid, yet extensible foundation of BEADL and hides hardware implementation related details form graphical representation.BEADL-specific extension for Neurodata Without Borders data standard defines how behavioral outcomes of task are stored in NWB including corresponding BEADL task description.

Proper citation: BEADL:BEhavioral tAsk Description Language (RRID:SCR_025464) Copy   

•   Source: SciCrunch Registry

https://portal.brain-map.org/genetic-tools/genetic-tools-atlas

Searchable catalog of enhancer-adeno-associated viruses (AAVs) that have been developed and tested at the Allen Institute for Brain Science. We present a suite of enhancer AAVs that can provide access to specific cell types when delivered to the whole brain. Multiple epigenomic and transcriptomic datasets were interrogated to reveal candidate enhancers that are selectively accessible in particular cell populations. Enhancer AAVs were constructed and screened for desirable expression and a sizeable subset of enhancer AAVs were subjected to further characterization by single cell transcriptomics and/or brain-wide expression imaging in mouse. In the GTA, we present a large toolkit for selective gene expression in cell types of interest. Genetic Tools Atlas is part of the growing Brain Knowledge Platform.

Proper citation: Genetic Tools Atlas (RRID:SCR_025643) Copy   

•   Source: SciCrunch Registry

https://brainlife.io/docs/using_ezBIDS/

Web-based BIDS conversion tool to convert neuroimaging data and associated metadata to BIDS standard. Guided standardization of neuroimaging data interoperable with major data archives and platforms.

Proper citation: ezBIDS (RRID:SCR_025563) Copy   

•   Source: SciCrunch Registry

https://kimlab.io/brain-map/DevATLAS/

Whole brain developmental map of neuronal circuit maturation. Generated by whole brain spatiotemporal mapping of circuit maturation during early postnatal development. Standard reference for normative developmental trajectory of neuronal circuit maturation, as well as high throughput platform to pinpoint when and where circuit maturation is disrupted in mouse models of neurodevelopmental disorders, such as fragile X syndrome.

Proper citation: DevATLAS (RRID:SCR_025718) Copy   

•   Source: SciCrunch Registry

https://github.com/Washington-University/HCPpipelines

Software package as set of tools, primarily shell scripts, for processing multi-modal, high-quality MRI images for the Human Connectome Project. Minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space.

Proper citation: HCP Pipelines (RRID:SCR_026575) Copy   

•   Source: SciCrunch Registry

https://github.com/kaizhang/SnapATAC2

Software Python/Rust package for single-cell epigenomics analysis.

Proper citation: SnapATAC2 (RRID:SCR_026622) Copy   

•   Source: SciCrunch Registry

https://github.com/noahbenson/neuropythy

Software neuroscience library for Python, intended to complement the existing nibabel library. Can automatic download data and interpret them into Python data structures.

Proper citation: neuropythy (RRID:SCR_027787) Copy   

•   Source: SciCrunch Registry

https://weghornlab.org/software.html

Software tool which derives gene-specific probabilistic estimates of the strength of negative and positive selection in cancer.

Proper citation: CBaSE (RRID:SCR_027765) Copy   

•   Source: SciCrunch Registry

https://github.com/ReproBrainChart

Open data resource for mapping brain development and its associations with mental health. Integrates data from 5 large studies of brain development in youth from three continents (N = 6,346). Bifactor models were used to create harmonized psychiatric phenotypes, capturing major dimensions of psychopathology. Neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner.

Proper citation: Reproducible Brain Charts (RRID:SCR_027837) Copy   

•   Source: SciCrunch Registry

https://doi.org/10.17605/OSF.IO/WDR78

Open source resource of manually curated and expert reviewed infant brain segmentations hosted on OpenNeuro.org. and OSF.io. Anatomical MRI data was segmented from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Images showed dramatic differences in myelination and intensities across 1–9 months, emphasizing the need for densely sampled gold-standard segmentations across early life. This dataset provides a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this dataset provides a vitally needed foundation for early-life large-scale studies such as HBCD.

Proper citation: Baby Open Brains (RRID:SCR_027836) 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://afni.nimh.nih.gov/afni/

Set of (mostly) C programs that run on X11+Unix-based platforms (Linux, Mac OS X, Solaris, etc.) for processing, analyzing, and displaying functional MRI (FMRI) data defined over 3D volumes and over 2D cortical surface meshes. AFNI is freely distributed as source code plus some precompiled binaries.

Proper citation: Analysis of Functional NeuroImages (RRID:SCR_005927) Copy   

•   Source: SciCrunch Registry

https://www.braintest.org/brain_test/BrainTest

A portal of online studies that encourage community participation to tackle the most challenging problems in neuropsychiatry, including attention-deficit / hyperactivity disorder, schizophrenia, and bipolar disorder. Our approach is to engage the community and try to recruit tens of thousands of people to spend an hour of their time on our site. You folks will provide data in both brain tests and questionnaires, as well as DNA, and in return, we will provide some information about your brain and behavior. You will also be entered to win amazon.com gift cards. While large collaborative efforts were made in genetics in order to discover the secrets of the human genome, there are still many mysteries about the behaviors that are seen in complex neuropsychiatric syndromes and the underlying biology that gives rise to these behaviors. We know that it will require studying tens of thousands of people to begin to answer these questions. Having you, the public, as a research partner is the only way to achieve that kind of investment. This site will try to reach that goal, by combining high-throughput behavioral assessment using questionnaires and game-like cognitive tests. You provide the data and then we will provide information and feedback about why you should help us achieve our goals and how it benefits everyone in the world. We believe that through this online study, we can better understand memory and attention behaviors in the general population and their genetic basis, which will in turn allow us to better characterize how these behaviors go awry in people who suffer from mental illness. In the end, we hope this will provide better, more personalized treatment options, and ultimately prevention of these widespread and extremely debilitating brain diseases. We will use the data we collect to try to identify the genetic basis for memory and impulse control, for example. If we can achieve this goal, maybe we can then do more targeted research to understand how the biology goes awry in people who have problems with cognition, including memory and impulse control, like those diagnosed with ADHD, Schizophrenia, Bipolar Disorder, and Autism Spectrum Disorders. By participating in our research, you can learn about mental illness and health and help researchers tackle these complex problems. We can''t do it without your help.

Proper citation: Brain Test (RRID:SCR_006212) Copy   

•   Source: SciCrunch Registry

http://intramural.nimh.nih.gov/gcap/index.htm

Schizophrenia related portal that aims to solve the mystery of genetic predisposition to psychosis, develop new methods for early diagnosis and prevention, and discover new treatments that will cure people suffering from it. Our objectives are to fully characterize: # neurobiological mechanisms related to susceptibility genes for schizophrenia and related clinical disorders; # genetic variation in aspects of cognition and emotionality associated with schizophrenia; and # small molecular targets for novel therapies. A unique feature of this Program is that its diverse scientific resources will be focused on a highly specific scientific agenda, that is to acquire the critical biological information about the susceptibility genes associated with schizophrenia and related illnesses. Our mission and goal, to understand the basic mechanisms of serious mental illness, has again guided us into new areas of research and to new insights. We have found evidence of new genes implicated in the cause of schizophrenia and involved in brain functions related to cognition and emotion and we have begun to explore how genes interact with each other and with the environment to individualize risk for these conditions. We are working now with over 20 genes related to schizophrenia. One of the key developments in our research over the past year has been the emergence of some targets for the development of novel therapeutics. We have discovered a new schizophrenia susceptibility gene, KCNH2, which represents the first clear target for the development of novel treatments. Just in this past year, for example, we published the first extensive statistical analysis of how schizophrenia genes may vary in their risk effects based on different genetic background (Nicodemus et al Hum Gen 2006), the first studies of schizophrenia genes interacting in effecting gene expression in brain (Lipska et al Hum Mol Genetics 2006a, Lipska et al Hum Mol Gen 2006 b); the first evidence that the mechanism of genetic association of NRG1 with schizophrenia involves a novel isoform of the gene in human brain (Law et al PNAS 2006), and the first evidence that MAOA may be linked to mood and impulse control because it effects critical mood regulatory neural networks (Meyer-Lindenberg et al PNAS 2006).

Proper citation: Genes Cognition and Psychosis Program (RRID:SCR_006292) Copy   

•   Source: SciCrunch Registry

http://vinovia.ncl.ac.uk/emagewebapp/pages/eadhb_home.jsf

Database of a set of standard 3D virtual models at different stages of development from Carnegie Stages (CS) 12-23 (approximately 26-56 days post conception) in which various anatomical regions have been defined with a set of anatomical terms at various stages of development (known as an ontology). Experimental data is captured and converted to digital format and then mapped to the appropriate 3D model. The ontology is used to define sites of gene expression using a set of standard descriptions and to link the expression data to an ''''anatomical tree''''. Human data from stages CS12 to CS23 can be submitted to the HUDSEN Gene Expression Database. The anatomy ontology currently being used is based on the Edinburgh Human Developmental Anatomy Database which encompasses all developing structures from CS1 to CS20 but is not detailed for developing brain structures. The ontology is being extended and refined (by Prof Luis Puelles, University of Murcia, Spain) and will be incorporated into the HUDSEN database as it is developed. Expression data is annotated using two methods to denote sites of expression in the embryo: spatial annotation and text annotation. Additionally, many aspects of the detection reagent and specimen are also annotated during this process (assignment of IDs, nucleotide sequences for probes etc). There are currently two main ways to search HUDSEN - using a gene/protein name or a named anatomical structure as the query term. The entire contents of the database can be browsed using the data browser. Results may be saved. The data in HUDSEN is generated from both from researchers within the HUDSEN project, and from the wider scientific community. The HUDSEN human gene expression spatial database is a collaboration between the Institute of Human Genetics in Newcastle, UK, and the MRC Human Genetics Unit in Edinburgh, UK, and was developed as part of the Electronic Atlas of the Developing Human Brain (EADHB) project (funded by the NIH Human Brain Project). The database is based on the Edinburgh Mouse Atlas gene expression database (EMAGE), and is designed to be an openly available resource to the research community holding gene expression patterns during early human development.

Proper citation: HUDSEN Human Gene Expression Spatial Database (RRID:SCR_006325) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/functionalconnectivitytoolbox/

MATLAB toolbox for performing functional connectivity analyses includes many of the most commonly-used approaches researchers have utilized to date for the identification of condition-dependent functional interactions between fMRI time-series obtained from two or more brain regions. The approaches are either bivariate or multivariate methods defined in time or frequency domains that emphasize distinct features of relationships among the time-series.

Proper citation: Functional Connectivity Toolbox (RRID:SCR_006394) Copy   

•   Source: SciCrunch Registry