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http://www.nimh.nih.gov/funding/clinical-trials-for-researchers/practical/tordia/treatment-of-ssri-resistant-depression-in-adolescents-tordia.shtml

A multi-site, clinical research study examining treatment options for teens whose depression has not improved after one adequate trial of a selective serotonin reuptake inhibitor (SSRI), a type of antidepressant. The purpose of the study is to determine how best to treat adolescents with depression that is resistant to the first SSRI antidepressant they have tried. Participants receive one of three other antidepressant medications, either alone or in combination with cognitive behavioral therapy. The TORDIA study aims to develop useful clinical guidelines for the care and management of adolescent depression. Adolescents ages 12 to 18, currently taking a prescribed selective serotonin reuptake inhibitor (SSRI) and still experiencing depression, participate in a 12-week randomized treatment study that includes one of four conditions: (1) switching to an alternative SSRI, (2) switching to a different non-SSRI antidepressant, (3) switching to an alternative SSRI and receiving cognitive behavioral therapy (CBT), or (4) switching to a different non-SSRI antidepressant and receiving CBT. This is a double-blind study, which means that neither the participant nor the clinical staff will know which of the three possible medications has been assigned. Participants who respond to the assigned treatment will receive 12 additional weeks of the same treatment. Those who do not appear to be getting better will be offered 12 weeks of an alternative, individualized treatment plan based on each participant''s particular needs. All participants will receive follow-up psychiatric evaluations for 12 months after the 12-week continuation phase of the study, regardless of treatment adherence. For more information visit, http://www.clinicaltrials.gov/ct2/show/NCT00018902?term=clinical+trial+AND+treatment+of+ssri-resistant+AND+depression+AND+TORDIA+AND+study&rank=1

Proper citation: Treatment of SSRI-resistant Depression in Adolescents (TORDIA) (RRID:SCR_008831) Copy   

•   Source: SciCrunch Registry

http://trans.nih.gov/bmap/index.htm

The Brain Molecular Anatomy Project is a trans-NIH project aimed at understanding gene expression and function in the nervous system. BMAP has two major scientific goals: # Gene discovery: to catalog of all the genes expressed in the nervous system, under both normal and abnormal conditions. # Gene expression analysis: to monitor gene expression patterns in the nervous system as a function of cell type, anatomical location, developmental stage, and physiological state, and thus gain insight into gene function. In pursuit of these goals, BMAP has launched several initiatives to provide resources and funding opportunities for the scientific community. These include several Requests for Applications and Requests for Proposals, descriptions of which can be found in this Web site. BMAP is also in the process of establishing physical and electronic resources for the community, including repositories of cDNA clones for nervous system genes, and databases of gene expression information for the nervous system. Most of the BMAP initiatives so far have focused on the mouse as a model species because of the ease of experimental and genetic manipulation of this organism, and because many models of human disease are available in the mouse. However, research in humans, other mammalian species, non-mammalian vertebrates, and invertebrates is also being funded through BMAP. For the convenience of interested investigators, we have established this Web site as a central information resource, focusing on major NIH-sponsored funding opportunities, initiatives, genomic resources available to the research community, courses and scientific meetings related to BMAP initiatives, and selected reports and publications. When appropriate, we will also post initiatives not directly sponsored by BMAP, but which are deemed relevant to its goals. Posting decisions are made by the Trans-NIH BMAP Committee

Proper citation: BMAP - Brain Molecular Anatomy Project (RRID:SCR_008852) Copy   

•   Source: SciCrunch Registry

http://cbdb.nimh.nih.gov/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 07, 2013. A multidisciplinary neuroscience laboratory in which basic and clinical scientists work side by side exploring neural mechanisms and models of mental and cognitive function and of neuropsychiatric illness. Experiments are performed at many levels of inquiry, from basic molecular biology of the gene to clinical examinations of patients. A major area of investigation of this laboratory is the genetic mechanisms implicated in the pathogenesis of schizophrenia and its treatment. The laboratory is organized as a multi-disciplinary team of investigators with a common mission: to identify and fully characterize basic genetic and neurobiological mechanisms of schizophrenia and related cognitive and emotional disorders. The various components of this effort are centered various different units or divisions represented by groups of investigators, at various levels of training and experience, working on related experiments. The Director of the Branch and of the Genes, Cognition and Psychosis Program (GCAP) is Daniel R. Weinberger, M.D. The CBDB is the principle research laboratory in the created (2003) Genes, Cognition, and Psychosis Program (GCAP) of the NIMH. After twelve years of residing on the pastoral grounds of St. Elizabeths Hospital, in Southeast Washington, CBDB moved back to the main NIH campus in Bethesda, Maryland in 1998. While the unique setting of St. Elizabeths is irreplaceable, we have occupied beautiful new laboratories and clinic spaces that were created for us, and we are in the mainstream of NIH life., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NIMH Intramural Research Program Clinical Brain Disorders Branch (RRID:SCR_008728) Copy   

•   Source: SciCrunch Registry

http://erpinfo.org/erplab

A set of open source, freely available Matlab routines for analyzing Event Related Potential (ERP) data. It is tightly integrated with the EEGLAB Toolbox. ERPLAB routines can be accessed from the Matlab command window and from Matlab scripts in addition to being accessed from the EEGLAB GUI. Consequently, ERPLAB provides the ease of learning of a GUI-based system but also provides the power and flexibility of a scripted system.The development of ERPLAB Toolbox is being coordinated by Steve Luck and Javier Lopez-Calderon at the UC-Davis Center for Mind & Brain, with financial support from NIMH.

Proper citation: ERPLAB (RRID:SCR_009574) Copy   

•   Source: SciCrunch Registry

https://github.com/BRAINSia/BRAINSTools/tree/master/BRAINSDemonWarp

A command line program for image registration by using different methods including Thirion and diffeomorphic demons algorithms. The function takes in a template image and a target image along with other optional parameters and registers the template image onto the target image. The resultant deformation fields and metric values can be written to a file. The program uses the Insight Toolkit (www.ITK.org) for all the computations, and can operate on any of the image types supported by that library. This a an ITK based implementation of various forms of Thirion Demons based registration (including diffeomorphic demons registration originating from Tom Vercauteren at INRIA ).

Proper citation: BRAINSDemonWarp (RRID:SCR_009524) Copy   

•   Source: SciCrunch Registry

https://www.intomics.com/inbio/map/#home

Database for investigating and visualizing protein-protein interactions. It aims to maintain coverage, quality, convenience, and transparency in the field of PPI research.

Proper citation: inBio Map (RRID:SCR_016147) 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://fcon_1000.projects.nitrc.org/indi/indiPRIME.html

Open resource for nonhuman primate imaging.Used for aggregation independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI).Consortium and data collection for the neuroimaging community to map the non-human primate connectome. Anatomical, functional, and diffusion MRI datasets openly shared via the International Neuroimaging Data sharing Initiative (INDI).

Proper citation: Primate Data Exchange (RRID:SCR_016435) Copy   

•   Source: SciCrunch Registry

https://github.com/hakyimlab/PrediXcan

Software tool to detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations. Used to test the molecular mechanisms through which genetic variation affects phenotype.

Proper citation: PrediXcan (RRID:SCR_016739) Copy   

•   Source: SciCrunch Registry

https://github.com/fsotoc/FaReT

Software toolkit of three dimensional models and software to study face perception. Collection of plugins used with MakeHuman to create face stimuli for experiments.

Proper citation: Face Research Toolkit (RRID:SCR_023322) Copy   

•   Source: SciCrunch Registry

https://github.com/parklab/NGSCheckMate

Software package for validating sample identity in next generation sequencing studies within and across data types. Used for identifying next generation sequencing data files from the same individual. Used for checking sample matching for NGS data.

Proper citation: NGSCheckMate (RRID:SCR_022994) Copy   

•   Source: SciCrunch Registry

https://github.com/rondolab/MR-PRESSO

Software R package for performing Mendelian randomization pleiotropy residual sum and outlier method.Used to identify horizontal pleiotropic outliers in multi instrument summary level MR testing.

Proper citation: MR-PRESSO (RRID:SCR_023697) Copy   

•   Source: SciCrunch Registry

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

Software that facilitates accurate extraction of text from PDF files of research articles for use in text mining applications. It is intended for both scientists and natural language processing (NLP) engineers interested in getting access to text within specific sections of research articles. The system extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles. The current version of LA-PDFText is a baseline system that extracts text using a three-stage process: * identification of blocks of contiguous text * classification of these blocks into rhetorical categories * extraction of the text from blocks grouped section-wise.

Proper citation: lapdftext (RRID:SCR_006167) 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

https://data.broadinstitute.org/alkesgroup/Eagle/

Software package for statistical estimation of haplotype phase either within a genotyped cohort or using a phased reference panel in large scale sequencing. The package includes Eagle1 (to harness identity-by-descent among distant relatives to rapidly call phase using a fast scoring approach) and Eagle2 (to analyze a full probabilistic model similar to the diploid Li-Stephens model used by previous HMM-based methods.

Proper citation: Eagle (RRID:SCR_015991) Copy   

•   Source: SciCrunch Registry

https://github.com/UMCU-RIBS/ALICE

Software tool for automatic localization of intra-cranial electrodes for clinical and high density grids. Software for coregistering high density ECoG grids to MRI anatomy.

Proper citation: ALICE (RRID:SCR_017463) Copy   

•   Source: SciCrunch Registry

https://miracl.readthedocs.io/en/latest/

Automated software resource that combines histologically cleared volumes with connectivity atlases and MRI, enabling analysis of histological features across multiple fiber tracts and networks, and their correlation with in vivo biomarkers.Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI. Open source pipeline for automated registration of mice clarity data to Allen reference atlas, segmentation and feature extraction of mice clarity data in 3D, registration of mice multimodal imaging data to Allen reference atlas, tract or label specific connectivity analysis based on Allen connectivity atlas,comparison of diffusion tensort imaging/tractography, virus tracing using CLARITY and Allen connectivity atlas, statistical analysis of CLARITY and Imaging data, atlas generation and label manipulation.

Proper citation: MIRACL (RRID:SCR_020945) Copy   

•   Source: SciCrunch Registry

https://github.com/gillislab/CrossExpression

Software package to discover genes that coordinate their expression across spatially adjacent cells. Used for analysis of spatial transcriptomic data allowing in-depth analyses how genes coordinate their expression in space to perform tissue-level functions. Facilitates analysis and exploration of cross-expression patterns.

Proper citation: CrossExpression (RRID:SCR_027990) Copy   

•   Source: SciCrunch Registry

https://github.com/ericcfields/FMUT/wiki/

Software toolbox (FMUT) is an extension to David Groppe’s Mass Univariate ERP Toolbox (MUT). MUT implements t-tests for several different mass univariate approaches to the analysis of ERP data. FMUT adds to this by implementing one-way and factorial ANOVA versions of the same mass univariate approaches.

Proper citation: Factorial Mass Univariate Toolbox (RRID:SCR_028612) Copy   

•   Source: SciCrunch Registry

https://www.synapse.org/

Sage Bionetworks, Mount Sinai School of Medicine (MSSM), University of Pennsylvania (Penn), the National Institute of Mental Health (NIMH), and Takeda Pharmaceuticals Company Limited (TAKEDA) have launched a Public-Private Pre-Competitive Consortium, the CommonMind Consortium, to generate and analyze large-scale genomic data from human subjects with neuropsychiatric disease and to make this data and the associated analytical results broadly available to the public. This collaboration brings together disease area expertise, large scale and well curated brain sample collections, and data management and analysis expertise from the respective institutions. As many as 450 million people worldwide are believed to be living with a mental or behavioral disorder: schizophrenia and bipolar disorder are two of the top six leading causes of years lived with disability according to the World Health Organization. The burden on the individual as well as on society is significant with estimates for the health care costs for these individuals as high as four percent GNP. This highlights a grave need for new therapies to alleviate this suffering. Researchers from MSSM including Dr. Pamela Sklar, Dr. Joseph Buxbaum and Dr. Eric Schadt will join with Dr. Raquel Gur and Dr. Chang-Gyu Hahn from Penn to combine their extensive brain bank collections for the generation of whole genome scale RNA and DNA sequence data. Dr.Pamela Sklar, Professor of Psychiatry and Neuroscience at MSSM commented this is an exciting opportunity for us to use the newest genomic methods to really expand our understanding of the molecular underpinnings of neuropsychiatric disease, while Dr Raquel Gur, Professor of Psychiatry from Penn observed this will be a great complement to some of the large-scale genetic analyses that have been carried out to date because it will give a more complete mechanistic picture. The CommonMind Consortium is committed to generating an open resource for the community and invites others with common goals to contact us at info (at) CommonMind.org.

Proper citation: CommonMind Consortium (RRID:SCR_000139) Copy   

•   Source: SciCrunch Registry