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http://www.nimh.nih.gov/funding/clinical-trials-for-researchers/practical/stard/index.shtml

A nationwide public health clinical trial conducted to determine the effectiveness of different treatments for people with major depression, in both primary and specialty care settings, who have not responded to initial treatment with an antidepressant. This is the largest and longest study ever done to evaluate depression treatment. The study is completed and no longer recruiting participants. Each of the four levels of the study tested a different medication or medication combination. The primary goal of each level was to determine if the treatment used during that level could adequately treat participants����?? major depressive disorder (MDD). Those who did not become symptom-free could proceed to the next level of treatment. The design of the STAR*D study reflects what is done in clinical practice because it allowed study participants to choose certain treatment strategies most acceptable to them and limited the randomization of each participant only to his/her range of acceptable treatment strategies. No prior studies have evaluated the different treatment strategies in broadly defined participant groups treated in diverse care settings. Over a seven-year period, the study enrolled 4,041 outpatients, ages 18-75 years, from 41 clinical sites around the country, which included both specialty care settings and primary medical care settings. Participants represented a broad range of ethnic and socioeconomic groups. All participants were diagnosed with MDD, were already seeking care at one of these sites, and were referred to the trial by their doctors. * STAR*D Study Medications: Citalopram (Celexa), Sertraline (Zoloft), Bupropion SR (Wellbutrin SR), Venlafaxine XR (Effexor XR), Buspirone (BuSpar), Mirtazapine (Remeron), Triiodothyronine (T3) (Cytomel), Nortriptyline (Pamelor, Aventyl), Tranylcypromine (Parnate), Lithium (Eskalith, Lithobid) *STAR*D Talk Therapy:Cognitive Therapy

Proper citation: Sequenced Treatment Alternatives to Relieve Depression Study (RRID:SCR_008051) Copy   

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http://www.jneurosci.org/supplemental/18/12/4570/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 29, 2013. Supplemental data for the paper Changes in mitochondrial function resulting from synaptic activity in the rat hippocampal slice, by Vytautas P. Bindokas, Chong C. Lee, William F. Colmers, and Richard J. Miller that appears in the Journal of Neuroscience June 15, 1998. You can view digital movies of changes in fluorescence intensity by clicking on the title of interest.

Proper citation: Hippocampal Slice Wave Animations (RRID:SCR_008372) Copy   

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http://grey.colorado.edu/emergent

emergent is a comprehensive, full-featured neural network simulator that allows for the creation and analysis of complex, sophisticated models of the brain in the world. With an emphasis on qualitative analysis and teaching, it also supports the workflow of professional neural network researchers. Its high level drag-and-drop programming interface, built on top of a scripting language that has full introspective access to all aspects of networks and the software itself, allows one to write programs that seamlessly weave together the training of a network and evolution of its environment without ever typing out a line of code. Networks and all of their state variables are visually inspected in 3d, allowing for a quick visual regression of network dynamics and robot behavior. This same 3d world sports a highly accurate Newtonian physics simulation, allowing you to create rich robotics simulations (for example, a car). As a direct descendant of PDP (1986) and PDP (1999), emergent has been in development for decades. In the most recent versions available strive to distill it down to its essential elements. Those that take the time to learn the best practices will be rewarded with the ability to create and understand the most complicated neural models ever published.

Proper citation: Emergent (RRID:SCR_008500) Copy   

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

A repository of schizophrenia neuroimaging data collected from over 450 individuals with schizophrenia, healthy controls and their respective siblings, most with 2-year longitudinal follow-up. The data include neuroimaging data, cognitive data, clinical data, and genetic data.

Proper citation: Northwestern University Schizophrenia Data and Software Tool (NUSDAST) (RRID:SCR_014153) Copy   

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http://krasnow1.gmu.edu/CENlab/software.html

Stochastic reaction-diffusion simulator in Java which is used for simulating neuronal signaling pathways.

Proper citation: NeuroRD (RRID:SCR_014769) Copy   

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http://www.patternlabforproteomics.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented July 5, 2018. Gene Ontology Explorer (GOEx) combines data from protein fold changes with GO over-representation statistics to help draw conclusions in proteomic experiments. It is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. A recent hack included in GOEx is to load the sparse matrix index file directly into GOEx, instead of going through the report generation using the AC/T-fold methods. This makes it easy for GOEx to analyze any list of proteins as long as the list follows the index file format (described in manuscript) . Please note that if using this alternative strategy, there will be no protein fold information. Platform: Windows compatible

Proper citation: GOEx - Gene Ontology Explorer (RRID:SCR_005779) Copy   

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http://brainvis.wustl.edu/wiki/index.php/Caret:About

Software package to visualize and analyze structural and functional characteristics of cerebral and cerebellar cortex in humans, nonhuman primates, and rodents. Runs on Apple (Mac OSX), Linux, and Microsoft Windows operating systems.

Proper citation: Computerized Anatomical Reconstruction and Editing Toolkit (RRID:SCR_006260) Copy   

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https://biccn.org

Consortium for the cell census in the brain. Integrated network of data generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate brains.

Proper citation: BICCN (RRID:SCR_015820) Copy   

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https://nemoarchive.org/

Data repository specifically focused on storage and dissemination of omic data generated from BRAIN Initiative and related brain research projects. Data repository and archive for BCDC and BICCN project, among others. NeMO data include genomic regions associated with brain abnormalities and disease, transcription factor binding sites and other regulatory elements, transcription activity, levels of cytosine modification, histone modification profiles and chromatin accessibility.

Proper citation: NeMOarchive (RRID:SCR_016152) Copy   

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

Data analysis software that can simultaneously characterize a large number of white matter bundles within and across different subjects for group analysis. It has three major components: construction of the structural connectome for the whole brain, low-dimensional representation of streamlines in each connection, and multi-level connectome analysis.

Proper citation: Mapping Population-based Structural Connectomes (RRID:SCR_016232) Copy   

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http://fantom.gsc.riken.jp/

International collaborative research project and database of annotated mammalian genome. Used to improve estimates of total number of genes and their alternative transcript isoforms in both human and mouse. Consortium to assign functional annotations to full length cDNAs that were collected during Mouse Encyclopedia Project at RIKEN.

Proper citation: Functional Annotation of the Mammalian Genome (RRID:SCR_000788) Copy   

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http://nipy.org

Community site to make brain imaging research easier that aims to build software that is clearly written, clearly explained, a good fit for the underlying ideas, and a natural home for collaboration.

Proper citation: Neuroimaging in Python (RRID:SCR_013141) Copy   

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https://www.delaneycare.org/index.php

The Collaboratory of AIDS Researchers for Eradication (CARE) is a consortium of scientific experts in the field of HIV latency from several U.S. and European academic research institutions as well as Merck Research Laboratories working together to find a cure for HIV.

Proper citation: Collaboratory of AIDS Researchers for Eradciation (CARE) (RRID:SCR_013681) Copy   

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http://hbatlas.org/pages/publications

A research paper with supplementary materials reporting the generation and analysis of exon-level transcriptome and associated genotyping data. The experiment represented both males and females of multiple ethnicities and examines gene regulation and expression in different areas of the brain. A data set on the human brain transcriptome as well as insights into the transcriptional foundations of human neurodevelopment is provided.

Proper citation: Spatio-temporal transcriptome of the human brain (RRID:SCR_013743) Copy   

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http://cobre.mrn.org/megsim/

Realistic simulated MEG datasets ranging from basic sensory to oscillatory sets that mimic functional connectivity; as well as basic visual, auditory, and somatosensory empirical sets. The simulated sets were created for the purpose of testing analysis algorithms across the different MEG systems when the truth is known. MEG baseline recordings were obtained from 5 healthy participants, using three MEG systems: VSM/CTF Omega, Elekta Neuromag Vectorview, 4-D Magnes 3600. Simulated signals were embedded within the CTF and Neuromag 306 baseline recordings (4-D to be added). Participant MRIs are available. Averaged simulation files are available as netcdf files. Neuromag 306 averaged simulations are also available in fif format. Also available: single trials of data where the simulated signal is jittered about a mean value, continuous fif files where the simulated signal is marked by a trigger, and simulations with oscillations added to mimic functional connectivity.

Proper citation: MEGSIM (RRID:SCR_002420) Copy   

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http://humanconnectome.org/connectome/connectomeDB.html

Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.

Proper citation: ConnectomeDB (RRID:SCR_004830) Copy   

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http://www.armystarrs.org/

Study of mental health risk and resilience factors ever conducted among military personnel. The purpose of Army STARRS is to identify as quickly as possible factors that protect or pose risks to Soldiers'' emotional well-being and overall mental health so that the Army may apply the knowledge to its ongoing health promotion, risk reduction, and suicide prevention efforts. Army STARRS investigators will use four separate study components the Historical Data Study, New Soldier Study, All Army Study, and Soldier Health Outcomes Study to identify factors that help protect a Soldier''s mental health and factors that put a Soldier''s mental health at risk. Army STARRS is a five-year study that will run through 2014. Findings will be reported as they become available, so that the Army may apply them to its ongoing health promotion, risk reduction, and suicide prevention efforts. Given its length and scope, Army STARRS will generate a vast amount of information and will allow investigators to focus on periods in a military career that are known to be high risk for psychological problems. The information gathered from volunteer participants throughout the study will help researchers identify not only potentially relevant risk factors, but potential protective factors as well. Because promoting mental health and reducing suicide risk are important for all Americans, the findings from Army STARRS will benefit not only servicemembers but the nation as a whole. NIMH has assembled a group of renowned experts to carry out this research including teams from the Uniformed Services University of the Health Sciences (USUHS), the University of California, San Diego, University of Michigan, Harvard Medical School, and NIMH. Additional Army and NIMH program staff will contribute to the oversight and implementation of the study. This research team brings together international leaders in military health, health and behavior surveys, epidemiology, suicide, and genetic and neurobiological factors involved in psychological health.

Proper citation: Army STARRS (RRID:SCR_006708) Copy   

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http://www.nimh.nih.gov/funding/clinical-trials-for-researchers/practical/step-bd/index.shtml

A long-term outpatient study designed to find out which treatments, or combinations of treatments, are most effective for treating episodes of depression and mania and for preventing recurrent episodes in people with bipolar disorder. This study has been completed. (2005) STEP-BD is evaluating all the best-practice treatment options used for bipolar disorder: mood-stabilizing medications, antidepressants, atypical antipsychotics, and psychosocial interventions - or talk therapies - including Cognitive Behavioral Therapy, Family-focused Therapy, Interpersonal and Social Rhythm Therapy, and Collaborative Care (psychoeducation). There are two kinds of treatment pathways in STEP-BD, and participants may have the opportunity to take part in both. The medications and psychosocial interventions provided in these pathways are considered among the best choices of treatment for bipolar disorder in everyday clinical practice. In the Best Practice Pathway, participants are followed by a STEP-BD certified doctor and all treatment choices are individualized. Everyone enrolled in STEP-BD may participate in this pathway. Participants and their doctors work together to decide on the best treatment plans and to change these plans if needed. Also, anyone who wishes to stay on his or her current treatment upon entering STEP-BD may do so in this pathway. Adolescents and adults age 15 years and older may participate in the Best Practice Pathway. For adults age 18 and older, another way to participate is in the STEP-BD Randomized Care Pathways. Depending on their symptoms, participants may be offered treatment in one or more of these pathways during the course of the study. The participants remain on mood-stabilizing medication. However, because doctors are uncertain which of several treatment strategies work best for bipolar disorder, another medication and/or talk therapy may be added. Each Randomized Care Pathway involves a different set of these additional treatments. Unlike in the Best Practice Pathway, the participants in the Randomized Care Pathways are randomly assigned to treatments. Also, in some cases, neither the participant nor the doctor will be told which of the different medications is being added. This is called a double-blind study and is done so that the medication effects can be evaluated objectively, without any unintended bias that may come from knowing what has been assigned. Participants will not be assigned medications that they have had bad reactions to in the past, that they are strongly opposed to, or that the doctor feels are unsuitable for them. The medication(s) participants may be randomly assigned to in the Randomized Care Pathways are free of charge. There are other treatment options for participants if they do not respond well to the treatment assigned to them. Also, participants may return to the Best Practice Pathway at any time. About 1,500 individuals will be enrolled in at least one Randomized Care Pathway during their period of participation in STEP-BD. It is important to note that STEP-BD provides continuity of care. For example, if a participant starts out in the Best Practice Pathway and later chooses to enter one of the Randomized Care Pathways, he or she continues with the same STEP-BD doctor and treatment team. Then, after completing the Randomized Care Pathway, the participant may return to the Best Practice Pathway for ongoing, individually-tailored treatment. Follow the link to view study info at Clinicaltrials.gov, http://www.clinicaltrials.gov/ct/show/NCT00012558?order=1

Proper citation: Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) (RRID:SCR_008844) Copy   

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http://research.mssm.edu/cnic/

Center to advance research and training in mathematical, computational and modern imaging approaches to understanding the brain and its functions. Software tools and associated reconstruction data produced in the center are available. Researchers study the relationships between neural function and structure at levels ranging from the molecular and cellular, through network organization of the brain. This involves the development of new computational and analytic tools for imaging and visualization of 3-D neural morphology, from the gross topologic characteristics of the dendritic arbor to the fine structure of spines and their synapses. Numerical simulations of neural mechanisms based on these structural data are compared with in-vivo and in-vitro electrophysiological recordings. The group also develops new theoretical and analytic approaches to exploring the function of neural models of working memory. The goal of this analytic work is to combine biophysically realistic models and simulations with reduced mathematical models that capture essential dynamical behaviors while reproducing the functionally important features of experimental data. Research areas include: Imaging Studies, Volume Integration, Visualization Techniques, Medial Axis Extraction, Spine Detection and Classification, Applications of Rayburst, Analysis of Spatially Complex Structures, Computational Modeling, Mathematical and Analytic Studies

Proper citation: Computational Neurobiology and Imaging Center (RRID:SCR_013317) Copy   

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http://interactome.baderlab.org/

Project portal for the Human Reference Protein Interactome Project, which aims generate a first reference map of the human protein-protein interactome network by identifying binary protein-protein interactions (PPIs). It achieves this by systematically interrogating all pairwise combinations of predicted human protein-coding genes using proteome-scale technologies.

Proper citation: Human Reference Protein Interactome Project (RRID:SCR_015670) Copy   

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