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http://www.open-ephys.org/pulsepal
Open source pulse train generator that allows users to create and trigger software defined trains of voltage pulses with high temporal precision. Generates precisely timed pulse sequences for use in research involving electrophysiology or psychophysics.
Proper citation: Open Ephys: Pulse Pal (RRID:SCR_017203) Copy
http://kim.bio.upenn.edu/software/pivot.shtml
Software R package for interactive analysis and visualization of transcriptomics data. Operating systems are macOS, Linux, Windows.
Proper citation: PIVOT software (RRID:SCR_017210) Copy
https://github.com/SciCrunch/NIF-Ontology/tree/neurons/ttl
An ontology for describing the complex phenotypes of neurons.
Proper citation: Neuron Phenotype Ontology (RRID:SCR_017403) Copy
http://www.brainimagelibrary.org
Public, NIH-funded repository and analysis ecosystem for brain microscopy data, designed to store, share, and process massive volumetric datasets. It enables researchers to access whole-brain images, neuron morphologies, and spatial data without needing to download, fostering collaborative discovery. Used to deposit, analyze, mine, share and interact with large brain image datasets.
Proper citation: Brain Image Library (RRID:SCR_017272) Copy
BossDB (Brain Observatory Storage Service and Database) is a cloud-based ecosystem for the storage and management of public large-scale volumetric neuroimaging and connectomics datasets. This includes volumetric Electron Microscopy and X-Ray Micro/Nanotomography data with support for multi-channel image data, segmentations, annotations, meshes, and connectomes. BossDB integrates with community resources for data access, processing, visualization, and analysis, and includes an API that enables metadata management, rendering, datatype conversions, and ingest.
Proper citation: Brain Observatory Storage Service and Database (BossDB) (RRID:SCR_017273) Copy
Portal for visualization and analysis of multi omic data in public and private domains. Enables upload, visualization and analysis of scRNA-seq data.
Proper citation: gene Expression Analysis Resource (RRID:SCR_017467) Copy
https://github.com/PriceLab/TReNA
Methods for reconstructing transcriptional regulatory networks.
Proper citation: TReNA (RRID:SCR_017458) Copy
http://becs.aalto.fi/en/research/bayes/drifter/
Model based Bayesian method for eliminating physiological noise from fMRI data. This algorithm uses image voxel analysis to isolate the cardiac and respiratory noise from the relevant data.
Proper citation: DRIFTER (RRID:SCR_014937) Copy
http://surfer.nmr.mgh.harvard.edu/fswiki/Tracula
Software tool developed for automatically reconstructing a set of major white matter pathways in the brain from diffusion weighted images using probabilistic tractography. This method utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual intervention with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. The trac-all script is used to preprocess raw diffusion data (correcting for eddy current distortion and B0 field inhomogenities), register them to common spaces, model and reconstruct major white matter pathways (included in the atlas) without any manual intervention. trac-all may be used to execute all the above steps or parts of it depending on the dataset and user''''s preference for analyzing diffusion data. Alternatively, scripts exist to execute chunks of each processing pipeline, and individual commands may be run to execute a single processing step. To explore all the options in running trac-all please refer to the trac-all wiki. In order to use this script to reconstruct tracts in Diffusion images, all the subjects in the dataset must have Freesurfer Recons.
Proper citation: TRACULA (RRID:SCR_013152) Copy
http://www.nitrc.org/projects/caworks
A software application developed to support computational anatomy and shape analysis. The capabilities of CAWorks include: interactive landmark placement to create segmentation (mask) of desired region of interest; specialized landmark placement plugins for subcortical structures such as hippocampus and amygdala; support for multiple Medical Imaging data formats, such as Nifti, Analyze, Freesurfer, DICOM and landmark data; Quadra Planar view visualization; and shape analysis plugin modules, such as Large Deformation Diffeomorphic Metric Mapping (LDDMM). Specific plugins are available for landmark placement of the hippocampus, amygdala and entorhinal cortex regions, as well as a browser plugin module for the Extensible Neuroimaging Archive Toolkit.
Proper citation: CAWorks (RRID:SCR_014185) Copy
https://github.com/kstreet13/slingshot
Software R package for identifying and characterizing continuous developmental trajectories in single cell data. Cell lineage and pseudotime inference for single-cell transcriptomics.
Proper citation: Slingshot (RRID:SCR_017012) Copy
https://community.brain-map.org/t/allen-human-reference-atlas-3d-2020-new/405
Parcellation of adult human brain in 3D, labeling every voxel with brain structure spanning 141 structures. These parcellations were drawn and adapted from prior 2D version of adult human brain atlas.
Proper citation: Allen Human Reference Atlas, 3D, 2020 (RRID:SCR_017764) Copy
https://openwetware.org/wiki/HughesLab:JTK_Cycle
Software R package for Detecting Rhythmic Components in Genome-Scale Data Sets. Non-parametric algorithm to identify rhythmic components in large datasets. Identifies and characterizes cycling variables in large datasets.
Proper citation: JTK_CYCLE (RRID:SCR_017962) Copy
https://cloudreg.neurodata.io/
Software automated, terascale, cloud based image analysis pipeline for preprocessing and cross modal, nonlinear registration between volumetric datasets with artifacts. Automatic terabyte scale cross modal brain volume registration.
Proper citation: CloudReg (RRID:SCR_022795) Copy
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. A private repository of clinical and genetic information on families with autism. Genetic and clinical data are obtained from families that have more than one family member diagnosed with an Autism Spectrum Disorder. The biological samples, along with the accompanying clinical data, are made available to AGRE-approved researchers worldwide. As they become available, additional family pedigrees will be posted in the online catalog. Cell lines have been established for the majority of families in this collection and serum/plasma is available on a subset of the subjects until stocks are depleted. The diagnosis of autism has been made using the standard Autism Diagnostic Interview-Revised (ADI-R) algorithm and the Autism Diagnostic Observation Scale (ADOS-G). Detailed birth and medical histories (including basic dysmorphology assessments) on children as well as family and medical information for parents and unaffected siblings, are available for nearly all families. DNA, cell lines, serum, plasma and clinical information are made available to AGRE-approved researchers for analysis.
Proper citation: Autism Genetic Resource Exchange (RRID:SCR_004403) Copy
https://clinicaltrials.gov/ct2/show/NCT00014001
The NIMH-funded Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Study was a nationwide public health-focused clinical trial that compared the effectiveness of older (first available in the 1950s) and newer (available since the 1990s) antipsychotic medications used to treat schizophrenia. These newer medications, known as atypical antipsychotics, cost roughly 10 times as much as the older medications. CATIE is the largest, longest, and most comprehensive independent trial ever done to examine existing therapies for this disease. Schizophrenia is a brain disorder characterized by hallucinations, delusions, and disordered thinking. The course of schizophrenia is variable, but usually is recurrent and chronic, often causing severe disability. Previous studies have shown that taking antipsychotic medications consistently is far more effective than taking no medicine and that the drugs are necessary to manage the disease. The aim of the CATIE study was to determine which medications provide the best treatment for schizophrenia. Additional information may be found by following the links, http://www.nimh.nih.gov/trials/practical/catie/index.shtml, http://www.clinicaltrials.gov/ct/show/NCT00014001?order=1
Proper citation: CATIE - Clinical Antipsychotic Trials in Intervention Effectiveness (RRID:SCR_005615) Copy
http://neuromorphometrics.com/?page_id=23
Collection of neuroanatomically labeled MRI brain scans, created by neuroanatomical experts. Regions of interest include the sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter and sub-divided cortex into parcellation units that are defined by gyral and sulcal landmarks.
Proper citation: Manually Labeled MRI Brain Scan Database (RRID:SCR_009604) Copy
A data repository containing transcriptome and associated metadata for the developing and adult human brain. It provides genome-wide, exon-level transcriptome data from both sexes and multiple ethnicities.
Proper citation: Human Brain Transcriptome (RRID:SCR_013742) Copy
http://kimlab.io/brain-map/atlas/
Website to visualize and share anatomical labels. Franklin and Paxinos (FP) based anatomical labels in Allen Common Coordinate Framework (CCF). Cell type specific transgenic mice and MRI atlas were used to adjust and further segment labels. New segmentations were created in dorsal striatum using cortico-striatal connectivity data. Anatomical labels were digitized based on Allen ontology, and web-interface was created for easy visualization. These labels provide resource to isolate and identify mouse brain anatomical structures. Open source data sharing will facilitate further refinement of anatomical labels and integration of data interpretation within single anatomical platform.
Proper citation: Enhanced and Unified Anatomical Labeling for Common Mouse Brain Atlas (RRID:SCR_019267) Copy
Project aimed at making neuroimaging data sets of brain freely available to scientific community. By compiling and freely distributing neuroimaging data sets, future discoveries in basic and clinical neuroscience are facilitated.
Proper citation: Open Access Series of Imaging Studies (RRID:SCR_007385) Copy
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