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http://findlab.stanford.edu/functional_ROIs.html

Atlas of functional ROI's, including individual networks (auditory network, sensorimotor network, etc.). Atlases of individual networks and combined networks are available for download directly from the website.

Proper citation: 90 fROI atlas (RRID:SCR_014757) Copy   

•   Source: SciCrunch Registry

http://mouse.brain-map.org/

Genome wide database of gene expression in mouse brain. Genome-wide atlas of gene expression in the adult mouse brain.

Proper citation: ABA Mouse Brain: Atlas (RRID:SCR_017479) Copy   

•   Source: SciCrunch Registry

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

Project that is a translation of the BraVa arterial vasculature database into the NIFTI MRI file format that can be applied to stroke studies, fMRI resting state imaging studies and other clinical neuroscience studies. Group artery region labels and arterial density maps are provided as well. Human Brain Major Artery Atlas 10.7490/f1000research.1114378.1

Proper citation: Bravissima (RRID:SCR_016229) Copy   

•   Source: SciCrunch Registry

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

Atlas for studies of older adult brain. Includes T1-weighted template of older adult brain and tissue probability maps. Exhibits high image sharpness, provides higher inter-subject spatial normalization accuracy compared to other standardized templates and similar normalization accuracy to well-constructed study-specific templates.

Proper citation: MIITRA atlas (RRID:SCR_017566) Copy   

•   Source: SciCrunch Registry

http://www.pymvpa.org

A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.

Proper citation: PyMVPA (RRID:SCR_006099) Copy   

•   Source: SciCrunch Registry

https://github.com/jefferis/elmr

Software tool as support for working with light and electron microscopy fly brain data. Part of suite of R packages based on NeuroAnatomy Toolbox. Provides tools to move between adult brain EM and light level data, emphasising interaction between CATMAID web application and R Neuroanatomy Toolbox package.

Proper citation: elmr (RRID:SCR_017249) Copy   

•   Source: SciCrunch Registry

https://www.mbfbioscience.com/neurolucida-explorer

Companion analytical software for Neurolucida and Neurolucida 360, designed to perform extensive morphometric analysis on neuron reconstructions, serial section reconstructions, and brain maps.

Proper citation: Neurolucida Explorer (RRID:SCR_017348) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/bva-io/

Software package for interfacing the Brain Vision Analyser data files (load/save) for ongoing development of Matlab routines . This package is also compatible with the EEGLAB software, and may be uncompressed in the plugin folder of this software.

Proper citation: BVA import/export EEGLAB plugin (RRID:SCR_016333) Copy   

•   Source: SciCrunch Registry

https://github.com/DiedrichsenLab/DCBC/tree/v1.0.0

Software Python toolbox for brain parcellation evaluation.

Proper citation: DCBC toolbox (RRID:SCR_022176) Copy   

•   Source: SciCrunch Registry

https://www.nitrc.org/projects/rshrf

Software toolbox for resting state HRF estimation and deconvolution analysis. Matlab and Python toolbox that implements HRF estimation and deconvolution from resting state BOLD signal. Used to retrieve optimal lag between events and HRF onset, as well as HRF shape. Once that HRF has been retrieved for each voxel/vertex, it can be deconvolved from time series or one can map shape parameters everywhere in brain and use it as pathophysiological indicator. Input can be 2D GIfTI, 3D or 4D NIfTI images, but also on time series matrices/vectors. Output are three HRF shape parameters for each voxel/vertex, plus deconvolved time series, and number of retrieved pseudo events. All can be written back to GIfTI or NIfTI images.

Proper citation: Resting State Hemodynamic Response Function Retrieval and Deconvolution (RRID:SCR_023663) Copy   

•   Source: SciCrunch Registry

http://mindhacks.com/

Mind Hacks: Neuroscience and psychology tricks to find out what's going on inside your brain. Mind Hacks is also a book by Tom Stafford and Matt Webb.

Proper citation: Mind Hacks (RRID:SCR_000170) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/retro/BeijingEOEC.html

Data set of 48 healthy controls from a community (student) sample from Beijing Normal University in China with 3 resting state fMRI scans each. During the first scan participants were instructed to rest with their eyes closed. The second and third resting state scan were randomized between resting with eyes open versus eyes closed. In addition this dataset contains a 64-direction DTI scan for every participant. The following data are released for every participant: * 6-minute resting state fMRI scan (R-fMRI) * MPRAGE anatomical scan, defaced to protect patient confidentiality * 64-direction diffusion tensor imaging scan (2mm isotropic) * Demographic information and information on the counterbalancing of eyes open versus eyes closed.

Proper citation: Beijing: Eyes Open Eyes Closed Study (RRID:SCR_001507) Copy   

•   Source: SciCrunch Registry

http://neurobureau.projects.nitrc.org/ADHD200/Introduction.html

Preprocessed versions of the ADHD-200 Global Competition data including both preprocessed versions of structural and functional datasets previously made available by the ADHD-200 consortium, as well as initial standard subject-level analyses. The ADHD-200 Sample is pleased to announce the unrestricted public release of 776 resting-state fMRI and anatomical datasets aggregated across 8 independent imaging sites, 491 of which were obtained from typically developing individuals and 285 in children and adolescents with ADHD (ages: 7-21 years old). Accompanying phenotypic information includes: diagnostic status, dimensional ADHD symptom measures, age, sex, intelligence quotient (IQ) and lifetime medication status. Preliminary quality control assessments (usable vs. questionable) based upon visual timeseries inspection are included for all resting state fMRI scans. In accordance with HIPAA guidelines and 1000 Functional Connectomes Project protocols, all datasets are anonymous, with no protected health information included. They hope this release will open collaborative possibilities and contributions from researchers not traditionally addressing brain data so for those whose specialties lay outside of MRI and fMRI data processing, the competition is now one step easier to join. The preprocessed data is being made freely available through efforts of The Neuro Bureau as well as the ADHD-200 consortium. They ask that you acknowledge both of these organizations in any publications (conference, journal, etc.) that make use of this data. None of the preprocessing would be possible without the freely available imaging analysis packages, so please also acknowledge the relevant packages and resources as well as any other specific release related acknowledgements. You must be logged into NITRC to download the ADHD-200 datasets, http://www.nitrc.org/projects/neurobureau

Proper citation: ADHD-200 Preprocessed Data (RRID:SCR_000576) Copy   

•   Source: SciCrunch Registry

http://www.nmr.mgh.harvard.edu/CFNT/index

Biomedical technology research center that develops and applies innovative neuroimaging technologies and techniques to enable closer examination of the human brain, and thereby contribute to better understanding of the brain in health and disease. They develop new techniques and advance existing technologies for acquisition and analysis of functionally specific images of the working brain, with unprecedented physiological precision and spatiotemporal resolution. The research and development aims to improve and extend existing methods for non-invasive magnetic resonance image analysis and acquisition, electromagnetic source imaging, optical neuroimaging, and most recently, combined MR-PET neuroimaging. The Resource provides an essential interactive environment, within which an interdisciplinary team of highly skilled scientists, engineers, and clinicians with diverse expertise in multiple modalities and disciplines. The resource supports service use of the Center's facilities by neuroscientists throughout the country, provide extensive training opportunities for students, fellows, and staff scientists, and seek to advance the field of brain mapping through active dissemination of new knowledge and technology.

Proper citation: Center for Functional Neuroimaging Technologies (RRID:SCR_001423) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/resources/Tools/record/nlx_144509-1/6b508d2a-fd5f-56f2-93cf-462fcd52548e/search?q=*&l=&facet%5B%5D=Parent%20Organization:University%20of%20Michigan;%20Michigan;%20USA&sort=added

THIS RESOURCE IS NO LONGER IN SERVICE. Documented September 12, 2017.

Dataset in Bilingual exposure optimizes left-hemisphere dominance for selective attention processes in the developing brain by Arredondo, Su, Satterfield, & Kovelman (XX) Does early bilingual exposure alter the representations of cognitive processes in the developing brain? Theories of bilingual development have suggested that bilingual language switching might improve children''s executive function and foster the maturation of prefrontal brain regions that support higher cognition. To test this hypothesis, we used functional Near Infrared Spectroscopy to measure brain activity in Spanish-English bilingual and English-monolingual children during a visuo-spatial executive function task of attentional control (N=27, ages 7-13). Prior findings suggest that while young children start with bilateral activation for the task, it becomes right-lateralized with age (Konrad et al., 2005). Indeed monolinguals showed bilateral frontal activation, however young bilinguals showed greater activation in left language areas relative to right hemisphere and relative to monolinguals. The findings suggest that bilingual experience optimizes attention mechanisms in the language hemisphere, and highlight the importance of early experiences for neurodevelopmental plasticity of higher cognition. These data are made available from Ioulia Kovelman''s Language and Literacy Lab at University of Michigan and may be exported through the NIF Data Federation. To cite these data please use this text Data were published by Arredondo et al. (XX) and made available via the NIF at XX

Proper citation: Arredondo ANT fNIRS dataset1 (RRID:SCR_002653) Copy   

•   Source: SciCrunch Registry

http://www.internationalbrainbee.com

A world-wide neuroscience competition for high school students that aims to motivate them to learn about the brain and to pursue neuroscience careers. Brain Bee tests knowledge of the human brain, including topics like intelligence, emotions, memory, sleep, vision, hearing, sensations, Alzheimer's disease, Parkinson's disease, stroke, schizophrenia, epilepsy, depression, addictions and brain research.

Proper citation: Brain Bee (RRID:SCR_002248) Copy   

•   Source: SciCrunch Registry

http://ranchobiosciences.com/gse4271/

Curated data set from a study that investigated 77 primary high-grade astrocytomas and 23 matched recurrences so that changes in gene expression related to both survival and disease progression can be identified. Samples in the study include WHO grade III and IV astrocytomas with a wide range of survival times.

Proper citation: GSE4271 (RRID:SCR_003643) Copy   

•   Source: SciCrunch Registry

http://www.chibi.ubc.ca/WhiteText/

Freely available corpus of manually annotated brain region mentions created to facilitate text mining of neuroscience literature. The corpus contains 1,377 abstracts with 18,242 brain region annotations. Interannotator agreement was evaluated for a subset of the documents, and was 90.7% and 96.7% for strict and lenient matching respectively. We observed a large vocabulary of over 6,000 unique brain region terms and 17,000 words. For automatic extraction of brain region mentions we evaluated simple dictionary methods and complex natural language processing techniques. The dictionary methods based on neuroanatomical lexicons recalled 36% of the mentions with 57% precision. The best performance was achieved using a conditional random field (CRF) with a rich feature set. Features were based on morphological, lexical, syntactic and contextual information. The CRF recalled 76% of mentions at 81% precision, by counting partial matches recall and precision increase to 86% and 92% respectively. We suspect a large amount of error is due to coordinating conjunctions, previously unseen words and brain regions of less commonly studied organisms. We found context windows, lemmatization and abbreviation expansion to be the most informative techniques. We encourage you to test new methods and applications of the dataset. Please contact us if you do, we would like to hear about and link to your work. The abstracts are from PubMed/Medline, specifically The Journal of Comparative Neurology.

Proper citation: Automated recognition of brain region mentions in neuroscience literature. (RRID:SCR_002731) Copy   

•   Source: SciCrunch Registry

http://nif.nimh.nih.gov/

Neurophysiology imaging core facility that provides anatomical and functional MRI scanning for researchers in the National Institute of Mental Health (NIMH), the National Eye Institute (NEI), and the National Institute for Neurological Disorders and Stroke (NINDS). The shared intramural resource centers on a cutting-edge 4.7T vertical bore scanner dedicated to imaging of nonhuman primates.

Proper citation: Neurophysiology Imaging Facility (RRID:SCR_004080) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-8/search?q=*

A data set of connectivity statements from BAMS, CoCoMac, BrainMaps, Connectome Wiki, the Hippocampal-Parahippocampal Table of Temporal-Lobe.com, and Avian Brain Circuitry Database. The data set lists which brain sites connectivity is to and from, the organism connectivity is mapped in, and journal references.

Proper citation: Integrated Nervous System Connectivity (RRID:SCR_006391) Copy   

•   Source: SciCrunch Registry