Allows users to analyze, visualize, annotate and share whole-brain data at cellular resolution. WholeBrain supplies a method to generate brain maps containing data from neuron function, neuron identity, and connectivity. It quantifies and spatially maps multidimensional data from whole-brain experiments. It also compares results across experiments in a single standardized anatomical reference atlas.
Offers databases, software applications, and other associated tools for supporting and promoting quantitative coordinate-based meta-analysis of the structural and functional neuroimaging literature. BrainMap will continue to evolve in response to the meta-analytic needs of biomedical researchers in the structural and functional neuroimaging communities. It enables quantitative meta-analyses and meta-analysis-based neuroimaging data interpretation.
Provides implementations of algorithms for non-machine learning experts and reusable across scientific disciplines and application fields. Scikit-Learn is a general-purpose machine learning library written in Python. This application can easily be integrated into applications outside the traditional range of statistical data analysis. It also offers a panel of strategies to select features.
Provides a method for multivariate analysis based on machine learning models for neuroimaging data. PRoNTo is open-source, cross-platform, MATLAB-based and Statistical Parametric Mapping (SPM) compatible, therefore being suitable for both cognitive and clinical neuroscience research. It can also be extended via the addition of new feature selection and extraction approaches, validation procedures or classification/regression models.
Runs the Gibbs sampling algorithm to fit a Bayesian group sparse multi-task regression model. bgsmtr is a package which provides approaches for choosing the tuning parameters with computations using only a single core. Tuning parameters can be chosen using either the Markov chain Monte Carlo (MCMC) samples and the widely applicable information criterion (WAIC) (multiple runs) or using an approximation to the posterior mode and five-fold cross-validation (single run).
Enables mapping of anatomically defined regions onto microtome-cut brain slices. SliceMap employs a combination of coarse pre-registration steps and fine elastic registration based on a library of pre-annotated reference slices. It is able to create large images from fluorescently labeled mouse brain sections. This tool contributes to a more accurate assessment of neurodegenerative brain disease development in mouse models.
Allows users to study and visualize clinical/anatomical correlations and brain deficits in Human Immunodeficiency virus/Acquired Immune Deficiency Syndrome (HIV/AIDS). JRD-fluid is an analysis method using fluid image warping. It consists of information-theoretic measure of image correspondence. It assists users to work about AIDS neuropathology. This algorithm is also useful for studies that concern neuroscientific topic.
Characterizes the neural mechanism that underlies decisions. rMSPRT is implemented as a probabilistic, recursive, parallel procedure. It can determine that the mean decision time on the dot motion task is a decreasing function of coherence. This tool accounts for the dependence of choice reaction times on task difficulty, trial outcome, and the number of alternatives. It is able to decide faster than monkeys in the same conditions.
Offers a platform dedicated to the storage and handling of bioinformatics data dealing with brain disorders. Brain-CODE gathers clinical information produced from Ontario Brain Institute and its partners. Besides, it proposes a management tool providing a virtual laboratory environment intending to simplify collaboration between researchers. The application allows users to handle imaging, genomics or clinical data and includes features for curating and sharing personal datasets as well as for browsing information about controlled and public datasets.
Allows exploration of correlation patterns between neuroimaging data with Allen human brain database (ABA) mRNA gene expression profiles. MENGA was applied to six different imaging datasets that target the dopamine and serotonin receptor systems and the myelin molecular structure in the human brain. It is useful to compare genomic and imaging data. This tool gives a quantitative assessment of the amount of the variability in the image phenotype.
Aims to systematically preprocess the data from the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) and openly share the results. The Preprocessed Connectomes Project has been initiated in 2011 with the ADHD-200 Preprocessed initiative, and has grown to include the Beijing Enhanced DTI dataset and ABIDE. To enable the comparison of different preprocessing choices and to accommodate different opinions about the best preprocessing strategies, most of the data is preprocessed using a variety of tools and parameters. Data is hosted in an Amazon Web Services Public S3 Bucket and at NITRC. A software package to run the Preprocessed Connectomes Project's protocol for assessing data quality is available for local use.
Assesses structural and diffusion magnetic resonance imaging (MRI) as imaging markers of Alzheimer’s disease (AD). adni_on_alcf exploits high-throughput brain phenotyping, including morphometry and whole-brain tractography, and machine learning analytics for classification to process. This multimodal approach intends to evaluate white-matter individualized structure connectome by providing pre- and post-processing algorithms in a pipeline tool.
Enables distributed collaboration around annotation, discovery and analysis of publicly available brain imaging data. The Open Neuroimaging Laboratory is a collaborative platform that facilitates finding, improving, and reusing the massive amount of neuroimaging data available on the Web. This data represents an enormous funding effort and the time and goodwill of thousands of participants: researchers, clinicians, patients, and their families.