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PRoNTo / Pattern Recognition for Neuroimaging Toolbox
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.
rMSPRT / Recursive Multi-hypothesis Sequential Probability Ratio Test
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.
MENGA / Multimodal Environment for Neuroimaging and Genomic Analysis
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.
Preprocessed Connectomes Project
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.
adni_on_alcf / Alzheimer's Disease Neuroimaging Initiative on Argonne national laboratory Leadership Computing Facility
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.
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