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NiChart is a set of modular but integrated software tools for neuroimaging research, and a cloud-based web application to provide wide access to these tools. This work was supported, in part, by NIH grants U24NS130411 and RF1AG054409. The Cloud implementation was also supported by Amazon Web Services (AWS).
NiChart enables mapping of large-scale multi-modal brain MRI data into a dimensional system of neuroimaging derived measures, including signatures implemented by machine learning (ML) models.
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We provide image processing tools for deriving a panel of imaging derived phenotypes from structural (sMRI), diffusion (dMRI) and functional (fMRI) imaging data: from ROIs and functional networks to structural covariance and ML indices.
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ML models, which are previously trained on pre-processed, QC'ed and curated reference samples, allow users to calculate imaging signatures that quantify complex multi-variate imaging patterns of brain changes mapping the image data into a small but informative set of neuroimaging chart dimensions.
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ML models capture heterogeneity of brain aging and neurodegeneration, and atrophy patterns due to various diseases and conditions, such as Alzheimer's disease, neuropsychiatic disorders, or cardio-vascular risk factors.
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Standardized values from the large reference set allow users to compare their data with NiChart-based normative ranges or distributions from specific disease subgroups.