NiCHART: A Software Suite to Translate Neuroimaging Big Data to Individualized Biomarkers in Disease

COEX 

Growing availability of open-access, large-scale neuroimaging data in healthy development and disease allows for rapid discovery of radiologic, neurologic, and psychiatric insights . This is especially true in the context of machine learning (ML), which promises improved prediction of diagnoses, prognoses, disease subtypes, and more. However, harnessing ML to pursue such precision medicine efforts remains a challenge for many neuroimaging scientists – barriers in coding skills, field-specific knowledge of state-of-the-art methodology, and access to large-scale neuroimaging data all limit the rate of biomarker discovery. We introduce niCHART (NeuroImaging Computational Harmonization and ARtificial intelligence Toolbox), a mutually-compatible ecosystem of state-of-the-art methods allowing for holistic processing of multi-modal MRI images as well as calculation of statistical and ML-based imaging-derived phenotypes (IDPs). Ultimately, niCHART will allow for improved reproducibility and accessibility of neuroimaging analysis as well as allow end-users to contextualize their own data among open-access, curated neuroimaging big data.