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Preprint

NeuroDISK: An AI Approach to Automate Continuous Inquiry-Driven Discoveries in Neuroimaging Genetics

Daniel GarijoiD, Qifan YangiD, Hernán VargasiD, Shruti P. GadewariD, Kevin LowiD, Varun RatnakariD, Maximiliano OsorioiD, Alyssa H. ZhuiD, Mary Agnes B. McMahoniD, Yolanda GiliD, Neda JahanshadiD

Abstract

Collaborative and multi-site neuroimaging studies have greatly accelerated the rate at which new and existing data can be aggregated to answer a neuroscientific question. New research initiatives are continuously collecting more data, allowing opportunities to refine previous published findings through continuous and dynamic updates. Yet, we lack a practical framework for researchers to systematically, automatically, and continuously update published findings. We developed NeuroDISK, an automated artificial intelligence based framework that: 1) performs automated and inquiry-driven analyses, and 2) continuously updates these analyses as new data becomes available. NeuroDISK was evaluated using published results from the ENIGMA consortiums work on the genetic architecture of the cerebral cortex. We incorporate both meta-analysis and meta-regression options to showcase our framework on the effect of specific genotypes and moderators on select brain regions. Initial NeuroDISK meta-analysis results replicate the original publication, and we show result updates after adding new data. The NeuroDISK framework can be generalized for users to define question(s), run corresponding workflow(s) and access results interactively and continuously.

PDFDOI

02 /Specification & Classification

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