Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference.

ABSTRACT: Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain's global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a general data-driven framework to optimize and validate parameters of dMRI-based fiber tracking algorithms using neural tracer data as a reference. Japan's Brain/MINDS Pr

SUBMITTER: Gutierrez CE 

PROVIDER: S-EPMC7749185 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature