  Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive
investigation of whole-brain connectivity, which can potentially help to reveal
the brain's global network architecture and 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 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 Project provides
invaluable datasets containing both dMRI and neural tracer data from the same
primates. We considered four criteria for goodness of fiber tracking:
distance-weighted coverage, true/false positive ratio, projection coincidence,
and commissural passage, applied using a multi-objective optimization
algorithm. We implemented a variant of non-dominated sorting genetic algorithm
II (NSGA-II) to optimize five major parameters of a global fiber-tracking
algorithm over multiple brain samples in parallel. Using optimized parameters
compared to the default parameters, dMRI-based fiber tracking performance was
significantly improved, while minimizing false positives and impossible
cross-hemisphere connections. Parameters optimized for 10 tracer injection
sites showed good generalization capability for other brain samples. These
results demonstrate the importance of data-driven adjustment of fiber-tracking
algorithms and support the validity of dMRI-based tractography, if appropriate
adjustments are employed.
