Diffusion magnetic resonance imaging ( MRI ) allows non-invasive exploration of brain structural information, crucial for understanding the human-brain; however, present-day diffusion-based tractography approaches are still lacking in accuracy. Improvement efforts included comparison with anatomical connectivity data, the usage of higher quality data, more sophisticated algorithms and parameters assessment. Despite that, well-known problems such as low sensitivity, dominance of false positives, the accurate reconstruction of long-range connections could not be avoided or minimized. Current methods include several parameters, typically tuned in a heuristic way, with settings to increase the sensitivity, without attenuating the overall problem. We propose a new way to optimize fiber-tracking results by multi-objectives tailored to minimize the current issues. Our approach uses comparison with marmoset fluorescent tracer injection data from the Japan Brain/MINDS to validate and optimize ex-vivo diffusion weighted MRI (DWI)-based global fiber tracking results of the same subject. The approach includes multiple comparisons at a higher-than-DWI resolution standard brain space to evaluate objectives of coverage and passage constraint. The coverage objectives maximize sensitivity while constraining the growth of false positives, promoting the generation of long connections to the projection areas. The passageconstraint minimizes the number of fibers crossing hemispheres outside the commissures . We implemented a  non-dominated sorting genetic algorithm  (NSGA-II) strategy to explore the parameters space. Evolutionary optimization runs for 10 brain samples in parallel while sharing champion settings over samples, encouraging convergence of parameters in a similar locus. We evaluate the generalization capability of the optimized parameters on unseen marmoset test data leading to promising results .
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 .
R
Diffusion
Diffusion-weighted
R
MRI
dMRI
R
exploration of brain structural information, crucial for understanding the human-brain; however, present-day diffusion-based tractography approaches are still lacking in accuracy. Improvement efforts included comparison with anatomical connectivity data, the usage of higher quality data, more sophisticated algorithms and parameters assessment. Despite that, well-known problems such as
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
R
the accurate
and inaccurate and incomplete
R
could not be avoided or minimized. Current methods include several parameters,
. Furthermore, parameters of tracking algorithms are
R
with settings to increase the sensitivity, without attenuating the overall problem. We propose a new way to optimize
which leaves room for manipulation of an intended result. Here we propose a data-driven framework to optimize and validate parameters of dMRI-based
R
results by multi-objectives tailored to minimize the current issues. Our approach uses comparison with marmoset fluorescent tracer injection data from the Japan
algorithms using neural tracer data as a reference. Japan's
R
to validate and optimize ex-vivo diffusion weighted MRI (DWI)-based global fiber tracking results of the same subject. The approach includes multiple comparisons at a higher-than-DWI resolution standard brain space to evaluate objectives of coverage and passage constraint. The coverage objectives maximize sensitivity while constraining the growth of false positives, promoting the generation of long connections to the projection areas. The passageconstraint minimizes the number of fibers crossing hemispheres outside the commissures
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
A
variant of
A
II
R
strategy to explore the parameters space. Evolutionary optimization runs for 10
to optimize five major parameters of a global fiber-tracking algorithm over multiple
R
while sharing champion settings over samples, encouraging convergence of parameters in a similar locus. We evaluate the generalization capability of the optimized parameters on unseen marmoset test data leading to promising results
. 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