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 Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. A fundamental difference when comparing dMRI-based tractography and neural tracer data is that the former cannot specify the direction of connectivity; therefore, evaluating the fitting of dMRI-based tractography becomes challenging. The framework implements multi-objective optimization based on the non-dominated sorting genetic algorithm II. Its performance is examined in two experiments using data from ten subjects for optimization and six for testing generalization. The first uses a seed-based tracking algorithm, iFOD2, and objectives for sensitivity and specificity of region-level connectivity. The second uses a global tracking algorithm and a more refined set of objectives: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage. In both experiments, with optimized parameters compared to default parameters, fiber tracking performance was significantly improved in coverage and fiber length. Improvements were more prominent using global tracking with refined objectives, achieving an average fiber length from 10 to 17 mm, voxel-wise coverage of axonal tracts from 0.9 to 15%, and the correlation of target areas from 40 to 68%, while minimizing false positives and impossible cross-hemisphere connections. Optimized parameters showed good generalization capability for test brain samples in both experiments, demonstrating the flexible applicability of our framework to different tracking algorithms and objectives. These results indicate the importance of data-driven adjustment of fiber tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.

Artificial Intelligence: A Review of Progress and Prospects in Medicine and Healthcare

“…AI can also help in the research of functional brain mapping. Researchers from Japan Gutierrez et al, [112] used machine intelligence based on CNN to accelerate and improve the correctness of an effective brain connection mapping technique. AI can assist in the branch of neuro-oncology for assessment and diagnosis of brain tumors.…”

Mapping the human corticoreticular pathway with multimodal delineation of the gigantocellular reticular nucleus and high-resolution diffusion tractography

“…Compared with prior human CRP tractography studies, this work features several methodologic advances to improve accuracy & reliability, including: greater sample size, upgraded MRI hardware, [51,94] higher spatial & angular resolution, [51,74,94] better distortion correction, [51,53,74] advanced model-free reconstruction of diffusion orientations (for improved resolution of multiple fiber populations within a voxel and partial volume effects), [30,55,72,[95][96][97] group-averaging of diffusion orientations prior to tractography, [30,54] and more anatomically-informed delineation of tractography regions [2,7,[27][28][29]48,50,74] (Table 1).…”

“…MRI-based diffusion tractography is a non-invasive method that estimates tract trajectories by simulating streamlines through pre-processed diffusion-weighted images. [27][28][29][30] One research group has begun mapping the healthy human CRP using this method (Table 1). [31][32][33][34] Primarily focusing on CRP origins in the secondary motor cortices (supplementary motor area & premotor cortex), studies from this group have reported a CRP trajectory through the superior corona radiata, posterior limb of the internal capsule (anteromedial to the CST) and midbrain tegmentum (posterior to the CST) to the area of the pontomedullary reticular formation.…”

Mapping the corticoreticular pathway from cortex‐wide anterograde axonal tracing in the mouse

“…While this method provides useful non-invasive, in vivo measures, it is limited by insufficient resolution to resolve axonal bundles and inability to determine the direction of neural conduction (Calamante, 2019). Consequently, it is crucial for diffusion tractography to be guided by anatomical knowledge in order to maximize tract coverage and minimize inclusion of false pathways (Aydogan et al, 2018;Azadbakht et al, 2015;Gutierrez et al, 2020). Unfortunately, this anatomical knowledge is incomplete for the CRP, which limits confidence in prior human CRP mapping (e.g., Jang & Seo, 2014Yeo et al, 2012Yeo et al, , 2014.…”