Abstract
Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models.
Highlights
Recent advances in neuroscience technologies to study brain connectivity are providing new techniques to better understand the role of both structural and functional networks in complex neurological disorders (Bargmann and Newsome, 2014; Baliyan et al, 2016)
In the situation of two crossing fibers, we discover from simulation analyses that the Ball-and-Stick model faces significant challenges in estimation of the fiber-related parameters, when we apply the Bayesian framework proposed by Behrens (Behrens et al, 2007)
The performance of estimation on the fiber-specific parameters in two crossing fiber scenarios will be compared between the simplified model and the full model
Summary
Recent advances in neuroscience technologies to study brain connectivity are providing new techniques to better understand the role of both structural and functional networks in complex neurological disorders (Bargmann and Newsome, 2014; Baliyan et al, 2016). Diffusion MRI (dMRI) allows for identifying anatomical connections in the living human brain. These connections form the substrate for information transfer between remote brain regions and are central to our understanding of brain function. It has become an important tool in the study of a wide range of diseases affecting the brain, as it allows us to probe the shape and integrity of the white-matter pathways that connect the functionally-related cortical and subcortical regions (Yendiki et al, 2011, 2016). A lot of research has focused on discovering an approach using parametrized models for extracting tissue structural information from dMRI data
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