Abstract

White matter tractography, based on diffusion-weighted magnetic resonance images, is currently the only available in vivo method to gather information on the structural brain connectivity. The low resolution of diffusion MRI data suggests to employ probabilistic methods for streamline reconstruction, i.e., for fiber crossings. We propose a general probabilistic model for spherical regression based on the Fisher-von-Mises distribution, which efficiently estimates maximum entropy posteriors of local streamline directions with machine learning methods. The optimal precision of posteriors for streamlines is determined by an information-theoretic technique, the expected log-posterior agreement concept. It relies on the requirement that the posterior distributions of streamlines, inferred on retest measurements of the same subject, should yield stable results within the precision determined by the noise level of the data source.

Highlights

  • IntroductionThe structural connectivity between different cortical brain regions is established by white matter, that is composed of myelinated axons to distribute action potentials as messages between communicating neurons

  • 1.1 Cerebral White Matter and Diffusion MRIThe structural connectivity between different cortical brain regions is established by white matter, that is composed of myelinated axons to distribute action potentials as messages between communicating neurons

  • Concluding the general description of methods, we present the expected log-posterior agreement for the FvM posterior in Sect. 3.5, which allows us to calibrate the precision parameter according to the noise level in the data

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Summary

Introduction

The structural connectivity between different cortical brain regions is established by white matter, that is composed of myelinated axons to distribute action potentials as messages between communicating neurons. The advent of diffusion-weighted magnetic resonance imaging (DWI) (Chilla et al 2015; Soares et al 2013) has empowered neuroscientists and neurologists to monitor changes in the structural connectivity with potential relevance for diagnosis, prognosis and therapy of neurodegenerative diseases (Oishi et al 2011). DWI is currently the only non-invasive, and non-radiative imaging modality, which enables neurologists to investigate the connective micro-architecture of the white matter in a minimally inva-. Fiber tracking algorithms are required to reconstruct consistent long-range tissue connectivity from local, voxel-centric 1 DWI measurements. Long-range connections in the white matter are commonly referred to as streamlines, fibers or tracts.

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