Abstract

Diffusion weighted imaging (DWI) has provided unparalleled insight into the microscopic structure and organization of the central nervous system. Diffusion tensor imaging (DTI) and other models of the diffusion MRI signal extract microstructural properties of tissues with relevance to the normal and injured brain. Despite the prevalence of such techniques and applications, accurate and large-scale validation has proven difficult, particularly in the human brain. In this report, human brain sections obtained from a digital public brain bank were employed to quantify anisotropy and fiber orientation using structure tensor analysis. The derived maps depict the intricate complexity of white matter fibers at a resolution not attainable with current DWI experiments. Moreover, the effects of multiple fiber bundles (i.e., crossing fibers) and intravoxel fiber dispersion were demonstrated. Examination of the cortex and hippocampal regions validated-specific features of previous in vivo and ex vivo DTI studies of the human brain. Despite the limitation to two dimensions, the resulting images provide a unique depiction of white matter organization at resolutions currently unattainable with DWI. The method of analysis may be used to validate tissue properties derived from DTI and alternative models of the diffusion signal.

Highlights

  • The brain is a network with multiple levels of connectivity

  • Microscopic tissue orientation and anisotropy was derived using structure tensor analysis applied to images of human brain sections acquired at sub-micron resolution

  • The anisotropy of the corpus callosum (CC) was greater than the other white matter tracts in the brain

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Summary

Introduction

The brain is a network with multiple levels of connectivity. The connections of each neuron determine its functional properties, and the functionality of the brain as a whole is determined by both these local and large-scale connections. Since anatomical and functional connectivity are intrinsically linked, a major goal of neuroscience has been to construct comprehensive maps of brain connectivity, or “connectomes” (Sporns et al, 2005; Sporns, 2011). Given the many levels of connectivity, from the microscopic to the macroscopic, the tools used to create such maps vary (Leergaard et al, 2012). A number of methods are needed to bridge the gap from the connectivity of individual neurons to those that measure large-scale patterns of anatomical connectivity

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