The understanding of the relationship between structure and function has always characterized biology in general and neurobiology in particular. One such fundamental relationship is that between axon diameter and the axon's conduction velocity (ACV). Measurement of these neuronal properties, however, requires invasive procedures that preclude direct elucidation of this relationship in vivo. Here we demonstrate that diffusion-based MRI is sensitive to the fine microstructural elements of brain wiring and can be used to quantify axon diameter in vivo. Moreover, we demonstrate the in vivo correlation between the diameter of an axon and its conduction velocity in the human brain. Using AxCaliber, a novel magnetic resonance imaging technique that enables us to estimate in vivo axon diameter distribution (ADD) and by measuring the interhemispheric transfer time (IHTT) by electroencephalography, we found significant linear correlation, across a cohort of subjects, between brain microstructure morphology (ADD) and its physiology (ACV) in the tactile and visual sensory domains. The ability to make a quantitative assessment of a fundamental physiological property in the human brain from in vivo measurements of ADD may shed new light on neurological processes occurring in neuroplasticity as well as in neurological disorders and neurodegenerative diseases.
The layer composition of the cerebral cortex represents a unique anatomical fingerprint of brain development, function, connectivity and pathology. Historically the cortical layers were investigated solely ex-vivo using histological means, but recent magnetic resonance imaging (MRI) studies suggest that T1 relaxation images can be utilized to separate the layers. Despite technological advancements in the field of high resolution MRI, accurate estimation of whole brain layer composition has remained limited due to partial volume effects, leaving some layers far beyond the image resolution. In this study we offer a simple and accurate method for layer composition analysis, resolving partial volume effects and cortical curvature heterogeneity. We use a low resolution echo planar imaging inversion recovery (EPI IR) MRI scan protocol that provides fast acquisition (~12 minutes) and enables extraction of multiple T1 relaxation time components per voxel, which are assigned to types of brain tissue and utilized to extract the subvoxel composition of each T1 layer. While previous investigation of the layers required the estimation of cortical normals or smoothing of layer widths (similar to VBM), here we developed a sphere-based approach to explore the inner mesoscale architecture of the cortex. Our novel algorithm conducts spatial analysis using volumetric sampling of a system of virtual spheres dispersed throughout the entire cortical space. The methodology offers a robust and powerful framework for quantification and visualization of the layers on the cortical surface, providing a basis for quantitative investigation of their role in cognition, physiology and pathology.
In this paper we demonstrate a generalized and simplified pipeline called axonal spectrum imaging (AxSI) for in-vivo estimation of axonal characteristics in the human brain. Whole-brain estimation of the axon diameter, in-vivo and non-invasively, across all fiber systems will allow exploring uncharted aspects of brain structure and function relations with emphasis on connectivity and connectome analysis. While axon diameter mapping is important in and of itself, its correlation with conduction velocity will allow, for the first time, the explorations of information transfer mechanisms within the brain. We demonstrate various well-known aspects of axonal morphometry (e.g., the corpus callosum axon diameter variation) as well as other aspects that are less explored (e.g., axon diameter-based separation of the superior longitudinal fasciculus into segments). Moreover, we have created an MNI based mean axon diameter map over the entire brain for a large cohort of subjects providing the reference basis for future studies exploring relation between axon properties, its connectome representation, and other functional and behavioral aspects of the brain.
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