Shape analysis of the human association pathways

Fang-Cheng Yeh

doi:10.1016/j.neuroimage.2020.117329

Abstract

Shape analysis has been widely used in digital image processing and computer vision, but they have not been utilized to compare the structural characteristics of the human association pathways. Here we used shape analysis to derive length, area, volume, and shape metrics from diffusion MRI tractography and utilized them to study the morphology of human association pathways. The reliability analysis showed that shape descriptors achieved moderate to good test-retest reliability. Further analysis on association pathways showed left dominance in the arcuate fasciculus, cingulum, uncinate fasciculus, frontal aslant tract, and right dominance in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The superior longitudinal fasciculus has a mixed lateralization profile with different metrics showing either left or right dominance. The analysis of between-subject variations shows that the overall layout of the association pathways does not variate a lot across subjects, as shown by low between-subject variation in length, span, diameter, and radius. In contrast, the area of the pathway innervation region has a considerable between-subject variation. A follow-up analysis is warranted to thoroughly investigate the nature of population variations and their structure-function correlation.

Highlights

Deciphering the structural layout of the human brain has been a challenging goal to understand how structure defines the brain function (DeFelipe, 2010)
The network analysis tackled the structure-function correlation from a panoramic view, but the shape characteristics and morphology of the connecting bundles were mostly ignored, the association pathways in the human brain that control most of the cognitive functions
This qualitative evaluation suggests that the augmented fiber tracking could provide grossly consistent tractography results in test-retest scans

Summary

Introduction

Deciphering the structural layout of the human brain has been a challenging goal to understand how structure defines the brain function (DeFelipe, 2010). Further studies have correlated structural connectivity with brain function in the healthy population or disease population (Fornito et al., 2015). The network analysis tackled the structure-function correlation from a panoramic view, but the shape characteristics and morphology of the connecting bundles were mostly ignored, the association pathways in the human brain that control most of the cognitive functions. On the other hand, existing tractography studies only used basic shape features such as volume or size and discarded rich morphology information in the fiber pathways (Abhinav et al, 2014; Huang et al, 2005; Lopez et al, 2013; Wolff et al, 2015). There is yet a comprehensive study utilizing shape analysis to investigate the structural characteristics of the human association pathways

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Conclusion