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Shape analysis of the human association pathways.
- Source :
-
NeuroImage [Neuroimage] 2020 Dec; Vol. 223, pp. 117329. Date of Electronic Publication: 2020 Sep 01. - Publication Year :
- 2020
-
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.<br /> (Copyright © 2020. Published by Elsevier Inc.)
- Subjects :
- Adult
Brain diagnostic imaging
Female
Humans
Male
Neural Pathways anatomy & histology
Neural Pathways diagnostic imaging
White Matter anatomy & histology
White Matter diagnostic imaging
Young Adult
Brain anatomy & histology
Brain Mapping methods
Diffusion Tensor Imaging
Image Processing, Computer-Assisted methods
Subjects
Details
- Language :
- English
- ISSN :
- 1095-9572
- Volume :
- 223
- Database :
- MEDLINE
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 32882375
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2020.117329