Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age

Chaddad, Ahmad, Desrosiers, Christian and Toews, Matthew. 2017. « Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age ». Scientific Reports, vol. 7.
Compte des citations dans Scopus : 37.

Preview

PDF Toews M. 2017 14932 Multi-scale radiomic analysis of sub-cortical.pdf - Published Version Use licence: Creative Commons CC BY. Download (3MB) | Preview

Abstract

We propose using multi-scale image textures to investigate links between neuroanatomical regions and clinical variables in MRI. Texture features are derived at multiple scales of resolution based on the Laplacian-of-Gaussian (LoG) filter. Three quantifier functions (Average, Standard Deviation and Entropy) are used to summarize texture statistics within standard, automatically segmented neuroanatomical regions. Significance tests are performed to identify regional texture differences between ASD vs. TDC and male vs. female groups, as well as correlations with age (corrected p < 0.05). The open-access brain imaging data exchange (ABIDE) brain MRI dataset is used to evaluate texture features derived from 31 brain regions from 1112 subjects including 573 typically developing control (TDC, 99 females, 474 males) and 539 Autism spectrum disorder (ASD, 65 female and 474 male) subjects. Statistically significant texture differences between ASD vs. TDC groups are identified asymmetrically in the right hippocampus, left choroid-plexus and corpus callosum (CC), and symmetrically in the cerebellar white matter. Sex-related texture differences in TDC subjects are found in primarily in the left amygdala, left cerebellar white matter, and brain stem. Correlations between age and texture in TDC subjects are found in the thalamus-proper, caudate and pallidum, most exhibiting bilateral symmetry.

Item Type:	Peer reviewed article published in a journal
Additional Information:	Identifiant de l'article: 45639
Uncontrolled Keywords:	Fonds d'auteur ÉTS, FAETS
Professor:	Professor Desrosiers, Christian Toews, Matthew
Affiliation:	Génie logiciel et des technologies de l'information, Génie de la production automatisée
Date Deposited:	03 Apr 2017 18:07
Last Modified:	28 Jan 2020 16:31
URI:	https://espace2.etsmtl.ca/id/eprint/14932

Actions (login required)

View Item