Open AccessGraph theory analysis of cortical thickness networks in adolescents with d‐transposition of the great arteries
Author(s) -
Watson Christopher G.,
Stopp Christian,
Newburger Jane W.,
Rivkin Michael J.
Publication year - 2018
Publication title -
brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.915
H-Index - 41
ISSN - 2162-3279
DOI - 10.1002/brb3.834
Subject(s) - great arteries , transposition (logic) , thresholding , graph theory , correlation , graph , mathematics , cardiology , computer science , medicine , combinatorics , artificial intelligence , geometry , heart disease , image (mathematics)
Objective Adolescents with d‐transposition of the great arteries (d‐ TGA ) who had the arterial switch operation in infancy have been found to have structural brain differences compared to healthy controls. We used cortical thickness measurements obtained from structural brain MRI to determine group differences in global brain organization using a graph theoretical approach. Methods Ninety‐two d‐ TGA subjects and 49 controls were scanned using one of two identical 1.5‐Tesla MRI systems. Mean cortical thickness was obtained from 34 regions per hemisphere using Freesurfer. A linear model was used for each brain region to adjust for subject age, sex, and scanning location. Structural connectivity for each group was inferred based on the presence of high inter‐regional correlations of the linear model residuals, and binary connectivity matrices were created by thresholding over a range of correlation values for each group. Graph theory analysis was performed using packages in R. Permutation tests were performed to determine significance of between‐group differences in global network measures. Results Within‐group connectivity patterns were qualitatively different between groups. At lower network densities, controls had significantly more long‐range connections. The location and number of hub regions differed between groups: controls had a greater number of hubs at most network densities. The control network had a significant rightward asymmetry compared to the d‐ TGA group at all network densities. Conclusions Using graph theory analysis of cortical thickness correlations, we found differences in brain structural network organization among d‐ TGA adolescents compared to controls. These may be related to the white matter and gray matter differences previously found in this cohort, and in turn may be related to the cognitive deficits this cohort presents.