Resting-State Functional Network Connectivity in Adolescents and Young Adults with Congenital Heart Disease

Jordan Pincus, Georgia State University, Atlanta, United States
Zening Fu, Georgia State University, Atlanta, United States
Armin Iraji, Georgia State University, Atlanta, United States
Vince Calhoun, Georgia State University, Atlanta, United States
William Mahle, Children's Healthcare of Atlanta, Atlanta, United States
Thomas Burns, Children's Healthcare of Atlanta, Atlanta, United States
Tricia King, Georgia State University, Atlanta, United States


Adolescents and young adults (AYAs) with congenital heart disease (CHD) experience hypoxic injury to the brain throughout early development. Consequently, these individuals show structural brain abnormalities and cognitive difficulties, particularly with executive function (EF), that persist into adulthood. Structural brain changes often have functional brain consequences, yet functional magnetic resonance imaging (fMRI) in AYAs with CHD remains largely unexplored. Two studies have respectively found hyperactivity in task-based fMRI and hyperconnectivity in resting-state fMRI (rsfMRI) within the default mode (DM) network in CHD. While this previous work used seed-based methodology, our current study employs a state-of-the-art data-driven approach to explore resting-state functional network connectivity (FNC) in AYAs with CHD. Given the role of DM, cognitive control (CC), and cerebellar (CB) networks in EF, we investigate FNC between and within these functional domains in AYAs with CHD compared to controls.

Participants and Methods:

rsfMRI data was collected from 21 AYAs with CHD (Mage(SD)=  17.90(1.64), 28.6% females) and 41 healthy controls (Mage(SD)=  19.19(1.91), 56.1% females). rsfMRI data was preprocessed using SPM12. A spatially-constrained independent component analysis (scICA) was conducted using the Neuromark template. Unlike seed-based methods, which average the signal from all voxels within the same spherical location for each participant, scICA allows for variations in regions-of-interest across subjects. Specifically, this hybrid data-driven aproach searches a predefined feature space to estimate subject-specific networks and associated time courses. For each individual, time courses were extracted from 28 intrinsic connectivity networks (ICNs) within the DM, CC, and CB “domains”, according to scICA terminology. FNC was measured by the Pearson’s correlation between time courses of two ICNs. Each participant’s within-domain FNC is the average of their FNC values between all combinations of two ICNs from the same functional domain (DM, CC, or CB). Between-domain FNC is the average of FNC values between all combinations of two ICNs from different domains (e.g., DM vs CC). Correlations were converted to z-scores before analysis. Linear regressions, including age and sex as covariates, explored group differences for all within and between-domain FNC values. P-values were adjusted using a false discovery rate for multiple comparisons correction (α = .05).


Within-DM FNC was greater in AYAs with CHD than controls (p=.036, r2semipartial=.09). All other FNC group comparisons (within- and between-domains) were not significant (all p>0.4).


Results reveal hyperconnectivity within the DM domain in AYAs. This pattern is consistent with previous literature, highlighting that DM hyperconnectivity and hyperactivity exist across fMRI modalities and methodologies. Our finding emphasizes the importance of better understanding the role of DMN in CHD. A power analysis revealed that only large effect sizes could be detected with this sample size and analysis plan. Therefore, there might exist other, more nuanced between- and within-domain FNC differences in CHD. Future work should explore if hyperconnectivity of the DM domain helps explain performance-based EF outcomes, since DMN hyperactivity is related to worse cognitive outcomes in other clinical populations.  Better understanding brain-behavior relationships in AYAs with CHD may guide clinical recommendations for improving independence and long-term neurocognitive outcomes as individuals with CHD transition to adulthood.

Category: Neuroimaging

Keyword 1: congenital disorders
Keyword 2: neuroimaging: functional connectivity
Keyword 3: adolescence