RESUMEN
Advanced brain imaging of neonatal macrostructure and microstructure, which has prognosticating importance, is more frequently being incorporated into multi-center trials of neonatal neuroprotection. Multicenter neuroimaging studies, designed to overcome small sample sized clinical cohorts, are essential but lead to increased technical variability. Few harmonization techniques have been developed for neonatal brain microstructural (diffusion tensor) analysis. The work presented here aims to remedy two common problems that exist with the current state of the art approaches: 1) variance in scanner and protocol in data collection can limit the researcher's ability to harmonize data acquired under different conditions or using different clinical populations. 2) The general lack of objective guidelines for dealing with anatomically abnormal anatomy and pathology. Often, subjects are excluded due to subjective criteria, or due to pathology that could be informative to the final analysis, leading to the loss of reproducibility and statistical power. This proves to be a barrier in the analysis of large multi-center studies and is a particularly salient problem given the relative scarcity of neonatal imaging data. We provide an objective, data-driven, and semi-automated neonatal processing pipeline designed to harmonize compartmentalized variant data acquired under different parameters. This is done by first implementing a search space reduction step of extracting the along-tract diffusivity values along each tract of interest, rather than performing whole-brain harmonization. This is followed by a data-driven outlier detection step, with the purpose of removing unwanted noise and outliers from the final harmonization. We then use an empirical Bayes harmonization algorithm performed at the along-tract level, with the output being a lower dimensional space but still spatially informative. After applying our pipeline to this large multi-site dataset of neonates and infants with congenital heart disease (n= 398 subjects recruited across 4 centers, with a total of n=763 MRI pre-operative/post-operative time points), we show that infants with single ventricle cardiac physiology demonstrate greater white matter microstructural alterations compared to infants with bi-ventricular heart disease, supporting what has previously been shown in literature. Our method is an open-source pipeline for delineating white matter tracts in subject space but provides the necessary modular components for performing atlas space analysis. As such, we validate and introduce Diffusion Imaging of Neonates by Group Organization (DINGO), a high-level, semi-automated framework that can facilitate harmonization of subject-space tractography generated from diffusion tensor imaging acquired across varying scanners, institutions, and clinical populations. Datasets acquired using varying protocols or cohorts are compartmentalized into subsets, where a cohort-specific template is generated, allowing for the propagation of the tractography mask set with higher spatial specificity. Taken together, this pipeline can reduce multi-scanner technical variability which can confound important biological variability in relation to neonatal brain microstructure.
RESUMEN
To identify regional cerebral blood flow (rCBF) alterations in children and adolescents with congenital heart disease (CHD) in relation to neurocognitive outcomes using a nonbiased data-driven approach. This is a prospective, observational study of children and adolescents with CHD without brain injury and healthy controls using pseudo-continuous arterial spin labeling (pCASL) MRI. Quantitative rCBF was compared between participants with CHD and healthy controls using a voxelwise data-driven method. Mediation analysis was then performed on a voxelwise basis, with the grouping variable as the independent variable, neurocognitive outcomes (from the NIH Toolbox Cognitive Battery) as the dependent variables, and rCBF as the mediator. After motion correction, a total of 80 studies were analyzable (27 for patients with CHD, 53 for controls). We found steeper age-related decline in rCBF among those with CHD compared to normal controls in the insula/ventromedial prefrontal regions (salience network) and the dorsal anterior cingulate and precuneus/posterior cingulate (default mode network), and posterior parietal/dorsolateral prefrontal (central executive network) (FWE-corrected P< 0.05). The reduced rCBF in the default mode/salience network was found to mediate poorer performance on an index of crystallized cognition from the NIH Toolbox Cognitive Battery in those with CHD compared to controls. In contrast, reduced rCBF in the central executive network/salience network mediated reduced deficits in fluid cognition among patients with CHD compared to controls. Regional cerebral blood flow alterations mediate domain-specific differences in cognitive performance in children and adolescents with CHD compared to healthy controls, independent of injury, and are likely related to brain and cognitive reserve mechanisms. Further research is needed to evaluate the potential of interventions in CHD targeting regional cerebral blood flow across lifespan.