A Multivariate Surface-Based Analysis of the Putamen in Premature Newborns: Regional Differences within the Ventral Striatum

Abstract

Many children born preterm exhibit frontal executive dysfunction, behavioral problems including attentional deficit/hyperactivity disorder and attention related learning disabilities. Anomalies in regional specificity of cortico-striato-thalamo-cortical circuits may underlie deficits in these disorders. Nonspecific volumetric deficits of striatal structures have been documented in these subjects, but little is known about surface deformation in these structures. For the first time, here we found regional surface morphological differences in the preterm neonatal ventral striatum. We performed regional group comparisons of the surface anatomy of the striatum (putamen and globus pallidus) between 17 preterm and 19 term-born neonates at term-equivalent age. We reconstructed striatal surfaces from manually segmented brain magnetic resonance images and analyzed them using our in-house conformal mapping program. All surfaces were registered to a template with a new surface fluid registration method. Vertex-based statistical comparisons between the two groups were performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We found statistically significant differences in regional morphology between the two groups that are consistent across statistics, but more extensive for multivariate measures. Differences were localized to the ventral aspect of the striatum. In particular, we found abnormalities in the preterm anterior/inferior putamen, which is interconnected with the medial orbital/prefrontal cortex and the midline thalamic nuclei including the medial dorsal nucleus and pulvinar. These findings support the hypothesis that the ventral striatum is vulnerable, within the cortico-stiato-thalamo-cortical neural circuitry, which may underlie the risk for long-term development of frontal executive dysfunction, attention deficit hyperactivity disorder and attention-related learning disabilities in preterm neonates.

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

“…In the past few years, our group has developed a series of structural MRI analysis software tools for AD research, such as multivariate TBM on cortical surface (Wang et al, 2013b) and hippocampal abnormality analyses (Shi et al, 2014; Shi et al, 2013a; Shi et al, 2013b; Wang et al, 2011), together with cortical thickness estimation with volumetric Laplace-Beltrami operator and heat kernel (Wang et al, 2013a; Wang et al, 2014). In the future, we plan to apply our structural MRI software tools in our ongoing preclinical AD research (Caselli and Reiman, 2013; Langbaum et al, 2013).…”

“…Our surface smoothing process consists of mesh simplification using “progressive meshes” (Hoppe, 1996) and mesh refinement by Loop subdivision surface (Loop, 1987). The similar procedures were frequently adopted in a number of our prior works (Colom et al, 2013; Luders et al, 2013; Monje et al, 2013; Shi et al, 2014; Shi et al, 2013a; Shi et al, 2013b; Wang et al, 2011; Wang et al, 2010) and our experience showed that the smoothed meshes are accurate approximations to the original surfaces with higher signal-to-noise ratio (SNR) (Shi et al, 2013a). …”

“…The Ricci flow method is theoretically sound and computationally efficient (Jin et al, 2008; Wang et al, 2006; Wang et al, 2012). In addition, TBM has been used extensively to detect regional differences in surface and volume brain morphology between groups of subjects (Chung et al, 2003b; Hua et al, 2011; Leow et al, 2009; Shi et al, 2014; Shi et al, 2013a; Shi et al, 2013b; Wang et al, 2012; Wang et al, 2011; Wang et al, 2013b). We hypothesize that the hyperbolic Ricci flow together with TBM may offer a set of accurate surface statistics for ventricular morphometry and that it may boost statistical power to detect the subtle difference between MCI patients who progress to dementia from those who fail to progress.…”

Impact of Early and Late Visual Deprivation on the Structure of the Corpus Callosum: A Study Combining Thickness Profile with Surface Tensor-Based Morphometry

“…With permutation test to correct for multiple comparisons, the overall significance p -value of a p -map is a quantitative measure indicating how likely the patterns in the p -map occurring by accident. Group difference analysis has been applied widely in the literature to study the impacts of Alzheimer’s disease (AD) on hippocampus and ventricles (Thompson et al 2004a; Wang et al 2011), the genetic influence of ApoE4 on hippocampus (Shi et al 2014), and the impact of prematurity on putamen (Shi et al 2013b), etc. With group difference analysis, our results confirm and extend prior works by demonstrating how blindness acquired at different periods during development influence the structural reorganization of the CC (e.g.…”

Predicting Alzheimer’s Disease Using Combined Imaging-Whole Genome SNP Data

“…We adopted a surface fluid registration based hippocampal subregional analysis package [25], which uses isothermal coordinates and fluid registration to generate one-to-one hippocampal surface registration for following surface statistics computation. This software package has been adopted by various studies [22, 26–30].…”