Single-subject morphological brain networks: connectivity mapping, topological characterization and test-retest reliability.

Hao Wang,Jinhui Wang,Xiaoqing Jin,Ye Zhang

doi:10.1002/brb3.448

Abstract

IntroductionStructural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual‐level morphological brain networks and systematically examined their topological organization and long‐term test–retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type.MethodsThis study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback–Leibler divergence measure. Graph‐based global and nodal network measures were then calculated, followed by the statistical comparison and intra‐class correlation analysis.ResultsThe morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph‐based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small‐worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long‐term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability.ConclusionsOur findings support single‐subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders.

Highlights

During the last two decades, tremendous progress has been made in both neuroimaging and high-throughput genotyping technology, which has resulted in the development of an emergent interdisciplinary field known as imaging genetics, focusing on the genetic dissection of neuroimaging and clinical phenotypes
In spite of having a lower area under the curve (AUC) for variable selection compared to BLASSO, the proposed approach does significantly better in terms of out of sample prediction
We have developed a new Bayesian semi-parametric conditional graphical model for imaging genetics studies, and applied it for analyzing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset

Summary

Introduction

During the last two decades, tremendous progress has been made in both neuroimaging and high-throughput genotyping technology, which has resulted in the development of an emergent interdisciplinary field known as imaging genetics, focusing on the genetic dissection of neuroimaging and clinical phenotypes. Typical approaches for estimating the group level brain network often fail to account for heterogeneity across subjects resulting from demographic, clinical and genetic variations This may lead to spurious associations and erroneous inferences. In addition to functional connectivity, several genetic biomarkers have been shown to be predictive of neurological disorders Such biomarkers are often inferred by modeling the association between gene products/ variants and the brain imaging phenotype (Stein et al, 2010; Zhu et al 2014; Stingo, 2013), due to the knowledge that some neuroimaging traits are closer to the action of the gene compared to clinical phenotypes (Mier et al, 2010; Munafo et al, 2008). Existing approaches for detecting such associations usually do not take into account the underlying brain functional network influencing the imaging phenotype

Methods

Results

Conclusion