Hemisphere and Gender Differences in the Rich-Club Organization of Structural Networks

Abstract

Structural and functional differences in brain hemispheric asymmetry have been well documented between female and male adults. However, potential differences in the connectivity patterns of the rich-club organization of hemispheric structural networks in females and males remain to be determined. In this study, diffusion tensor imaging was used to construct hemispheric structural networks in healthy subjects, and graph theoretical analysis approaches were applied to quantify hemisphere and gender differences in rich-club organization. The results showed that rich-club organization was consistently observed in both hemispheres of female and male adults. Moreover, a reduced level of connectivity was found in the left hemisphere. Notably, rightward asymmetries were mainly observed in feeder and local connections among one hub region and peripheral regions, many of which are implicated in visual processing and spatial attention functions. Additionally, significant gender differences were revealed in the rich-club, feeder, and local connections in rich-club organization. These gender-related hub and peripheral regions are involved in emotional, sensory, and cognitive control functions. The topological changes in rich-club organization provide novel insight into the hemisphere and gender effects on white matter connections and underlie a potential network mechanism of hemisphere- and gender-based differences in visual processing, spatial attention and cognitive control.

Altered Rich-Club Organization and Regional Topology Are Associated With Cognitive Decline in Patients With Frontal and Temporal Gliomas

“…The preprocessing steps used were as previously described (Bai et al, 2012;Wang X.N. et al, 2016) and were performed using PANDA software as follows: (Ostrom et al, 2014) format conversion of original data (DICOM); (Raysi Dehcordi et al, 2013) the extraction of brain tissue and structure; (Fornito et al, 2013) realignment; (Hart et al, 2016) Eddy current and motion artifact correction of DTI data by applying an affine alignment of each diffusion-weighted image to the b0 image; (Wang et al, 2019) fractional anisotropy (FA) calculation; (Yan et al, 2018) diffusion tensor tractography; and (Iturria-Medina et al, 2011) conduct of tractography to produce 3D streamlines representing fiber tract connectivity (Mori et al, 1999).…”

“…The development of complex network analysis using graph theory (i.e., connectome) in the brain offers the potential to identify the impacts of focal and diffuse pathologies in glioma patients (Fornito et al, 2013;Hart et al, 2016), such as rich-club organization (Yan et al, 2018;Wang et al, 2019), small-world characteristics (Iturria-Medina et al, 2011;Yan et al, 2018), betweenness (Iturria-Medina et al, 2011), and global and local network efficiency (Iturria-Medina et al, 2011;Shu et al, 2018). Recent studies have suggested that the human brain shares the same key organizational principles as common, natural, and manmade systems, that is to say, that the human brain is thought to be a network of unceasing communication (Hart et al, 2016).…”

Disrupted Rich Club Organization of Hemispheric White Matter Networks in Bipolar Disorder

“…The tractography was terminated when it reached a voxel with an FA value less than 0.1 or when the angle was greater than 35 • . Based on the reconstructed fiber tracts, the WM connection between a pair of nodes was adopted if the fiber number (FN) was larger than 3 (Shu et al, 2011;Li et al, 2018;Wang et al, 2019). We selected the threshold value for the FN > 3 was to reduce pseudo-connections due to possible noise effects on the whole-brain WM tractography (Sun et al, 2015).…”

“…The node of structural network was defined as one region of the normalized AAL atlas. The weights of the structural network was defined the mean FA values of the connected fibers between 2 regions (Li et al, 2018;Yan et al, 2018;Wang et al, 2019). The reason for choosing FA is that it is an important indicator commonly used to examine the microstructure aspects of brain WM connections (Cui et al, 2013).…”

“…This paper applied the formula (Yasser et al, 2011;Wang et al, 2019) to estimate the hemispheric asymmetry of the network topology graph metrics, where X(R) and X(L) refer to the graph metric for the right and left hemispheric networks, respectively. A negative AS(X) value indicates leftward asymmetry in the graph metric of X, while a positive AS(X) value demonstrates a rightward advantage in the graph metric of X.…”