Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to [corrected] IQ and Gender.

Vasileios C Pezoulas,Michalis Zervakis,Manousos A Klados,Sifis Michelogiannis

doi:10.3389/fnhum.2017.00189

Abstract

During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high fluid Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks.

Highlights

During the last decades, many neuroimaging studies have been performed toward establishing the relationship between brain volume, connectivity structures and intelligence
Our goal is to examine whether the Minimum Spanning Tree (MST) topology can highlight significant differences among different Intelligence Quotient (IQ) groups in the cerebellum
The Human Connectome Project (HCP) is the result of efforts of co-investigators from the University of California, Los Angeles, Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH), Washington University, and the University of Minnesota

Summary

Introduction

Many neuroimaging studies have been performed toward establishing the relationship between brain volume, connectivity structures and intelligence. Gray and white-matter characteristics have been used to study the correlation between structural findings and intellectual abilities (Mechelli et al, 2005; Hulshoff Pol et al, 2006; Choi et al, 2008; Malpas et al, 2016), while studies associating anatomical and functional connectivity with intelligence have been reported (Haier et al, 2005; Song et al, 2008; Chiang et al, 2009; Ryman et al, 2016; Tsvetanov et al, 2016), with indicative biomarkers involving the total brain volume and the concentration of the N-acetyl aspartate (McDaniel, 2005; Paul et al, 2016). Functional and structural networks have been used to study the correlation between brain organization and intelligence These studies revealed important correlations of local and widespread brain properties related to the cognitive functions and intelligence (Li et al, 2009; Douw et al, 2011). More differences related to the organization of brain networks across genders have been identified in default mode network, revealing local as well as widespread connection effects (Allen et al, 2011; Tomasi and Volkow, 2012; Szalkai et al, 2015)

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