Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders.

Yu Sun,Lei Feng,John Suckling,Junhua Li

doi:10.3389/fnagi.2017.00361

Abstract

Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging), we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years) community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging.

Highlights

The human brain is structurally and functionally asymmetrical or lateralized (Galaburda et al, 1978; Toga and Thompson, 2003)
Assessments of cognitive ability administered by trained raters included the mini-mental state examination (MMSE) (Folstein et al, 1975; Feng et al, 2012), the Montreal cognitive assessment (MoCA) (Nasreddine et al, 2005; Liew et al, 2015), the Rey auditory verbal learning test (Schmidt, 1996), the Digit Span and Block Design tests from the Wechsler Adults Intelligence Scale (WAIS-III), the Boston Naming Test, the Color Trials Test (CTT), and the Symbol Digit Modalities Test (SDMT)
In the current study, using multimodal neuroimaging techniques and graph theory analysis, we examined the hemispheric asymmetry in healthy aging adults

Summary

Introduction

The human brain is structurally and functionally asymmetrical or lateralized (Galaburda et al, 1978; Toga and Thompson, 2003). A number of brain phenotypes have been shown to exhibit asymmetry, including gray matter volume (Good et al, 2001; Lancaster et al, 2003; Takao et al, 2011a), cortical thickness (Zhou et al, 2013), and white matter integrity (Cao et al, 2003; Takao et al, 2011b; Thiebaut de Schotten et al, 2011b; Song et al, 2014), which show varying degrees of correspondence to brain functions (Toga and Thompson, 2003; Herve et al, 2013) and that this asymmetry is hormone related (Hausmann and Gunturkun, 2000; Hausmann et al, 2003). While rightward asymmetry of gray matter volume in the lateral inferior frontal gyrus and diffusion parameters of frontal white matter tracts have been frequently reported (Zhou et al, 2013), indicating a rightward predilection to processing non-verbal functions, including spatial attention, and visuospatial memory (Thiebaut de Schotten et al, 2011a). Studies have reported that brain asymmetries would be altered due to behavior changes in maturation/normal aging (Cabeza, 2002; Zhong et al, 2016), and in various neuropsychiatry (e.g., schizophrenia) as well as neurological (e.g., dementia) diseases (Crow et al, 1989; Thompson et al, 2003; Kim et al, 2012; Wachinger et al, 2016; Sun et al, 2017)

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