Abstract

Brain structural network changes provide key information about the aging process of the brain. Unfortunately, there has yet to be a detailed characterization of these structural networks across different age groups. Efforts to classify these networks have also been hampered by their reliance on technically limited traditional methods, which are unable to track multiple fiber orientations within a voxel and consequently are prone to false detection and artifacts. In this study, a newly developed Ensemble Average Propagator (EAP) based probabilistic tractography method was applied to construct a structural network, with the strength of the link between any two brain functional regions estimated according to the alignment of the EAP along connecting pathways. Age-related changes in the topological organization of human brain structural networks were thereby characterized across a broad age range (ages 8–75 years). The data from 48 healthy participants were divided into four age groups (Group 1 aged 8–15 years; Group 2 aged 25–35 years; Group 3 aged 45–55 years; and, Group 4 aged 65–75 years; N = 12 per group). We found that the brain structural network continues to strengthen during later adolescence and adulthood, through the first 20–30 years of life. Older adults, aged 65–75, had a significantly less optimized topological organization in their structural network, with decreased global efficiency and increased path lengths versus subjects in other groups. This study suggests that probabilistic tractography based on EAP provides a reliable method to construct macroscale structural connectivity networks to capture the age-associated changes of brain structures.

Highlights

  • Human brain structural networks are functionally modular and connect effectively through neural bundles to meet the needs for complex cognitive tasks (Schmahmann et al, 2007; Lerch et al, 2017)

  • diffusion-weighted imaging (DWI) characterizes structural connectivity networks across brain regions in-vivo by calculating the number of streamlines or the probability of connections (Frank, 2002; Tuch et al, 2002; Anderson, 2005; Maier-Hein et al, 2017)

  • The aim of this study was to evaluate the novel Ensemble Average Propagator (EAP) approach for reconstructing structural connectivity networks, using a healthy subject dataset that covered a wide range of ages

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Summary

Introduction

Human brain structural networks are functionally modular and connect effectively through neural bundles to meet the needs for complex cognitive tasks (Schmahmann et al, 2007; Lerch et al, 2017) This neural fiber connectivity enables the communication between the various regions of the brain (Bassett et al, 2011) and its integrity is pivotal for individual health. The demonstrated connectivity patterns can be assessed through graph-based analyses that outline the complex structural substrates of cognition (Betzel et al, 2014) This approach has been effectively employed to identify densely interconnected structural hub regions that are critical to efficient neuronal signaling and communication (van den Heuvel and Sporns, 2013)

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