Changes in Topological Organization of Functional PET Brain Network with Normal Aging

Abstract

Recent studies about brain network have suggested that normal aging is associated with alterations in coordinated patterns of the large-scale brain functional and structural systems. However, age-related changes in functional networks constructed via positron emission tomography (PET) data are still barely understood. Here, we constructed functional brain networks composed of regions in younger (mean age years) and older (mean age years) age groups with PET data. younger and older healthy individuals were separately selected for two age groups, from a physical examination database. Corresponding brain functional networks of the two groups were constructed by thresholding average cerebral glucose metabolism correlation matrices of regions and analysed using graph theoretical approaches. Although both groups showed normal small-world architecture in the PET networks, increased clustering and decreased efficiency were found in older subjects, implying a degeneration process that brain system shifts from a small-world network to regular one along with normal aging. Moreover, normal senescence was related to changed nodal centralities predominantly in association and paralimbic cortex regions, e.g. increasing in orbitofrontal cortex (middle) and decreasing in left hippocampus. Additionally, the older networks were about equally as robust to random failures as younger counterpart, but more vulnerable against targeted attacks. Finally, methods in the construction of the PET networks revealed reasonable robustness. Our findings enhanced the understanding about the topological principles of PET networks and changes related to normal aging.

Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults

“…In this study, the range from 0.1 to 0.5 (i.e., 0.1 < S < 0.5, interval ¼ 0.01) was chosen to be the standard threshold sequence, as a previous study reported. 11 Then, the threshold sequence was applied to the adjacency matrix to generate 41 binarized network matrices for each subject. Figure 5(c) shows an example from one subject's data, revealing a spatial representation of nodes and edges from one binary matrix with a middle threshold (S ¼ 0.25).…”

“…We focused on demonstrating our AVC by using a few major parameters affected by age effects in a previous study. 11 Specifically, a set of global parameters investigated were global network metrics, including (i) clustering coefficient (C p ), (ii) shortest path length (L p ), (iii) global efficiency (E g ), (iv) normalized clustering coefficient (γ), (v) normalized characteristic path length (λ), and (vi) small-world (σ). For the local parameters, we focused on the hub information, including (i) nodal degree, (ii) nodal efficiency, and (iii) betweenness centrality.…”

“…Two-sample t-tests for each of the global brain network parameters were performed at each sparsity value from 0.1 to 0.5, as a previous study suggested. 11 …”

“…The brain networks experience changes in their properties under a variety of conditions, such as taking on different physical or mental tasks, facing strong emotions, 9 or undergoing normal aging. [10][11][12] Thus, noninvasive mapping of the human brain's structural and functional connectivity enables researchers to better understand the architecture of the brain and to clearly reveal connectivity changes in entire brain networks. Increasingly, many neuroimaging techniques, such as fMRI, diffusion MRI, electroencephalographic (EEG), and DOT, have been recently used to investigate large-scale brain networks.…”

“…Based on GTA, a number of studies have found alterations in network parameters in aged patients. Liu et al 11 conducted a resting-state positron emission tomography (PET) study in young and aged patients and reported a decrease in network efficiency for older patients, who also maintained a higher clustering coefficient than younger subjects. This result indicates that the brain network becomes more random in nature as humans grow older.…”