Associations between age and gray matter volume in anatomical brain networks in middle‐aged to older adults

Abstract

Aging is associated with cognitive decline, diminished brain function, regional brain atrophy, and disrupted structural and functional brain connectivity. Understanding brain networks in aging is essential, as brain function depends on large-scale distributed networks. Little is known of structural covariance networks to study inter-regional gray matter anatomical associations in aging. Here, we investigate anatomical brain networks based on structural covariance of gray matter volume among 370 middle-aged to older adults of 45–85 years. For each of 370 subjects, we acquired a T1-weighted anatomical MRI scan. After segmentation of structural MRI scans, nine anatomical networks were defined based on structural covariance of gray matter volume among subjects. We analyzed associations between age and gray matter volume in anatomical networks using linear regression analyses. Age was negatively associated with gray matter volume in four anatomical networks (P < 0.001, corrected): a subcortical network, sensorimotor network, posterior cingulate network, and an anterior cingulate network. Age was not significantly associated with gray matter volume in five networks: temporal network, auditory network, and three cerebellar networks. These results were independent of gender and white matter hyperintensities. Gray matter volume decreases with age in networks containing subcortical structures, sensorimotor structures, posterior, and anterior cingulate cortices. Gray matter volume in temporal, auditory, and cerebellar networks remains relatively unaffected with advancing age.

Early grey matter changes in structural covariance networks in Huntington's disease

“…Therefore, we choose to set the number of independent components in our study to ten components. This number is consistent with previous studies of brain networks, in which eight to ten components are most often applied (Cole et al, 2010, Hafkemeijer et al, 2014). A standard threshold level of 0.5 was used to describe significance of individual voxels within a spatial map.…”

“…This statistical technique with independent component analysis (ICA) defines fully automated spatial component maps of maximal statistical independence, which is commonly used to study functional network integrity. When applied on structural grey matter images, this method defines spatial components based on the co-variation of grey matter patterns among all participants (Hafkemeijer et al, 2014, Segall et al, 2012, Xu et al, 2009). Then, ICA provides for each participant a score (‘network integrity score’), which can be negative or positive, describing the strength of the individual expression in each network (Beckmann and Smith, 2004, Segall et al, 2012), with high scores indicating strong individual expression of the identified network.…”

“…As neurodegeneration is probably a network-based process involving several brain regions and is not regional specific (Rombouts et al, 2009), examining such approach might be particularly interesting in HD. Recently, a novel technique is developed to study disease-specific inter-regional network changes in grey matter by using structural covariance networks independent of a-priori defined regions (Hafkemeijer et al, 2014, Xu et al, 2009). Structural covariance networks are based on the observation that grey matter regions in the brain co-vary in morphometric characteristics.…”

“…Structural covariance networks are based on the observation that grey matter regions in the brain co-vary in morphometric characteristics. Therefore, structural covariance networks might be a valuable tool in investigating the topological organization of the brain (Hafkemeijer et al, 2014). …”

Adolescent Tuning of Association Cortex in Human Structural Brain Networks

“…Finally, structural network architecture is known to mature across the lifespan (DuPre and Spreng 2017), including during both early childhood (Geng et al 2017) and late adulthood (Hafkemeijer et al 2014). Our focused age-range prohibits us from conclusively ascertaining the specificity of these changes to adolescence.…”