Age‐related structural and functional variations in 5,967 individuals across the adult lifespan

Abstract

Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year‐wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U‐shape quadratic relationship with age; SNC presented a U‐shape quadratic relationship with age within cerebellum, and inverted U‐shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting‐state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U‐shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age‐related regional variation and SNC/FNC changes based on a large dataset.

Atlas55+: Brain Functional Atlas of Resting-state Networks for Late Adulthood

“…This finding was expected as the age range across all participants was 40 years. This was also consistent with prior neuroimaging studies that described an effect of aging on both brain structure and function in populations over age 50 (Betzel et al, 2014;Chan et al, 2014;Damoiseaux, 2017;Dima et al, 2020;Frangou et al, 2020;Luis et al, 2015;Luo et al, 2020b;Varangis et al, 2019a). The overall negative impact of age on each network largely confirms a reduction of functional cohesiveness of the major brain networks, particularly those supporting higher-order cognitive functions (Betzel et al, 2014;Damoiseaux et al, 2008;He et al, 2013;Mowinckel et al, 2012;Yaple et al, 2019).…”

“…However, to date, the only brain functional atlases available are derived from samples of young healthy adults, typically below the age of 40 years (Doucet et al, 2019). Nonetheless, changes in brain function over the course of adulthood have been well-documented and suggest that brain networks are continuously reconfigured throughout adult life (Betzel et al, 2014;Damoiseaux, 2017;Damoiseaux et al, 2008;He et al, 2013;Luo et al, 2020b;Meunier et al, 2009;Varangis et al, 2019b;Yaple et al, 2019). To our knowledge, there is currently no reference brain functional atlas derived from rs-fMRI data from older adults, and this may undermine neuroimaging efforts to characterize the brain functional connectome and its cognitive role in late adulthood.…”

The Spontaneous Activity Pattern of the Middle Occipital Gyrus Predicts the Clinical Efficacy of Acupuncture Treatment for Migraine Without Aura

“…The fMRI data preprocessing was carried out with SPM12 (SPM12, http://www.fil.ion.ucl.ac.uk/spm ) and DPARSF 4.5 ( 32 ) ( http://rfmri.org/DPARSF ). The main steps included the following: (1) discarding the first 10 timepoints; (2) slice-timing correction, realignment, and discarding subjects with a mean framewise displacement value exceeding 0.5 mm or a maximum displacement greater than one voxel size ( 33 , 34 ); (3) reorienting functional and T1 images with six rigid-body parameters; (4) coregistering T1 images to functional space, segmentation, and normalizing the functional images to Montreal Neurological Institute (MNI) space; (5) correcting head motion with Friston 24-parameter model ( 35 , 36 ), removing linear trend, and regressing out the white matter and cerebrospinal fluid signals; (6) re-sampling the functional images to 3-mm 3 cubic voxels and smoothing functional images with a 6-mm Gaussian kernel of full width at half maximum; (7) temporally filtering (0.01–0.08 Hz) ( 37 ) to generate the ALFF maps; (8) transforming the ALFF map to the zALFF map with normal z transformation.…”

Tai Chi Training Evokes Significant Changes in Brain White Matter Network in Older Women

“…As cognition relies on proper brain functioning, it is natural to assume that the aging-related changes in cognition may be accompanied by age-related changes in the brain, too. Indeed, solid evidence shows that aging leads to considerable changes in functional connectivity and grey matter as well as white matter in the human brain [5,6]. In this context, a recent study showed that there is a significant ageing-related loss of white matter volume in fronto-striatal projections [7].…”