Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging

Abstract

A pressing need exists to disentangle age-related changes from pathologic neurodegeneration. This study aims to characterize the spatial pattern and age-related differences of biologically relevant measures in vivo over the course of normal aging. Quantitative multiparameter maps that provide neuroimaging biomarkers for myelination and iron levels, parameters sensitive to aging, were acquired from 138 healthy volunteers (age range: 19–75 years). Whole-brain voxel-wise analysis revealed a global pattern of age-related degeneration. Significant demyelination occurred principally in the white matter. The observed age-related differences in myelination were anatomically specific. In line with invasive histologic reports, higher age-related differences were seen in the genu of the corpus callosum than the splenium. Iron levels were significantly increased in the basal ganglia, red nucleus, and extensive cortical regions but decreased along the superior occipitofrontal fascicle and optic radiation. This whole-brain pattern of age-associated microstructural differences in the asymptomatic population provides insight into the neurobiology of aging. The results help build a quantitative baseline from which to examine and draw a dividing line between healthy aging and pathologic neurodegeneration.

High-resolution characterisation of the aging brain using simultaneous quantitative susceptibility mapping (QSM) and R2* measurements at 7 T

“…The high R2* values for the GP (independent of age) in contrast to thalamus, caudate and putamen are consistent with previous in vivo R2* reports and post-mortem correlations with iron concentration (Gelman et al, 1999;Langkammer et al, 2010;Péran et al, 2009;Yao et al, 2009). Furthermore, agerelated increase in R2* have been reported previously in the GP in some studies (Callaghan et al, 2014;Draganski et al, 2011). On the other hand, the lack of a significant age-related difference in the GP with QSM in the present study is consistent with previous post-mortem evidence (Hallgren and Sourander, 1958), and confirmed in vivo (Li et al, 2014), that iron deposition in the GP increases rapidly from birth until the second decade of life but thereafter plateaus.…”

“…Furthermore both aforementioned studies were limited to regions-of-interest (ROI) analyses. Yet, recent aging lifespan studies using quantitative multimodal MRI (Callaghan et al, 2014) and QSM (Acosta-Cabronero et al, 2016) have shown whole-brain analyses may be used to reveal global patterns of iron distribution that may otherwise be missed in pre-defined ROI analyses.…”

Tracking trauma-induced structural and functional changes above the level of spinal cord injury

“…Novel quantitative MRI protocols of the spinal cord and brain have the potential to measure neural changes at the microstructural level. This is because the degree of myelination, iron content and neuronal microstructure are reflected in MR relaxation times, magnetization transfer and diffusion-weighted images which can be measured at high resolution [14][15][16]. These novel quantitative MR methods include multiparametric mapping [17][18][19][20][21] and diffusion tensor imaging [22] next to volumetric measures (i.e.…”

Synthetic quantitative MRI through relaxometry modelling

“…due to co‐localized and interacting effects of atrophy and MRI parameter changes, as occur during ageing 46. It has also been shown that age‐related atrophy and differences in tissue microstructure can be captured by the qMRI parameters used in this study 30, 46. To circumvent any atrophy‐related confound being introduced here, the cohort used was restricted to a narrow age range (18–25 years).…”

“…Significance was defined as voxels having a p value less than 0.05 after small volume and family‐wise error corrections for multiple comparisons. An explicit mask described by Callaghan et al30 was used to exclude cerebrospinal fluid voxels. Given the a priori hypothesis that GM tissue classification would be variable for deep GM structures, the statistical analysis was restricted to a central search volume focusing on these structures.…”