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. 2019 Sep;40(13):3832-3842.
doi: 10.1002/hbm.24634. Epub 2019 May 21.

Brain biomarkers and cognition across adulthood

Angeliki Tsapanou  1 , Christian Habeck  1 , Yunglin Gazes  1 , Qolamreza Razlighi  1 , Jayant Sakhardande  1 , Yaakov Stern  1 , Timothy A Salthouse  2
Affiliations

Brain biomarkers and cognition across adulthood

Angeliki Tsapanou et al. Hum Brain Mapp. 2019 Sep.

Abstract

Understanding the associations between brain biomarkers (BMs) and cognition across age is of paramount importance. Five hundred and sixty-two participants (19-80 years old, 16 mean years of education) were studied. Data from structural T1, diffusion tensor imaging, fluid-attenuated inversion recovery, and resting-state functional magnetic resonance imaging scans combined with a neuropsychological evaluation were used. More specifically, the measures of cortical, entorhinal, and parahippocampal thickness, hippocampal and striatal volume, default-mode network and fronto-parietal control network, fractional anisotropy (FA), and white matter hyperintensity (WMH) were assessed. z-Scores for three cognitive domains measuring episodic memory, executive function, and speed of processing were computed. Multiple linear regressions and interaction effects between each of the BMs and age on cognition were examined. Adjustments were made for age, sex, education, intracranial volume, and then, further, for general cognition and motion. BMs were significantly associated with cognition. Across the adult lifespan, slow speed was associated with low striatal volume, low FA, and high WMH burden. Poor executive function was associated with low FA, while poor memory was associated with high WMH burden. After adjustments, results were significant for the associations: speed-FA and WMH, memory-entorhinal thickness. There was also a significant interaction between hippocampal volume and age in memory. In age-stratified analyses, the most significant associations for the young group occurred between FA and executive function, WMH, and memory, while for the old group, between entorhinal thickness and speed, and WMH and speed, executive function. Unique sets of BMs can explain variation in specific cognitive domains across adulthood. Such results provide essential information about the neurobiology of aging.

Keywords: aging; brain biomarkers; cognition.

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Figures

Figure 1
Figure 1
Scatterplot for the association between speed of processing and the covariate‐residualized WMH burden in the total sample. Age, sex, education, and the remaining BMs were used as covariates. Lower speed of processing was associated with higher WMH burden in the total sample. BM, brain biomarker; WMH, white matter hyperintensity
Figure 2
Figure 2
Scatterplot for the association between speed of processing and the covariate‐residualized WMH burden in the old group. Age, sex, education, and the remaining BMs were used as covariates. Lower speed of processing was associated with higher WMH burden in the old group. BM, brain biomarker; WMH, white matter hyperintensity
Figure 3
Figure 3
Scatterplot for the association between episodic memory and the covariate‐residualized WMH burden in the young group. Age, sex, education, and the remaining BMs were used as covariates. Lower performance in episodic memory was associated with higher WMH burden in the young group. BM, brain biomarker; WMH, white matter hyperintensity
Figure 4
Figure 4
Scatterplot for the interaction effect between hippocampal volume and age on episodic memory, in total sample with a median split age of 60 years old
See this image and copyright information in PMC

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