Brain biomarkers and cognition across adulthood

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.

Structural Neural Correlates of Double Decision Performance in Older Adults

“…The present study found several related findings including the right precentral gyrus and right superior parietal lobule but failed to replicate this finding in the left precentral gyrus, bilateral inferior frontal gyrus, left superior frontal gyrus and middle frontal gyrus. Additionally, several studies to date have suggested that cortical volume within hippocampal regions are especially influential in speed of processing performance, accounting for a significant variance in speed of processing scores (Papp et al, 2014;O'Shea et al, 2016;Tsapanou et al, 2019). The current study supports these findings.…”

“…Specifically, decreased cortical volume has been associated with poorer speed of processing performance within the bilateral precentral gyrus, bilateral inferior frontal gyrus, left superior frontal gyrus, bilateral superior parietal regions, and bilateral middle frontal gyrus ( Chee et al, 2009 ; Hong et al, 2015 ). Additionally, greater hippocampal volume has been associated with better speed of processing, suggesting hippocampal volume may be especially related to speed of processing performance ( Papp et al, 2014 ; O’Shea et al, 2016 ; Tsapanou et al, 2019 ). Notably, O’Shea et al (2016) found that hippocampal volume accounts for 11% of the variance in speed of processing scores.…”

Functional connectivity changes in cerebral small vessel disease - a systematic review of the resting-state MRI literature

“…The same pattern of altered inter-network connectivity was reported for the salience network [ 48 ]; intrinsic connectivity in the SN was increased in patients with CSVD and in association with the extent of white matter disease [ 83 ]. In healthy individuals or patients without symptomatic CSVD (Table 2 ), most studies did not report significant associations between FC and WMH burden [ 66 , 79 , 82 , 86 , 92 ]. Two studies found an association between higher FC, especially in occipital and frontal areas, and WMH burden [ 87 , 93 ], whereas in patients with late-life depression the pattern was more similar to the one seen in patients with CSVD [ 69 , 71 ].…”

“…occipital cortex ↗ WMH in the right ant. corona radiata FC in the left superior occipital cortex ↗ WMH in the right superior corona radiata [ 67 ] 400 healthy participants (Baltimore Longitudinal Study of Aging) Multimodal supervised classification algorithm [ 89 ] Philips Achieva, 3 T TR 2000 ms, TE 30 ms [matrix], 3 × 3 × 3 mm 3 180 volumes [eyes] Confound regression - 24 motion parameters - GSR: global, WM, CSF Motion scrubbing - ‘summary motion value’ > 0.2 mm - Volume censoring (FD > 0.5 mm, < 5 min) Geodesic graph-based segmentation Regional homogeneity Sparse connectivity patterns Pattern of advanced brain ageing characterised by both increased WMH burden and reduced FC compared to resilient agers [ 66 ] 11 healthy participants Automated regression algorithm [ 90 ] using a Hidden Markov Random Field with Expectation Maximization [ 91 ] Siemens Trio, 3 T TR 2000 ms, TE 27 ms 92 × 92 × 43, 2.5 × 2.5 × 3 mm 3 240 volumes eyes closed SPM12 Confound regression - Linear/quadratic, 18 motion parameters - GSR: CSF, WM Motion scrubbing -> 3 mm max, > 3° max -> 24 spikes (FD > 1 mm) Brainnetome atlas (228) Graph theory to define DMN Pearson correlation No association between WMH load and DMN FC trajectories [ 92 ] 562 healthy participants SPM Lesion Segmentation Tool [ 76 ] Phillips Achieva, 3 T TR 2000 ms, TE 20 ms 112 × 112 × 37, 2 × 2 × 3 mm 3 [volumes], [eyes] [Confound regression] [Motion scrubbing] Mean FD as covariate in analysis Desikan–Killiany parcellation FC measure not specified No association between WMH load and FC [ 93 ] 182 participants (UK Biobank) BIANCA with manual correction [ 94 ] Siemens Skyra, 3 T TR 735 ms, TE 39 ms 88 × 88 × 64, 2.4 × 2.4 × 2.4 mm 3 490 volumes, [eyes] FMRIB (FSL), ICA-FIX Confound regression - ICA [Motion scrubbing] ICA, AAL atlas Pearson correlation Degree centrality FC ↗ WMH in right orbitofr...…”

Pathways of Prevention: A Scoping Review of Dietary and Exercise Interventions for Neurocognition

“…Translational studies across animal and human models are potentially compounded by inherent and well-characterized neurobiological differences across animal and human studies, in which the behavioral markers most sensitive to the effects of aging [57][58][59] and CVD risk factors [10,60] in humans are also the most distinct and hardest to measure in animals (e.g. higher-order executive functions).…”