Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer's disease neuropathology

Abstract

Background: Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD).

Methods: Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n = 291), resilient in both (n = 260) or advanced in one/resilient in the other (n = 163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task).

Findings: Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits.

Interpretation: The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life.

Funding: The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute.

Keywords: Ageing; Alzheimer’s disease; Brain age; Cognition; Multimodal.

Fig. 1

Schematic diagram showing a summary of the brain-age prediction framework used to obtain brain-age groups. The top left hand corner depicts the age distribution for each study. Features are extracted from structural and resting-state functional MRI data (Box 1) which are then harmonised (Box 2). The vectorised, harmonised structural and functional features become the input features to two support vector regressions to obtain separate structural and functional brain-age predictions for each individual (Box 3 and 4). The two predicted brain-ages are plotted and used to identify the 4 brain-age sub-groups (Box 5).

Fig. 2

UpSet plot describing the overlap of baseline and longitudinal cognitive and neuroimaging variables across individuals in Set 2. The vertical bar chart shows the number of individuals who have the combination (or intersection) of variables described by the connected line and dots below each corresponding bar. Filled-in dots show which variable is part of an intersection, and the remainder are unfilled. For example, 229 individuals have data available for SPARE-AD, APOE4 allele, WMH and NODDI. The horizontal bars represent the set size. Cognitive tests: DSST: baseline Digit Symbol Substitution Task; DSST_L: longitudinal Digit Symbol Substitution Task; MMSE: baseline Mini Mental State Examination; MMSE_L: longitudinal Mini Mental State Examination; TMT-A/B: baseline Trail Making Task A and B; TMT-A/B_L: longitudinal Trail Making Task A and B. Neuroimaging data: NODDI: Neurite Orientation Dispersion And Density Imaging measures; Amyloid PET: cortical centiloid values; SPARE-AD: Spatial Patterns of Abnormality for Recognition of Early Alzheimer’s Disease index; WMH: White Matter Hyperintensity volumes. Genetic data: APOE4 Status: APOE4 allele status.

Fig. 5

Comparison of neuroimaging features across brain-age groups. (a) Whole cortex centiloid values. (b) White matter lesion volume. (c). SPARE-AD index. The brackets indicate the significant (P < 0.05) pairwise P values between the brain-age groups following Tukey’s HSD post-hoc test.

Fig. 3

Baseline cognitive performance across brain-age groups. (a) Mean time (seconds) to complete trail making test part A (n = 493, Advanced = 165, Resilient = 147, AFRS = 91, RFAS = 90). (b) Mean time (seconds) to complete trail making test part B (n = 484, Advanced = 162, Resilient = 147, AFRS = 89, RFAS = 96). (c) Z score standardised total DSST score (n = 368, Advanced = 124, Resilient = 109, AFRS = 65, RFAS = 70). (d) Z score standardised total MMSE score (n = 342, Advanced = 118, Resilient = 108, AFRS = 55, RFAS = 61). The brackets indicate the significant (P < 0.05) pairwise P values between the brain-age groups following Tukey’s HSD post-hoc test.

Fig. 4

Longitudinal changes in cognitive performance across brain-age groups. The Y axis on all plots represents the value of the gradient (the rate of change) between the mean baseline score of each different task and the follow-up score for that task. (a) The gradient measures the difference between the mean baseline time to complete part A of the trail making test and the follow-up time to complete the test (n = 1160, Advanced = 324, Resilient = 371, AFRS = 228, RFAS = 237). A positive gradient value represents an increase in the time taken to complete the task at follow-up, which means a worsening over time. (b) The same as panel (A) but for the trail making test part B (n = 1109, Advanced = 309, Resilient = 365, AFRS = 216, RFAS = 219). (c, d) A negative gradient value for the DSST (n = 664, Advanced = 201, Resilient = 212, AFRS = 108, RFAS = 143) and MMSE (n = 1149, Advanced = 315, Resilient = 387, AFRS = 207, RFAS = 240), respectively, represents worsening performance over time. The brackets indicate the significant (P < 0.05) pairwise P values between the brain-age groups following Tukey’s HSD post-hoc test.