The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans

Abstract

Introduction: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).

Methods: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.

Results: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.

Discussion: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Keywords: Alzheimer's disease pathology; Dementia; MRI; Machine Learning; Neuroimaging; PET; beta-amyloid; brain aging; brain signatures; cognitive testing; harmonized neuroimaging cohorts; preclinical Alzheimer's disease; small vessel ischemic disease; tau.

Figure 1.

Flow chart showing the inclusion and exclusion criteria and final sample included in this study

Figure 2.

iSTAGING dimensions in cognitively normal subjects. A) SPARE-BA scores were calculated for n=8,284 subjects from 11 studies from the iSTAGING consortium using a supervised learning method. The model was applied with cross-validation using harmonized regional anatomical volumes of the subjects as input features. “Advanced” versus “resilient” aging groups were identified as individuals who deviated from normative aging trends. B) Subjects in “advanced” and “resilient” groups displayed widespread differences in atrophy patterns most pronounced in the frontal operculum, superior temporal, and insular cortex and further extending to frontal and inferior parietal cortex, as well as enlargement of the ventricles. C) SPARE-AD scores were calculated for n=5,460 subjects from 11 studies from the iSTAGING consortium using a supervised learning method. The model was trained using harmonized regional anatomical volumes as input features on ADNI CN and AD subjects and applied to all other studies; it was applied to ADNI subjects using cross-validation; D) Subjects with “high” and “low” SPARE-AD scores differed by atrophy patterns most pronounced in the hippocampus, amygdala, entorhinal cortex and inferior temporal cortex. E) White matter disease dimension, represented by white matter hyperintensities (WMH) as a function of age. WMH volume was calculated for n= 7,357 subjects from 10 studies using a deep learning method. F) Frequency maps of WMH in the iSTAGING consortium, showing WMH progression during the life span (in the 40s n=1,110, 50s n=1,918, 60s n=2,093, 70s n=1,330 and 80s n=321)

Figure 3

A) Brain charts show associations between chronological age, SPARE-BA, and executive function. The relative diagonal isocontours indicate a similar contribution of age and SPARE-BA to the executive function (FDR corrected P-Value <0.05). Put differently, executive function at a given age cannot be estimated without SPARE-BA, and vice-versa. B-C) Brain charts that show associations between chronological age, SPARE-AD, and cognitive testing. The isocontours of the executive function indicate a stronger association with age compared to SPARE-AD, but the effect of SPARE-AD was significant (FDR corrected P-Value <0.05) and increasing after the age of 65 years old. The isocontours of the memory function showed a stronger association with SPARE-AD compared with age after the age of 70 years old, further underlying the role of AD-like atrophy on memory.

Figure 4.

A-B) Brain charts that show associations between chronological age, WMH volume, and brain atrophy captured with SPARE-BA and SPARE-AD. The isocontours of SPARE-BA and SPARE-AD indicate strong associations with WMH (FDR corrected P-Value <0.05 for both charts). C-D) Brain charts that show associations between chronological age, WMH volume, and cognitive testing. The isocontours of the executive function indicate strong associations with both age and WMH starting from the forties; the effect of WMH was significant (FDR corrected P-Value <0.05). The isocontours of the memory function showed strong associations with age and WMH from the end of the forties; the effect of WMH was significant (FDR corrected P-Value <0.05). E) Brain charts that show associations between chronological age, WMH, and AD pathology. The isocontours of the Aβ status showed strong associations with age and WMH from the sixties; the effect of WMH was significant (FDR corrected P-Value <0.05).

Figure 5

Brain charts in patients with MCI and AD: Associations with cognitive testing scores. A-B) Brain charts that show associations between chronological age, predicted brain age (SPARE-BA), and cognitive testing. The isocontours of the executive function indicate a stronger association with SPARE-BA than with age (FDR corrected P-Value <0.05). Similarly, the isocontours of the memory function showed a stronger association with SPARE-BA compared with age; the effect of SPARE-BA was significant on memory (FDR corrected P-Value <0.05). C-D) Brain charts that show associations between chronological age, SPARE-AD, and cognitive testing. The isocontours of the executive and memory functions indicate association with SPARE-AD only (horizontal isocontours); the effect of SPARE-AD was significant (FDR corrected P-Value <0.05). E-F) Brain charts that show associations between chronological age, WMH, and cognitive testing. The isocontours of the executive function indicate that the effect of WMH volume on executive function was relatively more pronounced at younger ages (relatively horizontal isocontours), whereas at older ages, both WMH and age are equal contributors to diminished executive function; the effect of WMH on executive function was significant (FDR corrected P-Value <0.05). The isocontours of the memory function showed stronger associations with WMH than age (FDR corrected P-Value <0.05)

Figure 6

Brain charts in patients with MCI and AD: the WMH dimension. A) Frequency maps of WMH in the MCI and AD patients, showing WMH progression over age (in the 50s n=47, 60s n=326, 70s n=569, and 80s n=289). B-C) Brain charts that show associations between chronological age, WMH volume, and brain atrophy captured with SPARE-BA and SPARE-AD. The isocontours of SPARE-BA and SPARE-AD indicate strong associations with WMH (FDR corrected P-Value <0.05 for both charts). D) Brain chart that shows associations between chronological age, WMH, and AD pathology. The isocontours of the Aβ status showed stronger associations with WMH than age (FDR corrected P-Value <0.05).