Longitudinal Analysis of Brain-Predicted Age in Amnestic and Non-amnestic Sporadic Early-Onset Alzheimer's Disease

Abstract

Objective: Predicted age difference (PAD) is a score computed by subtracting chronological age from “brain” age, which is estimated using neuroimaging data. The goal of this study was to evaluate the PAD as a marker of phenotypic heterogeneity and severity among early-onset Alzheimer's disease (EOAD) patients.Methods: We first used 3D T1-weighted (3D-T1) magnetic resonance images (MRI) of 3,227 healthy subjects aged between 18 and 85 years to train, optimize, and evaluate the brain age model. A total of 123 participants who met the criteria for early-onset (<65 years) sporadic form of probable Alzheimer's disease (AD) and presented with two distinctive clinical presentations [an amnestic form (n = 74) and a non-amnestic form (n = 49)] were included at baseline and followed-up for a maximum period of 4 years. All the participants underwent a work-up at baseline and every year during the follow-up period, which included clinical examination, neuropsychological testing and genotyping, and structural MRI. In addition, cerebrospinal fluid biomarker assay was recorded at baseline. PAD score was calculated by applying brain age model to 3D-T1 images of the EOAD patients and healthy controls, who were matched based on age and sex. At baseline, between-group differences for neuropsychological and PAD scores were assessed using linear models. Regarding longitudinal analysis of neuropsychological and PAD scores, differences between amnestic and non-amnestic participants were analyzed using linear mixed-effects modeling.Results: PAD score was significantly higher for non-amnestic patients (2.35 ± 0.91) when compared to amnestic patients (2.09 ± 0.74) and controls (0.00 ± 1). Moreover, PAD score was linearly correlated with the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating Sum of Boxes (CDR-SB), for both amnestic and non-amnestic sporadic forms. Longitudinal analyses showed that the gradual development of the disease in patients was accompanied by a significant increase in PAD score over time, for both amnestic and non-amnestic patients.Conclusion: PAD score was able to separate amnestic and non-amnestic sporadic forms. Regardless of the clinical presentation, as PAD score was a way of quantifying an early brain age acceleration, it was an appropriate method to detect the development of AD and follow the evolution of the disease as a marker of severity as MMSE and CDR-SB.

The value of arterial spin labelling perfusion MRI in brain age prediction

“…However, individual anatomical and physiological differences together with the uncertainty of the MRI measurements set a limit on the theoretically achievable prediction accuracy; and at some point, it becomes questionable if a lower MAE is desirable—because instrumental variability is removed—or if even higher MAE can improve brain age's performance in disease—because meaningful physiological variability is added. Within‐subject longitudinal brain age changes, as a proxy for brain age repeatability, assuming the intra‐individual ageing is negligible compared with the inter‐individual age differences, take the inter‐individual variations out of the equation and might therefore achieve more accurate predictions of age changes across time both for healthy participants, and have accrued interest lately (Aamodt et al, 2022 ; Franke & Gaser, 2019 ; Gautherot et al, 2021 ).…”

Potential of brain age in identifying early cognitive impairment in subcortical small-vessel disease patients

“…Patients with SSVD exhibit high heterogeneity in imaging characteristics; as a result, cognition-related imaging differences at the group-level are difficult for use as biomarkers at the individual level. Previous studies have indicated that brain age prediction can determine brain aging based on individual brain MRI data, which can be used as an effective biomarker to estimate cognitive impairment in neurological and psychiatric disorders ( Liem et al, 2017 ; Shahab et al, 2019 ; Beheshti et al, 2020 ; Gautherot et al, 2021 ; Mishra et al, 2021 ). Thus, it is essential to investigate the association of SVCI with individual brain age.…”

Recent research advances in young-onset dementia

“…“Predicted age difference” (PAD) calculated using deep learning techniques and magnetic resonance imaging (MRI) also demonstrated differences in the typical and nonamnestic young-onset AD. Gautherot et al [46 ▪ ] found different regional atrophy in these two groups, with the nonamnestic group having more severe neocortical and basal nuclei atrophy, leading to a higher PAD score, which was able to separate these two subtypes of AD.…”