Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained?

Abstract

With the aging population, prevalence of neurodegenerative diseases is increasing, thus placing a growing burden on individuals and the whole society. However, individual rates of aging are shaped by a great variety of and the interactions between environmental, genetic, and epigenetic factors. Establishing biomarkers of the neuroanatomical aging processes exemplifies a new trend in neuroscience in order to provide risk-assessments and predictions for age-associated neurodegenerative and neuropsychiatric diseases at a single-subject level. The "Brain Age Gap Estimation (BrainAGE)" method constitutes the first and actually most widely applied concept for predicting and evaluating individual brain age based on structural MRI. This review summarizes all studies published within the last 10 years that have established and utilized the BrainAGE method to evaluate the effects of interaction of genes, environment, life burden, diseases, or life time on individual neuroanatomical aging. In future, BrainAGE and other brain age prediction approaches based on structural or functional markers may improve the assessment of individual risks for neurological, neuropsychiatric and neurodegenerative diseases as well as aid in developing personalized neuroprotective treatments and interventions.

Keywords: MRI; biomarker; brain age estimation; intervention; metabolic health; neurodegeneration; neurodevelopment; psychiatric disorders.

Figure 1

Depiction of the BrainAGE concept. All MRI data are automatically preprocessed via VBM. (A) The model of healthy brain aging is trained with the chronological age and preprocessed structural MRI data of a training sample (left; with an illustration of the most important voxel locations that were used by the age regression model). Subsequently, the individual brain ages of previously unseen test subjects are estimated, based on their MRI data. (B) The difference between the estimated and chronological age results in the BrainAGE score, with positive BrainAGE scores indicating advanced brain aging (orange line), increasing BrainAGE scores indicating accelerating brain aging (red line), and negative BrainAGE scores indicating delayed brain aging (green line). [Figure and legend adapted from Franke et al. (45), with permission from Hogrefe Publishing, Bern].

Figure 2

Reference curves for BrainAGE. (A) Individual structural brain age based on anatomical T1-images of 394 healthy subjects (aged 5–18 years). Chronological age is shown on the x-axis and the estimated brain age on the y-axis. The overall correlation between estimated brain age and chronological age is r = 0.93 (p < 0.001), and the overall MAE = 1.1 years. The 95% confidence interval of the quadratic fit is stable across the age range (±2.6 years). [Figure and legend reproduced from Franke et al. (45), with permission from Elsevier, Amsterdam.] (B) Estimated brain age and chronological age are shown for the whole test sample with the confidence interval (red lines) at a real age of 41 years of ± 11.5 years. The overall correlation between estimated brain age and chronological age is r = 0.92 (p < 0.001), and the overall MAE = 5.0 years. [Figure and legend modified from Franke et al. (32), with permission from Elsevier, Amsterdam.] (C) Scatterplot of estimated brain age against chronological age (in years) resulting from leave-one-out cross-validation in 29 healthy control baboons using their in vivo anatomical MRI scans. The overall correlation between chronological age and estimated brain age is r = 0.80 (p < 0.001), with an overall MAE of 2.1 years. [Figure and legend reproduced from Franke et al. (33), permitted under the Creative Commons Attribution License.] (D) (a) Chronological and estimated brain age are shown for a sample of untreated control rats, including the 95% confidence interval (gray lines). The overall correlation between chronological and estimated brain age was r = 0.95 (p < 0.0001). [Figure and legend reproduced from Franke et al. (34), with permission from IEEE.] (E) Longitudinal brain aging trajectories for the individual rats. [Figure and legend reproduced from Franke et al. (34), with permission from IEEE].

Figure 3

Influences of the various parameters on BrainAGE estimation accuracy. (1) The accuracy of age estimation essentially depends on the number of subjects used for training the age estimation model (blue lines: full training sample; green lines: ½ training sample; red lines: ¼ training sample). (2) The method for preprocessing the T1-weighted MRI images also showed a strong influence on the accuracy of age estimation. (3) Data reduction via principal component analysis (PCA) only had a moderate effect on the mean absolute error (MAE). AF, affine registration; NL, non-linear registration; R4/8, re-sampling to spatial resolution of 4/8 mm; S4/8, smoothing with FWHM smoothing kernel of 4/8 mm. [Figure and legend modified from Franke et al. (32), with permission from Elsevier, Amsterdam].

Figure 4

Change in BrainAGE scores during the menstrual cycle. BrainAGE scores significantly decreased by −1.3 years (SD = 1.2) at time of ovulation (i.e., t2-t1; ^*p < 0.05). The data are displayed as boxplots, containing the values between the 25th and 75th percentiles of the samples, including the median (red lines). Lines extending above and below each box symbolize data within 1.5 times the interquartile range. The width of the boxes depends on the sample size. Note: reduced sample size at t4. [Figure and legend reproduced from Franke et al. (35), with permission from Elsevier, Amsterdam].

Figure 5

Longitudinal BrainAGE. Box plots of (A) baseline BrainAGE scores and (B) BrainAGE scores of last MRI scans for all diagnostic groups. Post-hoc t-tests showed significant differences between NO/sMCI vs. pMCI/AD (^*p < 0.05) at both time measurements. (C) Longitudinal changes in BrainAGE scores for NO, sMCI, pMCI, and AD. Thin lines represent individual changes in BrainAGE over time; thick lines indicate estimated average changes for each group. Post-hoc t-tests showed significant differences in the longitudinal BrainAGE changes between NO/sMCI vs. pMCI/AD (^*p < 0.05). [Figures and legend reproduced from Franke et al. (45), with permission from Hogrefe Publishing, Bern].

Figure 6

Longitudinal BrainAGE in APOE ε4-carriers and ε4-non-carriers. BrainAGE scores at (A) baseline for APOE ε4-carriers [C] and non-carriers [NC] in the 4 diagnostic groups NO, sMCI, pMCI, and AD. BrainAGE scores differed significantly between diagnostic groups (p < 0.001). Post-hoc tests showed significant differences between BrainAGE scores in NO as well as sMCI from BrainAGE scores in pMCI as well as AD (p < 0.05). (B) Estimated longitudinal changes in BrainAGE scores for the 4 diagnostic groups: NO (light blue), sMCI (green), pMCI (red) and AD (blue), subdivided into APOE ε4 carriers and non-carriers. Post-hoc t-tests resulted in significant differences for ε4 carriers and non-carriers as well as for NO/sMCI vs. pMCI/AD (p < 0.05). [Figures and legend reproduced from Loewe et al. (36), permitted under the Creative Commons Attribution License].

Figure 7

Cumulative probability for MCI patients of remaining AD-free based. (A) Kaplan-Meier survival curves based on Cox regression comparing cumulative AD incidence in participants with MCI at baseline by BrainAGE score quartiles (p for trend < 0.001). [Figure and legend reproduced from Gaser et al. (37), permitted under the Creative Commons Attribution License.] (B) Kaplan-Meier survival curves based on Cox regression comparing the cumulative incidence of AD incidence in ε4-carriers [red] and ε4-non-carriers [blue] with MCI at baseline, divided into patients with baseline BrainAGE scores below the median (light lines) and above the median (dark lines). Duration of follow-up is truncated at 1,250 days. [Figure and legend reproduced from Loewe et al. (36), permitted under the Creative Commons Attribution License].

Figure 8

BrainAGE in psychiatric disorders. (A) Box-plot of BrainAGE scores in healthy controls (CTR), bipolar disorder patients (BPD), and schizophrenia patients (SZ) with significant group effect (ANOVA, p = 0.009), and schizophrenia patients showing higher BrainAGE scores than either CTR or BPD. [Figure and legend reproduced from Nenadic et al. (38), with permission from Elsevier, Amsterdam.] (B) Associations between BrainAGE scores and psychiatric diagnosis and metabolic factors. Obesity was significantly associated with BrainAGE scores additively to the effect of first-episode schizophrenia (FES; age adjusted mean and 95% confidence intervals). [Figure and legend reproduced from Kolenic et al. (40), with permission from Elsevier, Amsterdam.] (C) Negative association between BrainAGE and gray matter volume in participants with first episodes of schizophrenia-spectrum disorders (P ≤ 0.001, cluster extent = 50). [Figure and legend from Hajek et al. (39), with permission from Oxford University Press].

Figure 9

The effects of low vs. high levels in distinguished variables on BrainAGE. (A) Mean BrainAGE scores in participants with values in the 1st (plain squares) and 4th (filled squares) quartiles of distinguished variables from the diabetes study. [Figure and legend reproduced from Franke et al. (41), permitted under the Creative Commons Attribution License.] (B) Mean BrainAGE scores of cognitively healthy CTR men in the 1st vs. 4th quartiles of the most significant physiological and clinical chemistry parameters (left panel). BrainAGE scores of cognitively healthy CTR men with “healthy” markers (i.e., values below the medians of BMI, DBP, GGT, and uric acid; n = 9) vs. “risky” markers (i.e., values above the medians of BMI, DBP, GGT, and uric acid; n = 14; p < 0.05; right panel). [Figures and legend modified from Franke et al. (42), permitted under the Creative Commons Attribution License.] (C) Mean BrainAGE scores of cognitively healthy CTR women in the 1st vs. 4th quartiles of the most significant physiological and clinical chemistry parameters (left panel). BrainAGE scores of cognitively healthy CTR women with “healthy” markers (i.e., values below the medians of GGT, ALT, AST, and values above the median of vitamin B₁₂; n = 14) vs. “risky” clinical markers (i.e., values above the medians of GGT, ALT, AST, and values below the median of vitamin B₁₂; n = 13; p < 0.05; right panel). [Figures and legend modified from Franke et al. (42), permitted under the Creative Commons Attribution License]. ^*p < 0.05; ^**p < 0.01.

Figure 10

Group-specific links between age-related measures. Scatterplots and regression lines were generated separately for (A) controls (circles) and (B) meditation practitioners (triangles). The x-axes display the chronological age; the y-axes display the BrainAGE index (negative values indicate that participants' brains were estimated as younger than their chronological age, positive values indicate that participants' brains were estimated as older). [Figures and legend reproduced from Luders et al. (43), with permission from Elsevier].

Figure 11

Effects of prenatal undernutrition on brain aging. (A) Dutch famine sample: BrainAGE scores in late adulthood differed significantly between the three groups only in men (blue), but not in women (red). In men, post-hoc tests showed significantly increased scores in those with exposure to famine in early gestation (^*p < 0.05). [Figure and legend reproduced from Franke et al. (85), with permission from Elsevier, Amsterdam.] (B) Baboon sample: BrainAGE scores in late adolescence/young adulthood differed significantly between female (red) CTR and offspring with maternal nutrient restriction (MNR) by 2.7 years (^**p < 0.01), but not between male (blue) CTR and MNR offspring. [Figure and legend reproduced from Franke et al. (33), permitted under the Creative Commons Attribution License].

Figure 12

Graphical summary of BrainAGE results in human studies. Dots, study means; Lines, longitudinal results; Blue, males; Red, females. [AD, Alzheimer's disease; BPD, bipolar disorder; CTR, control subjects; DM2, diabetes mellitus type 2; FES, first episode of schizophrenia-spectrum disorders; GA, gestational age; MCI, mild cognitive impairment; pMCI, progressive MCI (i.e., convert from MCI to AD during follow-up); pMCI_fast, diagnosis was MCI at baseline, conversion to AD within the first 12 months (without reversion to MCI or CTR at any available follow-up; pMCI_slow, diagnosis was MCI at baseline, conversion to AD was reported after the first 12 months of follow-up (without reversion to MCI or CTR at any available follow-up); sMCI, stable MCI (i.e., diagnosis is MCI at all available time points, but at least for 36 months); SZ, schizophrenia].