Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset

Abstract

Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases.

Keywords: accelerated brain aging; advanced brain aging; heterogeneity; structural MRI; subtypes.

Figure 4

Comparative analysis of cortical thickness across the three ABA subgroups and the control group. Statistical significance denoted as * indicates q < 0.01, while ** signifies q < 0.001.

Figure 5

Examination of cortical thickness through Z-values for each thickness IDPs across the three ABA subgroups in comparison to the RBA group. Error bars represent the 95% confidence intervals. The black dotted line represents the mean Z-value for each subgroup.

Figure 6

Examination of FA through Z-values for each FA IDPs within the three ABA subgroups in contrast to the RBA group. The error bars depict the 95% confidence intervals. The black dotted line represents the mean Z-value for each subgroup.

Figure 7

Examination of MD through Z-values for each MD IDPs within the three ABA subgroups in contrast to the RBA group. The error bars depict the 95% confidence intervals. The black dotted line represents the mean Z-value for each subgroup.

Figure 8

Examination of ISOVF through Z-values for each ISOVF IDPs within the three ABA subgroups in contrast to the RBA group. The error bars depict the 95% confidence intervals. The black dotted line represents the mean Z-value for each subgroup.

Figure 9

Heat map illustrating connection strength. The connection strengths within the three ABA groups were stratified based on the positive and negative phases of the RBA groups.

Figure 1

Flowchart depicting the subject screening process.

Figure 2

The comprehensive workflow of the present investigation.

Figure 3

The ARI values correspond to varying numbers of subtypes.

Figure 10

Quantitative analysis of Non-IDP variables among the three subtypes and the RBA group employed ANOVA, followed by pairwise comparisons using Dunnett’s test. Significance is denoted by ** at p < 0.001.

Figure 11

Comparative assessment of qualitative Non-IDP variables between the three subtypes and RBA group were then underwent by ANOVA, two-by-two comparisons were conducted employing Dunnett’s test. a: SubGroup 2 is significantly different from SubGroup 3 (p < 0.05); b: SubGroup 1 is significantly different from RBA group (p < 0.05); c: SubGroup 2 is significantly different from RBA group (p < 0.05).