Feeling How Old I Am: Subjective Age Is Associated With Estimated Brain Age

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 Feeling How Old I Am: Subjective Age Is Associated With Estimated Brain Age

Seyul Kwak¹Hairin Kim¹Jeanyung Chey^1*Yoosik Youm²

• ¹Department of Psychology, Seoul National University, Seoul, South Korea
• ²Department of Sociology, Yonsei University, Seoul, South Korea

While the aging process is a universal phenomenon, people perceive and experience one’s aging considerably differently. Subjective age (SA), referring to how individuals experience themselves as younger or older than their actual age, has been highlighted as an important predictor of late-life health outcomes. However, it is unclear whether and how SA is associated with the neurobiological process of aging. In this study, 68 healthy older adults underwent a SA survey and magnetic resonance imaging (MRI) scans. T1-weighted brain images of open-access datasets were utilized to construct a model for age prediction. We utilized both voxel-based morphometry (VBM) and age-prediction modeling techniques to explore whether the three groups of SA (i.e., feels younger, same, or older than actual age) differed in their regional gray matter (GM) volumes, and predicted brain age. The results showed that elderly individuals who perceived themselves as younger than their real age showed not only larger GM volume in the inferior frontal gyrus and the superior temporal gyrus, but also younger predicted brain age. Our findings suggest that subjective experience of aging is closely related to the process of brain aging and underscores the neurobiological mechanisms of SA as an important marker of late-life neurocognitive health.

Introduction

Subjective age (SA) refers to how individuals experience themselves as younger or older than their chronological age. Subjective perception of aging does not coincide with the chronological age and shows large variability among individuals (Rubin and Berntsen, 2006). The concept of SA has been highlighted in aging research as an important construct because of its relevance to late-life health outcomes. Previous studies have suggested that SA is associated with various outcomes, including physical health (Barrett, 2003; Stephan et al., 2012; Westerhof et al., 2014), self-rated health (Westerhof and Barrett, 2005), life satisfaction (Barak and Rahtz, 1999; Westerhof and Barrett, 2005), depressive symptoms (Keyes and Westerhof, 2012), cognitive decline (Stephan et al., 2014), dementia (Stephan et al., 2016a), hospitalization (Stephan et al., 2016b) and frailty (Stephan et al., 2015a). Although chronological age is a primary factor in explaining these late-life health outcomes, these studies suggest that SA can be another construct that characterizes individual differences in the aging process.

The interoceptive hypothesis posits that a significant number of functions, both physical and cognitive, decline with age and this is subsequently followed by an awareness of such age-related changes (Diehl and Wahl, 2010). In other words, feeling subjectively older may be a sensitive marker or indicator reflecting age-related biological changes. This hypothesis is supported by several studies that have reported significant associations between older SA and poorer biological markers, including C-reactive protein (Stephan et al., 2015c), diabetes (Demakakos et al., 2007), and body mass index (Stephan et al., 2014). Moreover, the indices of biological age (MacDonald et al., 2011) including peak expiratory flow and grip strength, also were associated with SA, even after demographic factors, self-rated health and depressive symptoms were controlled for Stephan et al. (2015b). Among a variety of biological aging markers, a decrease in neural resource constitutes a major dimension of age-related changes in addition to physical, socio-emotional and lifestyle changes (Diehl and Wahl, 2010). Together with the interoceptive hypothesis, the subjective experience of aging may partly result from one’s subjective awareness of age-related cognitive decline. For example, subjective reports of one’s own cognitive decline have received attention as an important source of information for the prediction of subtle neurophysiological changes. Even when no signs of decline are found in cognitive test scores, subjective complaints of cognitive impairment may reflect early stages of dementia or pathological changes in the brain (de Groot et al., 2001; Reid and MacLullich, 2006; Stewart et al., 2008; Yasuno et al., 2015). It is, thus, possible to examine a link between the subjective experience of aging and neurophysiological aging.

To assess age-related brain structural changes and widespread loss of brain tissue, neuroanatomical morphometry methods have been widely used (Good et al., 2001; Fjell et al., 2009a; Raz et al., 2010; Matsuda, 2013). Moreover, the large neuroimaging datasets and newly developed machine learning techniques have made it possible to estimate individualized brain markers (Gabrieli et al., 2015; Cole and Franke, 2017; Woo et al., 2017). This approach can be advantageous for interpreting an individualized index for brain age, since predictive modeling can represent multivariate patterns expressed across whole brain regions, unlike massive and iterative univariate testing. In recent studies, estimated brain age was found to predict indicators of neurobiological aging, including cognitive impairment (Franke et al., 2010; Franke and Gaser, 2012; Löwe et al., 2016; Liem et al., 2017), obesity (Ronan et al., 2016) and diabetes (Franke et al., 2013).

Although SA has predictive values for future cognitive decline or dementia onset, few studies have examined the neurobiological basis of such outcomes. Combining both regional morphometry and the brain age estimation method, this study will provide an integrated picture of how each individual undergoes a heterogeneous brain aging process and supply further evidence of the neural underpinnings of SA (Kotter-Grühn et al., 2015). Using analyses for voxel-based morphometry (VBM) and age-predicting modeling, we aimed to identify whether younger SA is associated with larger regional brain volumes and lower estimated brain age. We also examined possible mediators, including self-rated health, depressive symptoms, cognitive functions and personality traits that were candidates for explaining the hypothesized relationship between SA and brain structures.

Materials and Methods

Subjects

The participants in this study were subsampled from the 3rd wave Korean Social Life, Health and Aging Project (KSHAP) which consisted of 591 older adults. KSHAP is a community-based cohort study that collected data from the entire population of older adults in Township K. In all, 195 elderly individuals received thorough health survey, psychosocial surveys and neuropsychological assessment. The health survey was conducted in 2014, and both the psychosocial survey and neuropsychological assessment were conducted in 2015. The following exclusion criteria were applied: psychiatric or neurological disorders, vision or hearing problems, having metal in the body that cannot be removed, hypertension and/or diabetes that cannot be controlled with drugs and/or insulin, and a history of losing consciousness due to head trauma. Older adults with cognitive impairment were excluded using neuropsychological tests and semi-structured interviews. The details of the screening procedures are described in a previous publication (Joo et al., 2017). The final data were comprised of 68 subjects who did not meet any of the exclusion criteria (mean age = 71.38, SD = 6.41, range = 59–84). MRI acquisition was done in 2015, and subsequent analysis was blinded from identification of all participants. The study was approved by the Institutional Review Board of Yonsei University, and all participants provided written informed consent to the research procedures.

Subjective Age Groups

The following question was verbally asked to assess comparative SA: “How old do you feel, compared to your real age?” (Westerhof and Barrett, 2005). Participants responded with one of three categorized age identity options: “I’m younger than my real age” (younger SA), “I’m the same as my real age” (same SA) and “I’m older than my real age” (older SA; Boehmer, 2007). Among the subjects of KSHAP who did not have cognitive impairment (n = 137), those who identified themselves as younger SA were the greatest in proportion (40.1%), followed by same SA (34.3%) and older SA (25.5%). The gender ratio did not significantly differ among the three SA groups (χ² = 4.324, p = 0.112).

Cognitive Functions

The Mini-Mental State Examination for Dementia Screening (MMSE-DS; Han et al., 2010), category fluency test (Kang et al., 2012) and episodic memory and working memory indices from the Elderly Memory Disorder Scale (Chey, 2007) were used to assess cognitive functions. Category fluency test asked participants to generate words from the two semantic categories (i.e., supermarket and animal) each within a minute (Kang et al., 2000). The episodic memory index was calculated by adding the correct rates on the Long-Delay Free Recall of the Elderly Verbal Learning Test (EVLT), Delayed Recall on the Story Recall Test (SRT) and Delayed Reproduction on the Simple Rey Figure Test (SRFT). The EVLT is a nine-word learning test utilizing the California Verbal Learning Test paradigm (Chey et al., 2006). The SRT requires subjects to recall a paragraph containing 24 semantic units (An and Chey, 2004). The SRFT is a simplified version of the Rey-Osterreith Complex Figure Test modified for the elderly population (Park et al., 2011). All delayed recall subtests were administered 15−30 min after the immediate recall session. The working memory index was the sum of the longest correct backward digit sequence repetition span and longest correct Corsi-block tapping order (Song and Chey, 2006).

Health and Psychosocial Covariates

We assessed potential mediators and covariates that could account for or confound the association between SA and brain structural characteristics. The covariates were selected on the basis of previously reported associations with self-rated health (Stephan et al., 2015b), personality traits (Stephan et al., 2012), cognitive functions (Stephan et al., 2014) and depressive symptoms (Keyes and Westerhof, 2012). Participants rated the level of their global health on a 5-point Likert scale: poor, slightly poor, good, very good and excellent. Higher values represented better self-rated health. Depressive symptoms were measured using the 30-items Geriatric Depression Scale (Yesavage et al., 1982), to which respondents indicated whether they had experienced a given symptom during the past week using a “yes” or “no”, i.e., on a binary scale. The personality traits of extraversion and openness were assessed using the NEO-Five-Factors-Inventory (Costa and McCrae, 1992) on a 4-point Likert scale, ranging from 1 (strongly disagree) to 4 (strongly agree).

Voxel-Based Morphometry Analysis

Magnetic resonance imaging (MRI) scans were acquired in a 3Tesla MAGNETOM Trio 32 channel coil at Seoul National University Brain Imaging Center. Whole-brain T1-weighted magnetic-prepared rapid-gradient echo (MPRAGE) sequence images were acquired for each subject, with the following parameters: TR = 2300 ms, TE = 2.3 ms, FOV = 256 × 256 mm², and FA = 9°. Whole-brain VBM analysis was carried out to determine the association between regional gray matter (GM) density and SA groups. The preprocessing of imaging data was conducted using the Statistical Parametric Mapping software (SPM12; Welcome Department of Imaging Neuroscience, London, UK) implemented in Matlab Version r2015b (MathWorks). T1 images were bias-corrected and segmented into five tissue classes, based on a non-linearly registered tissue probability map (Ashburner and Friston, 2005). The segmented native images were summed to infer individual total intracranial volume (TIV). To spatially normalize the GM image into the standard space with an enhanced accuracy of inter-subject registration (Ashburner, 2007), we used diffeomorphic anatomical registration using exponentiated lie algebra (DARTEL). A customized template was created, and a deformation field was applied to previously segmented GM images to warp non-linear transformation to standardized MNI space. During the transformations, the total amount of GM was preserved. All images were smoothed using an 8-mm full width half-maximum Gaussian kernel.

We first used F-contrast to test voxel-wise differences in GM volume among the three SA groups. For exploratory purposes, the F-test result was examined in a liberal cluster-defining threshold (z = 2.33, k > 500). The main effect of the F-map indicated differences in volumes of regional GM among the three SA groups. Based on the directional hypothesis that younger SA would be associated with larger brain volumes, we additionally conducted three pairwise t-tests (younger > same, same > older, younger > older). We identified the post hoc test results based on whether the voxels above cluster-level (cluster defining threshold of z = 3.09) or voxel-level family-wise error (FWE) p < 0.05 were included in the aforementioned clusters of F-test results. The cluster-level FWE rate was estimated based on Gaussian random field theory. Each VBM analysis was conducted after adjusting for age, gender, education and TIV.

Age-Prediction Datasets

To estimate the degree of the age-related brain structural changes that have occurred in an individual, we implemented out-of-sample modeling and the prediction scheme that has recently been used to estimate brain ages (Franke et al., 2010; Liem et al., 2017; Rudolph et al., 2017). We utilized two publicly assessable datasets which consisted of T1-weighted MRI images: The Open Access Series of Imaging Studies (OASIS¹) and The Information eXtraction from Images (IXI²). The cross-sectional (Marcus et al., 2007) and longitudinal datasets (Marcus et al., 2010) of the OASIS consisted of 512 healthy adults aged 18–94 (mean age = 51.64, SD = 24.91, female: 256). Subjects diagnosed with dementia (clinical dementia rating ≥ 0.5) were excluded from the OASIS dataset. The IXI dataset consisted of 545 healthy adults aged 19–86 (mean age = 48.78, SD = 16.53, female: 342). To reduce non-linear age effects in model construction (Fjell et al., 2013, 2014) and avoid biased modeling in predicting subjects of KSHAP who have a relatively old age range (59–84), we selected subjects above the age of 40 as the training sample. The finalized age-prediction data included 598 subjects (mean age = 63.28, SD = 12.97, female: 383).

Age-Prediction Image Preprocessing

To apply a standardized preprocessing analysis pipeline across different MRI scan protocols in both KSHAP and the age-prediction datasets, we used a fully automated preprocessing procedure implemented in CAT12 r1113 (Computational Anatomy Toolbox, Structural Brain Mapping Group, Departments of Psychiatry and Neurology, Jena University Hospital³). First, a spatial-adaptive non-local means (SANLM) denoising filter (Manjón et al., 2010) was employed. Segmentation algorithms based on the adaptive maximum a posterior (AMAP) technique, implemented in CAT12, were used to classify brain tissue into three classes: GM, white matter (WM) and cerebrospinal fluid (CSF). Additionally, partial volume estimation (PVE) was used to create a more accurate segmentation for the two mixed classes: GM–WM and GM–CSF. Projection-based estimation of cortical thickness was conducted in the segmented images (Dahnke et al., 2012, 2013), which showed a comparable accuracy with other surface-based tools (Righart et al., 2017). In total, 156 values were extracted from CAT12 region of interest (ROI) analysis pipeline, including 148 cortical thickness and averaged GM density in eight bilateral subcortical structures (caudate, putamen, amygdala and hippocampus). Cortical areas were defined based on automatic parcellation of gyri and sulci (Destrieux et al., 2010), while subcortical volumes were defined using the Neuromorphometric atlas⁴. Identical procedures for preprocessing and extracting ROI values were made using the KSHAP data.

Partial Least Square Regression Modeling

To effectively summarize and explain age-related characteristics of brain structure, we constructed a cross-validated partial least square regression (PLSR) model using the Caret package for R (Kuhn, 2015). PLSR reduces high-dimensional data into orthogonal components that have the greatest covariance with the output (the target of the prediction) before multiple regression analysis is conducted. In contrast to reducing dimensions with principal component analysis, PLSR decomposes orthogonal components in a way that is more relevant to the outcomes in the model construction stage. The PLS method is utilized in neuroimaging studies to effectively summarize the highly collinear data structures that are observed across brain regions (McIntosh and Lobaugh, 2004; Krishnan et al., 2011; Rudolph et al., 2017). In this study, the PLSR model was constructed to find linearly combined latent components highly predictive of the age of an individual. The training and estimation procedure of the age-prediction model was based on 156 ROI values from the open-access datasets (n = 598).

Cross-Validation of the Age-Prediction Model

To construct a PLSR model applicable to the independent data and to achieve generalizability, we optimized the number of PLS components, using leave-one-out cross-validation (LOOCV). Although sequentially adding more components of latent variables would derive a more complex model in explaining the given data, cross-validation procedures must be applied to determine whether such a complex model ultimately explains the novel data that are independent from training data. Using the LOOCV procedure, we iteratively partitioned the training data (n = 597) to construct a model and predicted the left-out single-subject data. Each left-out procedure of modeling and predicting was repeated 598 times. Within each model, with differing number of components, the root mean squared error (RMSE) of the iterated LOOCV procedures was calculated. Examining the out-of-sample prediction error for each PLS component, the PLSR model was optimized between under-fitted and over-fitted models (Whelan and Garavan, 2014; Gabrieli et al., 2015; Rudolph et al., 2017). This approach identified the optimal model that showed the lowest RMSE and the greatest explained variance (R²) in predicting the age of the left-out subject.

Statistical Analysis of Predicted Age

The brain ages of the individual subjects from the KSHAP data (n = 68) were predicted based on the weights of the cross-validated PLSR model, using the inputs for 156 brain regional values. We examined bivariate correlations between the real ages and predicted ages for the KSHAP data. Then, we used analysis of covariance (ANCOVA) to compare the differences in the predicted brain age between the SA groups, adjusting for the linear effects of gender, education and age. The adjusted mean of predicted age indicated the difference. We additionally examined whether the ANCOVA result changed when self-rated health, depressive symptoms, cognitive function and personality traits were additionally included as covariates. We determined the significance of the group difference at p < 0.05 level.

Results

Rank-order correlation analysis showed that SA group was positively (from younger to older) associated with fewer years of education, lower working memory performance and poorer self-rated health (Table 1). Group comparisons specifically showed significant differences in chronological age (same < older; p = 0.043), depressive symptoms (same < older; p = 0.035) and MMSE-DS (younger, same > older; p = 0.013, p = 0.002, respectively).