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Article

The Protective Power of Cognitive Reserve: Examining White Matter Integrity and Cognitive Function in the Aging Brain for Sustainable Cognitive Health

by
Lan Lin
1,2,*,
Yue Jin
1,
Min Xiong
1,
Shuicai Wu
1,2 and
Shen Sun
1,2
1
Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
2
Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(14), 11336; https://doi.org/10.3390/su151411336
Submission received: 23 May 2023 / Revised: 11 July 2023 / Accepted: 18 July 2023 / Published: 20 July 2023
(This article belongs to the Section Health, Well-Being and Sustainability)
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Abstract

:
Sustainable cognitive health is heavily influenced by cognitive reserve (CR), which delays cognitive decline or reduces its severity by enhancing cognitive function through life experiences. The main objective of this study is to explore whether CR moderates the relationship between white matter integrity and cognitive function in cognitively intact older adults. A cross-sectional analysis was conducted on 5004 cognitively normal individuals aged 48–80 years from the UK Biobank, utilizing hierarchical regression analysis to estimate CR through five CR proxies and five skeleton-based diffusion measures. The study’s findings revealed that early fluid intelligence had a significant independent and moderating impact on cognitive performance, demonstrating its suitability as an individual CR proxy. Moreover, the composite proxy composed of early fluid intelligence and physical activity showed promise in promoting sustainable cognitive health. Importantly, this study represents one of the most extensive MRI investigations to unveil the substantial moderating effect of CR on the intricate relationship between white matter integrity and cognitive function based on a complete model. Notably, this study employed the NODDI method, which proved more advantageous than DTI in examining this interaction. Overall, this research constitutes a noteworthy and substantive contribution to our current understanding of the complex and intricate relationship between CR, cognitive function, and aging-associated cognitive decline, highlighting the importance of considering CR proxies in promoting sustainable cognitive health in aging populations.

1. Introduction

In the context of aging societies, public healthcare policies are increasingly focused on ensuring a sustainable quality of life for the elderly [1,2,3,4]. As individuals age, the trajectories of typical aging are marked by a decline in multiple cognitive domains, albeit with considerable variability in how this decline manifests among individuals. While some experience a rapid deterioration in cognitive function, others are able to maintain relatively intact cognitive performance. The ability to sustain cognitive function assumes greater significance with advancing age, as it directly impacts overall well-being and the maintenance of a high quality of life [5,6]. The heterogeneous nature of the aging process encompasses a spectrum of manifestations, including instances of recovery from brain injuries, as well as the delayed onset of symptoms in individuals with neurodegenerative diseases. This heterogeneity has given rise to the hypothesis of an underlying “reserve” that serves as a protective mechanism against anticipated cognitive impairment. This hypothesis has rapidly gained prominence in the research field, stimulating the exploration of related constructs such as compensation, brain maintenance, and cognitive reserve (CR) [7]. Consequently, novel approaches, assessment methods, theoretical definitions, and investigations have emerged to delve into the impact of these constructs on human cognition. Among the various explanatory models, CR has garnered substantial consensus and attention within the scientific community due to its potential implications for comprehending cognitive aging and resilience. CR refers to a theoretical construct that encapsulates the capacity of an individual to deploy more efficient or flexible brain networks and cognitive paradigms [8]. The adaptive nature of CR empowers individuals with higher reserve capacity to effectively navigate the effects of normal aging or pathological processes [9]. By engaging alternative brain networks, these individuals can successfully accomplish cognitive tasks despite the challenges posed by age-related changes or neural dysfunction. In recent years, a surge of interest has emerged around the intricate relationship between CR and cognition in late adulthood. This growing level of attention reflects the recognition of the potential for cognitive changes and the underlying brain function to be influenced and compensated for during the aging process. A substantial body of research has contributed compelling evidence in support of this notion, highlighting the malleability and adaptability of cognitive processes in older adults [10]. Moreover, several studies have emphasized the importance of CR as a key factor that can modulate the efficacy of cognitive training interventions [11,12]. CR acts as a protective mechanism, enabling individuals to optimize the benefits derived from cognitive training and enhance their cognitive functioning in the face of age-related challenges.
Perception, attention, and memory are highly complex cognitive processes that require the coordinated activity of specific brain regions [13]. Ultimately, this communication takes place through a network of white matter pathways that can be compromised by neurological diseases or an age-related decline in white matter integrity [14]. Damage to white matter can result in a loss of white matter integrity, thereby precipitating cognitive and neurological symptoms of diverse intensity, contingent upon the site and magnitude of the injury. While the impact of neurological diseases on cognition is well-established, recent research has demonstrated that even moderate disruptions to these networks can cause measurable changes in cognitive performance that may not meet the diagnostic criteria for neurological disease. Therefore, the disconnection model of neurocognitive aging, drawing upon Geschwind’s clinical neurological model [15,16] and further developed by Catani and Ffytche [17], which posits that cognitive aging is characterized by disruptions in the communication between different brain regions, leading to a decline in cognitive functioning, has gained prominence [18,19]. These findings highlight the critical importance of even subtle changes in brain connectivity and the effects they can have on cognitive function. Furthermore, studies of the aging brain have emphasized the role of white matter integrity in preserving cognitive function [20], further underscoring the significance of understanding how it is related to cognitive processes.
The disruption of complex networks of white matter can result in damage to neural circuits and lead to the emergence of a disconnection syndrome, which can manifest in behavioral and cognitive changes [21]. Despite the negative impact of such disruptions on cognitive functioning, research in cognitive neuroscience has shown that the concept of CR holds promise for individuals affected by this condition [8]. CR denotes the brain’s capacity to attenuate the impact of damage by employing diverse mechanisms. For example, individuals with high CR may be better able to recruit alternative brain networks to preserve cognitive function despite disruptions to traditional neural pathways. Additionally, CR may lead to changes in neural plasticity, augmenting the brain’s inherent capability to adjust and restructure in the face of damage. Recent studies have suggested that developing CR may protect against cognitive decline and dementia, as individuals with higher CR are better equipped to cope with cortical disconnection and other forms of brain damage [22]. Specifically, research supports the notion that individuals with higher CR experience less cognitive decline and better cognitive functioning as they age [23]. To gain a deeper understanding of the complex interplay among CR, age-related white matter integrity, and cognitive functioning, it is of paramount importance to undertake investigations that elucidate the influence of CR on the trajectory of cognitive aging in the population of middle-aged to older adults. Ultimately, these findings could establish a basis for building resilience against the impacts of aging and pathology on cognitive abilities, offering hope to individuals experiencing cognitive decline and impairment.
CR embodies a conceptual framework that defies direct quantification, yet it is believed to be associated with enriching life experiences, including intellectual, physical, and social activities [24]. These activities are thought to mitigate the detrimental effects of neurological disease on cognitive function [25,26]. Educational attainment is commonly used as a proxy for measuring CR, as higher levels of education have been linked to increased CR capacity and compensatory cognitive strategies [27,28]. Individuals with greater CR have been observed to tolerate age-related brain pathologies more effectively [29,30], and higher cognitive capacity is typically associated with greater educational attainment. Conversely, lower educational attainment is linked to an increased risk of Alzheimer’s disease (AD) [31]. Furthermore, fluid intelligence [32,33,34] plays a crucial role in the development of CR [35,36]. Boosting fluid intelligence has been shown to improve executive function, emotional recognition, and theory of mind in older adults [37], while also providing protection against mental health issues and violent tendencies [38]. Other sources of CR include leisure activities that require intellectual engagement [39,40]. Research indicates that such activities can enhance cognitive functioning and overall quality of life for older individuals [41]. Promoting physical activity upholds vascular and circulatory health, which in turn can defer cognitive decline, thus augmenting CR [42,43]. Strong social engagement has also been linked to a lower risk of AD [44,45]. While CR is an abstract concept, it explains why brain damage does not consistently result in cognitive dysfunction. However, defining CR is challenging [46], and proxies are often utilized to measure and adjust the link between brain integrity indicators and cognitive performance. A composite CR combining several proxies is considered the preferable choice over individual proxies in terms of avoiding biased measurement.
Previous research on CR has shown promise, but has primarily focused on its direct impact on cognitive performance [47,48], offering a weaker form of the CR hypothesis [46]. A more robust approach would be to prioritize the moderating effect hypothesis, which suggests that individuals with greater CR demonstrate less reliance on the brain structure to maintain cognitive abilities compared to those with lower CR scores [49,50]. To comprehensively test the CR hypothesis, a complete model should encompass three fundamental elements: the brain deterioration status (e.g., white matter integrity), cognitive performance, and a CR proxy [51]. Despite progress in understanding the relationship between CR and brain structure, research on white matter integrity within this framework remains limited. While many studies have focused on gray matter atrophy [50,52,53,54,55,56], it is essential to conduct thorough investigations into the role of CR in the intricate interplay between white matter integrity and cognition. To bridge this gap, Baker and colleagues conducted a study on the moderating effect of CR on the length of white matter fiber bundles in healthy older adults [57]. However, the diffusion metrics were not thoroughly evaluated, leaving room for a more detailed understanding of the microstructural integrity of white matter tracts. This investigation employs a comprehensive and systematic experimental approach to explore the moderating impact of CR on five skeleton-based diffusion measures and cognition, employing a large cross-sectional sample and a wide range of CR proxies. The primary objective is to identify the potential negative moderation effects of CR proxies on white matter integrity–cognition models during the aging process.

2. Materials and Methods

2.1. Participants

The present study harnessed data derived from the UK Biobank (UKB), an extensive population-based prospective cohort study that accrued baseline data during the period of 2006 to 2010 [58]. Ethical approval was obtained by UKB from the North West Multi-Center Research Ethics Committee (REC reference 11/NW/0382). Approval for this particular study was granted by UKB under application number 68382. Comprehensive in-person interviews were diligently conducted, employing a standardized questionnaire, to meticulously acquire data concerning lifestyle and socio-demographic characteristics. Cognitive abilities were evaluated through the administration of UKB’s cognitive function tests via a touchscreen questionnaire. Subsequently, the UKB expanded its research endeavors to encompass imaging in 2015, resulting in the selection of a specific subgroup of participants to undergo a brain MRI scan approximately eight years after the initial data collection at baseline. Participant selection was carried out via subject screening, as depicted in Figure 1, resulting in a study cohort of 5004 subjects (age = 64.2 ± 7.4, sex (F/M) = 2376/2628) who met the specified criteria from the initial pool of 502,490 screened individuals.

2.2. CR Proxies

The primary objective of this study was to investigate the potential moderating influence exerted by five distinct reserve proxies on the relationship between white matter integrity and cognitive function. The reserve proxies included educational attainment, fluid intelligence assessed at the initial visit between 2006 and 2010, physical activity, leisure activities, and social interaction.
The educational level of participants was ascertained by correlating a designated level of schooling (UKB ID: 6138) with corresponding years according to the International Standard Classification of Education scale, and subsequently categorized into six distinct levels, ranging from no qualification (7 years) to a college or university degree (20 years) [59]. Furthermore, fluid intelligence predicts various forms of achievement, especially academic success. Individuals exhibiting elevated levels of fluid intelligence generally surpass those with average or lower levels of fluid intelligence in cognitive tasks. The fluid intelligence test of the UKB [60], conducted during the initial assessment visit (UKB ID: 20016), was administered. This test encompassed a combination of verbal and numerical logic and involved the completion of 13 multiple-choice fluid intelligence questions. Importantly, the purpose of this test was to assess problem-solving skills that rely on logic and reasoning, independent of acquired knowledge. Participants were given a time constraint of two minutes to complete the test. Participants who did not respond to all the questions within the designated two-minute time limit were assigned a score of zero for each unanswered question. Based on their fluid intelligence scores, participants were subsequently categorized on a scale ranging from 0 to 13.
Engaging in leisure activities and physical exercise has been demonstrated to improve cognitive function and attenuate the impact of cognitive decline related to aging. The assessment encompassed five distinct leisure activities (UKB ID: 6160), encompassing visits to a sports club or gym, attendance at pubs or social clubs, involvement in a religious organization, enrollment in adult education classes, and engagement in other group activities. Participants who provided complete responses were included in the analysis, while those who declined to participate were excluded. The cumulative count of activities in which participants actively participated was utilized to compute a comprehensive score representing engagement in leisure activities [61]. Grip strength was assessed using a hydraulic handheld dynamometer for both hands (UKB ID: 46,47), serving as a measure of physical activity and performance. Social interaction frequency was also evaluated, as socially demanding interactions can motivate the brain and enhance CR, by assessing the frequency with which participants engaged with or were visited by friends and family (UKB ID: 1031). To account for the possibility that CR may be a multifactorial construct, composite CR scores were created by combining individual CR scores [62]. The study included 31 CR scores, consisting of five individual scores and 26 composite scores, and higher scores were indicative of increased CR. The values for each CR proxy were standardized using the Z-score method.

2.3. Neuropsychological Exam

This study utilized a battery of neuropsychological assessments, consisting of several tests, to evaluate cognitive function. The tests included paired-associate learning (PAL), trail making test-B (TMT-B), numeric memory (NM), and reaction time (RT), as listed in Table 1. Reverse scoring was applied to TMT-B and RT. To integrate data from multiple sources, a global cognitive score was computed by summing the standardized scores obtained from all administered neuropsychological tests. This approach allows for the creation of a comprehensive measure that encompasses performance across various cognitive domains. By combining the individual scores into a single global cognitive score, researchers can obtain a broader assessment of overall cognitive functioning. This composite score provides a comprehensive representation of an individual’s cognitive abilities [63].

2.4. White Matter Integrity

In accordance with the UKB Imaging Protocol, the imaging procedures involved the utilization of a Siemens Skyra 3T scanner for high-quality magnetic resonance imaging (MRI) acquisition. Diffusion-weighted MRI (dMRI) was employed, which assesses the diffusivity of water molecules within their respective tissue microenvironments, providing valuable information regarding tissue compartments’ integrity through voxel-wise diffusion tensor and Neurite Orientation Dispersion and Density Imaging (NODDI) measures. A total of 50 distinct diffusion encoding directions were employed for the two diffusion-weighted shells, with all 100 directions being unique. The dMRI data were processed by correcting for head motion and eddy currents and removing outlier slices using the eddy tool [64]. The processed data were then analyzed using tract-based spatial statistics (TBSS) to generate measures based on diffusion-tensor modeling and microstructural model fitting in distinct tract regions. The b = 1000 shell (50 directions) was processed using the DTIFIT tool [65] to generate outputs such as fractional anisotropy (FA) and mean diffusivity (MD). The complete two-shell dMRI data were processed using the NODDI modeling tool [66] and the AMICO tool [67], which facilitated the generation of microstructural parameters such as the intra-cellular volume fraction (ICVF), isotropic or free water volume fraction (ISOVF), and orientation dispersion index (OD) for a comprehensive analysis of the processed data. These parameters provide valuable insights into white matter neurite density, within-voxel tract disorganization, and extracellular volume fraction. To improve the precision of the alignment of the DTI FA image with a standard-space white-matter skeleton, we used the TBSS skeletonization process [68], which involves multiple steps such as image registration, mean FA image creation, skeleton identification, and the generation of a skeletonized representation of white matter tracts by projecting FA values onto a common skeleton. All other DTI/NODDI output maps were also subjected to a standard-space warp to achieve consistency. The set of processed maps were subjected to further analysis to generate a comprehensive range of image-derived features, referred to as image-derived phenotypes (IDPs), by aligning the maps with a set of 48 standard-space tract masks. To obtain a more focused set of measures, we computed five skeleton-based metrics, which included FA, MD, ICVF, ISOVF, and ODI, and we averaged the dMRI IDPs within a set of 48 standard-space tracts.

2.5. Analysis

In this study, we implemented a hierarchical regression model with three stages, employing SPSS 26 Statistics software, aimed at exploring the moderating influence of CR proxies on a healthy older cohort, as depicted in Figure 2. A three-stage hierarchical regression model was used to examine whether the relationship between white matter integrity and specific cognitive functions was moderated by varying levels of CR proxy, when controlling for age and gender. To achieve this, a total of 775 white matter integrity–cognition models were generated, incorporating all possible combinations of skeleton white matter integrity and cognitive function. The first stage involved regressing age, gender, and white matter characteristics on a specific cognitive function. Subsequently, an independent variable denoting a proxy for CR was included to evaluate its direct impact on cognitive function in the second step. In the final stage, the interaction term between the white matter integrity and CR proxy was incorporated to determine the interaction effect’s significance. If the interaction term explained a significant portion of the variance in the regression model, it validated the presence of a moderating effect exerted by CR proxies on the relationship between white matter integrity and cognitive function, while also discerning the direction of the moderating effect. The direction of the moderating effect was identified as well. To facilitate the interpretation of the Spearman’s correlation coefficients among all cognitive reserve proxies, the study generated heatmaps, as depicted in Figure 3. To address concerns regarding multiple comparisons, the false discovery rate (FDR) was calculated, employing the widely recognized the Benjamini–Hochberg approach, with significance set at q-values below 0.05 in Step 2 and 3. Furthermore, the change in the coefficient of determination (R2) was evaluated in the progression from Step 2 to Step 3, gauging the interaction term’s impact on elucidating the variance in a specific cognitive function.

3. Results

3.1. White Matter Integrity–Cognition Relationships

In the initial phase of our hierarchical regression analysis, we included variables related to sex, age, and white matter integrity, which were found to display significant correlations with cognitive measures. As illustrated in Table 2, all of the white matter integrity–cognition models produced statistically significant results (p < 0.001), revealing gender as a substantial independent predictor of cognitive function in 60% of the models. Overall, men exhibited superior cognitive performance, with the exception of verbal declarative memory, regardless of white matter integrity. Notably, age displayed a negative correlation with cognitive function across all models, highlighting its crucial role as a predictor of cognitive outcomes. Among the white matter characteristics, FA and ICVF exhibited significant correlations with cognitive function in 60% and 40% of the models, moving in the same direction, respectively. However, MD, OD, ICVF and cognitive function displayed inconsistent directional movements. Specifically, 60% of the models indicated negative correlations with cognitive function concerning MD and OD.

3.2. CR-Cognition Relationships

After accounting for confounding factors such as age, gender, and white matter integrity in our analysis, we proceeded to examine the influence of CR proxies on cognitive function. Within our investigation, a noteworthy outcome emerged, indicating positive independent effects associated with a diverse array of positive independent effects of 30 CR proxies, consisting of four individual proxies and 26 composite proxies, in the white matter integrity–cognition models. As presented in Figure 4, early fluid intelligence demonstrated the highest mean independent effect (mean R2 change = 0.115) among the five individual CR proxy variables, which remained consistently significant across 25 regression models. Early fluid intelligence demonstrated the largest independent effect on global cognition, indicating that it accounted for an additional 20.0% of the variance in FA, 20.1% of the variance in ICVF and OD, and 20.2% of the variance in MD and ISOVF. In terms of composite CR proxies, early fluid intelligence and physical activity emerged as the variables with the most notable mean independent effects, with a mean R2 change of 0.095, and were significant across all regression models.
The impact of various CR proxies on white matter integrity–cognition models for each cognitive function is detailed in Figure 5. Specifically, this figure illustrates the mean R2 changes contributed by the CR proxies. Our findings emphasize the considerable influence of CR proxies on global cognitive levels, as evidenced by a significant mean R2 change of 0.078. Conversely, the smallest mean effect was observed on RT, with a mean R2 change of 0.012.

3.3. Moderation Effects

In the third step of our analysis, we implemented an FDR correction using a 0.05 threshold to address multiple comparisons. Our findings reveal that the inclusion of the interaction term in Step 3 produced a significant enhancement in the predictive performance of the 63 models examined, demonstrating 7.9% positive moderating effects and 92.1% negative moderating effects (see Table 3 and Table 4). However, none of the models survived the FDR correction for multiple comparisons. Our study employed rigorous Bonferroni corrections that encompassed all CR proxies and white matter integrity–cognition models, which included 25 white matter integrity–cognition models, 31 CR proxies, and 775 comparisons. With respect to the negative moderating effect, different beta values were observed for various white matter indicators. Specifically, for FA and ICVF, negative beta corresponds to the negative moderating effect, while for MD, OD, and ISOVF, positive beta corresponds to the negative moderating effect. Based on the fundamental assumption of the CR hypothesis, the negative moderating effect indicates a modified relationship between white matter lesions and cognitive outcomes, suggesting that higher CR levels may enable individuals to maintain cognitive function despite white matter disconnection. Our study identified 58 negative moderating effects that support the CR hypothesis, while positive moderating effects were observed in only a limited number of outcomes (5 out of 63), contradicting the CR hypothesis and implying a stronger association between white matter disconnection and cognitive outcomes in individuals with higher CR, indicating a greater reliance on white matter integrity to sustain cognitive function.
An examination of Table 4 yielded noteworthy observations regarding the relationships between cognitive outcome, white matter integrity, and various moderating strategies exhibited by CR proxies. Specifically, 34 out of 63 models displayed negative moderating strategies pertaining to OD, while 15 out of 63 models showed similar traits pertaining to ISOVF. Notably, our analysis also revealed that early fluid intelligence exhibited a diverse array of negative moderating effects in 6 out of 63 models on the relationship between white matter integrity and cognitive outcome. Furthermore, among composite CR proxies, particularly early fluid intelligence and physical activity, three and four proxies, respectively, exhibited significant negative moderating impacts on the relationship between white matter integrity and cognitive outcome. Our analysis of the neuropsychological battery yielded a more nuanced understanding of the moderating effects on the relationship between white matter integrity and cognitive outcome, particularly in relation to RT and TMT-B, where we observed negative moderating effects in 27.0% and 28.6% of the models, respectively.

4. Discussion

In the present study, we employed DTI and NODDI techniques to systematically examine the influence of five CR proxies, individually and in various combinations, on the association between cognitive function and white matter integrity alterations. Our study design allowed us to explore the impact of multiple CR proxies and their combinations on the observed associations. The inclusion of a substantial sample size from the UKB enhanced the generalizability and statistical power of our findings. By elucidating these relationships, we can contribute to a deeper understanding of the mechanisms underlying CR and potential avenues for interventions targeting cognitive decline and related neurodegenerative disorders.

4.1. White Matter Integrity

White matter integrity is a crucial factor in facilitating neural communication between different brain regions, thereby supporting various cognitive functions, including attention, memory, and decision-making [69,70]. The use of dMRI has proven to be an effective tool in identifying the structural basis of cognitive functions and comprehending the impact of aging, disease, or injury on white matter integrity. A study conducted by Voineskos et al. [71] using dMRI tractography and structural equation modeling investigated the association between white matter tracts and cognitive performance among a cohort of older adults. Their findings suggest that reduced white matter tract length in the corpus callosum, a key white matter bundle connecting the left and right hemispheres, is associated with weaker memory and executive function. Similarly, Zimny et al. [72] used DTI to evaluate changes in selected white matter tracts in patients with AD and mild cognitive impairment. Their study revealed that impaired white matter tracts, as measured using dMRI, are related to cognitive decline. These studies provide compelling evidence for the critical role of white matter integrity in cognitive function, underscoring the potential utility of dMRI in evaluating and predicting the risk of age-related cognitive decline.
In the present study, we conducted an evaluation of two diffusion models, namely the classical DTI model and the NODDI model, to determine their effectiveness in computing quantitative dMRI measures for characterizing microstructural alterations in the FA skeleton of white matter. The DTI model is widely recognized for its ability to characterize water molecule diffusion properties and generates two established metrics, FA and MD. FA measures axial and radial diffusivity, and assesses the coherence of the underlying tissue or fiber organization, indicating the degree of elongation in diffusion. Conversely, MD assesses the general freedom of diffusion in tissue and is an average of all three axes of the diffusion ellipsoid, irrespective of directionality. Our study findings reveal a positive correlation between FA and multiple cognitive domains, while a negative correlation exists between MD and the same cognitive domains. This observation is consistent with previous studies suggesting that both FA and MD serve as sensitive biomarkers for age-related white matter changes [73,74,75], where typically FA decreases and MD increases with age [76,77]. Despite their utility, the common tensor models employed in DTI are simplistic and, as a result, fall short in adequately discerning the individual microstructural attributes associated with white matter.
Parametric diffusion modeling is a sophisticated technique that enables a detailed analysis of the diffusion signal within the brain. This method allows for the identification and characterization of various compartments, including intracellular and extracellular spaces, using relevant neurobiological metrics. In particular, NODDI proposes that white matter tissue can be segmented into three distinct diffusion compartments that can be detected using modern clinical MRI scanners in a reasonable amount of time. These compartments can be characterized by three distinct parameters: ICVF, which serves as a proxy for axonal density; ISOVF, which describes extracellular water diffusion; and OD, which provides insight into axonal organization. Compared to the broader evaluation offered by DTI, NODDI’s microscopic characterization of white matter is more precise. Our research demonstrates that ICVF is positively associated with multiple cognitive domains, while there is a negative correlation between OD, ISOVF, and the same cognitive domains. In contrast, earlier studies have reported that increased OD is linked to improved cognitive abilities [78,79]. The apparent discrepancy between our study and previous ones may be due to various factors, such as differences in participant age ranges. For example, previous studies that observed an upward trend in OD associated with aging predominantly examined cohorts of younger individuals, in contrast with our current study [79]. In contrast, investigations that specifically enrolled older subjects reported a negative correlation or a transition from a positive to a negative correlation around the approximate age of 60 [80].
The present study yields significant insights into the relationship between diffusion measures, cognitive domains, and CR. The findings indicate that while the four diffusion measures analyzed are comparably effective in accounting for cognitive domains, there are substantial differences in the moderating effects of CR proxies. Notably, the correlation between traditional DTI metrics, such as FA and MD, and NODDI’s ICVF with cognitive domains is subject to fewer moderating effects from CR proxies, in contrast to the more comprehensive range of moderating effects observed for the association between OD and ISOVF. This distinction can be attributed to inherent microstructural characteristics within the brain, such as the density of neurites and the complexity or branching pattern of neural tracts, which can render the FA signal vulnerable. The observed moderating effect is predominantly attributed to OD, rather than ICVF, indicating that the moderating effect of CR on white matter OD in relation to cognition may be linked to changes in tract complexity, rather than a decline in neurite density. MD is a measure that represents the average diffusion rate of water molecules in tissue, encompassing both intracellular and extracellular water compartments. In contrast, ISOVF is more sensitive to changes in white matter microstructure that specifically affect the extracellular space. The observed moderating effect is predominantly attributed to the extracellular water compartments. CR’s moderating effect on white matter integrity in relation to cognition has been relatively underexplored in middle-aged and older adults, possibly due to differences in the effects between DTI and the more specialized technique known as NODDI. While NODDI requires more advanced equipment and expertise than DTI, its unique advantages make it essential to investigate the moderating effect of CR on white matter integrity in aging populations. Moreover, future NODDI studies may provide a more thorough understanding of the mechanisms through which CR can protect against age-related cognitive decline and dementia, thereby informing interventions aimed at promoting cognitive health in older adults.

4.2. CR Proxy

General intelligence is composed of two discernible components known as fluid intelligence and crystallized intelligence. Fluid intelligence encompasses the capacity to solve novel problems, think abstractly, reason logically, and adapt to new situations, whereas crystallized intelligence pertains to the accumulation of knowledge and skills acquired through learning and experience amassed over an individual’s lifespan. Historically, crystallized intelligence, which is often associated with formal education, has been the primary measure of CR [81]. However, recent research suggests a significant association between fluid intelligence and education attainment [82], highlighting a strong interdependency between these two constructs. Individuals exhibiting elevated levels of fluid intelligence typically demonstrate success in both academic and professional contexts, with fluid intelligence being a robust predictor of academic achievement [83,84]. Recent studies have also suggested a bidirectional causal relationship between fluid intelligence and educational attainment [85,86]. The present study highlights the independent and robust effect of fluid intelligence on cognitive functioning, with a mean ΔR2 value of 0.115. Additionally, the present study reveals a significant number of negative moderation effects of early fluid intelligence and its associated composite proxies. Specifically, the results suggest that out of the 58 negative moderation effects observed, 54 were found to be associated with early fluid intelligence and its related composite proxies. These findings corroborate earlier research indicating that fluid intelligence exhibits stronger predictive capabilities for CR when contrasted with crystallized intelligence [37]. Moreover, studies further suggest that higher fluid intelligence may offer protection against the onset of dementia [87,88], and that it functions as a reliable indicator for gauging the development of CR [89], which is closely correlated with favorable lifestyles [89]. The relationship between white matter integrity and cognitive performance appears to be moderated by early fluid intelligence, suggesting that an intelligence measure incorporating educational attainment, favorable lifestyles, and certain cognitive domains could serve as an index for the benefits of education.
Educational attainment has been proposed as a crucial factor in promoting CR, which can help the brain to withstand damage and maintain function as we age or experience injury. Numerous studies have demonstrated that individuals with higher levels of educational attainment have a lower incidence of dementia and better cognitive function in old age [90]. This positive effect may be attributed to the development of CR, which allows the brain to compensate for damage by using alternate or more efficient neural pathways. The present study found that educational attainment had the second-highest mean independent effect on cognitive function, and educational attainment or its composite proxies were implicated in 29 of the 58 negative moderation effects observed. These findings are consistent with previous research that has demonstrated education-related moderation effects on various cognitive functions, such as the relationship between cardiovascular risk factors and late-life cognition [91], between brain activation and subjective cognitive decline symptoms [92], and between multiple sclerosis-related disease burden and cognition [93].
Modifiable behaviors within one’s lifestyle, such as engaging in physical activity, have the potential to serve as protective factors against cognitive decline associated with aging [94]. Our study revealed that the impact of physical activity on cognitive functioning was relatively modest in terms of the average independent effect (ΔR2 = 0.005). However, it exhibited a substantial moderation effect. Out of the total of 58 negative moderation effects observed, a notable subset of 28 were specifically linked to physical activity or its corresponding composite proxies. The findings concerning the application of physical activity as a proxy for CR yielded diverse results. Certain researchers have reported an association between higher levels of physical activity and reduced brain atrophy among middle-aged and older adults who do not exhibit signs of dementia [95,96]. However, a study conducted among non-demented participants revealed that physical activity did not exert any notable influence on the short-term rate of change in biomarkers associated with AD [97]. One plausible explanation for the divergent outcomes could be attributed to the utilization of self-reported physical activity data collected via a non-validated questionnaire. This method primarily captures recent lifestyle habits and is susceptible to inherent measurement inaccuracies. When examining the effect of physical activity on CR, grip strength represents a more relevant biomarker that merits consideration. Involvement in leisure activities and the establishment of social connections are also acknowledged as influential factors in fostering CR and facilitating the improvement of cognitive function.
The observed relationships between individual CR proxies exhibited limited strength and primarily lacked statistical significance, indicating the involvement of diverse determinants influencing CR. The composite CR includes both static and dynamic aspects, enabling the assessment of CR to change over time. The composite CR score was derived by transforming individual measures into Z-scores and subsequently computing their average. It is conceivable that two individuals with identical CR composite scores may exhibit contrasting characteristics, with one displaying higher engagement in physical activity but lower fluid intelligence, while the other demonstrates lower engagement in physical activity but higher fluid intelligence. Our analyses revealed that 26 composite proxies had significant positive independent effects on cognition, although their effects were smaller in magnitude than those observed for fluid intelligence alone. Nonetheless, our study found that a composite CR led to a similar level of moderation effect compared with individual CR proxies. Physical activities and their possible combinations were responsible for 28 out of 58 negative moderation effects, while early fluid intelligence and its possible combinations were responsible for 54 out of 58 negative moderation effects. Our results therefore support the use of early fluid intelligence and physical activity composition as the preferred composite proxies, given that the mean independent effects ranked second and were responsible for 6 out of 58 negative moderation effects.

4.3. CR’s Moderation Effect

The CR hypothesis [98] posits that individuals with a higher CR exhibit better cognitive functioning and experience fewer clinical impairments than those with a lower CR, given a certain level of neuropathology. A multitude of studies have explored the relationship between CR and cognitive outcomes, with most providing evidence of a positive correlation [99,100]. Research has also suggested that a higher CR is associated with greater white matter integrity in older adults [101]. Moreover, studies have shown that CR may have a significant impact on the trajectory and severity of cognitive decline observed in patients with neurodegenerative diseases such as AD, with higher CR found to be associated with slower rates of decline [49].
This study adds to the growing body of research exploring the moderating effects of CR proxies on the association between cognitive performance and white matter integrity in healthy middle-aged and older adults. The results indicate that CR proxies have a statistically significant but minor moderating impact (ΔR2 = 0.06~0.23%) on cognitive performance, further supporting the validity of the CR hypothesis. It is possible that the relatively small effect size may be attributed to several factors, such as the study’s focus on healthy middle-aged and older adults without dementia, potentially limiting the range of changes in white matter integrity and cognitive decline observed. Moreover, the analysis indicates that a combination of white matter integrity measures, age, and gender accounted for only 1.6~14.5% of the variance, which may considerably contribute to the small size of the moderation effect observed.

4.4. Limitations

It is essential to note and address several limitations associated with the present study. Firstly, the cross-sectional design of the study complicates efforts to establish definitive causality. Longitudinal investigations would be required to ascertain the rate of cognitive decline and provide stronger evidence. Secondly, although the study utilized a large population sample, an independent validation set in a separate cohort would enhance the validity of the results. Further research is warranted to validate these findings and delve into the underlying mechanisms of CR in greater depth. Thirdly, an important aspect missed in this study was the comparison between crystallized intelligence, a commonly used CR proxy, and fluid intelligence. This comparison could have provided additional insights. Lastly, given that older individuals may increasingly rely on CR to maintain their cognitive function, investigating the interplay between age and CR will be an important avenue for future research.

5. Conclusions

The primary objective of this study was to examine the impact of five CR proxies, individually and in combination, on the relationship between cognitive function and changes in white matter integrity. The investigation utilized DTI and NODDI techniques on a substantial sample size derived from the UKB. The analysis employed hierarchical regression analysis to assess the moderating influence of CR proxies on cognition in a sample consisting of cognitively intact older adults, with a specific focus on examining the impact of CR proxies on this relationship. The findings of this study reveal that CR had a significant moderating effect on the relationship between white matter integrity and cognitive function, with NODDI outperforming DTI in exploring this interaction. Notably, the results highlighted that early fluid intelligence exhibited the most substantial independent and moderating effect on cognitive performance, thereby indicating its suitability as an individual CR proxy. Additionally, the study supported the inclusion of a composite proxy composed of fluid intelligence and physical activity composition. However, it is important to note that the study’s findings may be potentially restricted by the specific focus on cognitively intact older adults as the experimental subjects. As a result, the magnitude of the moderation effect may be constrained. Nonetheless, the findings underscore the significant influence of CR proxies on diverse cognitive domains, emphasizing the importance of investigating the relationship between cognitive function, white matter integrity, and CR. Overall, this research provides valuable insights into the role of CR in the context of cognitive decline associated with aging. Furthermore, it establishes a framework for future investigations in this field, paving the way for the continued exploration of the intricate interplay between cognitive function, white matter integrity, and CR.

Author Contributions

Conceptualization, L.L., S.W. and Y.J.; methodology, Y.J.; software, Y.J.; validation, L.L., Y.J. and M.X.; formal analysis, Y.J.; investigation, L.L.; resources, L.L. and S.S.; data curation, Y.J.; writing—original draft preparation, Y.J.; writing—review and editing, L.L.; supervision, L.L.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.

Funding

Grants from National Natural Science Foundation of China (81971683) and the Natural Science Foundation of Beijing Municipality (L182010) provided funding for this study.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The imaging datasets generated by UKB analyzed during the current study are available via the UKB data access process (see http://www.ukbiobank.ac.uk/register-apply/, accessed on 15 January 2022). The Research Access Administration Team at UKB responds to all data access requests from academic and industrial researchers without bias or exclusivity. The requests are assessed to see if they promote health research that is in the public interest, and if they do, they are swiftly granted. Detailed information about the data available from UKB is available at http://www.ukbiobank.ac.uk, accessed on 15 January 2022. There may be a slight difference between the precise number of participants having imaging data currently accessible in UKB and that listed in this study.

Acknowledgments

We are grateful to UK Biobank for making this invaluable resource available, and to the UK Biobank participants for dedicating their time to make these data possible.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flowchart of subject screening.
Figure 1. Flowchart of subject screening.
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Figure 2. White matter integrity–cognitive function models for hierarchical regression.
Figure 2. White matter integrity–cognitive function models for hierarchical regression.
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Figure 3. The heatmap plot of the Spearman correlation coefficients between individual CR proxies.
Figure 3. The heatmap plot of the Spearman correlation coefficients between individual CR proxies.
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Figure 4. Mean R2 change for proxies with significant effects in all global-level models. (“|” is the mean of significant R2 change values across all models for that proxy, and frequency is the number of the regression models when a given CR proxy has significant independent effects on cognition, which was then divided by 25).
Figure 4. Mean R2 change for proxies with significant effects in all global-level models. (“|” is the mean of significant R2 change values across all models for that proxy, and frequency is the number of the regression models when a given CR proxy has significant independent effects on cognition, which was then divided by 25).
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Figure 5. Violin plots visually represent the distribution of R2 change in all white matter integrity associated with five cognitive domains that displayed significant main effects. These plots showcase the mean (depicted as a black dot) and the range of data distribution (represented by a black line).
Figure 5. Violin plots visually represent the distribution of R2 change in all white matter integrity associated with five cognitive domains that displayed significant main effects. These plots showcase the mean (depicted as a black dot) and the range of data distribution (represented by a black line).
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Table
Table 1. Cognitive domain, neuropsychological tests, and detailed test descriptions.
Table 1. Cognitive domain, neuropsychological tests, and detailed test descriptions.
TestingUKB IDDescriptionCognitive DomainVariablesRange
PAL506Participants viewed 12-word pairs on a screen and were instructed to memorize them for a later test. In the test, they then selected the correct partner word from four options.Verbal declarative memoryTotal correct score0–10
TMT-B505Participants switched between touching 1–13 numbers in numeric order and the letters A-L in alphabetical order while in a pseudo-random arrangement.Executive functionsTime to complete (deci-seconds)187–2986
NM100029Participants completed a backward digit span task where they had to enter a two-digit number in reverse order after a short delay. The task progressively added one digit with each correctly recalled sequence. The participant’s task ended once they failed two trials of the same length or successfully recalled a 12-digit number.Working memoryMaximum digits remembered correctly2–11
RT100032Participants pushed a button-box when two matching symbol cards appeared on screen. Out of 12 trials, the first 5 were practice trials, and the remaining 7 had 4 matching card trials for the score.Processing speedAverage response time (milliseconds)348–1508
Table
Table 2. Results of the first step of hierarchical regression.
Table 2. Results of the first step of hierarchical regression.
CognitionModel StatisticsWhite Matter IntegritySexAge
R2FVariableββΒ
PAL0.059104.477 **FA0.062 **−0.167 **−0.134 **
TMT-B0.145283.335 **FA0.094 **0.005−0.336 **
NM0.01729.392 **FA0.0440.069 **−0.092 **
RT0.107199.579 **FA0.0440.108 **−0.297 **
Global cognitive0.142276.878 **FA0.094 **0.006−0.333 **
PAL0.058102.895 **MD−0.055 *−0.164 **−0.133 **
TMT-B0.140270.715 **MD−0.051 *0.009−0.349 **
NM0.01627.524 **MD−0.0260.071 **−0.097 **
RT0.106197.512 **MD−0.0290.110 **−0.300 **
Global cognitive0.138266.688 **MD−0.063 **0.010−0.340 **
PAL0.05699.334 **ICVF0.024−0.166 **−0.150 **
TMT-B0.141274.217 **ICVF0.063 **0.006−0.354 **
NM0.01626.852 **ICVF0.0130.070 **−0.105 **
RT0.106196.900 **ICVF0.0200.109 **−0.308 **
Global cognitive0.137264.173 **ICVF0.047 *0.007−0.355 **
PAL0.057101.421 **OD−0.042−0.167 **−0.148 **
TMT-B0.140271.945 **OD−0.053 **0.006−0.360 **
NM0.01830.897 **OD−0.051 *0.070 **−0.098 **
RT0.107199.924 **OD−0.0430.108 **−0.304 **
Global cognitive0.140271.168 **OD−0.073 **0.007−0.352 **
PAL0.057101.054 **ISOVF−0.042−0.165 **−0.139 **
TMT-B0.138266.618 **ISOVF−0.0190.007−0.363 **
NM0.01626.860 **ISOVF−0.0140.070 **−0.102 **
RT0.106196.669 **ISOVF−0.0170.109 **−0.306 **
Global cognitive0.136262.060 **ISOVF−0.0360.008−0.352 **
Note: * p < 0.001, ** p < 0.0001, β: standardized β.
Table
Table 3. The moderating effects of CR proxies on FA and ICVF at the global-level.
Table 3. The moderating effects of CR proxies on FA and ICVF at the global-level.
Cognition FunctionBrain Structure × CR ProxiesΔR2β
PALFA × Education0.00170.041 **
TMT-BICVF × Physical activity0.00100.032 *
RTFA × Early fluid intelligence + Physical activity0.0009−0.031 *
PALFA × Education + Physical activity0.00090.029 *
RTFA × Early fluid intelligence + Leisure activities + Physical activity0.0009−0.029 *
TMT-BFA × Early fluid intelligence0.0008−0.028 *
RTFA × Early fluid intelligence0.0007−0.026 *
Note: * p < 0.05, ** p < 0.01.
Table
Table 4. The moderating effects of CR proxies on MD, OD, and ISOVF at the global-level.
Table 4. The moderating effects of CR proxies on MD, OD, and ISOVF at the global-level.
Cognition FunctionBrain Structure × CR ProxiesΔR2β
TMT-BOD × Early fluid intelligence + Physical activity0.00230.048 **
RTISOVF × Early fluid intelligence + Physical activity0.00180.042 **
RTISOVF × Early fluid intelligence + Leisure activities + Physical activity0.00170.041 **
TMT-BOD × Early fluid intelligence0.00160.040 **
RTISOVF × Education + Early fluid intelligence + Leisure activities + Physical activity0.00160.040 **
RTISOVF × Education + Early fluid intelligence + Physical activity0.00150.038 **
TMT-BOD × Education + Early fluid intelligence + Physical activity0.00140.039 **
Global cognitionOD × Early fluid intelligence + Physical activity0.00140.037 **
RTISOVF × Education + Early fluid intelligence + Leisure activities0.00130.037 **
Global cognitionOD × Early fluid intelligence + Leisure activities + Physical activity0.00130.036 **
RTISOVF × Early fluid intelligence + Leisure activities0.00130.036 **
TMT-BOD × Education + Early fluid intelligence0.00120.037 **
RTISOVF × Education + Early fluid intelligence0.00120.034 **
Global cognitionOD × Education + Early fluid intelligence + Leisure activities0.00120.035 **
RTISOVF × Early fluid intelligence0.00110.033 **
Global cognitionOD × Education + Early fluid intelligence + Leisure activities + Physical activity0.00110.034 **
NMOD × Early fluid intelligence + Leisure activities + Physical activity0.00110.033 **
RTMD × Early fluid intelligence + Leisure activities0.00110.033 **
Global cognitionOD × Early fluid intelligence + Leisure activities0.00100.032 **
Global cognitionOD × Early fluid intelligence0.00100.032 **
TMT-BOD × Early fluid intelligence + Physical activity + Social interaction0.00100.031 *
RTMD × Early fluid intelligence + Leisure activities + Physical activity0.00100.031 *
TMT-BISOVF × Education0.00100.031 *
NMOD × Early fluid intelligence + Leisure activities + Physical activity + Social interaction0.00090.031 *
NMOD × Early fluid intelligence + Leisure activities + Social interaction0.00090.031 *
TMT-BOD × Early fluid intelligence + Leisure activities + Physical activity0.00090.030 *
TMT-BOD × Education + Early fluid intelligence + Leisure activities + Physical activity0.00090.031 *
NMOD × Education + Early fluid intelligence + Leisure activities + Physical activity0.00090.031 *
NMISOVF × Early fluid intelligence0.0009−0.030 *
NMISOVF × Physical activity0.0009−0.030 *
NMOD × Education + Early fluid intelligence + Leisure activities0.00090.030 *
NMOD × Early fluid intelligence + Leisure activities0.00090.030 *
NMOD × Education + Early fluid intelligence + Leisure activities + Social interaction0.00090.030 *
NMOD × Education + Leisure activities + Social interaction0.00090.030 *
Global cognitionOD × Education + Early fluid intelligence0.00080.030 *
NMOD × Education + Leisure activities0.00080.029 *
Global cognitionMD × Leisure activities0.00080.029 *
TMT-BISOVF × Education + Early fluid intelligence + Physical activity0.00080.029 *
NMOD × Education + Early fluid intelligence + Leisure activities + Physical activity + Social interaction0.00080.029 *
TMT-BISOVF × Early fluid intelligence + Physical activity0.00080.028 *
TMT-BOD × Education + Early fluid intelligence + Physical activity + Social interaction0.00080.029 *
Global cognitionOD × Education + Early fluid intelligence + Leisure activities + Social interaction0.00080.029 *
RTISOVF × Education + Leisure activities + Physical activity0.00080.028 *
Global cognitionOD × Education + Early fluid intelligence + Physical activity0.00080.029 *
TMT-BISOVF × Education + Early fluid intelligence0.00080.027 *
RTMD × Early fluid intelligence + Physical activity0.00070.028 *
TMT-BOD × Early fluid intelligence + Social interaction0.00070.028 *
RTMD × Early fluid intelligence0.00070.027 *
TMT-BISOVF × Education + Early fluid intelligence + Leisure activities0.00070.026 *
Global cognitionOD × Early fluid intelligence + Leisure activities + Social interaction0.00070.026 *
Global cognitionOD × Early fluid intelligence + Leisure activities + Physical activity + Social interaction0.00070.026 *
TMT-BOD × Education + Early fluid intelligence + Social interaction0.00070.028 *
RTOD × Education + Early fluid intelligence0.00070.027 *
TMT-BOD × Education + Early fluid intelligence + Leisure activities0.00070.027 *
Global cognitionOD × Education + Early fluid intelligence + Leisure activities + Physical activity + Social interaction0.00070.026 *
TMT-BISOVF × Education + Early fluid intelligence + Leisure activities + Physical activity0.00060.025 *
Note: * p < 0.05, ** p < 0.01.
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Lin, L.; Jin, Y.; Xiong, M.; Wu, S.; Sun, S. The Protective Power of Cognitive Reserve: Examining White Matter Integrity and Cognitive Function in the Aging Brain for Sustainable Cognitive Health. Sustainability 2023, 15, 11336. https://doi.org/10.3390/su151411336

AMA Style

Lin L, Jin Y, Xiong M, Wu S, Sun S. The Protective Power of Cognitive Reserve: Examining White Matter Integrity and Cognitive Function in the Aging Brain for Sustainable Cognitive Health. Sustainability. 2023; 15(14):11336. https://doi.org/10.3390/su151411336

Chicago/Turabian Style

Lin, Lan, Yue Jin, Min Xiong, Shuicai Wu, and Shen Sun. 2023. "The Protective Power of Cognitive Reserve: Examining White Matter Integrity and Cognitive Function in the Aging Brain for Sustainable Cognitive Health" Sustainability 15, no. 14: 11336. https://doi.org/10.3390/su151411336

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Lin, L., Jin, Y., Xiong, M., Wu, S., & Sun, S. (2023). The Protective Power of Cognitive Reserve: Examining White Matter Integrity and Cognitive Function in the Aging Brain for Sustainable Cognitive Health. Sustainability, 15(14), 11336. https://doi.org/10.3390/su151411336

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