Associations Among Executive Function Abilities, Free Water, And White Matter Microstructure in Early Old Age Part 3

7.1. Associations between executive function and white matter microstructure

Associations between the EF factors and the FW and white matter microstructure measures are displayed in Table 2. The corresponding associations between all covariates and the FW and white matter microstructure measures from these models are displayed in Table 3. 

In recent years, numerous studies have shown that there is a close connection between human memory and the microstructure of brain white matter. White matter is made up of many nerve fibers and their supporting tissue, and its function is to connect different areas of the brain with nerve fibers. For the human brain's learning and memory, a healthy and complete white matter neural network is crucial.

An unhealthy state of white matter microstructure may contribute to memory degradation and cognitive decline. Studies have shown that white matter degeneration is also one of the essential causes of neurodegenerative diseases such as Alzheimer's disease. This degeneration can lead to damage and loss of connections in nerve fibers, which affects the transmission of information in various areas of the brain, exacerbating the decline in cognitive function and memory loss.

However, there are also studies showing that people can maintain the health of their white matter and maintain good memory and cognitive abilities by improving their lifestyle and behavioral habits. For example, good nutrition and adequate sleep can provide the energy needed by the brain and the protein needed to repair cells; regular exercise can stimulate the formation of neurons and neuromas; learning new knowledge, practicing memory, and thinking can stimulate and maintain nerve fibers. Connectivity; staying socially engaged can enhance cognitive brain activity and mental health.

In addition, the continuous advancement of today's medical technology, such as neuroimaging technology, can help doctors observe the microstructure of patients' white matter for early detection and treatment of some diseases and symptoms related to white matter degeneration.

Therefore, maintaining the microstructure of white matter is critical to maintaining brain health and memory. People can continuously improve their health through good living habits and medical technology to maintain a healthy and complete white matter neural network, making themselves smarter, more creative, and more energetic. It can be seen that we need to improve our memory. Cistanche deserticola can significantly improve memory because Cistanche deserticola is a traditional Chinese medicinal material with many unique effects, one of which is to improve memory. The efficacy of minced meat comes from the various active ingredients it contains, including acid, polysaccharides, flavonoids, etc. These ingredients can promote brain health in a variety of ways.

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Analyses were conducted separately for each tract and each metric (FW, FAFWcorr, and MDFWcorr), but associations between any given metric and the Common EF and Working Memory-Specific factors were estimated within the same model. 

FDR corrections in Table 2 were based on all p-values from that column only (e.g., across all associations between the Common EF factor and FW metrics, with separate FDR corrections for FAFWcorr and MDFWcorr; 3 additional sets of FDR corrections were examined for associations between Working Memory-Specific and FW, FAFWcorr, and MDFWcorr). An example of these analyses is displayed in Fig. 3 (for FW across all tracts).

The Common EF factor was associated with FW across 9 of the 11 white matter tracts (and the 'all tracts' measure). In all cases, more FW corresponded to lower Common EF ability (range in β = -0.15 to -0.26). Common EF was not associated with FAFWcorr or MDFWcorr in any of the tracts. The Working Memory-Specific factor was associated with greater FAFWcorr in the IFG Triangularis (β = 0.21), but not with any other FW or white matter microstructure metrics after FDR correction.

Analyses after excluding individuals with MCI are displayed in Table 4. For Common EF and FW, the negative associations were nonsignificant after FDR correction, though significant associations were observed for 2 of the 11 white matter tracts based on uncorrected p-values (cingulum and unicinate fasciculus). Associations with the remaining tracts were attenuated by approximately half (range β = -0.09 to -0.15). For the Working Memory-Specific factor, the association with FA in the IFG Triangularis remained significant even after FDR correction (β = 0.25). Additionally, significant associations were observed for FA in the inferior longitudinal fasciculus and the superior longitudinal fasciculus (β = 0.26 and 0.28, respectively).

7.2. Interactions between cognitive reserve and white matter microstructure

Next, in the full sample (i.e., including individuals with MCI and those missing MCI diagnoses), we repeated the same set of analyses after adding age 20 general cognitive ability and its interaction with the relevant white matter measure to the regression model. We only conducted moderation analyses for the white matter measures significantly associated with EF factors in the primary analyses (i.e., Common EF and FW in 9 tracts and the 'all tracts' measure, Working Memory-Specific and FAFWcorr in the IFG Triangularis tract only). 

Results are displayed in Table 5. Although AFQT scores were strongly predictive of Common EF (βs = 0.41 to 0.64), they did not moderate any of the associations between the EF factors and FW described above (all uncorrected ps > 0.243, FDR-corrected ps > 0.782). Finally, we conducted sensitivity analyses using a dichotomous score for cognitive reserve (i.e., grouping subjects as above or below the mean), which also revealed no evidence that cognitive reserve moderated associations between EF factors and FW or FA (see supplemental Table S3).

8. Discussion

The goal of the study was to better understand the associations between EF abilities and white matter microstructure in older adults. Results indicated that EF abilities (specifically a Common EF factor comprising performance across 6 tasks) were associated with FW across almost all cortical tracts examined here, but not with FAFWcorr or MDFWcorr. Greater Common EF ability was associated with less FW. These findings mirror the associations with age, which was associated with free water across all cortical tracts, but only associated with FAFWcorr or MDFWcorr in some tracts.

These results are consistent with two recent studies that have suggested EFs are associated with FW (but not FAFWcorr or MDFWcorr) in older adult samples (Archer et al., 2020; Maillard et al., 2019). These prior studies focused on samples with high rates of MCI (~50 %) and our study extends these findings to a slightly younger sample with a substantially lower prevalence of MCI. Importantly, after excluding individuals with MCI (13.7 % of the sample), the associations between Common EF and FW described above were nonsignificant, suggesting individuals with MCI may be driving many of the observed FW associations, though there was some evidence that Common EF remained associated with FW in the cingulum and uncinate (based on raw values). Thus, widespread associations between Common EF and FW may be primarily observed in samples with some MCI/ AD cases.

Another explanation for the lack of significance of many tracts after excluding MCI cases is reduced power in the smaller cognitively normal sample (with a restricted range in scores). However, associations between Common EF and FW were about half the magnitude as they were when including participants with MCI (Table 2 vs Table 4), suggesting that the differences are not simply due to a reduction in sample size. Thus, EF may be modestly related to FW in cognitively normal individuals, with associations observed potentially for frontally connected tracts (e.g., the uncinate and cingulum) in normal aging. This is consistent with the notion that FW metrics primarily capture neurodegeneration (Pasternak et al., 2012), with individual differences in FW not being strongly associated with cognitive abilities until after some neurodegeneration has taken place. 

Another recent study of cognitively normal older adults revealed similar negative associations between FW and fluid cognition (which included multiple EF tasks) in some, but not all, white matter tracts (including the cingulum and SLF) and no associations with FAFWcorr in any of the candidate tracts (Gullett et al., 2020). Therefore, associations between Common EF and FW may be relatively restricted to certain brain regions in adulthood and normal aging, but expand to others with age and/or early AD pathology. In either case, associations appear unique to FW rather than FAFWcorr or MDFWcorr metrics.

Because this was one of the first studies to examine these associations at the level of latent factors, we were also able to examine associations between variance unique to working memory tasks not already captured by Common EF (i.e., the Working Memory-Specific factor). Results in the full sample (including individuals with MCI) were consistent with an earlier study of younger adults in which working memory updating specific ability was not associated with white matter microstructure (Smolker et al., 2018), though the FA and MD metrics were not corrected for FW in this prior study. However, the Working Memory-Specific factor was associated with FAFWcorr in the IFG-Triangularis in the primary analysis (including participants with MCI) and with FAFWcorr in three tracts (IFG-Triangularis, inferior and superior longitudinal fasciculus) after excluding participants with MCI.

It will be important to further examine these novel associations with working memory-specific to understand these positive associations with FAFWcorr, including why some associations were only observed in cognitively normal subjects. Importantly, there was little evidence for group-level differences in FAFWcorr across cognitively normal and MCI subjects (see Table S3), suggesting the lack of association in the full sample was not driven by MCI subjects having lower FAFWcorr. Rather, the individual differences captured by microstructural measures may be more subtle and shed light on individual variability in normal aging. That is, while FW metrics capture neurodegeneration or axonal degradation (and therefore relate to cognitive ability after some atrophy has taken place), microstructural measures may tell us more about normal function and/or may be of use in predictive studies. This would be consistent with prior work showing FW was strongly associated with neurodegeneration (e.g., hippocampal volume) but FAFWcorr in the fornix interacted with hippocampal volume to predict future executive function decline (Archer et al., 2020). 

Regardless, these findings highlight the importance of considering the role of MCI in these associations and separately evaluating cognitive correlates of white matter microstructure within cognitively normal individuals. Furthermore, because earlier work has suggested that working memory-specific operations may be more associated with subcortical brain regions such as gating in the basal ganglia (Friedman and Miyake, 2017), it will be interesting to further probe these associations with FW and white matter in other tracts beyond those cortical tracts examined here.

Another goal of the study was to examine whether an indicator of cognitive reserve (age 20 general cognitive ability) moderates associations between EFs and white matter microstructure. For example, earlier work in this VETSA sample has demonstrated that individuals with low general cognitive ability at age 20 demonstrated stronger associations between hippocampal volume and memory at mean age 56 (Vuoksimaa et al., 2013). The present results indicated no evidence for such interactions for Common EF. Moderating effects of cognitive reserve may be only apparent in the context of disease- or age-related pathology. Although FW in all tracts was significantly associated with age, the cross-sectional nature of this study makes it difficult to determine whether this association reflects age-related neuropathology or instead reflects pre-existing individual differences or other factors beyond age-related neuropathology. Indeed, white matter abnormalities have been observed in wave 2 of VETSA (mean age 62) (FennemaNotestine et al., 2016; Sanderson-Cimino et al., 2021), suggesting some pathology was present in at least some subjects, yet a significant interaction with age 20 cognitive ability was not observed. Alternatively, or additionally, moderation of FAFWcorr and MDFWcorr by cognitive reserve may be possible in samples with greater rates of impairments (MCI or AD), as EFs appear more related to these measures in AD patients (Ji et al., 2017).

While associations between EF factors and FW differed after removing MCI subjects from these analyses (i.e., Table 2 vs Table 4), there were no group differences between MCI and cognitively normal subjects on our index of young adult cognitive reserve (p =.263). The fact that our MCI subjects had comparable levels of young adult cognitive reserve to cognitively normal subjects, but appeared to show much stronger associations between Common EF and FW, adds further support to the idea that associations between EF and FW differ in normal versus pathological aging. It may therefore be interesting to examine moderating effects of cognitive reserve within samples of MCI cases only, though it will be necessary to do so in a sample with a greater number of MCI subjects.

8.1. Strengths and weaknesses
The comprehensive assessment of EFs in VETSA allowed for the examination of associations between EF and white matter microstructure using a latent variable approach that isolated Common EF variance from Working Memory-Specific variance at a time in early old age when most individuals were cognitively normal. This study also represents one of the first examinations into the role of cognitive reserve in these associations. Some weaknesses of the study include the fact that all participants are men, and the vast majority are non-Hispanic and White. It will be important to evaluate these associations in more diverse samples.

Additionally, we used established white matter tract templates (Archer, Vaillancourt, & Coombes, 2018) and a recently available white matter tract atlas that provides strong coverage of the brain, replicating prior associations between EF and FW in older adults (Archer et al., 2020). While our results showed consistent associations between the Common EF factor and extracellular (FW) but not intracellular metrics (FAFWcorr and MDFWcorr), it is still unclear what specific cellular processes contribute to each variable. Better understanding the cognitive correlates of FW, FAFWcorr, and MDFWcorr will help shed light on the nature of these measures, and their role as predictors and indicators of aging, but additional studies are also needed to quantify what gives rise to individual differences in these measures.

Finally, this study is cross-sectional and cannot speak to whether these associations reflect pre-existing associations between Common EF and FW, or if these effects are specific to aging. Findings from one existing study suggest that baseline levels of FW are also associated with longitudinal changes in EF, though this sample was about 10 years older than the present sample and most individuals were diagnosed with MCI or dementia (Maillard et al., 2019). In our study, age was consistently associated with all free water measures (and some white matter measures), suggesting that the free water measures examined here are sensitive to age. The correlations with age may be relatively low estimates given the narrow age range of our sample (~10 years). In any case, it will be necessary to examine these associations in early adulthood and middle age to determine whether associations between FW and EF reflect age-related changes in EF or perhaps whether they account for individual differences in EF across the lifespan.

8.2. Concluding remarks

EF are complex cognitive control abilities that are highly relevant to aging. This study sheds light on the neural underpinnings of common and specific components of EF by demonstrating that FW measures across many cortical white matter tracts are associated with individual differences in Common EF abilities. By contrast, working memory-specific abilities were associated with FAFWcorr, but only after excluding individuals with MCI from the analyses. Associations between Common EF ability and FW were not moderated by cognitive reserve in the current investigation, but it will be important to consider whether these factors may contribute more strongly to associations between EFs and white matter in later stages of aging or progression toward cognitive decline or dementia.

Credit authorship contribution statement

Daniel E. Gustavson: Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing. Derek B. Archer: Data curation, Visualization, Writing – original draft, Writing – review & editing. Jeremy A. Elman: Data curation, Writing – review & editing. Olivia K. Puckett: Data curation, Writing – review & editing. Christine Fennema-Notestine: Data curation, Methodology, Writing – review & editing. Matthew S. Panizzon: Data curation, Writing – review & editing. Niranjana Shashikumar: Data curation, Writing – review & editing. Timothy J. Hohman: Methodology, Writing – review & editing. Angela L. Jefferson: Writing – review & editing. Lisa T. Eyler: Methodology, Writing – review & editing. Linda K. McEvoy: Methodology, Writing – review & editing. Michael J. Lyons: Funding acquisition, Writing – review & editing. Carol E. Franz: Funding acquisition, Methodology, Supervision, Project administration, Writing – review & editing. William S. Kremen: Funding acquisition, Methodology, Supervision, Project administration, Writing – review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Acknowledgments

This research was supported by Grants R03 AG065643, R01 AG050595, R01 AG076838, R01 AG022381, P01 AG055367, K01 AG073584, and K08 AG047903 from the National Institutes of Health. Publication of this article was funded by the University of Colorado Boulder Libraries Open Access Fund.

The content of this manuscript is the responsibility of the authors and does not represent official views of NIA/NIH, or the Veterans' Administration. Numerous organizations provided invaluable assistance in the conduct of the VET Registry, including the U.S. Department of Veterans Affairs, Department of Defense; National Personnel Records Center, National Archives and Records Administration; Internal Revenue Service; National Opinion Research Center; National Research Council, National Academy of Sciences; the Institute for Survey Research, Temple University. The authors gratefully acknowledge the continued cooperation of the twins and the efforts of many staff members.

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