Associations Among Executive Function Abilities, Free Water, And White Matter Microstructure in Early Old Age Part 2

Moreover, existing studies have focused on samples with high proportions of subjects with MCI and/or AD, and it will be important to examine whether these associations still exist when focusing on a sample more representative of the population (i.e., MCI rates closer to 10–15 %), or when focusing solely on cognitively normal adults.

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Finally, we should pay attention to keeping a happy mood and avoiding excessive stress and anxiety. People who are in a good mood are more likely to maintain brain sharpness and memory, so we should maintain a positive attitude and avoid the influence of negative emotions.

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Finally, when examining the neural underpinnings of EFs, we considered the role of young adult cognitive reserve, which we defined as an individual's total cognitive resources during early adulthood (Kremen et al., 2022). 

Theories of cognitive reserve suggest that some individuals do not exhibit the cognitive or functional deficits expected based on their brain pathology (Barulli and Stern, 2013; Stern, 2012, 2013; Whalley, Deary, Appleton, & Starr, 2004), which might manifest as reduced associations between EF and white matter (and FW) in individuals with high reserve. 

We focus on young adulthood as a time when aging effects will have had essentially no impact on cognitive capacity. While some studies suggest that cognitive decline in EFs is sensitive to cognitive reserve (McKenzie et al., 2020; O'Shea et al., 2015; Roldan-Tapia, Garcia, Canovas, & Leon, 2012), little research has examined whether cognitive reserve moderates associations between EFs and brain (Krch et al., 2019). 

In a prior study of individuals in the same sample as the present study, the association between hippocampal volume and episodic memory was higher among individuals with lower levels of cognitive reserve; those with higher reserve were more resilient against the potentially deleterious effects of hippocampal atrophy (Vuoksimaa et al., 2013). 

However, whether cognitive reserve moderates associations between Common EF and white matter microstructure has not been examined. If EFs are sensitive to cognitive reserve, then EF abilities should be more strongly associated with neuroimaging measures in individuals with low reserve. 

Such findings would indicate that individuals with high reserve are more able to retain the same level of EF ability in the face of neurodegeneration compared to individuals with low reserve.

5. The current study

In the current study, we used data from the third wave of the Vietnam Era Twin Study of Aging (VETSA) sample (mean age 68) to examine associations between EF and white matter microstructure using 3 metrics: FW, FAFWcorr, and MDFWcorr. 

We predicted that EFs would be uniquely associated with FW within multiple cortical white matter tracts, consistent with earlier work (Archer et al., 2020). Less is known about the associations between working memory-specific abilities and white matter, so these analyses are considered exploratory. 

Finally, we examined whether associations between EFs and white matter would be moderated by cognitive reserve based on general cognitive ability assessed when subjects were about age 20 (over 45 years before the assessments of EFs and white matter described here) (Kremen et al., 2022).

6. Methods

6.1. Subjects

Data analyses focus on 489 male twins who participated in the third wave of the longitudinal VETSA project. VETSA participants were recruited randomly from a previous study of members of the Vietnam Era Twin Registry (Tsuang, Bar, Harley, & Lyons, 2001). 

All individuals served in the United States military at some time between 1965 and 1975, but nearly 80 % reported no combat exposure. Sample characteristics are displayed in Table 1, alongside descriptive statistics for executive function tests and our index of cognitive reserve. 

Participants are generally representative of American men in their age cohort concerning health, education, and lifestyle characteristics (Kremen et al., 2011; Kremen et al., 2006; Schoenborn & Heyman, 2009).

All wave 3 MRI data were collected at the University of California, San Diego (UCSD). All participants gave their written informed consent before participation, and the study protocol was approved by the Institutional Review Boards at all participating institutions.

6.2. Measures

6.2.1. Executive function

EF abilities were measured with 6 tasks spanning prepotent response inhibition, task-set switching, and working memory span domains. Inhibition was assessed with the Stroop task (Golden and Freshwater, 2002; Stroop, 1935). 

Shifting was assessed using the (a) Trail Making Test switching trial and (b) the category-switching subtest for verbal fluency from the Delis-Kaplan Executive Function System (D-KEFS) (DKEFS; Delis, Kaplan, & Kramer, 2001). All measures of inhibition and switching were adjusted for appropriate baseline conditions. Working memory span was assessed with the letter number sequencing and digit span subtests of the Wechsler Memory Scale-III (Wechsler, 1997) and the reading span test (Daneman and Carpenter, 1980). 

Before analyses, all cognitive scores in the full VETSA wave 3 study were adjusted for practice effects, leveraging data from attrition replacement participants who completed the task battery for the first time at wave 2 or wave 3 to estimate the increase in performance expected in returnees who completed the tests two or more times (Elman et al., 2018).

Our model of EF was initially validated in waves 1 and 2 of VETSA (Gustavson et al., 2018a; Gustavson et al., 2018b) and includes 2 latent factors: a "Common EF" latent factor (based on performance across all 6 tests) and a "Working Memory-Specific" factor (based on additional variance in the 3 working memory span tests not already captured by the latent factor). 

Prior waves also administered an additional test (the AXContinuous Performance Test), but this was not included in the wave 3 assessment due to time constraints. Preliminary analyses indicated the latent factor model of EF continued to fit the data well in this subsample of individuals who completed the MRI assessment at wave 3 of VETSA, so we did not fit any additional confirmatory models of EF. 

Additionally, our confirmatory model of EF is supported by a recent study that fits a latent growth model of Common EF and Working Memory-Specific factors across all 3 waves of VETSA in the full sample (Gustavson et al., 2022b).

6.2.2. General cognitive ability (age 20)

General cognitive ability-our index of cognitive reserve-was assessed in young adulthood when VETSA participants were first inducted into the military (mean age 20 years) with the 100-item multiple-choice Armed Forces Qualifications Test (AFQT; Bayroff and Anderson, 1963). 

The AFQT demonstrates a strong correlation (r = 0.84) with measures of intelligence such as the Wechsler Adult Intelligence Scale (Lyons et al., 2009) and consists of 4 subscales assessing vocabulary, arithmetic ability, tool/mechanical knowledge and reasoning, and visual-spatial ability. 

AFQT scores also correlate moderately with the self-reported number of years of education (r = 0.31), but here we capitalized on having a far more precise index than years of education, i.e., a direct measure of overall cognitive ability from young adulthood (Kremen et al., 2022). AFQT percentile scores were converted into scores. Thus, the mean of 0.34 (see Table 1) is approximately equivalent to an IQ of 105.

6.3. Image acquisition

Images were acquired with two GE 3 T Discovery 750 × scanners (GE Healthcare, Waukesha, WI, USA) with eight-channel phased array head coils. The imaging protocol included a sagittal 3D fast spoiled gradient echo (FSPGR) T1-weighted (T1w) volume optimized for maximum gray/ white contrast (TE = 3.164 msec, TR = 8.084 msec, TI = 600 msec, flip angle = 8◦, matrix = 256x192, in-plane resolution = 1x1 mm, slice thickness = 1.2 mm, slices = 172). 

Diffusion data were acquired with a multi-shell diffusion-weighted scan (54-directions, b values = [0 (x3), 666 (x6), 1333 (x15), 2666 (x15), 4000 (x15)] s/mm2, integrated with a pair of b = 0 images with opposite phase-encode polarity, TR = 6600 msec, TE = 81.1 msec, matrix = 96x96, in-plane resolution = 2.5x2.5 mm, slice thickness = 2.5 mm, 54 slices).

6.4. Image processing

Data were preprocessed using the PreQual pipeline to correct for distortions/motions and eddy currents (Cai et al., 2021; Schilling et al., 2019). The multi-shell data was then subset to a single shell (b = 1333) and inputted into DTIFIT to calculate FA and MD for each participant. The single shell data was also input into MATLAB code to calculate FW, FAFWcorr, and MDFWcorr (Jenkinson et al., 2012; Pasternak et al., 2009). 

In short, this code leverages a variational network framework to split the diffusion image into a bi-tensor model – one which is the FW contamination, and the other is the tissue compartment. New, FW-corrected metrics (FAFWcorr, MDFWcorr) can then be quantified. Importantly, the FW metric itself can also be leveraged in analysis. 

A standard space representation for the FW, FAFWcorr, and MDFWcorr maps was created by non-linearly registering the DTIFIT-derived raw FA image and applying this transform to the FW-corrected maps (Avants, Epstein, Grossman, & Gee, 2008).

Following standardization, mean FW, FAFWcorr, and MDFWcorr values were quantified within several well-established white matter tractography templates for each imaging session (Archer et al., 2019; Archer et al., 2020; Brown et al., 2017). 

These templates included the cingulum bundle, fornix, superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), and uncinate fasciculus as well as homologous transcallosal connections of the inferior frontal gyrus (IFG) pars opercularis, IFG pars orbitalis, IFG pars triangularis, inferior temporal gyrus, medial frontal gyrus, and middle frontal gyrus (see Fig. 1 for a visual representation of all 11 tracts). 

We focused on this set of 11 cortical white matter tracts which have previously been linked to executive function and subjective cognitive decline in recent studies (Archer et al., 2021; Archer et al., 2020). 

Additionally, we included a final set of measures capturing FW, FAFWcorr, and MDFWcorr across all white matter tracts in the brain (including both cortical and subcortical tracts) to capture global FW and white matter microstructure as our recent work has highlighted strong genetic influences shared across all white matter tracts (Gustavson et al., 2019).

6.5. Data analysis

Phenotypic correlational and regression analyses were conducted in Mplus version 8.3 (Muth´en and Muth´en, 1998–2017), which accounts for missing observations using full-information maximum likelihood. The significance of individual parameter estimates was established with standard error-based 95 % confidence intervals and confirmed with χ2 difference tests by fixing that parameter to zero. 

Standard errors and chi-squares were adjusted for clustering within families (twin pairs), and the χ2 difference tests were appropriately scaled based on scaling factors provided in the Mplus output (Satorra & Bentler, 2001).

To examine associations between EFs and white matter microstructure, we fit a series of regression models in which the latent EF factors were regressed on the candidate white matter measures (one model per white matter measure). 

The following covariates were included in all analyses: age (M = 67.53, SD = 2.63, Range = 61.37 to 71.71), diabetes status (22.3 % yes), hypertension status (55.6 % yes), a variable capturing whether individuals were Hispanic and/or nonwhite (11.7 % yes), and two variables capturing scanner differences (one of the two scanners' software was upgraded during the study, so orthogonal contrasts were created to account for potential differences across the three scanner/software groups). 

Diabetes and hypertension status were based on whether the participant (1) reported being diagnosed by a doctor, (2) reported that they were currently taking medication for diabetes or high blood pressure, and/or (3) reported whether they had high blood pressure on the day of testing (hypertension only).,

After identifying which FW and white matter measures were associated with EF factors, we fit additional regression models (one for each measure) in which age 20 general cognitive ability (AFQT) was added to the model. 

Both EF factors were regressed on AFQT scores, and an interaction term was added (AFQT * diffusion measure) for whichever EF factor was associated with that measure in prior analyses.

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