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Published in final edited form as: Brain Imaging Behav. 2024 Oct 1;18(6):1515–1523. doi: 10.1007/s11682-024-00945-z

Differential correlations of changes in in vivo neuroimaging markers of hippocampal volume and arteriolosclerosis with declining financial and health literacy in old age

Lei Yu 1,2, Tianhao Wang 1,2, Alifiya Kapasi 1,3, Melissa Lamar 1,4, Gary Mottola 5, Konstantinos Arfanakis 1,6,7, David A Bennett 1,2, Patricia A Boyle 1,4
PMCID: PMC11680459  NIHMSID: NIHMS2037309  PMID: 39352643

Abstract

Financial and health literacy is essential for older adults to navigate complex decision processes in late life. However, the neurobiological basis of age-related decline in financial and health literacy is poorly understood. This study aimed to characterize progression of neurodegenerative and vascular conditions over time, and to assess how these changes coincide with declining financial and health literacy in old age. Data came from 319 community-living older adults who were free of dementia at baseline, and underwent annual literacy assessments, as well as biennial 3-Tesla neuroimaging scans. Financial and health literacy was assessed using a battery of 32 items. Two in vivo neuroimaging markers of neurodegenerative and cerebrovascular conditions were used, i.e., hippocampal volume and the ARTS marker of arteriolosclerosis. A multivariate linear mixed effects model estimated the simultaneous changes in financial and health literacy, hippocampal volume, and the ARTS score. Over a mean of 7 years of follow-up, these older adults experienced a significant decline in financial and health literacy, a significant reduction in hippocampal volume, and a significant progression in ARTS score. Individuals with faster hippocampal atrophy had faster decline in literacy. Similarly, those with faster progression in ARTS also had faster decline in literacy. The correlation between the rates of hippocampal atrophy and declining literacy, however, was stronger than the correlation between the progression of ARTS with declining literacy. These findings suggest that neurodegeneration and, to a lesser extent, cerebrovascular conditions are correlated with declining financial and health literacy in old age.

Keywords: Financial and health literacy, Brain MRI, Hippocampal volume, Arteriolosclerosis

Introduction

Older adults are faced with some of life’s most challenging financial and health decisions that range from retirement spending, wealth transfer across generations, Medicare plans selection to end-of-life healthcare arrangements. Financial and health literacy, the ability to acquire, incorporate, and utilize financial and health knowledge, is essential for older adults to navigate these complex decision processes (Finucane et al., 2002; Lusardi & Mitchell, 2017). Alarming evidence suggests that financial and health literacy declines with advancing age (Angrisani et al., 2023; Kobayashi et al., 2015); this places older adults at great risk for poor decision making and jeopardizes their physical, financial, and psychological wellbeing (Baker et al., 2008; Lusardi & Tufano, 2015; Yu et al., 2021). Identifying factors that contribute to declining financial and health literacy in old age is urgently needed to facilitate strategies to promote healthy aging.

A key driver underlying the deterioration of many functional abilities in old age, financial and health literacy included, is accumulating disease pathology in the brain. Autopsy studies have reported that neurodegenerative (e.g., Alzheimer’s disease (AD) and cerebrovascular (e.g., strokes) pathologies, both very common in the aging brain, are implicated in poorer decision making (Kapasi et al., 2019, 2021). Similar findings also have been reported using in vivo neuroimaging markers of these pathologies (Duke Han et al., 2016; Lamar et al., 2020). However, as AD and other neurodegenerative proteinopathies commonly co-occur with vascular pathologies and both progress over time (Rahimi & Kovacs, 2014), the relative contribution of these conditions to age-related decline in financial and health literacy is not well understood. We previously reported that, among autopsied older adults, individuals with different profiles of change in financial and health literacy had varying levels of AD and transactive response DNA-binding protein 43 (TDP-43) but not vascular pathologies (Yu et al., 2020). Separately, regional cortical thinning, but not white matter hyperintensity burden, was associated with decline in financial and health literacy (Lamar et al., 2023). Together, these data lend support for a strong connection of neurodegeneration, but not vascular conditions, with declining literacy. Of note, in prior studies, measures of degeneration or vascular conditions were taken at one timepoint, either at autopsy or study baseline. As a result, how neurodegenerative and vascular conditions deteriorate over time, and how these changes coincide with declining financial and health literacy during the same period, have not been investigated.

To fill this knowledge gap, the current study expanded our investigation on the neurobiological basis of age-related decline in financial and health literacy by characterizing simultaneous changes in three longitudinal outcomes. Participants underwent annual financial and health literacy assessments, as well as biennial in vivo neuroimaging scans. Repeated measurements of financial and health literacy and in vivo neuroimaging markers of hippocampal volume and arteriolosclerosis were collected over an average of 7 years of follow-up. The two neuroimaging measures serve as in vivo markers for neurodegeneration and cerebrovascular conditions, respectively. Rates of change in all three outcomes and the correlations between these changes are estimated from a single model. We hypothesize that financial and health literacy, hippocampal volume, and vascular condition decline over time, and the rates of decline are correlated with one another. Further, based on prior data, we hypothesize that the correlation between the rate of hippocampal atrophy and declining literacy is stronger than the correlation of accumulating vascular disease and declining literacy.

Methods

Study participants

Data came from community-dwelling older adults participating in the Rush Memory and Aging Project (MAP). MAP is an epidemiologic cohort study of Alzheimer’s disease and other age-related chronic conditions (Bennett et al., 2012). The study primarily recruits residents of retirement communities throughout the greater Chicago metropolitan area. At enrollment, participants were free of known dementia and were followed annually until death. Participants are continuously enrolled to replenish those who die during the follow-up.

MAP started in 1997 and is ongoing. A decision making substudy was introduced in 2010, and systematic in vivo 3-Tesla (3T) MRI data collection started in 2012. For the current analyses, the first 3T MRI scan was used as our analytic baseline, and changes in both decision making and in vivo neuroimaging measures were captured by leveraging all the longitudinal data thereafter. At the time of these analyses in January 2024, there were 2,305 MAP participants who completed the baseline evaluation. A total of 376 participants had multiple MRI scans, of which 359 also had multiple decision making assessments. We further excluded participants who were either demented at the analytic baseline (N = 2) or who were scanned on multiple scanners during the follow-up visits (N = 38). As a result, the final analytic sample consists of 319 older adults. We compared MAP participants who were included in the current study with those who were not. On average, participants who were included were younger, had higher education, higher percentage of non-Latino White, less mild cognitive impairment, and less deaths. No difference was found in female/male composition (Supplementary Table 1).

Financial and health literacy

As part of the decision making substudy, annual financial and health literacy assessments were administered to MAP participants. The testing battery includes 32 questions, of which 23 were questions on financial literacy and 9 were questions on health literacy (James et al., 2012). The financial literacy questions, adapted from the Health and Retirement Survey, were designed to assess knowledge of financial institutions, financial concepts, numeracy, financial management and investment. Separately, the health literacy questions assess knowledge of health information and concepts, which cover topics including Medicare, following prescription instructions, flu vaccination, leading causes of heart disease and stroke, and assessing drug risk.

The answer choice was either multiple choice or true/false. Correct answers to the financial questions were tallied and then divided by 23. The resulting score represents the percentage of financial literacy questions answered correctly, with higher scores indicating higher financial literacy. Health literacy score was computed similarly. A summary score for financial and health literacy was obtained by averaging the two domain-specific literacy scores.

Brain MRI data acquisition and processing

Participants underwent 3T MRI scans every 2 years, and longitudinal high-resolution T1-weighted structural, T2-weighted fluid-attenuated inversion recovery (FLAIR), and diffusion tensor imaging (DTI) data were collected. Briefly, the T1-weighted data were acquired using a 3D magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence, the T2-weighted FLAIR data were acquired using a 2D inversion recovery fast spin-echo sequence, and the DTI data were acquired using a 2D spin-echo echo-planar diffusion-weighted sequence, as previously described (Fleischman et al., 2023). We note that 3T MRI data were collected on 2 sites, one with a Philips scanner and the other with a Siemens scanner (Supplementary Table 2). For consistency, the current analyses only included participants who were scanned on the same scanner over time.

Hippocampus was segmented from T1-weighted data based on multi-atlas segmentation (Iglesias & Sabuncu, 2015). The left and right hippocampal volumes were averaged and normalized by the total intracranial volume (ICV). The measure is unitless because of the ICV correction.

ARTS is a novel in vivo neuroimaging classifier of arteriolosclerosis pathology, as previously described (Makkinejad et al., 2021). ARTS was trained using machine learning based on ex-vivo MRI, demographic and pathology data and was translated to in vivo. Since multiple MRI-based features were used to derive ARTS, the measure is unitless. Higher ARTS scores indicate higher likelihood of having moderate or severe arteriolosclerosis. Prior analyses suggest that ARTS features a high accuracy in predicting arteriolosclerosis in-vivo as well as an excellent scan-rescan reproducibility. ARTS is fully automated and is available at https://www.nitrc.org/projects/arts.

Details on additional covariates are provided in the Supplementary Methods.

Statistical analysis

We fit a trivariate linear mixed effects model to simultaneously model the longitudinal changes in financial and health literacy, hippocampal volume, and ARTS score (Supplementary Methods). The parameterization follows a similar structure of a regular linear mixed effects model with random intercept and slope (Laird & Ware, 1982), but the trivariate model had 3 sets of random intercepts and random slopes, one for each outcome. The correlation of changes in hippocampal volume and financial and health literacy, and separately the correlation of change in ARTS and literacy were extracted from the resulting 6 × 6 covariance matrix of the random effects. Statistical comparison of the correlations of changes was conducted using bootstrapping (Supplementary Methods).

Results

Participants had a mean age of 80 years, and a mean years of education 16 years. Over 75% were female, and over 90% were non-Latino White (Table 1). At the analytic baseline, 41 (12.9%) had mild cognitive impairment and the remaining were cognitively unimpaired. Older age was correlated with lower financial and health literacy (Pearson r = −.23, p < .001), lower hippocampal volume (Pearson r = −.42, p < .001), and higher ARTS score (Pearson r = .49, p < .001). Higher education was correlated with higher literacy (Pearson r = .31, p < .001), lower ARTS score (Pearson r = −.19, p < .001), but we did not observe a significant correlation between education and hippocampal volume (p = .70). Compared to female participants, male participants on average scored 7% points higher in financial and health literacy (Means = 77.9 vs. 71.1, t306 = 4.05, p < .001). Male participants had lower hippocampal volume (Means = 2.8 vs. 3.0, t317 = −4.45, p < .001) and lower ARTS score (Means = −0.24 vs. −0.09, t140 = −6.99, p < .001) than females.

Table 1.

Characteristics of the study participants

Characteristics Mean (SD) or N (%)
Age, years 79.7 (7.1)
Female 247 (77.4%)
Non-Latino White 298 (93.4%)
Education, years 15.9 (2.8)
Mild cognitive impairment 41 (12.9%)
Financial and health literacy, % correct 72.7 (12.7)
Hippocampal volume 3.0 (0.30)
ARTS −0.12 (0.19)
Length of follow-up, year 7.2 (2.4)
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Longitudinal changes in literacy, hippocampal volume, and ARTS

Over a mean of 7 years of follow-up, we observed an overall decline in financial and health literacy, a reduction in hippocampal volume, and an increase in ARTS scores (Fig. 1). The estimates from a trivariate linear mixed effects model suggested that, adjusted for age, sex and education, the mean rate of decline in financial and health literacy was 0.06 SD unit (Standard error [SE] = 0.009, p < .001), equivalent to approximately 1% point per year. Hippocampal volume declined at a mean rate of 0.13 SD unit (SE = 0.007, p < .001) per year, and the ARTS score increased at a mean rate of 0.12 SD unit per year (SE = 0.009, p < .001).

Fig. 1.

Fig. 1

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Changes in financial and health literacy, hippocampal volume, and ARTS. The figure illustrates the longitudinal changes in financial and health literacy (left panel), hippocampal volume (middle panel), and the ARTS score (right panel) of the study participants. Each panel is a spaghetti plot where individual wiggly line represents repeated measures that were recorded from each participant over time, with x-axis being time in year since baseline and y-axis being the corresponding measure

Older age was associated with faster decline in financial and health literacy, faster decline in hippocampal volume, and faster progression of ARTS. We did not observe an association of sex or education with change in any of the three outcomes (Table 2).

Table 2.

Changes in financial and health literacy, hippocampal volume, and ARTS

Predictors Outcome Estimate SE p
Mean rate of change ARTS 0.118 0.005 < 0.001
Mean rate of change Hippocampal volume −0.135 0.007 < 0.001
Mean rate of change Literacy −0.064 0.009 < 0.001
Age ARTS 0.004 0.001 < 0.001
Age Hippocampal volume −0.004 0.001 < 0.001
Age Literacy −0.005 0.001 < 0.001
Male sex ARTS 0.010 0.010 0.316
Male sex Hippocampal volume 0.015 0.014 0.283
Male Sex Literacy −0.021 0.019 0.271
Education ARTS −0.0003 0.001 0.861
Education Hippocampal volume 0.002 0.003 0.292
Education Literacy 0.002 0.003 0.544
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The statistics in Table 2 were obtained using a single trivariate linear mixed effects model, which showed the adjusted annual rates of change in ARTS, hippocampal volume, and literacy, as well as the associations of age, sex with these changes. Specifically, after the adjustment for age, sex, and education, the ARTS score increased at a mean rate of 0.12 standardized unit per year, hippocampal volume declined at a mean rate of 0.13 standardized unit per year, and financial and health literacy declined at a mean rate of 0.06 standardized unit per year. Older age was associated with a faster rate of increase in the ARTS score (Estimate: 0.004), a faster rate of decline in hippocampal volume (Estimate: −0.004), and a faster rate of decline in literacy (Estimate: −0.005). We did not observe associations of sex or education with change in any of the three longitudinal outcomes (all ps > 0.05)

Changes in neuroimaging markers with change in literacy

Examination of the covariance matrix of the random effects revealed that person-specific change in hippocampal volume was positively correlated with change in financial and health literacy and change in ARTS was inversely correlated with change in literacy (Fig. 2). Specifically, the estimated correlation between random slopes for hippocampal volume and literacy was 0.63 (bootstrapped 95% confidence interval [CI] = 0.40 to 0.83), and − 0.44 between ARTS and literacy (bootstrapped 95% CI = −0.67 to −0.24). In addition, we observed that change in hippocampal volume and change in ARTS were also correlated, such that older adults with faster decline in hippocampal volume had faster progression of ARTS (estimated correlation = −0.29, bootstrapped 95% CI = −0.53 to −0.06).

Fig. 2.

Fig. 2

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Correlations of changes in financial and health literacy, hippocampal volume, and ARTS. Along the diagonal are univariate kernel densities for model-derived person-specific slopes of change in literacy, change in hippocampal volume, and change in ARTS. The scatter plots on the lower left corner illustrate the pairwise correlations between these slopes of change. On the upper right corner are the corresponding density plots. The values on the x- and y-axis are estimates of model-derived slopes for change in standardized literacy score, hippocampal volume, and ARTS, with an exception of the figure on the top left corner where the values on the y-axis represent estimates of univariate kernel densities

Next, we examined whether the correlation between change in hippocampal volume and change in literacy differed from that of change in ARTS and change in literacy. Since the two correlations were in the opposite direction, for comparison purposes, the paired difference from each bootstrapped sample was computed using the absolute values of the estimated correlations. As shown in Fig. 3, the bootstrapped distribution of the paired difference between correlation of changes in ARTS and literacy and the correlation of changes in hippocampal volume and literacy are shifted toward left from 0. A test for location rejects the mean equal to 0 (t = −25.1, p < .001), suggesting that the correlation between change in hippocampal volume and change in financial and health literacy was significantly stronger than the correlation between change in ARTS and change in literacy.

Fig. 3.

Fig. 3

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Comparison of the correlation between change in hippocampal volume and change in literacy and the correlation between change in ARTS and change in literacy. The histogram in blue illustrates the bootstrapped distribution of the correlation between change in hippocampal volume and change in literacy. The histogram in cyan illustrates the bootstrapped distribution of correlation between change in the ARTS scores and change in literacy. Finally, the histogram in red represents the bootstrapped distribution of the paired difference of the absolute values of the 2 correlations, i.e., |Correlation (slopes of change in ARTS, slopes of change in literacy)|-|Correlation (slopes of change in hippocampal volume, slopes of change in literacy)|. It is evident that the mean of the paired difference is shifted significantly left from zero, which suggests that the correlation between change in hippocampal volume and change in literacy is stronger than the correlation between change in ARTS and change in literacy

Finaly, to explore whether our findings on declining literacy were reflective of a specific functional ability or macro-level cognition, we conducted secondary analyses by comparing the results for financial and health literacy with the results for a global cognitive measure derived using a battery of 19 cognitive tests (Boyle et al., 2021) from the same group of participants during the same study period. Different correlation patterns emerged. Specifically, the correlation between hippocampal atrophy and declining cognition was 0.67 (bootstrapped 95% CI = 0.50 to 0.84), which was similar to the correlation between hippocampal atrophy and declining literacy. Interestingly, the correlation between ARTS and declining cognition was − 0.65 (bootstrapped 95% CI = −0.79 to −0.48), much stronger than the correlation between ARTS and declining literacy. Together, these results indicate that the correlation between the longitudinal imaging marker of vascular conditions and declining cognition is different from declining literacy. Additional studies are warranted to confirm these findings.

Discussion

In this study, we leveraged longitudinal data from decision making assessments and 3T MRI scans on over 300 predominantly cognitively unimpaired community living older adults to examine how financial and health literacy decline as neurodegenerative and cerebrovascular conditions accumulate in old age. We found that older adults experienced a significant decline in financial and health literacy, a significant reduction in hippocampal volume, and a significant progression in ARTS score. Individuals with faster hippocampal atrophy had faster decline in financial and health literacy. Similarly, those with faster progression of ARTS score also had faster decline in literacy. Of note, the correlation between the rate of hippocampal atrophy and the rate of decline in literacy is stronger than that of ARTS. Together, these data provide novel insights into the neuropathologic underpinnings of declining financial and health literacy and related decision making abilities in old age.

Poor decision making in Alzheimer’s dementia is well documented (Danesin et al., 2022; Marson et al., 2009; Widera et al., 2011). Considering Alzheimer’s dementia is a heterogeneous clinical syndrome attributable to a host of neuropathologies that are common in aged brains (Schneider et al., 2007), a question remains as to the extent to which neurodegeneration, as compared to cerebrovascular disease, contributes to decline in decision making capabilities. Literature on the neural determinants of decision making capabilities in old age highlight atrophy in select brain regions. For example, lower angular gyrus volume is associated with poorer financial capacity in older adults with amnestic MCI (Griffith et al., 2010), and lower medial frontal cortical volume is implicated in poorer financial capacity in mild AD (Stoeckel et al., 2013). Cortical thickness of right temporal regions is linked to self-awareness for financial decision making abilities in older adults (Sunderaraman et al., 2022). We previously reported that lower volumes in right medial temporal regions are associated with higher scam susceptibility among dementia-free older adults (Duke Han et al., 2016), and cortical thickness of grey matter regions vulnerable to AD are related to both baseline level and subsequent change in financial and health literacy (Lamar et al., 2023). Together, while specific brain regions involved vary, these studies reveal converging evidence that neurodegeneration, a key contributor of brain atrophy, plays an important role in declining decision making capabilities. This is further confirmed in AD biomarker and autopsy studies where Alzheimer’s and other neurodegenerative proteinopathies are related to financial capacity, decision making, scam susceptibility, and financial and health literacy (Kapasi et al., 2019, 2021; Vassilaki et al., 2022; Yu et al., 2020). In the current study, we observed a high correlation between the rate of hippocampal atrophy and the rate of declining literacy over time, which is highly consistent with these findings.

The implication of vascular conditions in declining decision making capabilities is more obscure. Decision making is traditionally thought of as closely linked to executive functions, which has led some to hypothesize that decision making capabilities may be preferentially vulnerable to vascular insults. Recent neuroimaging and neuropathology data, however, have failed to consistently demonstrate the connections between brain vascular measures and decision making related outcomes. By examining multiple white matter integrity measures derived using tract-based spatial statistics from the DTI data (i.e., FA, the trace of the diffusion tensor, axial, and radial diffusivity), we previously reported that white matter integrity in right hemisphere tracts, temporal-parietal pathways in particular, was associated with scam susceptibility (Lamar et al., 2020). However, it is unclear whether the association is driven by vascular disease. Our previous study did not find an association of a neuroimaging-based white matter hyperintensity measure with subsequent decline in financial and health literacy. The longitudinal neuroimaging data in the current study revealed that arteriolosclerosis progresses over time in community living older adults, and the rate of progression was indeed correlated with rate of change in financial and health literacy. This result provides empirical evidence for the involvement of cerebrovascular conditions in declining decision making capabilities. Interestingly, the correlation between vascular disease with declining literacy is not as strong as degeneration.

The current findings on neurobiological correlates of declining financial and health literacy suggest that effective dementia/vascular disease prevention is likely to alleviate adverse impacts of accumulating brain pathologies on decision making. Separately, this also raises a broader question about what happens to decision making capabilities in aging, i.e., whether declining decision making capabilities are inevitable as people age. Emerging evidence from cognitive aging research suggests that, barring the impacts of neuropathologies in aged brains, older adults are able to maintain late life cognition. Decision making capabilities are higher order and complex functions that involve dynamic interplays between brain/cognitive functions, psychosocial, and emotional factors. We previously reported that psychological wellbeing and engagement in late life cognitive activities help sustain financial and health decision making among old adults with low cognitive abilities (Glover et al., 2021, 2023). Future studies are warranted to comprehensively assess the burdens of age-related neuropathologies on declining decision making capabilities, and to identify factors that may maintain or boost decision making capabilities in the presence of accumulating pathologies.

Strengths of the current study include the use of high-quality longitudinal decision making and neuroimaging data collected following uniform and systematic protocols. This, coupled with a high follow-up rate among the MAP participants (over 90%), allows us to simultaneously evaluate changes in multiple outcomes of interest over time with high confidence. A novel neuroimaging classifier specifically detecting arteriolosclerosis in vivo adds analytic power in estimating progression of vascular disease and examining its association with declining financial and health literacy. The limitations are noted as well. Most participants are older non-Latino White adults with high education, and the current findings may not be generalizable to broader aging populations. We simultaneously modeled three longitudinal outcomes to explore the interrelationship between changes in hippocampal volume, ARTS, and literacy over time. This approach nonetheless does not accommodate certain analyses such as testing for interaction of ARTS and hippocampal volume on literacy. Separately, the current analyses only include an imaging classifier for arteriolosclerosis; other known vascular conditions were not considered but will be examined in future studies as data allow.

Conclusions

In conclusion, our findings suggest that accumulating neurodegeneration and, to a lesser extent, cerebrovascular conditions are correlated with declining financial and health literacy in old age.

Supplementary Material

supplementary material

Acknowledgements

We acknowledge the contributions of all the participants of the Rush Memory and Aging Project. We would also like to thank the investigators and staff at the Rush Alzheimer’s Disease Center.

Funding

This study was funded by grants from the National Institute on Aging (R01AG17917, R01AG33678, K01AG075177, R01AG064233, and R01AG34374) and the National Institute of Neurological Disorders and Stroke (U01NS100599).

Footnotes

Competing interests The authors declare no competing interests.

Ethics approval The parent study and both decision making and neuroimaging substudies were each approved by an institutional review board of the Rush University Medical Center.

Consent for publication All authors have approved of publishing this manuscript which has been read thoroughly by all of them.

Consent to participate A written informed consent was acquired from each participant.

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11682-024-00945-z.

Data availability

Data used in this study can be requested for research purposes via the Rush Alzheimer Disease Center Research Resource Sharing Hub at https://www.radc.rush.edu.

References

  1. Angrisani M, Burke J, Lusardi A, & Mottola G (2023). The evolution of financial literacy over time and its predictive power for financial outcomes: Evidence from longitudinal data. Journal of Pension Economics & Finance, 22(4), 640–657. [Google Scholar]
  2. Baker DW, Wolf MS, Feinglass J, & Thompson JA (2008). Health literacy, cognitive abilities, and mortality among elderly persons. Journal of General Internal Medicine, 23(6), 723–726. 10.1007/s11606-008-0566-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bennett DA, Schneider JA, Buchman AS, Barnes LL, Boyle PA, & Wilson RS (2012). Overview and findings from the rush memory and Aging Project. Current Alzheimer Research, 9(6), 646–663. 10.2174/156720512801322663 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Boyle PA, Wang T, Yu L, Wilson RS, Dawe R, Arfanakis K, & Bennett DA (2021). To what degree is late life cognitive decline driven by age-related neuropathologies? Brain, 144(7), 2166–2175. 10.1093/brain/awab092 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Danesin L, Giustiniani A, Arcara G, & Burgio F (2022). Financial decision-making in neurological patients. Brain Sci, 12(5). 10.3390/brainsci12050529 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Duke Han S, Boyle PA, Yu L, Arfanakis K, James BD, Fleischman DA, & Bennett DA (2016). Grey matter correlates of susceptibility to scams in community-dwelling older adults. Brain Imaging Behav, 10(2), 524–532. 10.1007/s11682-015-9422-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Finucane ML, Slovic P, Hibbard JH, Peters E, Mertz CK, & MacGregor DG (2002). Aging and decision-making competence: An analysis of comprehension and consistency skills in older versus younger adults considering health-plan options. Journal of Behavioral Decision Making, 15(2), 141–164. 10.1002/bdm.407 [DOI] [Google Scholar]
  8. Fleischman DA, Arfanakis K, Zhang S, Leurgans SE, Barnes LL, Bennett DA, & Lamar M (2023). Acculturation in Context and Brain Health in older latino adults: A diffusion Tensor Imaging Study. Journal of Alzheimer’s Disease, 95(4), 1585–1595. 10.3233/jad-230491 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Glover CM, Yu L, Stewart CC, Wilson RS, Bennett DA, & Boyle PA (2021). The Association of Late Life Cognitive activity with Healthcare and Financial decision-making in Community-Dwelling, nondemented older adults. The American Journal of Geriatric Psychiatry: Official Journal of the American Association for Geriatric Psychiatry, 29(2), 117–125. 10.1016/j.jagp.2020.06.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Glover CM, Stewart CC, Yu L, Wilson RS, Lamar M, Bennett DA, & Boyle PA (2023). Psychological well-being relates to Healthcare and Financial decision making in a study of predominantly white older adults. J Appl Gerontol, 42(8), 1770–1780. 10.1177/07334648231157368 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Griffith HR, Stewart CC, Stoeckel LE, Okonkwo OC, den Hollander JA, Martin RC, & Marson DC (2010). Magnetic resonance imaging volume of the angular gyri predicts financial skill deficits in people with amnestic mild cognitive impairment. Journal of the American Geriatrics Society, 58(2), 265–274. 10.1111/j.1532-5415.2009.02679.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Iglesias JE, & Sabuncu MR (2015). Multi-atlas segmentation of biomedical images: A survey. Medical Image Analysis, 24(1), 205–219. 10.1016/j.media.2015.06.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. James BD, Boyle PA, Bennett JS, & Bennett DA (2012). The impact of health and financial literacy on decision making in community-based older adults. Gerontology, 58(6), 531–539. 10.1159/000339094 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Kapasi A, Yu L, Stewart CC, Schneider JA, Bennett DA, & Boyle PA (2019). Association of TDP-43 Pathology with Domain-specific literacy in older persons. Alzheimer Disease and Associated Disorders, 33(4), 315–320. 10.1097/wad.0000000000000334 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Kapasi A, Yu L, Stewart C, Schneider JA, Bennett DA, & Boyle PA (2021). Association of Amyloid-β Pathology with decision making and scam susceptibility. Journal of Alzheimer’s Disease, 83(2), 879–887. 10.3233/jad-210356 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kobayashi LC, Wardle J, Wolf MS, & von Wagner C (2015). Cognitive function and Health Literacy Decline in a cohort of Aging English adults. Journal of General Internal Medicine, 30(7), 958–964. 10.1007/s11606-015-3206-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Laird NM, & Ware JH (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. [PubMed] [Google Scholar]
  18. Lamar M, Arfanakis K, Yu L, Zhang S, Han SD, Fleischman DA, & Boyle PA (2020). White matter correlates of scam susceptibility in community-dwelling older adults. Brain Imaging Behav, 14(5), 1521–1530. 10.1007/s11682-019-00079-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Lamar M, Arfanakis K, Yu L, Kapasi A, Duke Han S, Fleischman DA, & Boyle P (2023). The relationship of MRI-Derived Alzheimer’s and cerebrovascular-related signatures with level of and change in health and financial literacy. The American Journal of Geriatric Psychiatry: Official Journal of the American Association for Geriatric Psychiatry, 31(12), 1129–1139. 10.1016/j.jagp.2023.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lusardi A, & Mitchell OS (2017). How ordinary consumers make complex economic decisions: Financial literacy and retirement readiness. Quarterly Journal of Finance, 7(03), 1750008. [Google Scholar]
  21. Lusardi A, & Tufano P (2015). Debt literacy, financial experiences, and overindebtedness. Journal of Pension Economics & Finance, 14(4), 332–368. [Google Scholar]
  22. Makkinejad N, Evia AM, Tamhane AA, Javierre-Petit C, Leurgans SE, Lamar M, & Arfanakis K (2021). ARTS: A novel In-vivo classifier of arteriolosclerosis for the older adult brain. Neuroimage Clin, 31, 102768. 10.1016/j.nicl.2021.102768 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Marson DC, Martin RC, Wadley V, Griffith HR, Snyder S, Goode PS, & Harrell LE (2009). Clinical interview assessment of financial capacity in older adults with mild cognitive impairment and Alzheimer’s disease. Journal of the American Geriatrics Society, 57(5), 806–814. 10.1111/j.1532-5415.2009.02202.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Rahimi J, & Kovacs GG (2014). Prevalence of mixed pathologies in the aging brain. Alzheimer’s Research & Therapy, 6(9), 82. 10.1186/s13195-014-0082-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Schneider JA, Arvanitakis Z, Bang W, & Bennett DA (2007). Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology, 69(24), 2197–2204. 10.1212/01.wnl.0000271090.28148.24 [DOI] [PubMed] [Google Scholar]
  26. Stoeckel LE, Stewart CC, Griffith HR, Triebel K, Okonkwo OC, den Hollander JA, & Marson DC (2013). MRI volume of the medial frontal cortex predicts financial capacity in patients with mild Alzheimer’s disease. Brain Imaging Behav, 7(3), 282–292. 10.1007/s11682-013-9226-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Sunderaraman P, Lee S, Varangis E, Habeck C, Chapman S, Joyce JL, & Cosentino S (2022). Self-awareness for financial decision making abilities is linked to right temporal cortical thickness in older adults. Brain Imaging Behav, 16(3), 1139–1147. 10.1007/s11682-021-00590-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Vassilaki M, Aakre JA, Kremers WK, Mielke MM, Geda YE, Machulda MM, & Petersen RC (2022). Association of performance on the financial capacity instrument-short form with brain amyloid load and cortical thickness in older adults. Neurol Clin Pract, 12(2), 113–124. 10.1212/cpj.0000000000001157 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Widera E, Steenpass V, Marson D, & Sudore R (2011). Finances in the older patient with cognitive impairment: He didn’t want me to take over. Jama, 305(7), 698–706. 10.1001/jama.2011.164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Yu L, Schneider JA, Kapasi A, Bennett DA, & Boyle PA (2020). Limbic-predominant age-related TDP-43 encephalopathy and distinct longitudinal profiles of domain-specific literacy. Alzheimer Disease and Associated Disorders, 34(4), 299–305. 10.1097/wad.0000000000000389 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Yu L, Mottola G, Bennett DA, & Boyle PA (2021). Adverse Impacts of Declining Financial and Health Literacy in Old Age. The American Journal of Geriatric Psychiatry: Official Journal of the American Association for Geriatric Psychiatry, 29(11), 1129–1139. 10.1016/j.jagp.2021.02.042 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

supplementary material
NIHMS2037309-supplement-supplementary_material.docx (28.7KB, docx)

Data Availability Statement

Data used in this study can be requested for research purposes via the Rush Alzheimer Disease Center Research Resource Sharing Hub at https://www.radc.rush.edu.

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