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. Author manuscript; available in PMC: 2017 Apr 15.
Published in final edited form as: Neuroimage. 2016 Feb 17;130:223–229. doi: 10.1016/j.neuroimage.2016.02.030

Financial Literacy is Associated with White Matter Integrity in Old Age

S Duke Han 1,2,3, Patricia A Boyle 4,5, Konstantinos Arfanakis 4,8,9, Debra Fleischman 4,5,6, Lei Yu 4,6, Bryan D James 4,7, David A Bennett 4,6
PMCID: PMC4808430  NIHMSID: NIHMS762244  PMID: 26899784

Abstract

Financial literacy, the ability to understand, access, and utilize information in ways that contribute to optimal financial outcomes, is important for independence and wellbeing in old age. We previously reported that financial literacy is associated with greater functional connectivity between brain regions in old age. Here, we tested the hypothesis that higher financial literacy would be associated with greater white matter integrity in old age. Participants included 346 persons without dementia (mean age=81.36, mean education=15.39, male/female=79/267, mean MMSE=28.52) from the Rush Memory and Aging Project. Financial literacy was assessed using a series of questions imbedded as part of an ongoing decision making study. White matter integrity was assessed with diffusion anisotropy measured with diffusion tensor magnetic resonance imaging (DTI). We tested the hypothesis that higher financial literacy is associated with higher diffusion anisotropy in white matter, adjusting for the effects of age, education, sex, and white matter hyperintense lesions. We then repeated the analysis also adjusting for cognitive function. Analyses revealed regions with significant positive associations between financial literacy and diffusion anisotropy, and many remained significant after accounting for cognitive function. White matter tracts connecting right hemisphere temporal-parietal brain regions were particularly implicated. Greater financial literacy is associated with higher diffusion anisotropy in white matter of nondemented older adults after adjusting for important covariates. These results suggest that financial literacy is positively associated with white matter integrity in old age.

Keywords: financial literacy, diffusion anisotropy, fractional anisotropy, white matter, diffusion tensor imaging, DTI

1. Introduction

Financial literacy is the ability to understand, access, and utilize information regarding monetary concepts and institutions in ways that contribute to better financial outcomes (Braunstein and Welch, 2002; Hilgert et al., 2003). Financial literacy has been linked with enhanced mental health in young adults (Taylor et al., 2009) and with optimal retirement outcomes and better financial planning in older adults (Lusardi and Mitchel, 2007a; Lusardi and Mitchel, 2007b). Because of this, financial literacy could have a significant impact not only upon the independence and wellbeing of older adults, but also families, caregivers, and society, as older adults make many important decisions about retirement savings and inter-generational transfers of wealth. For these reasons, a greater understanding of financial literacy is of critical importance, and examination of the neural correlates of financial literacy could inform interventions that may result in the maintenance or improvement of decision making in old age.

The acquiring of financial literacy can hypothetically be viewed as involving the coordination of two subprocesses in the brain: (1) the development of learned contextual knowledge (in this case financial knowledge), mediated through a network of posterior cortical (temporal-parietal) brain regions implicated in the Default Network (Buckner et al., 2008); and (2) the utilization and manipulation of that knowledge, primarily mediated through a network of anterior cortical (frontal) brain regions (Decety and Michalska, 2010; Hurliman et al., 2005). Those who develop greater financial literacy may have greater integration within or between these networks and the brain regions in them. We previously demonstrated that financial literacy was associated with greater functional connectivity between brain grey matter regions in these networks after adjusting for important covariates including level of cognition (Han et al., 2014). Since functional connectivity measures can be dependent on the structural integrity of white matter connections that facilitate communication between functionally associated brain grey matter regions (Koch et al., 2002; Griecius et al., 2009), the acquiring of financial literacy might also have an impact upon indices of white matter integrity in the brain. Further supporting this notion, increases in acquired general literacy have been observed to result in increasing white matter density and anatomical connectivity between brain grey matter regions in adulthood (Carrieras et al., 2009; Thiebaut de Shotten et al., 2014).

We tested the hypothesis that financial literacy is associated with greater white matter integrity in regression models that adjusted for the effects of age, education, sex, and white matter hyperintensities. Using diffusion tensor imaging (DTI), we investigated this in 346 non-demented older adults from the Rush Memory and Aging Project, a longitudinal clinicopathologic study of aging. We additionally examined whether results remained significant after further adjusting for cognitive function. To our knowledge, this is the first study of financial literacy associations with white matter integrity in a large community-based sample of non-demented older adults.

2. Material and methods

2.1 Participants

Participants of the current study were recruited from the Rush Memory and Aging Project, a longitudinal clinical-pathologic study of aging and dementia (Bennett et al., 2012b). The Rush Memory and Aging Project recruits participants from residential facilities and community organizations local to the Chicago metropolitan area, including senior housing facilities and retirement homes. Annual clinical evaluations are conducted with all participants. Standard criteria were used in the diagnoses of dementia (McKhann et al., 1984) and determined by a clinician with expertise in aging as previously reported (Bennett et al., 2012b).

The Rush Memory and Aging Project began in 1997, and enrollment is ongoing. A decision making sub-study which had the financial literacy assessment was added in 2008. Neuroimaging was introduced in 2009. At the time of these analyses, 1,684 participants had completed the baseline of the parent project and 893 of these participants were enrolled in the decision making sub-study. Of these 893 participants, 791 were enrolled in the MRI component of the parent study, 420 had MRI scans and 401 had DTI scans at the time of these analyses, but 3 failed quality control, leaving 398 with usable DTI data. Of these 398 participants, 5 had dementia and 47 had missing data, leaving a total of 346 subjects with complete data on financial literacy and DTI.

2.2 Assessment of Financial Literacy

Financial literacy was measured with 23 questions based on numeracy, concepts, and information as described in previous work (James et al., 2012; Bennett et al., 2012a; Boyle et al., 2013; Han et al., 2014). Questions included items adapted from materials in the Health and Retirement Survey (Lusardi and Mitchell, 2007a; Lusardi and Mitchell, 2007b) and include inquiries of monetary calculations such as sales and interest rates, as well as knowledge of financial concepts such as “bonds”, “stocks”, and “FDIC”. An example of an item is “Imagine that the interest rate on your savings account is 1% per year and inflation is 2% per year. After 1 year, will you be able to buy more than, exactly the same as, or less than today with the money in your account?” Another example is “True or false. Using money in a bank account to pay off credit card debt is usually wise.” The percent correct was calculated for each participant and used in accordance with previous studies (Boyle et al., 2013; James et al., 2012; Bennett et al, 2012a; Han et al., 2014).

2.3 Assessment of Cognition

Trained technicians supervised by a board-certified clinical neuropsychologist administered a battery of cognitive performances tests. Cognitive function measures assessed a wide range of cognitive abilities (Bennett et al., 2012b) and included measures of episodic memory (Word List Memory, Word List Recall and Word List Recognition from the procedures established by the CERAD; immediate and delayed recall of Logical Memory Story A and the East Boston Story), semantic memory (Verbal Fluency, Boston Naming, and the National Adult Reading Test), working memory (Digit Span subtests forward and backward of the Wechsler Memory Scale-Revised and Digit Ordering), perceptual speed (oral version of the Symbol Digit Modalities Test, Number Comparison, Stroop Color Naming, and Stroop Word Reading), and visuospatial ability (Judgment of Line Orientation and Standard Progressive Matrices). The raw scores on 19 tests were converted to z-scores using the mean and standard deviation from the baseline cognitive evaluation. A global cognition score was computed by averaging the z-scores across the 19 measures of cognitive abilities as previously reported (Wilson et al., 2003).

2.4 Ethical Statement

All procedures were conducted in accordance with the ethical rules for human experimentation that are stated in the Declaration of Helsinki and were approved by the Institutional Review Board of Rush University Medical Center.

2.5 Imaging Approach

Brain MR imaging was conducted on all participants using a 1.5 Tesla General Electric MRI scanner (Waukesha, WI). On average, participants underwent neuroimaging within approximately two months of completing the clinical evaluation and financial literacy assessment (mean = 60 days; standard deviation = 51 days). High resolution T1-weighted anatomical data was obtained using a 3D magnetization-prepared rapid acquisition gradient-echo (MPRAGE) sequence with the following parameters: echo-time (TE) = 2.8 msec, repetition time (TR) = 6.3 msec, preparation time = 1000 msec, flip-angle = 8°, field-of-view (FOV) = 24 cm × 24 cm, 160 sagittal slices, slice thickness = 1 mm, no gap, 224 × 192 acquisition matrix reconstructed to 256 × 256, and two repetitions, for a total imaging time of 22 minutes. T2-weighted fluid attenuated inversion recovery (FLAIR) data was collected on all participants using a 2D fast spin-echo sequence with the following parameters: TE = 120 msec, TR = 8 sec, inversion time = 2 sec, FOV = 24 cm × 24 cm, 42 axial slices, slice thickness = 3 mm, no gap, 256 × 224 acquisition matrix reconstructed to 256 × 256, and a scan time of 4 minutes and 1 second. Finally, spin-echo echo-planar DTI data was collected on all participants using the following parameters: TE = 84.6 msec, TR = 5.4 sec, FOV = 24 cm × 24 cm, 36 axial slices, slice thickness = 3 mm, no gap, 128 × 128 acquisition matrix reconstructed to 256 × 256, b = 900 sec/mm2 for 12 diffusion directions uniformly distributed in 3D space (Hassan et al., 2001), two b = 0 sec/mm2 volumes. The DTI data acquisition was repeated 6 times for a total of 72 diffusion-weighted and 12 b = 0 s/mm2 image volumes. The total DTI scan time was 7 minutes and 33 seconds.

For each participant, white matter lesions commonly present in the brain of older adults (often referred to as white matter hyperintensities, WMHs, due to their hyperintense appearance in T2-weighted images) were automatically segmented using a support vector machine classifier based on both MPRAGE and FLAIR information (WMLS, SBIA, University of Pennsylvania, PA) (Zacharacki et al., 2008), and a mask (0s and 1s) was generated (voxels with WMH were given values of 1 in the mask). The same projection parameters were used to project the WMH mask values from the same voxels as the local FA maxima. Thus, each voxel on the white matter skeleton had a corresponding WMH mask value of either 0 or 1. These values were used as covariates in our models. All DTI analyses controlled for WMHs since these may impact calculation of diffusion anisotropy. For the DTI data, corrections for bulk motion and distortions caused by eddy-currents and magnetic field non-uniformities, B-matrix reorientation, and diffusion tensor calculation were conducted using TORTOISE (www.tortoisedti.org) (Pierpaoli et al, 2010). Maps of the fractional anisotropy (FA) and trace of the diffusion tensor were produced (Le Bihan et al. 2001; Basser and Pierpaoli, 2001). The WMH mask of each participant was transformed to the space of the corresponding processed DTI data based on the transformation of the FLAIR image volume to the pre-processed b = 0 sec/mm2 volume. A detailed description of the image processing steps can be found in Arfanakis et al. 2013.

2.6 Statistical Analyses

The Tract-Based Spatial Statistics (TBSS) approach was used to investigate the association of financial literacy with white matter diffusion measures (Smith et al., 2006). The FA volumes from all participants were non-linearly spatially transformed to the mean FA template of the IIT Human Brain Atlas (v.4.0) (www.iit.edu/~mri) (Varentsova et al., 2014). The local FA maxima from each participant’s spatially transformed FA volume were then projected onto the white matter skeleton of the IIT Human Brain Atlas (v.3.2). The same projection parameters were used to project the trace and WMH mask values from the same voxels as the local FA maxima. Linear regression was then used to test the association of FA along the white matter skeleton (outcome) with financial literacy (predictor), controlling for age, sex, level of education, and presence of WMHs voxel-wise. Analyses were then repeated further adjusting for cognitive function. Separate linear regression models were used to test the association of the trace of the diffusion tensor along the white matter skeleton with financial literacy, controlling for the same factors mentioned above. The trace of the diffusion tensor refers to the sum of the diagonal elements of the diffusion tensor. This was also considered since it has been associated with pathological indicators of white matter, such as edema or stroke (Mukherjee et al., 2009). The null distribution was built using the “randomise” tool in FSL (FMRIB, University of Oxford, UK) and 5000 permutations of the data. Differences were considered significant at p<0.05, Family Wise Error (FWE) corrected. The Threshold-Free Cluster Enhancement (TFCE) method was used to define clusters with significant effects (Smith and Nichols, 2009). The “regionstat” tool of the IIT Human Brain Atlas (v.4.0) was used to extract the list of most probable connections passing through clusters showing significant effects, according to the information contained in the 4-dimensional, connectivity-based white matter labels of the IIT Human Brain Atlas (v.4.0) (developed using high angular resolution diffusion imaging probabilistic tractography) (Varentsova et al., 2015).

3. Results

3.1 Descriptives

Demographic, cognitive, and financial literacy variables are presented in Table 1. The sample had a high percentage of female and white participants. The sample had a mean education level of higher than high-school. Participants generally answered more than half of the items correctly on the measure of financial literacy.

Table 1.

Demographic and cognitive variables

Sample (n = 346)
Age (years)
 Mean (SD) 81.36 (7.07)
 Range 59.95 – 100.19
Education (years)
 Mean (SD) 15.39 (3.03)
 Range 7 – 28
Sex (% Female) 77.17% (n = 267)
Race (% White) 95.66% (n = 331)
MMSE (total score)
 Mean (SD) 28.52 (1.53)
 Range 23 – 30
Global Cognition Z-score
 Mean (SD) 0.30 (0.50)
 Range −1.35 – 1.46
Financial Literacy Percentage
 Mean (SD) 62.94 (19.31)
 Range 17.39 – 100
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3.2 Association of Financial Literacy with White Matter Integrity

TBSS analysis demonstrated significant positive correlations between the measure of financial literacy and FA values in a number of white matter regions and the thalamus, controlling for age, sex, level of education, and WMHs (p<0.05 corrected for multiple comparisons). Association tracts were highly represented among results, in particular tracts connecting temporal and parietal brain regions (Figure 1, Table 2, Supplementary Materials 1a–c).

Figure 1.

Figure 1

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Voxelwise results of fractional anisotropy (FA) positive association with financial literacy in linear regression models adjusted for age, education, sex, and white matter hyperintensities. Each cluster has been numbered, and a list of the connections with the most fibers through each cluster is provided in Table 2.

Table 2.

List of most probable connections passing through the white matter clusters showing significant associations of fractional anisotropy (FA) with financial literacy in Figure 1 (models adjusted for age, education, sex, and white matter hyperintensities). List derived from “regionstat” tool running on the IIT Human Brain Atlas (v.4.0). The last column shows the probability that a fiber passing through a voxel of the cluster belongs to a certain connection.

Cluster # Connection between Percent
1 L fusiform L inferior temporal   6
L hippocampus L superior temporal   5
L hippocampus L inferior temporal   3
L superior parietal L fusiform   3
L fusiform L superior temporal   3
L lingual L superior temporal   3
L fusiform L middle temporal   3
L lateral occipital L superior temporal   2
2 R cerebellum R thalamus 23
L cerebellum R cerebellum 13
R cerebellum R ventral diencephalon 12
Medulla R thalamus   6
L cerebellum L pallidum   3
Medulla L pallidum   3
Medulla R precentral   2
L cerebellum L thalamus   2
3 R superior parietal R supramarginal   6
R superior parietal R superior temporal   4
R inferior parietal R superior parietal   3
R inferior parietal R superior temporal   3
L superior parietal R superior parietal   2
R thalamus R postcentral   2
L precuneus R superior parietal   2
R paracentral R postcentral   2
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Next, analyses for FA were repeated further adjusting for cognitive function in addition to age, education, sex, and WMHs. Results revealed many of the same white matter pathways remained significant (Figure 2, Table 3, Supplementary Materials 1d–g). However, significant results were observed only in the right hemisphere and implicated mostly pathways in superior temporal and parietal regions. Again, association tracts were highly represented among results.

Figure 2.

Figure 2

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Voxelwise results of fractional anisotropy (FA) positive association with financial literacy in linear regression models adjusted for age, education, sex, white matter hyperintensities and cognitive function. Each cluster has been numbered, and a list of the connections with the most fibers through each cluster is provided in Table 3.

Table 3.

List of most probable connections passing through the white matter clusters showing significant associations of fractional anisotropy (FA) with financial literacy in Figure 2 (models adjusted for age, education, sex, white matter hyperintensities, and global cognition). List derived from “regionstat” tool running on the IIT Human Brain Atlas (v.4.0). The last column shows the probability that a fiber passing through a voxel of the cluster belongs to a certain connection.

Cluster # Connection between Percent
1 R inferior parietal R superior temporal 28
R superior parietal R superior temporal 16
R precuneus R superior temporal 14
R putamen R inferior parietal   4
banks of the R superior temporal sulcus R inferior parietal   3
R inferior parietal R middle temporal   3
R inferior parietal R insula   2
R inferior parietal R transverse temporal   2
2 R thalamus R paracentral   7
R paracentral R postcentral   7
R thalamus R postcentral   6
R thalamus R precentral   6
R putamen R postcentral   6
R pallidum R postcentral   5
R caudate R paracentral   5
R pallidum R precentral   4
3 R superior parietal R superior temporal 17
R precuneus R superior parietal 14
R putamen R superior parietal   4
L superior parietal R superior parietal   4
R inferior parietal R superior temporal   4
R precuneus R superior temporal   4
R inferior parietal R precuneus   3
R cuneus R precuneus   2
4 R superior parietal R supramarginal 19
R paracentral R postcentral   8
R postcentral R precentral   8
R precentral R supramarginal   4
R precentral R superior frontal   4
R pars opercularis R supramarginal   3
R postcentral R supramarginal   3
R caudal middle frontal R inferior parietal   3
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TBSS analyses did not show any significant association between financial literacy and the trace of the diffusion tensor, adjusting for the effects of age, education, sex, WMHs, and cognitive function. Since older adult females tend to show lower rates of financial literacy (Lusardi and Mitchell, 2011; Lusardi and Mitchell, 2008), we conducted analyses to determine whether there was a sex by financial literacy interaction on DTI measures. Formal analyses revealed no notable sex interactions.

4. Discussion

We investigated the association of financial literacy with white matter integrity in the present study. Financial literacy was associated with diffusion anisotropy, an indicator of white matter integrity, in a number of brain pathways in models that adjusted for demographic factors and white matter hyperintensities. The main pathways implicated were association tracts connecting temporal and parietal regions. Furthermore, the association of financial literacy with a number of white matter pathways remained significant in the right hemisphere after further adjusting for cognitive function. These results are congruent with the observation of greater functional connectivity between brain grey matter regions previously reported, as we observed greater financial literacy associated with greater functional connectivity between the posterior cingulate and regions of the middle temporal, precuneus, and postcentral gyrus of the right hemisphere in prior work in participants from the same cohort (Han et al., 2014). Altogether, these results suggest a link between financial literacy and white matter integrity that is relatively independent of cognitive functioning.

Atlas-based analyses (Varentsova et al., 2015) revealed that most of the white matter pathways implicated were association tracts connecting temporal and parietal regions, and adjusting for cognitive function mostly implicated temporal-parietal pathways in the right hemisphere. Although the significance of this is yet unclear, we previously demonstrated that better responses to a temporal discounting paradigm were associated with functional connectivity of right temporal lobe regions (Han et al., 2013), and susceptibility to scams was associated with lower grey matter density in right temporal lobe regions (Han et al., 2015) in older adults in participants from the same cohort. The present study further supports the potential importance of right temporal lobe regions and their connections to other brain areas in the processing of financial matters. We speculate that these temporal lobe regions serve an important role in retaining and accessing prior-learned contextual knowledge (Peters and Buchel, 2010; Peters and Buchel, 2012). The significance of right-sided laterality is consistent with the “right hemi-aging hypothesis” (see Dolcos et al., 2002), which posits age-related pathological declines affect functions attributed to the right hemisphere earlier or to a greater degree than the left hemisphere. However, more research is needed to determine the mechanisms underlying these associations.

It is interesting to note that we observed significant associations between financial literacy and fractional anisotropy in cerebellar connections; however, these were not significant after accounting for cognitive functioning. The cerebellum’s role in cognitive functioning has garnered significant attention in the neuroimaging literature within recent years (Buckner, 2013; Stoodley, 2012; Stoodley and Schmahmann, 2009a; Keren-Happuch et al., 2012). Significant portions of the cerebellum map onto higher order cortical association areas, basal ganglia, thalamic, and brainstem structures (Bostan et al., 2013; Buckner et al., 2011), and congruent with this understanding, functional neuroimaging literature has demonstrated activation in the cerebellum associated with complex cognitive functions such as working memory, executive functions, and language (Kim et al., 1994; Petersen et al., 1989; Koziel et al., 2013; Stoodley and Schmahmann, 2009b). We have demonstrated cognitive functions are associated with financial literacy in old age (Bennett et al., 2012), therefore our findings of cerebellar circuitry associated with financial literacy in the present study are consistent with this and other literature implicating the cerebellum’s role in higher order functions.

No other study to our knowledge has investigated associations of financial literacy with white matter integrity in older adults. Our results suggest three possible interpretations of the relationship between financial literacy and white matter integrity. The first possibility is that financial literacy may help to maintain or improve white matter integrity in the brains of older adults, and this link is not fully explained or accounted for by cognitive function. Previous studies have demonstrated that an increase in literacy in adulthood is associated with greater white matter integrity; for example, one study followed Columbian former guerillas of adult age as they re-integrated into society and learned how to read. After their literacy improved, white matter tracts between brain regions were found to be stronger when compared to carefully matched adult illiterates (Carrieras et al., 2009). Another study that investigated illiterates, persons who acquired literacy in adulthood, and persons who acquired literacy as children, found that literacy was associated with multiple stronger white matter pathways even when only considering the first two groups (Thiebaut de Schotten et al., 2014). These studies suggest that literacy enhancement may help refine the organization of white matter even in adulthood. A second possibility is that participants with greater white matter integrity throughout their lives may have been more predisposed to acquiring literacy in financial matters across the lifespan. A third possibility is that financial literacy and white matter integrity are both associated with another confounding factor that drives change in both, independent of cognitive functioning. While all three of these interpretations are plausible, more work is needed to examine the mechanisms underlying these three possible interpretations.

There are some limitations of the present study. One limitation is that we were not able to compare our results to a younger sample to determine whether our results reflect age-specific effects. Future studies are necessary to replicate these results in samples with more male and younger adult participants. Another limitation was that we conducted this diffusion tensor imaging study in the same cohort as our previous functional connectivity study. Since these techniques measure associated brain characteristics, conducting this study on a separate cohort may offer more complementary evidence of a link between white matter integrity and financial literacy. An important limitation to note is our inability to make direct causal inferences based on these cross-sectional observational data. A longitudinal model showing an increase in financial literacy resulting in a later increase in white matter integrity would be provide stronger evidence for a causal link. A potential limitation may be our approach to correcting for WMHs. Since we were interested in localization of white matter tissues implicated by financial literacy, we believe our voxel-level consideration of WMHs is more appropriate than an overall volume that yields no spatial specificity. However, post-hoc analysis of the relationship between financial literacy and overall volume of WMHs was significant in our sample (r=−0.2569, p<0.001), and if volume of WMHs is used instead of voxel-level WMH information, the association between financial literacy and white matter integrity becomes non-significant in our models (after correction for multiple comparisons). The underlying mechanisms for this are unclear and more work is needed to demonstrate whether there is a possible link between financial literacy and volume of WMHs. Another potential limitation is our use of the TBSS approach to investigate white matter integrity. This approach has been recently suspected of having potential limitations and biases (Bach et al., 2014), and in particular, the skeleton projection step may reduce accuracy of results (Schwarz et al., 2014). If true, these limitations may affect generalizability of our results. A final limitation is that we do not know the exact underlying mechanisms for the associations that we observed. FA is dependent on microstructural tissue properties such as myelination, degree of axonal diameter, axonal density, interaxonal spacing, and intravoxel coherence of axonal orientation (Sen and Basser, 2005). Future histological studies of brain white matter as a function of financial literacy in humans may provide further mechanistic insights into these results.

The present study had multiple strengths, including participants from a community-based sample, a large sample size, and the control of multiple characteristics – such as age, education, sex, white matter hyperintensities, and global cognition – that may be associated with financial literacy and white matter integrity. Our results suggest financial literacy is associated with greater white matter integrity beyond the effects of cognitive functioning. Future work is necessary to establish the temporal direction of this relationship with longitudinal models, and if it is found that financial literacy results in greater white matter integrity in old age, then additional work is needed to determine whether targeted interventions may have an effect on this association. The development of interventions to improve financial literacy could have the effect of increasing white matter integrity in the brain, which may facilitate greater reserve against age-related brain pathology and poor financial decision making in old age. Supporting this view, our group has noted the beneficial effects of financial literacy on decision making is stronger in persons with lower cognitive functioning, suggesting that efforts to improve financial literacy may enhance decision making in these persons (James et al., 2012). To our knowledge, this is the first neuroimaging study of white matter integrity associations with financial literacy in old age.

Supplementary Material

1

Highlights.

  • Financial literacy is associated with diffusion anisotropy in older adults.

  • Results remained significant after accounting for cognition.

  • Financial literacy interventions may affect white matter integrity in old age.

Acknowledgments

This research was supported by National Institute on Aging grants R01AG017917, R01AG033678, K23AG040625, the American Federation for Aging Research, and the Illinois Department of Public Health. The authors have no competing interests. Dr. S. Duke Han had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors declare no conflicts of interest and gratefully thank the Rush Memory and Aging Project staff and participants.

Footnotes

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Conflicts of Interest: The authors declare no competing financial interests.

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