Self-awareness for Financial Decision Making Abilities Is Linked to Right Temporal Cortical Thickness in Older Adults&nbsp;

doi:10.21203/rs.3.rs-227286/v1

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Self-awareness for Financial Decision Making Abilities Is Linked to Right Temporal Cortical Thickness in Older Adults

https://doi.org/10.21203/rs.3.rs-227286/v1

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published 10 Nov, 2021

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Everyday financial decision making and the awareness of the integrity of one’s financial decision making abilities (or financial awareness) are both critical to study in older adults as they can help identify those at risk for making suboptimal financial decisions and prevent financial loss. In the current study, we examined the cognitive and cortical thickness correlates of financial decision making and financial awareness in 59 community-dwelling participants co-enrolled in a larger study (mean age=68.35 years (SD=5.5), mean education=15.91 (SD=2.36), 61% = women, 67% = White). Data from standardized measures of financial decision making and cognition was investigated along with FreeSurfer derived thickness regions. Based on metacognitive frameworks, financial awareness was measured along with a well-validated measure of memory awareness. Results revealed that numeracy, executive functioning and vocabulary were associated with financial decision making, whereas in analysis adjusted for financial decision making, memory awareness relative to cognition was most strongly linked to financial awareness. No significant associations between thickness and financial decision making were found. However, both financial and memory awareness were associated with the same right-hemisphere temporal thickness regions underscoring the idea of a common substrate of awareness. Interestingly, our findings converge with the emerging work on financial exploitation in which the right sided temporal regions have been found to play a prominent role. Incorporating the contributing role of self-awareness in various models of financial exploitation will be an important consideration for future studies.

Head & Neck Surgery

Cellular & Molecular Neuroscience

financial decision making

self-awareness

cortical thickness

medial temporal region

cognition

Financial decision making (FDM) refers to the ability to conduct financial tasks autonomously to manage one’s finances without error or preventable financial loss (Lichtenberg, Stoltman, Ficker, Iris, & Mast, 2015; Marson, 2016; National Academies of Sciences & Medicine, 2016). Several studies highlight that older adults (OAs), an especially vulnerable but wealthy segment of the population, experience financial loss ranging from $2.9 to $36.5 billion annually (Lichtenberg et al., 2015; Teaster, Roberto, Migliaccio, Timmermann, & Blancato, 2012). Age-related cognitive decline may increase susceptibility to poor decision making (Agarwal, Driscoll, Gabaix, & Laibson, 2009; Fenge, 2017; Tymula, Belmaker, Ruderman, Glimcher, & Levy, 2013). Identifying the cognitive abilities and brain regions that contribute to FDM earlier in the spectrum of cognitive aging, among cognitively healthy OAs, will help to identify individuals at risk for poor FDM at a time when they are likely to be responsible for managing the assets they have accumulated over their lifetimes.

In those with mild cognitive impairment (MCI) and Alzheimer’s disease (AD), impairments in cognitive abilities including numeracy, executive functioning, memory, and visuomotor sequencing are associated with reduced FDM (Sherod et al., 2009). However, the role of specific cognitive abilities in supporting FDM among healthy OAs is unclear. Some studies find no association between specific cognitive abilities and FDM, while others report links between FDM and numeracy, attention, executive abilities, memory, visuomotor construction and processing speed (Pachana et al., 2014; Sherod et al., 2009; Shivapour, Nguyen, Cole, & Denburg, 2012).

Examining specific cognitive abilities along with regional brain structure among cognitively healthy OAs will allow stronger inferences regarding the aspects of cognition and brain integrity that may be critical for maintaining intact FDM. Few studies have investigated these links in healthy OAs, with most studies focusing on neurological populations (i.e., MCI, AD). Across these volumetric studies, cortical and subcortical regions including the angular gyrus, precuneus, medial and dorsolateral prefrontal cortex, inferior temporal and middle temporal cortex, cingulate cortex, nucleus accumbens, left medial and lateral amygdala and anterior thalamic radiation have been implicated in FDM (Benavides-Varela et al., 2020; Griffith et al., 2010; Han et al., 2016; Stoeckel et al., 2013). However, the relationship between cortical thickness measures and FDM has not yet been examined.

It is equally important to concurrently understand the substrates of financial awareness (FA), i.e., the awareness of one’s own FDM abilities. There is evidence that FA is a reliable construct, independent from FDM itself, and associated with memory awareness (i.e., metamemory, or the knowledge of one’s memory abilities), a well-established aspect of self-awareness (Sunderaraman, Chapman, Barker, & Cosentino, 2020). Of the various metacognitive metrics that was used to calculate FA, a calibration score reflecting the average degree of confidence was found to be the most meaningful method. It is arguable that with reduced FA, individuals may jeopardize their own financial well-being, be vulnerable to financial exploitation, and potentially incur heavy financial losses. Empirical evidence from memory awareness studies in individuals with MCI and AD has found that, those with impaired memory awareness are less likely to modify their approach to cognitively demanding tasks (e.g., using a pillbox to manage medications) (Cosentino, Metcalfe, Cary, De Leon, & Karlawish, 2011; Shaked et al., 2019) and may engage in more dangerous behaviors (e.g., driving)(Starkstein, Jorge, Mizrahi, Adrian, & Robinson, 2007).

Little is known about the neurocognitive substrates of FA. Historically, elements of self-awareness more broadly, particularly metamemory, have been linked to executive abilities and the structural integrity of the prefrontal cortex (Cosentino, Metcalfe, Holmes, Steffener, & Stern, 2011a; Fleming & Dolan, 2012). Increasingly, however, evidence points to a prominent role for midline structures, including the insula and cingulate, in supporting metamemory and self-evaluative processing more broadly (Bertrand et al., 2018; Buchy & Lepage, 2015; Klein, Ullsperger, & Danielmeier, 2013; van der Meer, Costafreda, Aleman, & David, 2010). Finally, regarding laterality, there is evidence that regions in the right hemisphere may differentially contribute to elements of self-awareness (Cosentino et al., 2015b; Harwood et al., 2005; Klein et al., 2013). Whether or not similar regions are associated with FA remain to be seen. In those with neurodegenerative conditions such as AD and fronto-temporal dementia, the right-hemisphere medial temporal regions have been associated with self-awareness.

The current study examined the cognitive and structural correlates of FDM and FA in healthy OAs and cortical thickness. On an exploratory basis, a whole brain analysis was run by selecting all 34 bilateral regions of interest (ROIs) and examining their associations with FDM, and between FA and 7 cortical ROIs (per hemisphere) based on previous literature (Bertrand et al., 2018; Cosentino et al., 2015b). To check for convergent validity, we explored the associations between a well-validated memory awareness measure (metamemory; Cosentino, Metcalfe, Butterfield, & Stern, 2007) and cortical thickness in the same pre-specified 7 ROIs.

Based on our previous work, we operationalized FA along a spectrum from under- to overconfidence in FDM. We hypothesized the following: (1) FDM and cognition: Numeracy, executive functioning, and vocabulary will show stronger associations with FDM compared to processing speed or memory. (2) FDM and cortical thickness: FDM will be more strongly associated with thickness of bilateral dorsolateral prefrontal and bilateral inferior parietal regions than that of other brain regions. (3) FA and Cognition: FA will be more strongly associated with memory awareness than primary cognitive domains (i.e., memory, executive functioning, processing speed, and vocabulary). (4) FA and Thickness: FA will show stronger associations with thickness in the right-sided insula, anterior cingulate cortex, and prefrontal region.

Participants

Community-dwelling participants were recruited for the Reference Ability Neural Network Study (RANN; Stern et al., 2014) wherein extensive cognitive and neuroimaging data were collected. Data for the current study were then prospectively collected from 60 cognitively healthy OAs in a single session examining FDM, FA and numeracy, generally within one year of RANN participation. Participants were required to be native English speakers, strongly right-handed, and have a minimum of fourth-grade reading level (Stern et al., 2014). They were screened for dementia or MCI using the Dementia Rating Scale (Mattis, 1988), hearing and/or visual impairment, and MRI contraindications. Written informed consent was obtained from all participants and compensation was provided at the end of the study. The Columbia University Medical Center Internal Review Board approved this study.

Cognitive Measures

Four cognitive domains based on composite scores – executive function, memory, language, and processing speed – were measured using paper-and-pencil tests in the RANN study (Stern et al., 2014). Numeracy was assessed as part of the current study (Sunderaraman, Barker, Chapman, & Cosentino, 2020).

FDM

Twenty objective items from the Financial Competence Assessment Inventory were used in the current study (Kershaw & Webber, 2008; Sunderaraman, Chapman, et al., 2020; Sunderaraman, Cosentino, Lindgren, James, & Schultheis, 2018). Items were either performance-based and observable (e.g., writing a check) or were conceptual knowledge questions that could be scored objectively with an external criterion (e.g., what is the meaning of assets?). Accuracy was originally scored on a scale from 1 to 5, but was collapsed into 1 to 3 because of the distribution of the data (Sunderaraman, Chapman, et al., 2020). The total accuracy ranged from 20 to 60.

Before and after each FCAI item, participants made prospective and retrospective judgments about their performance (How confident are you that you answered that question correctly?) on the specified item on a scale from 1 (unsure) to 4 (very confident). Because of the high correlation between the judgments (Sunderaraman, Chapman, et al., 2020), only prospective judgments were used in this study. The 4-point scale was collapsed into a 3-point scale ranging from 1 to 3 to bring it numerically on the same scale as the FCAI accuracy ranges. This matching enabled calculation of the awareness score; details regarding the development of the confidence rating scale can be found in Sunderaraman, Chapman, et al. (2020). Based on established metacognitive frameworks (Cosentino et al., 2007; Dunlosky & Tauber, 2016), calibration was calculated by subtracting average accuracy from the average confidence rating to determine the extent to which individuals were over- or underconfident. A score of zero indicated perfect calibration, positive scores indicated overconfidence, and negative scores indicated under confidence in one’s financial abilities (total score range: -2 to 2).

Memory Awareness

Details about this task have been described previously (Bertrand et al., 2018; Cosentino et al., 2015a; Cosentino, Metcalfe, Holmes, Steffener, & Stern, 2011b). Briefly, this task consisted of 20 items wherein participants have to learn trivia information. Participants were asked to predict if they would recognize the correct answer (Yes, Maybe, No) and recognition accuracy was scored dichotomously (correct or incorrect). Similar to FA, calibration scores were derived where zero indicated perfect calibration, positive scores indicated overconfidence, and negative scores indicated underconfidence in one’s memory abilities (total score range: -1 to 1).

Structural Neuroimaging

Cortical thickness

T1‐weighted images of the whole brain were acquired for each subject using a Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence with the following parameters: TE/TR: 3/6.5 ms; Field of view: 256 mm; Flip angle: 8°; In‐plane resolution: 256 × 256 voxels; Slice thickness/gap: 1/0 mm; Slices: 180. All T1 image segmentation was performed using FreeSurfer (v. 5.3; (Dale, Fischl, & Sereno, 1999; Fischl et al., 2002; Fischl et al., 2004)), and inspected visually for any possible inaccuracies. Segmentation inaccuracies were manually corrected. For each region, the average distance between the gray/white matter boundary and the pial surface is calculated to reflect the cortical thickness of that region.

34 bilateral cortical thickness ROIs were examined, and based on previous literature 7 ROIs per hemisphere were derived as follows: (i) Dorsolateral prefrontal: superior frontal gyrus, rostral middle frontal gyrus, and caudal middle frontal gyrus; (ii) Medial prefrontal: medial orbital and frontal pole; (iii) Temporal: entorhinal, parahippocampal, and temporal pole; (iv) Anterior cingulate cortex (ACC): caudal and rostral; (v) Posterior cingulate cortex (PCC); (vi) Parietal: superior parietal and precuneus; and (vii) Insula.

Data Analysis

Data were analyzed using R and IBM SPSS v.25. The data were checked for skewness and negatively skewed data were square root transformed. All the associations were examined with Pearson correlations. Both, FDM and four aspects of cognition (executive function, vocabulary, speed, and numeracy), except memory, were associated with education. Therefore, the association between FDM and cognition, except memory, was adjusted for education. As metamemory was not associated with education, the analyses between FDM and metamemory were unadjusted for education. While examining the relationship between FA and cognition, we first calculated unadjusted correlations, and then calculated partial correlations adjusting for FDM. Given the hypothesis-driven nature of the correlations, we did not adjust for multiple comparisons. Medium to large effect sizes were considered for interpreting the findings (Cohen, 1992).

See Table 1 for the demographic details.

FDM, FA, Cognition and Metamemory

FDM and FA were associated with numeracy, executive functioning, and vocabulary (see Table 2). FDM was negatively associated with FA (r = -.547, p <.001). After adjusting for FDM, there were no significant associations between FA and cognition; only the significant association between FA and metamemory remained.

FDM, FA, Metamemory and Cortical Thickness

Both FA and metamemory were associated most strongly with the right-hemisphere temporal ROIs (see Table 3; Figure 1). Significant associations were not observed between FDM and any areas of cortical thickness (see supplementary Table 1).

To our knowledge, this is the first study to examine associations between FDM, FA, cognition and cortical thickness in cognitively healthy older adults. We also explored how memory awareness was associated with cortical thickness, and whether commonalities existed between the structural substrates of memory awareness and FA. The results showed that various aspects of cognition, especially numeracy, executive functioning and vocabulary were associated with FDM whereas the primary correlate of FA was metamemory. This finding supports the notion that FA has a metacognitive component that does not map onto other aspects of cognition. Regarding cortical thickness, no significant associations were found with FDM. However, FA was associated with right-hemisphere temporal thickness suggesting that FA may be more sensitive to structural brain features than FDM in cognitively healthy individuals. Moreover, memory awareness was also strongly related to the same region, underscoring the idea that there may be a common substrate for elements of self-awareness.

FDM and Cognition: Numeracy showed the strongest association between FDM and cognition, followed by executive functioning and then vocabulary. These findings, in line with Sherod and colleague’s (2009) study, reinforce the role of numeracy as being integral to FDM. In the current study, numeracy involves aspects of mental arithmetic, written math, and numerical reasoning involving probability estimations (Sunderaraman, Barker, et al., 2020). Proficiency with numerical information is required in several financial decisions involving buying, selling, saving and investing. A role for executive functioning is also undeniable, e.g., those related to budgeting and choosing health care plans. In this study, executive functioning involved performance on reasoning and sequencing abilities, both of which are essential for healthy financial planning. A third ability, vocabulary, involved aspects of crystallized abilities in the form of word reading and conceptual knowledge. The ability to understand financial terminology (e.g., deductibles, interest fees, etc.) is required to effectively conduct financial tasks. The remaining cognitive abilities (memory, processing speed) are involved to a relatively lower extent probably because of the nature of the financial tasks examined in this study. It is safe to assume that task-specific demands (e.g., recalling a bank account PIN versus budgeting) places differential demands on memory, for example. Differences in FDM tasks may explain the contrasting results in the literature pertaining to the involvement of various cognitive abilities (Sherod et al., 2009; Shivapour et al., 2012).

FA and Cognition: When FDM was partialled out of the analysis, the association of the cognitive abilities and FA diminished. Only the association between FA and memory awareness remained significant, suggesting that FA maps onto other aspects of awareness more so than primary aspects of cognition. This finding resonates with work demonstrating selective associations between metamemory and other forms of self-evaluation in healthy OAs and individuals with AD (Chapman et al., 2018; Cosentino et al., 2007).

FDM and Cortical Thickness: Significant associations between cortical thickness and FDM were not found. One reason for this finding may be related to the task used in this study. FDM includes an array of tasks including the ability to read bills, write a check, and define financial concepts. In functionally intact individuals FDM may depend on the integrity of large-scale cortical/subcortical regions and therefore no specific region may be clearly associated with FDM. However, with increasing brain pathology, the relevance of specific cortical regions may become more evident. It is also possible that the thickness metrics used in this study are not sensitive enough to detect functional correlates in a fine-grained manner due to the nature of the measurement instruments used to examine FDM and/or thickness metrics.

FA, Memory awareness, and Cortical Thickness: Right temporal thickness in the temporal pole, entorhinal and parahippocampal regions was associated with FA such that thinner cortex in these regions was associated with higher calibration scores, reflecting over confidence. These regions were not hypothesized to be the primary correlates of FA; rather, we expected a more prominent role for right sided midline structures including the insula and cingulate, as well as the medial PFC. Nonetheless, current results are consistent with a number of previous studies pointing to a role for various right temporal regions in supporting aspects of self-awareness. For example, in MCI, AD, and behavioral variant FTD, right medial temporal regions along with the hippocampus, were associated with self-awareness (Chavoix & Insausti, 2017). Other studies have linked decreased volume in the right temporal regions1 to overestimation of abilities (Shany-Ur et al., 2014), and higher senile plaque density of the right hippocampus to poor memory awareness in AD (Marshall et al., 2004). Together with these studies, our findings suggest that the right temporal ROI may have special significance to self-awareness.

Reduced FA may predispose an individual to financial exploitation. Interestingly, studies on financial exploitation find the right temporal cortex to be associated with increased scam susceptibility. A recent study found that in non-demented adults, 3 clusters including the right-sided parahippocampal/hippocampal/fusiform, hipoocampal, and middle temporal regions were inversely associated with scam susceptibility (Han et al., 2016). Reduced right hemisphere white matter integrity in temporal pathways connecting the parietal and occipital cortex was also associated with higher scam susceptibility in these OAs (Lamar et al., 2020). Yet another study found that financially exploited individuals had thinner cortex in the right anterior insula and right posterior superior temporal cortices (Spreng et al., 2017). Based on these findings, one can speculate that compromises to self-awareness, and FA in particular, may be an important mechanism by which vulnerability to exploitation arises (Lichtenberg, Campbell, Hall, & Gross, 2020; Shao, Zhang, Ren, Li, & Lin, 2019; Spreng, Karlawish, & Marson, 2016). The Social Cognitive Neuroscience model offers a novel framework to understand the factors that feed an individual’s risk of financial exploitation (Spreng et al., 2016) including both cognitive and social capacities. It is conceivable that self-awareness operates as a third pathway (see Figure 2), and future work should directly examine this possibility.

The current study has some limitations. i) It consisted of highly educated OAs which may have resulted in a homogenous sample. The resulting restricted range of values could have limited our ability to find significant associations, particularly for FDM. ii) Advanced analysis which have been proposed to be powerful to detect associations among highly correlated variables could not be conducted given the small sample size. Multivariate approaches using larger and more heterogeneous samples offer a promising avenue of future research. iii) Domains such as visuospatial functioning were not included and should be examined in future studies. Despite these limitations, this is the first study to directly examine FA in relation to cortical thickness, revealing a seemingly important role for right temporal temporal regions. The consistency of findings across both FA and memory awareness implicates a common cortical substrate for awareness and may inform models of financial exploitation moving forward.

Acknowledgments: We thank Sarah Ho and Shalom Omollo for their assistance with data collection.

Author Contributions: Author contributions included conception and study design (PS and SC), data collection or acquisition (PS, SC, YS), statistical analysis (PS, SL, AB, CH and SC), interpretation of results (AMB, EV, CH, PS, SC, YS), drafting the manuscript work or revising it critically for important intellectual content (AMB, PS, SC, SC, and YS) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).

Funding Sources: This study was supported by the RuthL. Kirschstein Predoctoral Individual National Research Service Award [5F32AG053035], the National Academy of Neuropsychology Clinical Research Grant presented to PS, the CTSA grant [UL1 TR001873], National Institute on Aging [Grant numbers R01 AG026158 and RF1AG038465]

Compliance with Ethical Standards: The Columbia University Medical Center’s Institutional Review Committee has reviewed and approved the study protocol described in the manuscript. All study participants have provided written informed consent.

Conflict of Interest: None of the authors have a conflict of interest to disclose.

Availability of data and materials: The data will shared upon request from the corresponding author.

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Table 1: Demographics characteristics of the sample
		Mean (SD; range)
Age		68.35 (5.5; 57-84)
Education (yrs)		15.91 (2.36; 12-22)
Women; n (%)		35 (61)
Race; n (%)
	White	38 (67)
	Black	17 (30)
	Asian	1 (2)
	Native Hawaiian/ Pacific Islander	1 (2)
Ethnicity; n (%)
	Non-Hispanic	54 (95)

Table 2: FDM, FA, cognition, and memory awareness

	FDM	FA (Unadjusted)	FA (Adjusted for FDM)
	r (p)
FDM	NA	-0.547 (0.000)	NA
Numeracy	0.387 (0.004)	-0.422 (0.002)	-0.153 (0.288)
Executive Function	0.371 (0.005)	-0.410 (0.003)	-0.215 (0.134)
Memory	-0.005 (0.969)	-0.128 (0.366)	-0.159 (0.271)
Vocabulary	0.326 (0.014)	-0.352 (0.011)	-0.145 (0.316)
Speed	0.137 (0.313)	-0.117 (0.408)	0.061 (0.671)
Metamemory	-0.192 (0.153)	0.414 (0.002)	0.372 (0.007)
NOTE. The sample consisted of 59 individuals. Due to missing data, n was 49 for the financial awareness data.

Table 3: Correlations between financial awareness, memory awareness, and thickness ROIs
Thickness ROIs	Financial Awareness		Memory Awareness
	r	p	r	P
Right Prefrontal	0.158	0.279	-0.015	0.913
Left Prefrontal	0.131	0.371	-0.088	0.525
Right Medial Prefrontal	-0.119	0.417	-0.003	0.981
Left Medial Prefrontal	0.152	0.297	0.056	0.686
Right Anterior Cingulate	-0.099	0.499	-0.046	0.742
Left Anterior Cingulate	-0.118	0.418	0.104	0.456
Right Posterior Cingulate	-0.027	0.854	0.141	0.309
Left Posterior Cingulate	0.090	0.540	0.081	0.559
Right Temporal	-0.286	0.047	-0.287	0.035
Left Temporal	-0.145	0.321	-0.128	0.358
Right Medial Parietal	-0.011	0.939	-0.008	0.955
Left Medial Parietal	0.063	0.667	0.043	0.756
Right Insula	-0.189	0.193	-0.192	0.165
Left Insula	-0.219	0.131	-0.164	0.235

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published 10 Nov, 2021

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Editorial decision: Major Revisions Needed
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Self-awareness for Financial Decision Making Abilities Is Linked to Right Temporal Cortical Thickness in Older Adults

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