Altered Angular Gyrus Resting State Functional Connectivity Associated with Financial Capacity in Mild Cognitive Impairment

Abstract

Background:

Financial capacity (FC) is a complex ability commonly impaired in older individuals with cognitive impairment; however, the underlying neural mechanisms are not well understood.

Objective:

To assess resting state functional connectivity using functional magnetic resonance imaging (rs-fMRI) in individuals with mild cognitive impairment (MCI) and impaired FC compared to cognitively normal older adults.

Methods:

rs-fMRI scans were obtained from individuals with MCI (N = 17) and normal older adults (N = 15). All participants completed the Financial Capacity Instrument Short Form (FCI-SF) and neuropsychological assessments. Based on previous findings, the left angular gyrus (lAG) was used as the seed region. Connectivity correlation coefficients were calculated for each seed-based connection that showed significantly altered connectivity. A Pearson’s correlation was calculated between the connectivity correlation values from relevant regions and FC and other cognitive measures.

Results:

A total of 26 brain regions showed significantly increased functional connectivity with the lAG. Of these regions, 14 were identified as relevant to higher-level cognitive function for analysis. Pearson’s correlations showed a significant negative correlation between the FCI-SF total score and increased connectivity between the IAG and the right temporal fusiform cortex (rTFC) (r = –0.455, p = 0.009).

Conclusion:

Results showed a significant correlation between FC and increased functional connectivity between the lAG and the rTFC in cognitively normal older adults compared to participants with MCI. These exploratory findings suggest that cognitive functions play important roles in FC as the functional connectivity between the lAG and rTFC was not associated with other tests of executive or visuospatial cognition.

Keywords

Financial capacity mild cognitive impairment resting state functional connectivity

INTRODUCTION

In 2018, 16% of the US population (52.4 million people) was older than 65 years and by 2030 all individuals of the Baby Boomer generation will reach retirement age, accounting for one in five Americans [1]. An estimated 6.2 million Americans have Alzheimer’s disease (AD) and this number is projected to grow to 13.8 million by mid-century [2]. Impairment in performing everyday tasks such as driving, traveling, cooking, medication management, and financial management, clinically referred to as Instrumental Activities of Daily Living (IADLs), is a core diagnostic criterion for dementia (along with cognitive decline), and is a strong predictor of progression and conversion from mild forms of cognitive impairment (MCI) to dementia [3]. Particularly, impaired ability to make financial decisions disproportionately impacts older individuals, many of whom are experiencing cognitive decline. This exposes them to an increased risk of exploitation and accounts for an estimated 30% of all elder abuse reports [4]. Moreover, the inability to manage finances is one of the strongest predictors of caregiver burden [5].

Broadly, the capacity to make one’s own decisions is fundamental to individual autonomy and is defined as a clinical assessment of capability to make specific decisions. This is in distinction to competency, which refers to a legal determination of capacity [6, 7]. Financial capacity (FC) refers to the ability to independently manage finances in a manner consistent with self-interest [8]. FC comprises a broad range of conceptual, pragmatic, and judgment abilities ranging from basic skills, such as counting coins and prioritizing bills, to more complex skills, such as using financial conceptual knowledge and investment decision-making in everyday life. Declines in one or both of these skills are first observed in individuals with MCI, with progressive worsening in all aspects of FC observed during transition to dementia [9 –11]. For example, a person with MCI may be capable of carrying out financial transactions such as purchasing items, but not have the judgment required to spend within their financial means.

Financial skills require components of judgment and decision-making, and the cognitive correlates of impaired FC in MCI have thus far been associated primarily with executive control and numerical calculation [12 –15]. Specific subdomains of executive control (selective attention, self-monitoring, and working memory) have been strongly correlated with FC [9 , 17]. In one of the few longitudinal studies examining cognitive correlates of FC, Niccolai et al. [18] found that decline in written arithmetic, visual confrontation naming, visuospatial memory, and visual attention showed the highest associations with declining FC.

Studies examining the neuroanatomic correlates of FC have been limited mostly to examination using structural magnetic resonance imaging (MRI). Volumetric MRI studies by Griffith et al. [19] and Stoeckel et al. [20] have shown significant correlation between FC and prefrontal cortex volume, which has been related to decision-making; as well as posterior parietal structures like the angular gyrus and precuneus, which may be related to basic skills including numerical computation [19]. More recently, Giannouli et al. [21] compared brain volumes of 15 amnestic MCI patients and their correlations with performance on a new FC instrument, the Legal Capacity for Property Law Transactions Assessment Scale (LCPLTAS), and found that volumes of the angular gyrus and amygdala were correlated with performance on the LCPLTAS. Finally, a cross-sectional analysis by Tolbert et al. of the Alzheimer’s Disease Neuroimaging (ADNI-3) dataset of normal, MCI, and AD patients showed that global cortical amyloid-β deposition as measured by ¹⁸F Florbetapir PET was associated with worse performance on a well-validated instrument of FC, the Financial Capacity Instrument Short Form (FCI-SF) [22]. Most recently, Gonzalez et al. evaluated the association between regional cerebral tau (using flortaucipir tau PET) and the FCI-SF. They found significant associations between the FCI-SF and the dorsolateral prefrontal cortex, posterior cingulate, precuneus and supramarginal gyrus [23]. Similar to FC, there have been attempts to understand the neural correlates of financial literacy (FL), as well as victimization and exploitation [24, 25]. Han et al. have shown FL to be associated with greater functional connectivity (using resting state functional MRI, rs-fMRI) between the posterior cingulate cortex and the ventromedial prefrontal cortex, postcentral gyrus, and precuneus in older adults [26]. Additionally, they found that better financial literacy was associated with greater white matter integrity using diffusion tensor imaging in right-hemispheric temporo-parietal regions [27].

Several of the studies referenced above have noted the angular gyrus (AG) is implicated in FC. As part of the heteromodal parietal association cortex, the AG is considered an important cross-modal hub that conveys and integrates information between different modalities and processing subsystems where converging multi-sensory information is combined and integrated. Among its other functions which include memory processes, reading and comprehension, and attention, number processing has been shown to be a core function of the AG in early neuroimaging studies [28]. These results have further been replicated with high consistency across functional studies testing different numerical operations and tasks in healthy adults [29, 30]. The left AG has been shown to be involved in the verbal coding of numbers because it is strongly activated during small problems of addition and multiplication that requires arithmetic fact retrieval and transfer though the exact laterality and specialization of left versus right AG is not entirely conclusive [31 –33].

Taken together, prior neuroimaging and neurocognitive research suggest a linkage between brain structure, cognition, and functional capacity. The elucidation of neural networks through fMRI has been a powerful tool to connect complex cognition and behavioral traits. In contrast to other more data-driven approaches of fMRI analysis including independent component analysis, seed-based functional connectivity, also called ROI-based functional connectivity, finds regions correlated with the activity in a seed region. The coupling of activation between two, often anatomically disparate, regions suggests that they are involved in the same underlying functional processes and are thus functionally connected. Seed-based analysis requires a priori determination of seeds and is often based on a hypothesis or prior results. To date, we are unaware of research specifically investigating the relationship of FC and brain network connectivity. The present analysis utilizes rs-fMRI, FC, and neurocognitive testing to examine if such a relationship exists. We hypothesize that connectivity between brain regions responsible for calculation (angular gyrus), as well as decision-making and attention (prefrontal cortical regions) will be associated with performance on the FCI-SF, suggesting that a key fronto-parietal network is associated with FC.

METHODS

Participants and study design

Study participants included well-characterized community-dwelling volunteers enrolled in the present study examining the neural substrates of FC. Participants were recruited through the Clinical Core of the Johns Hopkins Alzheimer’s Disease Research Center, the Johns Hopkins Memory and Alzheimer’s Treatment Center, associated Johns Hopkins clinics, and the community. All participants received medical, neurological, and psychiatric evaluations detailed below, as well as extensive neuropsychological testing, FC assessment, and comprehensive self-report and informant-based scales. Additionally, each participant underwent a structural and functional brain MRI scan (detailed below). All participants were ≥ 65 years of age, had no history of major neurological or neuropsychiatric disease other than MCI, listed English as their primary language, had a minimum of 12 years of education, were right-handed and had an informant who could provide information about their daily functioning. Based on this clinical information (excluding imaging), diagnoses of cognitively normal or MCI were made as follows. Cognitively normal participants had a Clinical Dementia Rating (CDR) [34, 35] of 0. Participants with MCI were non-demented, had mild memory problems, a CDR of 0.5, and met criteria for MCI as detailed in Albert et al. [36] for single or multiple domains [37]. Diagnoses for MCI were further determined by consensus which included study members MN, PR, and CM. Informed consent was obtained prior to the initiation of the study in accordance with the requirements of the Johns Hopkins Institutional Review Board. Consent followed guidelines endorsed by the Alzheimer’s Association for participation of cognitively impaired individuals.

Neuropsychological and financial capacity assessments

Cognitive function was assessed using the following instruments: 1) general cognitive abilities with the Mini-Mental State Examination [38]; 2) access to semantic information with Animal Fluency [39]; 3) executive functioning with the Trail Making Test A and B [40], and the Controlled Oral Word Association Test [41]; 4) memory function with the Hopkins Verbal Learning Test-Revised (HVLT-R) [42]; 5) visuo-spatial function with the Clock Drawing Task [43]; 6) overall functional ability with the Clinical Dementia Rating scale [34], the Informant Questionnaire, and the Functional Assessment Questionnaire; 7) depressive symptoms with the Geriatric Depression Scale Short Form; and 8) neuropsychiatric symptoms with the Neuropsychiatric Inventory Questionnaire.

The financial capacity instrument (FCI) is a standardized, well-validated psychometric instrument of FC. The FCI assesses financial abilities of older adults based on a conceptual model that includes financial tasks that are either basic or complex [44]. The FCI Short Form (FCI-SF) (FCAP™) is an abbreviated version of the FCI that comprises 37 items and can be used to evaluate performance of basic financial skills in 15 minutes. The FCI-SF assesses four domains of everyday financial activity: monetary calculation skills, financial conceptual knowledge, understanding and using a checkbook and register, and understanding and using a bank statement. Item scores are summed to establish a series of four component performance scores (Mental Calculation, Financial Conceptual Knowledge, Using a Checkbook/Register, and Using a Bank Statement), and also an FCI-SF Total Score (range 0–74), with higher scores indicating better financial skills. Of note, 50 points of the total scored points are designated for completion of financial skills involving checkbook management. In addition to financial performance, the FCI-SF assesses processing speed as a dimension of financial ability. Time to completion scores (in seconds) are obtained for four FCI-SF tasks, which generates a composite time score for the two checkbook tasks, and a composite score for all timed tasks (range 0–670 s), with higher scores indicating slower performance [9 , 44]. Normative data and basic statistical characteristics for the FCI-SF have been previously published by Gerstenecker et al. [22]

MRI scan acquisition and rs-fMRI image processing

Functional MRI data were collected during rest using a T2^*-weighted gradient echo planar imaging (EPI) pulse sequence: TR = 2000 ms, TE = 30 ms, flip angle = 61°, oblique axial acquisition, FOV = 240 mm, matrix = 80×80, 20 slices, 3.0 mm slice thickness, 1 mm gap, SENSE factor = 2, spatial resolution 3×3×3 mm³. Total scan time was approximately 7 min.

rs-fMRI data were analyzed using the CONN toolbox in SPM 12 [45]. Standard preprocessing was implemented using the CONN Toolbox’s default pipeline for volume-based analysis. First, all volumes of the functional scan were realigned and unwarped to reduce effects of participant motion during the scan. Next, slice-timing correction was applied to correct for temporal misalignment. High motion volumes were identified as outliers using the default setting (framewise displacement of above 0.9 mm or changes in signal above 5 standard deviations of the global mean) and scrubbed by removing volumes with displacement greater than 0.9 mm. No participants were excluded from the analysis due to excessive motion. The functional and structural images were registered to MNI template space. The structural image was segmented into grey matter, white matter, and cerebrospinal fluid (CSF) by iteratively performing tissue classification and estimating the posterior tissue probability maps from the intensity values of the respective reference image, registering the images to MNI space (MNI template –2 mm), and by estimating the non-linear spatial transformation that best approximates the posterior and prior tissue probability maps until convergence. The functional and structural images were resampled to 2 mm/1 mm isotropic voxels respectively. Next, the functional images were smoothed using a three-dimensional Gaussian kernel of 8 mm at full width at half-maximum to increase the signal-to-noise ratio.

Each participant’s tissue class segmentation maps were used as masks to extract global signal for whole brain, and signal associated with white matter and CSF. The mean activation time-series obtained from these regions (whole brain, white matter, and CSF) were added as temporal confounds and regressed from the data. Effects of noise and low frequency drift were eliminated by applying a band-pass filter (0.008 to 0.09 Hz). Seed-based connectivity maps were calculated using the left Angular Gyrus (lAG) as a seed region of interest defined by the FSL Harvard-Oxford atlas provided in the CONN toolbox. In the second-level analysis, bivariate correlation coefficients were calculated between the lAG and all other voxels of the brain, which generated a seed to voxel correlation map for each participant. The correlation coefficients were then converted to normalized z-scores using Fisher’s transformation. The group-level analysis consisted of comparing MCI participants with cognitively normal participants using a voxel-wise threshold of p < 0.001 and a cluster size of K > 30, corrected for multiple comparisons defining clusters of voxels that show significantly altered connectivity with the lAG between groups.

Statistical analysis

Differences in baseline characteristics between cognitively normal older adults and MCI participants were examined using two-sample t-tests with a significance threshold of p < 0.05. Based on previously reported imaging findings (described above) in this population, the lAG was used as the seed region for functional connectivity measurements. [20, 21] Connectivity correlation coefficients were calculated for each connection that showed significantly altered connectivity between cognitively normal and MCI participants. A Pearson’s correlation (uncorrected for multiple comparisons) was then calculated between the connectivity correlation coefficients from relevant regions and cognitive measures including the FCI-SF.

RESULTS

Participant characteristics (Table 1)

A total of 32 participants were recruited (15 cognitively normal older adults and 17 MCI participants). There were no differences between groups with regards to age, sex, or education. Consistent with their diagnosis, MCI participants were more cognitively impaired compared on the MMSE, HVLT-R, part B of the Trail Making Test, clock drawing, and animal fluency, but no significant differences were found on the COWAT and part A of the Trail Making Test when compared to cognitively normal older adults. Performance on the FCI-SF was also found to differ between groups such that MCI participants performed worse (lower total score) and took longer to complete the instrument (higher total time) than cognitively normal participants.

Table 1

Participant characteristics

	NC (N = 15)	MCI (N = 17)	2-Sample	p
	Mean (SD)	Mean (SD)	T-test
Age	72.71 (4.54)	68.69 (21.40)	–0.71	0.48
Sex (% Female)	53.30	58.80
Education, y	16.86 (1.68)	16.29 (3.91)	–0.52	0.60
IQCODE, mean	2.17 (1.36)	2.41 (1.86)	0.42	0.67
FAQ	0.27 (0.90)	6.50 (5.31)	3.83	0.001
NPIQ	0.63 (1.02)	3.80 (1.61)	5.40	<0.001
MMSE	29.00 (0.84)	25.64 (3.04)	–4.12	<0.001
FCI-SF Total Score	68.33 (5.67)	58.11 (10.51)	–3.35	0.002
FCI-SF Total Seconds	324.06 (118.48)	445.47 (117.34)	2.90	0.007
COWAT Total Score	41.86 (8.99)	44.47 (12.66)	0.66	0.51
HVLT-R	7.93 (2.65)	3.23 (3.28)	–4.40	<0.001
Clock Drawing	9.66 (0.48)	8.35 (1.61)	–3.02	0.005
TMT part A Seconds	34.40 (13.34)	40.17 (11.83)	1.29	0.20
TMT part B Seconds	82.00 (31.70)	106.64 (36.29)	2.03	0.05
Animal Fluency Total	23.00 (4.07)	16.23 (4.30)	–4.54	<0.001

NC, normal control; MCI, mild cognitive impairment; IQCODE, Informant Questionnaire for Cognitive Impairment in Dementia; FAQ, Functional Assessment Questionnaire; NPI-Q, Neuropsychiatric Inventory Questionnaire; MMSE, Mini-Mental State Exam; FCI-SF, Financial Capacity Instrument Short Form; COWAT, Controlled Oral Word Association Test; HVLT-R, Hopkins Verbal Learning Test, Revised Edition (words recalled after delay); TMT, Trail Making Test.

Association of clinical measures and FCI-SF

A separate analysis using linear regression was used to investigate the associations between cognitive measures and performance on the FCI-SF in MCI participants. No significant associations were found between the total time score (s) of the FCI-SF and cognitive measures; however, we did find that FCI-SF total points were associated with the MMSE (R = 1.96, p = 0.026) and time to complete part B of the Trail Making Test (R = –0.16, p = 0.038) where faster time was associated with better score on the FCI-SF (Fig. 1A, B).

Fig. 1

A) Linear regression of FCI-SF versus Trails B (r = –0.16, p = 0.038) in MCI participants. B) Linear regression of FCI-SF total score versus MMSE (r = 1.96, p = 0.026) in MCI participants.

Connectivity analysis and association with cognitive measures and FC

To examine the relationship between FC and brain resting state functional connectivity, we used the left angular gyrus as the seed region because of its well-established role in numerical calculation and prior research as a neural correlate of FC [20, 21]. Using the standard brain template in the CONN toolbox, we found that 26 brain regions showed significant connectivity with the lAG where a contrast of diagnosis was introduced into the GLM model as a regressor in the second-level analysis. Of these 26 regions, 14 were selected because of their role in higher-order cognition and carried forward into a Pearson’s correlation with the FCI total score. Of the 14 regions, connectivity of the right temporal fusiform cortex (rTFC) showed a negative association with the FCI-SF (rTFC, r = -0.455, p = 0.009) (Fig. 2). Additionally, a negative correlation was observed between clock drawing and increased connectivity with the intraparietal sulcus (r = –0.38, p = 0.03); a positive correlation was observed between Trails B and increased connectivity with the postcentral gyrus (r = 0.39, p = 0.03) and the left central operculum (r = 0.36, p = 0.04) (Table 2).

Fig. 2

Areas of altered functional connectivity with the lAG in NC compared to MCI. Gray indicates seed region. Blue regions reflect increased connectivity while red regions reflect decreased connectivity.

Table 2

Pearson correlation of cognitive measures with functional connectivity regions

	FCI-SF	AF	COWAT	CLOCK	Trails A	Trails B
	r	r	r	r	r	r
	p	p	p	p	p	p
Right central operculum	–0.16	–0.16	–0.084	–0.098	0.29	0.17
	0.37	0.38	0.65	0.59	0.10	0.34
Right postcentral gyrus	–0.25	–0.34	–0.14	–0.25	0.34	0.39
	0.16	0.057	0.43	0.16	0.058	0.029
Right precentral gyrus	–0.11	–0.34	–0.27	–0.13	0.13	0.22
	0.53	0.059	0.13	0.49	0.48	0.22
Right Intraparietal sulcus	–0.27	–0.057	0.0027	–0.38	0.37	0.18
	0.14	0.76	0.99	0.032	0.038	0.33
Right superior parietal lobule	–0.31	–0.17	–0.023	–0.21	0.41	0.17
	0.083	0.36	0.90	0.26	0.019	0.35
Left central operculum	–0.20	–0.027	–0.19	–0.13	0.28	0.36
	0.28	0.88	0.30	0.48	0.13	0.043
Left Frontal operculum	–0.25	–0.27	0.094	–0.27	0.0063	0.22
	0.17	0.13	0.61	0.14	0.97	0.23
Right parietal operculum	0.0001	–0.029	0.034	0.045	0.20	0.022
	1.00	0.87	0.86	0.81	0.28	0.94
Left insular cortex	–0.047	–0.080	0.074	–0.011	0.19	0.22
	0.80	0.66	0.69	0.95	0.29	0.23
Left postcentral gyrus	–0.023	–0.10	–0.33	–0.12	0.16	0.14
	0.90	0.58	0.065	0.50	0.37	0.46
Left middle temporal gyrus	0.27	0.13	0.17	0.24	–0.26	–0.35
	0.13	0.48	0.34	0.18	0.16	0.052
Right temporal fusiform cortex	–0.46	–0.088	0.082	–0.055	0.028	0.16
	0.0088	0.63	0.65	0.77	0.88	0.39
Left superior parietal lobule	–0.16	–0.12	–0.16	–0.23	0.25	0.19
	0.37	0.50	0.37	0.21	0.17	0.30
Left Planum Temporale	0.047	–0.081	–0.042	–0.052	0.18	0.20
	0.80	0.66	0.82	0.78	0.32	0.28

FCI-SF, Financial Capacity Instrument Short Form; AF, Animal Fluency; COWAT, Controlled Oral Word Association Test; CLOCK, Clock Drawing Task; Trails, Trail Making Test.

DISCUSSION

This exploratory, cross-sectional, observational study aimed to examine the neural basis associated with changes in financial capacity in participants with MCI. Because of the growing incidence of dementia syndromes, an improved understanding of the underlying biology of these key functions may enhance the development of targeted and effective interventions to improve impaired every-day functions, specifically impaired personal financial function. Although previous research has established that FC is associated with changes in several key brain regions, this study has used a seed-based fMRI approach with a well-validated measure of FC (FCI-SF) to understand potential mechanisms of every day financial function in older individuals with mild cognitive impairment. Given the association between executive functions and FC demonstrated in this study and prior work, we hypothesized that connectivity between the angular gyrus (seed region) and regions of the prefrontal cortex would show the most robust associations with FC. Instead, our preliminary results show that connectivity between the angular gyrus and the temporal fusiform cortex was significantly associated with FC as assessed by the FCI-SF.

The temporal fusiform cortex is part of the ventral visual stream and has an important role in higher order processing of visual information. It is also associated with memory and multisensory integration and perception [46]. As described in the introduction, the angular gyrus is known to be associated with number processing, calculation, as well as reading and comprehension, and memory retrieval [47]. Taken together, these two regions appear to have functional overlap particularly in the areas of vision and memory. In the context of FC, this finding might suggest an integration of visuoperception, memory, and mathematical function. This relationship is further supported by the study by Niccolai et al. [18] showing that written arithmetic, visual confrontation naming, visuospatial memory, and visual attention were closely related to FC.

Using the lAG as the seed region resulted in significant connectivity correlations associated with other cognitive measures. Notably, we found that cognitive measures involving executive (Trails A and B) and visuospatial skills (CLOCK) were associated with regions primarily responsible for symbolic processing of numerical information, visuospatial attention and working memory (intraparietal sulcus and superior parietal lobule). Trails B was associated with connectivity with the postcentral gyrus and parietal opercular region suggesting high integration with somatosensory function. Taken together, these findings likely represent the highly connected nature of the angular gyrus with regions of reciprocating function.

While these are interesting and novel findings, this study has several limitations. First, the sample size (N = 32) was limited, and the Pearson’s correlation results were not corrected for multiple comparisons. As such, these data should be viewed as preliminary and exploratory, and interpretation of our results should be taken with caution. In spite of this, and in comparison to similar other exploratory analyses, however, we found statistically significant differences and associations in participant characteristics, neurocognitive measures, and connectivity analyses. Future analyses with larger numbers of participants will be important to more fully test hypotheses involving the relationships between cognitive measures, FC subdomains, and putative brain region function. For example, examining one or sets of FCI-SF subdomains, as detailed earlier, may reveal interesting associations with neuropsychological measures and their functional neuroanatomic correlates. This approach would build on the present analysis by examining FC at a more granular level. Additionally, a larger sample size would allow brain connectivity analysis without the need for a seed region allowing for a data-driven approach to understanding mechanisms of FC that minimize upfront a priori assumptions. Finally, while the FCI-SF has been shown to be a valid measure of FC, the full expanded version of FCI measures FC across 9 domains (basic monetary skills, financial conceptual knowledge, cash transactions, checkbook management, bank statement management, financial judgment, bill payment, and knowledge of personal financial assets and estate arrangements) in comparison to the FCI-SF’s four domains. Future research and analyses may make use of the full FCI as well as discrete elements of financial ability embedded within structured measures such as the CDR to further explore neurocognitive-functional relationships.

Finally, we reassert that the ultimate goal of this line of research is to develop therapies and interventions directed at key lost functional abilities, specifically impaired personal financial functions. FC is a complex cognitive and neurobiological construct with impact at multiple levels: individual, family, financial and other institutions, and society at large. Additionally, FC is an important area of public health concern as reviewed by the Institute of Medicine, which has also sponsored an initiative with the Social Security Administration focused on FC in elders and their beneficiaries [48]. Moreover, other governmental (e.g., FINRA) and advocacy groups (AARP) have identified declining financial capacity as an area of significant population and societal importance. Developing interventions to address this growing problem will be important for providing not just safety to older individuals, but also for maintaining independence and dignity as they age. With this in mind, building a cohesive model of neural function for FC is a key foundational step to demonstrating target engagement in early-phase intervention trials. We hope that these data will be expanded upon by others who view impairment in FC and other key functional abilities as areas to make a positive difference in the lives of older individuals, especially in the absence of a cure for AD and related dementias.

Footnotes

ACKNOWLEDGMENTS

The research presented in this manuscript was funded by the National Institute on Aging grant K23AG055626-01.

Authors’ disclosures available online ().

References

(2020) 2019 Profile of Older Americans The Administration for Community Living. https://acl.gov/sites/default/files/Aging%20and%20Disability%20in%20America/2019ProfileOlderAmericans508.pdf

Alzheimer’s Association (2019) 2019 Alzheimer’s disease facts and figures. Alzheimers Dement 15, 321–387.

Nowrangi

, Rosenberg

, Leoutsakos

(2016) Subtle changes in daily functioning predict conversion from normal to mild cognitive impairment or dementia: An analysis of the NACC database. Int Psychogeriatr 28, 2009–2018.

Garre-Olmo

, Planas-Pujol

, Lopez-Pousa

, Juvinya

, Vila

, Vilalta-Franch

, Frailty, Dependence in Girona Study Group (2009) Prevalence and risk factors of suspected elder abuse subtypes in people aged 75 and older. J Am Geriatr Soc 57, 815–822.

Razani

, Kakos

, Orieta-Barbalace

, Wong

, Casas

, Lu

, Alessi

, Josephson

(2007) Predicting caregiver burden from daily functional abilities of patients with mild dementia. J Am Geriatr Soc 55, 1415–1420.

Filakovic

, Eric

, Mihanovic

, Glavina

, Molnar

(2011) Dementia and legal competency. Coll Antropol 35, 463–469.

Karlawish

(2008) Measuring decision-making capacity in cognitively impaired individuals. Neurosignals 16, 91–98.

Marson

, Hebert

(2008) Financial capacity. In Encylopedia of Psychology and the Law, Cutler

, ed. Sage, Thousand Oaks, CA, pp. 313–316.

Marson

, Sawrie

, Snyder

, McInturff

, Stalvey

, Boothe

, Aldridge

, Chatterjee

, Harrell

(2000) Assessing financial capacity in patients with Alzheimer disease: A conceptual model and prototype instrument. Arch Neurol 57, 877–884.

10.

Moye

, Marson

(2007) Assessment of decision-making capacity in older adults: An emerging area of practice and research. J Gerontol B Psychol Sci Soc Sci 62, P3–P11.

11.

Farias

, Mungas

, Reed

, Harvey

, Cahn-Weiner

, Decarli

(2006) MCI is associated with deficits in everyday functioning. Alzheimer Dis Assoc Disord 20, 217–223.

12.

Boyle

, Malloy

, Salloway

, Cahn-Weiner

, Cohen

, Cummings

(2003) Executive dysfunction and apathy predict functional impairment in Alzheimer disease. Am J Geriatr Psychiatry 11, 214–221.

13.

Royall

, Palmer

, Chiodo

, Polk

(2005) Executive control mediates memory’s association with change in instrumental activities of daily living: The Freedom House Study. J Am Geriatr Soc 53, 11–17.

14.

Okonkwo

, Wadley

, Griffith

, Ball

, Marson

(2006) Cognitive correlates of financial abilities in mild cognitive impairment. J Am Geriatr Soc 54, 1745–1750.

15.

Sherod

, Griffith

, Copeland

, Belue

, Krzywanski

, Zamrini

, Harrell

, Clark

, Brockington

, Powers

, Marson

(2009) Neurocognitive predictors of financial capacity across the dementia spectrum: Normal aging, mild cognitive impairment, and Alzheimer’s disease. J Int Neuropsychol Soc 15, 258–267.

16.

Marson

, Martin

, Wadley

, Griffith

, Snyder

, Goode

, Kinney

, Nicholas

, Steele

, Anderson

, Zamrini

, Raman

, Bartolucci

, Harrell

(2009) Clinical interview assessment of financial capacity in older adults with mild cognitive impairment and Alzheimer’s disease. J Am Geriatr Soc 57, 806–814.

17.

Nowrangi

, Lyketsos

, Rao

, Munro

(2014) Systematic review of neuroimaging correlates of executive functioning: Converging evidence from different clinical populations. J Neuropsychiatry Clin Neurosci 26, 114–125.

18.

Niccolai

, Triebel

, Gerstenecker

, McPherson

, Cutter

, Martin

, Marson

(2017) Neurocognitive predictors of declining financial capacity in persons with mild cognitive impairment. Clin Gerontol 40, 14–23.

19.

Griffith

, Stewart

, Stoeckel

, Okonkwo

, den Hollander

, Martin

, Belue

, Copeland

, Harrell

, Brockington

, Clark

, Marson

(2010) Magnetic resonance imaging volume of the angular gyri predicts financial skill deficits in people with amnestic mild cognitive impairment. J Am Geriatr Soc 58, 265–274.

20.

Stoeckel

, Stewart

, Griffith

, Triebel

, Okonkwo

, den Hollander

, Martin

, Belue

, Copeland

, Harrell

, Brockington

, Clark

, Marson

(2013) MRI volume of the medial frontal cortex predicts financial capacity in patients with mild Alzheimer’s disease. Brain Imaging Behav 7, 282–292.

21.

Giannouli

, Tsolaki

(2019) Are left angular gyrus and amygdala volumes important for financial capacity inmild cognitive impairment? Hell J Nucl Med 22(Suppl), 160–164.

22.

Gerstenecker

, Eakin

, Triebel

, Martin

, Swenson-Dravis

, Petersen

, Marson

(2016) Age and education corrected older adult normative data for a short form version of the Financial Capacity Instrument. Psychol Assess 28, 737–749.

23.

Gonzalez

, Tommasi

, Briggs

, Properzi

, Amariglio

, Marshall

, Alzheimer’s Disease Neuroimaging I (2021) Financial capacity and regional cerebral tau in cognitively normal older adults, mild cognitive impairment, and Alzheimer’s disease dementia. J Alzheimers Dis 79, 1133–1142.

24.

Weissberger

, Mosqueda

, Nguyen

, Axelrod

, Nguyen

, Boyle

, Spreng

, Han

(2020) Functional connectivity correlates of perceived financial exploitation in older adults. Front Aging Neurosci 12, 583433.

25.

Spreng

, Cassidy

, Darboh

, DuPre

, Lockrow

, Setton

, Turner

(2017) Financial exploitation is associated with structural and functional brain differences in healthy older adults. J Gerontol A Biol Sci Med Sci 72, 1365–1368.

26.

Han

, Boyle

, Yu

, Fleischman

, Arfanakis

, Leurgans

, Bennett

(2014) Financial literacy is associated with medial brain region functional connectivity in old age. Arch Gerontol Geriatr 59, 429–438.

27.

Han

, Boyle

, Arfanakis

, Fleischman

, Yu

, James

, Bennett

(2016) Financial literacy is associated with white matter integrity in old age. Neuroimage 130, 223–229.

28.

Roland

, Friberg

(1985) Localization of cortical areas activated by thinking. J Neurophysiol 53, 1219–1243.

29.

Dehaene

, Dehaene-Lambertz

, Cohen

(1998) Abstract representations of numbers in the animal and human brain. Trends Neurosci 21, 355–361.

30.

Dehaene

, Piazza

, Pinel

, Cohen

(2003) Three parietal circuits for number processing. Cogn Neuropsychol 20, 487–506.

31.

Cappelletti

, Lee

, Freeman

, Price

(2010) The role of right and left parietal lobes in the conceptual processing of numbers. J Cogn Neurosci 22, 331–346.

32.

Cappelletti

, Barth

, Fregni

, Spelke

, Pascual-Leone

(2007) rTMS over the intraparietal sulcus disrupts numerosity processing. Exp Brain Res 179, 631–642.

33.

Grabner

, Ansari

, Koschutnig

, Reishofer

, Ebner

, Neuper

(2009) To retrieve or to calculate? Left angular gyrus mediates the retrieval of arithmetic facts during problem solving. Neuropsychologia 47, 604–608.

34.

Hughes

, Berg

, Danziger

, Coben

, Martin

(1982) A new clinical scale for the staging of dementia. Br J Psychiatry 140, 566–572.

35.

Morris

(1993) The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology 43, 2412–2414.

36.

Albert

, DeKosky

, Dickson

, Dubois

, Feldman

, Fox

, Gamst

, Holtzman

, Jagust

, Petersen

, Snyder

, Carrillo

, Thies

, Phelps

(2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 270–279.

37.

Petersen

(2004) Mild cognitive impairment as a diagnostic entity. J Intern Med 256, 183–194.

38.

Folstein

, Folstein

, McHugh

(1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12, 189–198.

39.

Lezak

(2004c) Verbal functions and language skills. In Neuropsychological Assessment, Lezak

, ed. Oxford University Press, Oxford.

40.

Reitan

(1955) The relation of the trail making test to organic brain damage. J Consult Psychol 19, 393–394.

41.

Ruff

, Light

, Parker

, Levin

(1996) Benton Controlled Oral Word Association Test: Reliability and updated norms. Arch Clin Neuropsychol 11, 329–338.

42.

Miotto

, Campanholo

, Rodrigues

, Serrao

, Lucia

, Scaff

(2012) Hopkins verbal learning test-revised and brief visuospatial memory test-revised: Preliminary normative data for the Brazilian population. Arq Neuropsiquiatr 70, 962–965.

43.

Goodglass

HKE

(1983) The Assessment of Aphasia and Related Disorders, Williams & Wilkins.

44.

Griffith

, Belue

, Sicola

, Krzywanski

, Zamrini

, Harrell

, Marson

(2003) Impaired financial abilities in mild cognitive impairment: A direct assessment approach. Neurology 60, 449–457.

45.

Whitfield-Gabrieli

, Nieto-Castanon

(2012) Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2, 125–141.

46.

Weiner

, Zilles

(2016) The anatomical and functional specialization of the fusiform gyrus. Neuropsychologia 83, 48–62.

47.

Seghier

(2013) The angular gyrus: Multiple functions and multiple subdivisions. Neuroscientist 19, 43–61.

48.

Committee on the Public Health Dimensions of Cognitive Aging; Board on Health Sciences Policy; Institute of Medicine (2015) Cognitive Aging: Progress in Understanding and Opportunities for Action, Blazer

, Yaffe

, Liverman

, eds. National Academies Press (US), Washington, DC.