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61 Network Segregation Predicts Processing Speed in the Cognitively Healthy Oldest-old
- Sara A Nolin, Mary E Faulkner, Paul Stewart, Leland Fleming, Stacy Merritt, Roxanne F Rezaei, Pradyumna K Bharadwaj, Mary Kathryn Franchetti, Daniel A Raichlen, Courtney J Jessup, Lloyd Edwards, G Alex Hishaw, Emily J Van Etten, Theodore P Trouard, David S Geldmacher, Virginia G Wadley, Noam Alperin, Eric C Porges, Adam J Woods, Ronald A Cohen, Bonnie E Levin, Tatjana Rundek, Gene E Alexander, Kristina M Visscher
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 367-368
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Objective:
Understanding the factors contributing to optimal cognitive function throughout the aging process is essential to better understand successful cognitive aging. Processing speed is an age sensitive cognitive domain that usually declines early in the aging process; however, this cognitive skill is essential for other cognitive tasks and everyday functioning. Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brain’s organization, and the cognitive effects of aging may relate to this large-scale organization. To follow-up on our prior work, we sought to replicate our findings regarding network segregation’s relationship with processing speed. In order to address possible influences of node location or network membership we replicated the analysis across 4 different node sets.
Participants and Methods:Data were acquired as part of a multi-center study of 85+ cognitively normal individuals, the McKnight Brain Aging Registry (MBAR). For this analysis, we included 146 community-dwelling, cognitively unimpaired older adults, ages 85-99, who had undergone structural and BOLD resting state MRI scans and a battery of neuropsychological tests. Exploratory factor analysis identified the processing speed factor of interest. We preprocessed BOLD scans using fmriprep, Ciftify, and XCPEngine algorithms. We used 4 different sets of connectivity-based parcellation: 1)MBAR data used to define nodes and Power (2011) atlas used to determine node network membership, 2) Younger adults data used to define nodes (Chan 2014) and Power (2011) atlas used to determine node network membership, 3) Older adults data from a different study (Han 2018) used to define nodes and Power (2011) atlas used to determine node network membership, and 4) MBAR data used to define nodes and MBAR data based community detection used to determine node network membership.
Segregation (balance of within-network and between-network connections) was measured within the association system and three wellcharacterized networks: Default Mode Network (DMN), Cingulo-Opercular Network (CON), and Fronto-Parietal Network (FPN). Correlation between processing speed and association system and networks was performed for all 4 node sets.
Results:We replicated prior work and found the segregation of both the cortical association system, the segregation of FPN and DMN had a consistent relationship with processing speed across all node sets (association system range of correlations: r=.294 to .342, FPN: r=.254 to .272, DMN: r=.263 to .273). Additionally, compared to parcellations created with older adults, the parcellation created based on younger individuals showed attenuated and less robust findings as those with older adults (association system r=.263, FPN r=.255, DMN r=.263).
Conclusions:This study shows that network segregation of the oldest-old brain is closely linked with processing speed and this relationship is replicable across different node sets created with varied datasets. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating replicability and consistency of the finding that as essential cognitive skill, processing speed, is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging.
57 Association Between Adverse Childhood Experiences on Depression and Anxiety in Adulthood: Examining the Role of Cognitive Flexibility
- Rosario Pintos Lobo, Alexandria G. Nuccio, Zachary T. Goodman, Stacy S. Merritt, Xiaoyan Sun, Katalina F. McInerney, Bonnie E. Levin
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 842-843
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Objective:
The association between adverse childhood experiences (ACEs) and adult depression and anxiety has been well described (Aafjes-van Doorn et al., 2020; Dolbier et al., 2021; Herzog & Schmahl, 2018). However, cognitive flexibility, as a potential moderating factor of this relationship, has been underreported (Kalia et al., 2021). We hypothesize that increased ACEs will be associated with increased symptoms of depression and anxiety, and cognitive flexibility will exhert a moderating role in this relationship.
Participants and Methods:Participants from the Evelyn F. McKnight University of Miami Frailty Registry were included in the study. 224 adults (Mage= 66.30, SD = 11.63; 59.4% female; 62.1% Hispanic/Latinx) without primary neurological disorders were recruited from University of Miami clinics and community centers. Participants completed a demographic questionnaire and neuropsychological evaluation including the Adverse Childhood Experiences inventory, Beck Depression Inventory, Beck Anxiety Inventory, and the Wisconsin Card Sorting Test (WCST). Current data were initially analyzed using descriptive statistics and correlations among variables. A series of hierarchical multiple linear regressions (HLR) were conducted to examine the effect that age has on cognitive flexibility (measured by number of perseverative errors on the WCST), as well as the association between number of ACEs endorsed on symptoms of depression and anxiety in late life.
Results:Correlation analyses revealed a negative correlation between total ACE score and cognitive flexibility (r=-.16, p=0.03); a positive correlation between age and cognitive flexibility (r=0.19, p=0.01); and positive relationships between ACE score and both BDI (r=0.35, p<0.001) and BAI (r=0.28, p<0.001) scores. Correlations further revealed a negative correlation between cognitive flexibility and both BDI (r=-0.18, p=0.014) and BAI (r=-0.14, p=0.048) scores. A series of hierarchical multiple linear regressions revealed that total number of ACEs had a statistically significant effect on both depression (f=7.24, p<.001, ΔR2=0.072) and anxiety (f=4.57, p<.001, ΔR2=0.044) symptoms, in models adjusted for demographic correlates (i.e., age, sex, race, ethnicity). While the overall moderation model examining the effect of cognitive flexibility on the relationship between ACEs and psychopathology was significant (f=6.04, p<.001, ΔR2=0.191), the interaction was not significant (p=.4199). However, HLRs further revealed a statistically significant effect of age on cognitive flexibility (f=6.77, p=0.01, ΔR2=0.034).
Conclusions:Current findings support past research showing higher number of ACEs are associated with more symptoms of depression and anxiety in later life. However, cognitive flexibility did not moderate the relationship between ACEs and symptoms of depression and anxiery. This suggests cognitive flexibility might not play a significant role in the association between childhood trauma and symptoms of depression and anxiety in later life. Alternatively present results could be attributed to a small sample size, or the specific measure of cognitive flexibility used. This study expands on prior research highlighting the role of cognitive flexibility on age, with age serving as a prominent feature in the association between ACEs and adult depression and anxiety. Further research examining the role of cognitive flexibility in younger and middle years and its association with ACEs and psychopathology may provide unique insights on how to intervene earlier in the life course before cognitive flexibility begins to decline.
4 The Effect of Age on the Relationship Between Adverse Childhood Experiences and Frailty in Late Life: A Moderation Model
- Alexandria G. Nuccio, Rosario PInts Lobo, Zachary Goodman, Stacy S Merritt, Xiaoyan Sun, Katalina F. McInerney, Bonnie E. Levin
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 320-321
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Objective:
Although relationships between Fried frailty criteria (i.e., weakness, slowness, weight loss, exhaustion and low physical activity), cognitive decline, and adverse childhood experiences (ACEs) have been examined (Brigalo et al., 2015, Brown et al., 2022, Fabricio et al., 2020, & Tani et al., 2021), the moderating effect of age on the relationship between ACEs and frailty has yet to be explored. The present study examined whether age moderates the relationship between total number of ACEs and number of frailty criteria in older age.
Participants and Methods:137 older adults were recruited from University of Miami clinics and surrounding community care centers. Collected data included demographic information, number of frailty criteria met, and number of ACEs endorsed. Participants were primarily Hispanic-White (64.2%) and female (56.9%), with a mean age of 73.62 years (SD=6.252). Data were initially analyzed using descriptive statistics. A hierarchical linear regression was run to test the effect of ACE score on number of frailty criteria met. A simple moderation analysis using the PROCESS macro was then performed with total number of medical conditions included as a covariate to address any potentially confounding effects. To avoid multicollinearity issues, number of ACEs endorsed and age were mean centered and an interaction term between the two was produced.
Results:Scores on the ACE did substantially effect the total number of frailty criteria met by participants in this study (f=2.37, p=0.028, ΔR2=0.023), independent of number of medical conditions. The overall moderation model was significant (f=2.99, p=0.022, R2=0.103), and the addition of the interaction effect resulted in a statistically significant change to the model (f=4.08, p=0.045, ΔR2=0.035). Taken together, support for a moderating effect was found, specifically within the lower age group (65 - 71years), but not older (greater than 72 years) with ACE score positively predicting the number of frailty criteria met (b =0.230, t=2.62, p=0.010).
Conclusions:Results largely support the positive effect of ACE endorsement on the number of frailty criteria met in later life. Age acted as a moderating effect, for the younger old population, such that as number of ACEs endorsed increased, so too did the number of frailty criteria met. This finding highlights the importance of early intervention among those in younger late life who have experienced trauma. Given the positive relationship between frailty and cognitive decline in late life (Brigalo et al., 2015 & Fabricio et al., 2020), these findings also support the need for a better understanding of how childhood adversity impacts physical well-being over the life course.
Validity of the NIH toolbox cognitive battery in a healthy oldest-old 85+ sample
- Sara A. Nolin, Hannah Cowart, Stacy Merritt, Katalina McInerney, P. K. Bharadwaj, Mary Kate Franchetti, David A. Raichlen, Cortney J. Jessup, G. Alex Hishaw, Emily J. Van Etten, Theodore P. Trouard, David S. Geldmacher, Virginia G. Wadley, Eric S. Porges, Adam J. Woods, Ron A. Cohen, Bonnie E. Levin, Tatjana Rundek, Gene E. Alexander, Kristina M. Visscher
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue 6 / July 2023
- Published online by Cambridge University Press:
- 14 October 2022, pp. 605-614
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Objective:
To evaluate the construct validity of the NIH Toolbox Cognitive Battery (NIH TB-CB) in the healthy oldest-old (85+ years old).
Method:Our sample from the McKnight Brain Aging Registry consists of 179 individuals, 85 to 99 years of age, screened for memory, neurological, and psychiatric disorders. Using previous research methods on a sample of 85 + y/o adults, we conducted confirmatory factor analyses on models of NIH TB-CB and same domain standard neuropsychological measures. We hypothesized the five-factor model (Reading, Vocabulary, Memory, Working Memory, and Executive/Speed) would have the best fit, consistent with younger populations. We assessed confirmatory and discriminant validity. We also evaluated demographic and computer use predictors of NIH TB-CB composite scores.
Results:Findings suggest the six-factor model (Vocabulary, Reading, Memory, Working Memory, Executive, and Speed) had a better fit than alternative models. NIH TB-CB tests had good convergent and discriminant validity, though tests in the executive functioning domain had high inter-correlations with other cognitive domains. Computer use was strongly associated with higher NIH TB-CB overall and fluid cognition composite scores.
Conclusion:The NIH TB-CB is a valid assessment for the oldest-old samples, with relatively weak validity in the domain of executive functioning. Computer use’s impact on composite scores could be due to the executive demands of learning to use a tablet. Strong relationships of executive function with other cognitive domains could be due to cognitive dedifferentiation. Overall, the NIH TB-CB could be useful for testing cognition in the oldest-old and the impact of aging on cognition in older populations.