The first dataset consisted of data from 313 community-dwelling adults (mean age = 68.90 years, SD = 6.75 years, range = 54-88 years; 50.48% female), a subset of The Irish Longitudinal Study on Ageing (TILDA), a nationally representative longitudinal cohort study of older adults in Ireland (67,68). This data was collected during Wave 3 of the TILDA study (69). All participants were screened for MRI contraindications and study-specific inclusion criteria included: no history of neurological conditions and available data for CR proxies and cognitive function.
The second dataset consisted of data from 234 community-dwelling adults (mean age = 64.49 years, SD = 7.42 years, range = 50-80 years; 51.28% female) selected from participants in the Cognitive Reserve/Reference Ability Neural Network (CR/RANN) studies (70–72). Participants were screened for MRI contraindications, hearing and visual impairments, medical or psychiatric conditions, and dementia or MCI. Participants selected for the current analyses were aged 50 years or older with data available for CR proxies, cognitive function and MRI.
Measures: CR Proxies
Data was available for 5 socio-behavioural proxies in both datasets: Educational attainment, Occupational complexity, Verbal intelligence, Leisure activities, and Physical activity. In TILDA, further data was available for the proxies: Cognitively stimulating activities and Social engagement.
Educational attainment was measured using years of formal education in both datasets. In TILDA, participants were asked to indicate the age at which they first left continuous full-time education. This information was missing for 4 participants in the final sample (1.28%), so it was imputed using educational qualification, father’s education, age, sex, and rural residence during childhood as previously described (73).
Occupational complexity was measured using the complexity of work in the dimensions of data, people, and things (74) using ratings obtained from an online catalogue of the Dictionary of Occupational Tiles (DOT: www.occupationalinfo.org). Ratings for each dimension were reversed (such that higher scores reflected greater complexity) and then summed to create a total occupational complexity score, with scores ranging from 0 (minimal complexity) to 21 (maximal complexity). This was obtained for each participant’s current occupation or last occupation before retirement in TILDA and for participant’s occupation of longest duration of their lifetime in CR/RANN.
Verbal intelligence was measured using the total number of correctly pronounced words on the National Adult Reading Test (NART; Nelson & Willinson, 1982) in TILDA and the American National Adult Reading Test (AMNART; Grober & Sliwinski, 1991) in CR/RANN. In TILDA, a stress/anxiety-preventative and time-saving measure (78) was employed such that participants only completed the second half of the NART if they scored greater than 20 on the first half. A correction procedure was employed whereby scores of 0-11 were retained as full scores, but scores of 12-20 in participants who did not complete the second half were corrected using a conversion table outlined by Beardsall and Brayne (79) (80). Possible scores on the NART, in TILDA, ranged from 0 to 50 and on the AMNART, in CR/RANN, from 0 to 45. While the NART is often used to provide a measure of premorbid intelligence, we have labelled NART scores here as verbal intelligence in line with previous cognitive reserve studies (81,82).
Leisure activities were assessed in TILDA by participants rating their current frequency of engagement on an 8-point Likert scale (0=Never to 7=Daily/Almost Daily) in 9 activities: watching television, going to films/plays/concerts, travel, listening to music/radio, going to the pub, eating out, sports/exercise, visiting/talking on phone, and volunteering. In CR/RANN, participants rated their frequency of engagement over the preceding 6 months on a 3-point Likert scale (1 = Never to 3 = Often) in 17 activities: television/radio, cards/games, reading, lectures/concerts, theatre/movies, travel, walks/rides, crafts/hobbies, music, visiting, sports/dancing/exercise, cooking, group membership, collecting, religious activities, and volunteering. For both datasets, total scores were created by summing individual responses and possible scores ranged from 17 to 51.
Physical activity was assessed in TILDA by calculating the total metabolic minutes arising from self-reported physical activity over the last week using the International Physical Activity Questionnaire-Short Form (IPAQ-SF; Craig et al., 2003; Lee et al., 2011). This questionnaire assessed the time spent in 3 categories: vigorous, moderate, and walking. Responses were converted to metabolic equivalent minutes (83) and summed. In CR/RANN, physical activity was calculated using total metabolic hours arising from physical activity in an average week. The Godin leisure time exercise questionnaire (85) assessed the frequency of activity sessions lasting > 15 mins in 3 categories: strenuous, moderate, and mild exercise. Responses were then weighted by the average estimated duration of activity in each category (0.5, 0.75, 1 hr respectively) and their metabolic equivalent values (9, 5, 3; Ogino et al., 2019; Scarmeas et al., 2009).
Cognitively stimulating activities were assessed in TILDA with a questionnaire where participants rated their frequency of engagement on an 8-point Likert scale (0=Never to 7=Daily/Almost Daily) in 5 activities: attending classes and lectures, working in the garden/home or on a car, reading books/magazines, spending time on hobbies/creative activities, and playing cards/bingo/games. Total scores were created by summing individual responses and possible scores ranged from 0 to 35.
Social engagement was measured in TILDA using the Social Network Index (87) which provides a total score, ranging from 0 to 4, reflecting an individual’s degree of social connection (88).
Composite proxies were created by first standardising (z-scoring) individual proxies. Next, every unique combination of proxies was generated and the composite proxy was the average of those proxies. For TILDA, this produced 120 unique composite proxies. For CR/RANN, this resulted in 26 composite proxies.
To summarize, for TILDA there were 127 proxies in total (individual and composite) and 31 in total for CR/RANN. To attenuate possible effects of outliers, all proxies were Winsorized using a robust technique based on the median absolute deviation (89). Outliers were identified as values greater than a threshold of 3 median absolute deviations from the median. Identified outliers were replaced by the median +/- 3 median absolute deviations.
Measures: Cognitive Function
Verbal fluency was assessed using the total score on the Animal Naming Test which measures the ability to spontaneously produce the name of animals in one minute (78). The total number of animals named was used as the total score in both datasets.
Processing speed was measured using the time to complete the Colour Trails Task 1 (CTT 1; D’Elia et al., 1996) in TILDA and the Trail Making Task A (TMT A; Reitan, 1955) in CR/RANN. The CTT is considered a cross-culturally valid form of the TMT (78). Scores were reversed coded, such that higher scores reflected greater cognitive performance.
Executive function was assessed using the CTT 2 (D’Elia et al., 1996) in TILDA and the TMT B (Reitan, 1955) in CR/RANN. Both measures reflect the multi-dimensional executive function construct (92,93), specifically visual attention and cognitive flexibility with contributions from processing speed as well (78). The time taken to complete both tasks was used as the outcome measure. Scores were reversed coded such that higher scores reflected greater cognitive performance.
Episodic memory was measured in both datasets with a composite measure created using the average of standardized and Winsorized immediate and delayed recall variables. In TILDA, immediate and delayed recall were measured using a 10-item word list (94) as used originally in the Health and Retirement Study (95). The word list was assessed over 2 trials in TILDA and the average score for immediate and delayed recall from both trials were used. In CR/RANN, immediate and delayed recall were measured using the total and delayed recall scores from the Selective Reminding Test (SRT; Buschke & Fuld, 1974).
Global cognition was measured using a composite measure of all 5 cognitive variables in each dataset. Cognitive variables were Winsorized and standardised prior to creation of the composite. The composite variable was then Winsorized and standardised itself.
Measures: Brain Structure
T1-weighted 3D magnetization-prepared rapid gradient echo (MPRAGE) scans were acquired in both datasets using a 3T scanner (Achieva, Philips Medical Systems, The Netherlands). TILDA parameters: FOV = 240 * 240 * 162 mm3, matrix size = 288 * 288, slice thickness/gap = 0.9/0 mm, TR/TE = 6.7/3.1 ms. CR/RANN parameters: FOV = 256 * 256 * 180 mm3, matrix size = 256 * 256, slice thickness/gap = 1/0 mm, TR/TE = 6.5/3 ms.
T1-MRIs were inspected and processed in TILDA and CR/RANN using FreeSurfer v6.0 and v5.1 (97), respectively, as described previously (71,98). Total GM volume and hippocampal volume were obtained from Freesurfer and both were divided by Freesurfer’s estimated total intracranial volume. Brain images were parcellated using the Desikan Killiany atlas, with 34 cortical regions of interest (ROIs) per hemisphere (99). The mean cortical thickness of each cortical ROI was calculated. Overall cortical thickness was calculated as the mean over cortical ROIs. All variables were standardized and Winsorized (based on z-scores >|3|).
Fifteen individual brain structure-cognitive function models were created for each combination of brain structure and cognitive function variable, where one brain structure variable was selected as an independent variable and one cognitive function variable was selected as an outcome variable (Fig. 1). A moderated hierarchical regression (Fig. 1) was conducted within each brain structure-cognitive function model (n = 15) for each unique proxy (TILDA = 127; CR/RANN = 31). In Step 1, a cognitive measure was regressed on age, sex, and a measure of brain structure. In Step 2, a proxy variable was included as an independent variable. In Step 3, the interaction term for brain structure and the proxy was added.
To protect against violations of linear regression assumptions, the analysis was repeated using a robust regression, specifically an iteratively reweighted least squares regression with Tukey’s biweight function and median absolute deviation scaling. Significant effects within each dataset were only considered significant if they were statistically significant in both the linear regression and robust regression. To control for multiple comparisons and to ensure generalizability of findings, effects were only considered significant if they were statistically significant across both datasets. The analysis was conducted with customized Python code (available here: https://github.com/rorytboyle/hierarchical_regression) which used the statsmodels module (100). The change in R2 (i.e. amount of variance explained) from Step 1 to Step 2, and from Step 2 to Step 3 in linear regression models were used to assess the size of the independent and moderation effects of CR proxies, respectively. Where significant effects were observed, the mean R2 change across both datasets was calculated to assess the average additional variance explained by the proxy and its interaction with brain structure.