DOI: 10.5061/dryad.g4f4qrfvf
title: Effects of brain maintenance and cognitive reserve on age-related decline in three cognitive abilities

description: Objectives Age-related cognitive changes can be influenced by both brain
 maintenance (BM), which refers to the relative absence over time of
 changes in neural resources or neuropathologic changes, and cognitive
 reserve (CR), which encompasses brain processes that allow for
 better-than-expected behavioral performance given the degree of
 life-course related brain changes. This study evaluated the effects of
 age, BM, and CR on longitudinal changes over two visits, 5 years apart, in
 three cognitive abilities that capture most of age-related variability.
 Method Participants included 254 healthy adults aged 20–80 years at
 recruitment. Potential BM was estimated using whole brain cortical
 thickness and white matter mean diffusivity at both visits. Education and
 IQ (estimated with AMNART) were tested as moderating factors for cognitive
 changes in the three cognitive abilities. Results Consistent with BM—after
 accounting for age, sex, and baseline performance—individual differences
 in the preservation of mean diffusivity and cortical thickness were
 independently associated with relative preservation in the three
 abilities. Consistent with CR—after accounting for age, sex, baseline
 performance, and structural brain changes—higher IQ, but not education,
 was associated with reduced 5-year decline in Reasoning (?=0.387,
 p=0.002), and education was associated with reduced decline in Speed
 (?=0.237, p=0.039). Discussion These results demonstrate that both CR and
 BM can moderate cognitive changes in healthy aging and that the two
 mechanisms can make differential contributions to preserved cognition.
description: <strong>Participants.</strong> The participants were
 drawn from our ongoing studies at Columbia University Irving Medical
 Center: The Reference Ability Neural Network (RANN) study and the
 Cognitive Reserve (CR) study (Stern et al., 2018; Stern et al., 2014).
 Subjects were recruited primarily by randomized market mailing. An initial
 telephone screening determined whether participants met basic inclusion
 criteria (i.e., right‐handed, English speaking, no psychiatric or
 neurological disorders, and normal or corrected‐to‐normal vision).
 Potentially eligible participants were further screened in person with
 structured medical and neuropsychological evaluations to ensure that they
 had no neurological or psychiatric conditions, cognitive impairment, or
 contraindication for MRI scanning. Global cognitive functioning was
 assessed with the Mattis Dementia Rating Scale (Lucas et al., 1998), on
 which a minimum score of 130 was required for retention in the study. In
 addition, participants who met diagnostic criteria for mild cognitive
 impairment were excluded. The studies were approved by the Internal Review
 Board of the College of Physicians and Surgeons of Columbia University.
 Additional details about procedures can be found in previous
 reports (Habeck, Gazes, et al., 2016; Stern et al., 2014). This study is
 currently in the process of completing a five-year follow-up on all
 participants using the same procedures. The current analysis included 254
 participants who were assessed at both baseline and follow-up and had data
 from at least one of the 23 cognitive tasks that comprise the reference
 abilities at either time point, in which 209 participants also had
 pre-post testing on cognitive tasks during fMRI studies (fMRI data not
 examined in this manuscript).
 <strong>Procedure.</strong> Out-of-scanner tasks were
 administered in one visit while in-scanner tasks were administered over
 the course of two 2-hour scanning sessions, with six activation tasks
 administered in each session. Prior to each scan session, computerized
 training was administered for the six tasks to be administered during that
 session. At the completion of training for each task, participants had the
 option of repeating the training. For all tasks except Picture Naming,
 responses were differential button presses. During training, responses
 were on the computer keyboard. During scans, they were made on the
 LUMItouch response system (Photon Control Company). 
 <strong>Tasks administered out of
 scanner.</strong> As described in Salthouse (2015), twelve measures
 were selected from a battery of neuropsychological tests to assess
 cognitive functioning. Reasoning was assessed with scores on three
 different tests: WAIS III Block design task, WAIS III Letter–Number Sequencing test, and WAIS III Matrix Reasoning test. For processing speed, the Digit Symbol subtest from the Wechsler Adult Intelligence Scale-Revised (Wechsler, 1981), Part A of the Trail making test, and the Color naming component of the Stroop test were chosen. Three memory measures were based on sub-scores of the Selective Reminding Task (SRT) (Buschke & Fuld, 2011): the long-term storage sub-score, continuous long-term retrieval, and the number of words recalled on the last trial. Vocabulary (not included in analyses reported in this manuscript) was assessed with scores on the vocabulary subtest from the WAIS III, the Wechsler Test of Adult Reading (WTAR), and the American National Adult Reading Test (AMNART) (Grober & Sliwinski, 1991). <strong>Tasks administered in the scanner. </strong>As described in Habeck et al. (2016) and Stern et al. (2014) twelve tasks were administered in the scanner and their behavioral performance measures were computed. Reasoning was assessed with the proportion of correct trials from Paper Folding (Ekstrom et al., 1979), Matrix Reasoning (Raven, 1962) and Letter Sets (Ekstrom et al., 1979). For processing speed mean reaction times on accurate trials for Digit Symbol, Letter Comparison, and Pattern Comparison tasks (Salthouse et al., 1991) were used. Memory scores were measured as the proportion of correctly answered questions from Logical Memory, Word Order Recognition, and Paired Associates. Three vocabulary measures (not included in the current analyses) were the proportion of correct responses for Synonyms, Antonyms (Salthouse, 1993), and Picture Naming (Salthouse, 1998).  <strong>Individual difference factors.</strong> Years of education was assessed with the Education Questionnaire (Manly et al., 2002)and an IQ score was estimated from the American National Adult Reading Test (Grober & Sliwinski, 1991). <strong>Image acquisition and processing</strong><strong>. </strong> There were two 2-hour MR imaging sessions to accommodate the twelve fMRI tasks as well as the additional imaging modalities. Relevant to the current study, T1-weighted MPRAGE scan was acquired to determine cortical thickness, with a TE/TR of 3/6.5 ​ms and Flip Angle of 8°, in-plane resolution of 256 x 256, field of view of 25.4 ​× ​25.4 ​cm, and 165–180 slices in axial direction with slice-thickness/gap of 1/0 ​mm. Two diffusion MRI were acquired in 56 directions using the following parameters: b = 800 s/mm2, TE = 69 ms, TR = 11,032 ms, flip angle = 90 degrees, in-plane resolution 112 x 112 voxels, acquisition time = 12 min 56 s, slice thickness = 2 mm (no gap), and 75 slices. In addition, BOLD fMRI for twelve tasks, FLAIR, ASL and a 7-minute resting BOLD scan were acquired but not reported in the current study. A neuroradiologist reviewed each subject's scans. Any significant findings were conveyed to the subject's primary care physician. Each subject's structural T1 scans were reconstructed using FreeSurfer v5.1 (http://surfer.nmr.mgh.harvard.edu/). This older version was used to maintain consistency across data for the parent study but with visual inspection already performed for every image, the quality of the parcellations was assured. The accuracy of FreeSurfer's subcortical segmentation and cortical parcellation (Fischl et al., 2004) has been reported to be comparable to manual labeling. Each subject's white and gray matter boundaries as well as gray matter and cerebral spinal fluid boundaries were visually inspected slice by slice, manual control points were added when any visible discrepancy was found, and reconstruction was repeated until we reached satisfactory results within every subject. The subcortical structure borders were plotted by TkMedit visualization tools and compared against the actual brain regions. In case of discrepancy, they were corrected manually. Finally, we computed the mean cortical thickness for each participant to be used in group-level analyses. Diffusion data were analyzed with version 3.0.1 of the software, MRtrix3 (www.mrtrix.org), starting with a set of preprocessing steps to improve data robustness: (1) denoising (Veraart et al., 2016), (2) Gibbs ring correction (Kellner et al., 2016), (3) corrections for motion and eddy currents (FSL eddy) (Andersson & Sotiropoulos, 2016) and (4) bias field correction (Tustison et al., 2010). Diffusion tensor models were estimated for the preprocessed data from which mean diffusivity (MD) was calculated for each participant. To calculate the mean MD for the white matter across the whole brain, each participant’s T1 structural scan was registered to the mean of the non-diffusion weighted images, on which FreeSurfer parcellation was again performed. The white matter mask derived from FreeSurfer was then used to quantify each participant’s mean MD across all white matter in the brain, resulting in one MD value per participant and used in LCSM described below.
description: The data were analyzed with R.