DOI: 10.5061/dryad.qz612jmrm
title: A neural implementation of cognitive reserve: insights from a longitudinal fMRI study of set-switching in aging

description: The dataset (Data.mat) comprises variables extracted from demographic
 information, cognitive task performance, and MRI/fMRI imaging of a
 longitudinal study of 52 adults aged 60–71, evaluated at baseline and
 after a 5-year follow-up. The analysis code is structured into
 two main codes: (A) Main Analysis in MATLAB
 (Code1_MainAnalysis_Matlab.m), which utilizes the primary dataset
 (Data.mat) to examine brain variables, model cognitive trajectories, and
 prepare data for further interaction analysis. (B) Interaction
 Visualization in R (Code2_Johnson-Neyman_R.R), which produces interaction
 effect visualizations using the output from MATLAB analysis.
description: This dataset does not contain any direct fMRI data. The fMRI data
 in our study were analyzed using a specific technique called Ordinal Trend
 Canonical Variates Analysis (OrT CVA). Through this approach, we derived a
 measure known as the OrT score, which was calculated for both single and
 dual-task conditions. This OrT score is the only fMRI-related variable
 included in the dataset that we uploaded. To provide
 further details, OrT CVA is a multivariate data-driven technique that
 identifies patterns of regional functional activation that show a
 monotonic change across multiple experimental conditions (in the current
 study single and dual conditions). The extracted functional activation
 patterns, called ordinal trends (OrT), indicate sustained activity across
 graduated increase in task demand (Habeck, Krakauer, et al., 2005; Habeck,
 Rakitin, et al., 2005). The technique utilizes a specialized design matrix
 to enhance variance contributions from patterns that exhibit
 within-subject increases in pattern scores from single to dual conditions.
 The test statistic that is used to assess the significance of the task
 condition relationship of the derived activation pattern is the number of
 exceptions i.e. the number of individuals showing decreased pattern
 expression from single to dual condition and thus violate the majority
 rule of an increase. A null distribution is generated using a permutation
 test with 1,000 iterations, where condition assignments are randomized
 within participants. The p-value is determined by the fraction of times
 the permutation test yields a number of exceptions as low or lower than
 the point estimate. To ensure the robustness of voxel loadings in the
 derived pattern, a simple bootstrap technique is employed. The data are
 resampled with replacement (without randomizing subject and condition
 assignments), and the analytic point-estimate process is repeated 500
 times.  Z-values for the voxel loadings are computed as the ratio of the
 point estimate of the loading divided by the bootstrap standard deviation
 around this point estimate. The OrT score which is the average level of
 activation of the identified patterns is computed for both single and dual
 conditions at baseline.
description: # A neural implementation of cognitive reserve: insights from a
 longitudinal fMRI study of set-switching in aging This document provides
 documentation for the dataset and code used in the paper by Fatemeh
 Hasanzadeh, Christian Habeck, Yunglin Gazes, and Yaakov Stern. ##
 **Dataset: Data.mat** The following section describes the
 variables contained in the Data.mat file, which includes longitudinal
 measurements from 52 older adults at baseline and 5-year follow-up. The
 dataset contains demographic, behavioral, and neuroimaging data collected
 as part of the study. **Demographic and Study Variables** | **Variable** |
 **Description** | | :----------------- |
 :--------------------------------------------------- | | subid | Unique
 participant identifier | | TimePoint | Study timepoint (1 = baseline, 2 =
 5-year follow-up) | | scannerID | MRI scanner identifier (1= Philips/ 3=
 PRISMA) | | Age | Participant age at each time point | | Sex | Participant
 biological sex (1=Male , 2=Female) | | IntraCranialVolume | intracranial
 volume | **Behavioral Measures** | **Variable** | **Description** | |
 :------------------------ |
 :-------------------------------------------------------------------- | |
 ECF\_Sing\_propCor | Proportion of correct responses in single-task
 condition | | ECF\_Dual\_propCor | Proportion of correct responses in
 dual-task condition | | ECF\_Sing\_CORmedRespTime | Median response time\*
 for correct responses in single-task (seconds) | |
 ECF\_Dual\_CORmedRespTime | Median response time for correct responses in
 dual-task (seconds) | | SwitchCost\_RT | Switch cost (difference between
 dual and single-task response time) | *response time or reaction time are
 the same. **Functional MRI Measures** | **Variable** | **Description** | |
 :----------- |
 :--------------------------------------------------------------- | |
 OrTSingle | Ordinal Trend pattern expression score for single-task
 condition | | OrTDual | Ordinal Trend pattern expression score for
 dual-task condition | **Structural MRI Measures** Naming convention: *
 lh_* : Left hemisphere regions * rh_* : Right hemisphere regions For each
 cortical region, the dataset includes two types of measurements: * Volume
 measurements (e.g., 'lh_bankssts_volume') * Thickness
 measurements (e.g., 'lh_bankssts_thickness') **Important Notes**
 * All volume measurements are in cubic millimeters (mm³) * All thickness
 measurements are in millimeters (mm) * Brain regions are segmented based
 on the Desikan-Killiany Atlas * Structural measures were processed using
 FreeSurfer v5.1.3 * Scanner harmonization was performed using the COMBAT
 method * The dataset includes measurements from two different scanners: *
 Baseline: 3.0T Philips Achieva scanner (all participants) * Follow-up: *
 39 participants in Philips Achieva * 13 participants on 3.0T Siemens
 PRISMA ## **Code Structure and Analysis Pipeline** The analysis consists
 of two main codes: **A. Code1_MainAnalysis_Matlab.m– MATLAB Code including
 Main Analysis** **1-Data Preparation** * Loads and organizes longitudinal
 data from 52 older adults * Extracts baseline and follow-up measurements
 **2-Behavioral Analysis** * Performs repeated measures ANOVA on reaction
 times * Analyzes single and dual-task conditions * Calculates and
 visualizes individual and population trajectories of switch costs across
 time points **3-Longitudinal Change Analysis** * Obtain change scores
 between baseline and follow-up * Calculates differential Ordinal Trend
 (dOrT) scores * Processes demographic and clinical variables **4-Brain
 Structure Analysis** * Implements elastic net regression on brain measures
 * Combines volume and thickness measurements from multiple regions *
 Generates a brain change index predicting switch cost changes **5-Main
 Statistical Model** * Fits linear model examining: * Brain change index
 and dOrT effects on task performance * dOrT moderation effects * Control
 variables (age, sex, scanner, etc.) * Includes outlier removal using the
 IQR method * Generates data for the Johnson-Neyman plot **Input**:
 Data.mat (main dataset) **Generated Outputs**: *
 Selected_BrainVariables_ElasticNet.csv (selected brain regions) *
 Trajectories of Switch Costs.tif (visualization) * Model_Main.csv (model
 coefficients) * Data4JNplot.csv (data for Interaction Analysis)  **B.
 Code2_Johnson-Neyman_R.R – R code including Interaction visualization**
 Secondary analysis in R Creates simple slopes and Johnson-Neyman plots for
 interaction visualization **Input**: 'Data4JNplot.csv’ (output from
 MATLAB analysis) **Generated Outputs**: * SimpleSlopesPlot.tif
 (visualization) * Johnson-NeymanPlot.tif (visualization)