2-back task in split-belt adaptation
Rossi, Cristina et al. (2021), 2-back task in split-belt adaptation, Dryad, Dataset, https://doi.org/10.5061/dryad.18931zcxm
We studied split-belt treadmill adaptation and savings in young (21±2 y/o) and older (56±6 y/o) adults with or without a secondary 2-back task during adaptation. We here provide here kinematic data for split-belt treadmill walking - specifically, step length asymmetry, double support asymmetry, and limb excursion asymmetry measure. We provide raw response data (button presses and reaction time) for performance in the 2-back cognitive task. We also provide scripts that can be used to analyse motor and cognitive data using bootstrapping.
1. Description of methods used for collection/generation of data:
Motor data was collected using Optotrak Motion Capture system at 100Hz, cognitive data was collected using MATLAB. Please refer to the manuscript for detailed methodology.
2. Methods for processing the data:
For motor data, gaps in raw data were filled using the MATLAB function "interp1" with method "pchip". The data was filtered with a 4th order butterworth low pass filter with normalized cutoff frequency of 6/50. Then, heel strikes and toe offs were detected for each strided based on kinematic data. Heel strike was taken to be the position of the ankle furthest ahead in the direction of walking, and toe off was taken to be the position of the ankle furthest behind in the direction of walking. All other processing (including that for cognitive data) is described in the manuscript.
All Usage Notes can be found in the README.txt file uploaded.
Raw data (1. and 2.) is located in the "data" folder.
1. Motor data:
a. File List:
- YAST_MOTOR.mat : motor data for YASingle group
- YADT_MOTOR.mat : motor data for YADual group
- OAST_MOTOR.mat : motor data for OASingle group
- OADT_MOTOR.mat : motor data for OADual group
b. File Description: Each file contains the following kinematic measures (further description is provided in the manuscript):
- DSF: double support time of the fast leg
- DSS: double support time of the slow leg
- FSL: step length of the fast leg
- SSL: step length of the slow leg
- LEF: limb excursion of the fast leg
- LES: limb excursion of the slow leg
Each of the kinematic variables is a 10x10000x7 matrix, where each row contains data for one subject, each of the 7 3rd dimensions contains data for a separate phase of the experiment (baseline, adaptation, 4x washout blocks, readaptation), and each column contains data for one stride (padded with nans at the end).
Each file also contains a DT dummy variable, structured in the same way as the kinematic variables, that is 1 for strides when the participant was dual-tasking, and 0 for other trials.
2. Cognitive data:
a. File List:
- YADT_COGNITIVE.mat : n-back task data for YADual group
- OADT_COGNITIVE.mat : n-back task data for OADual group
- OADN_COGNITIVE.mat : n-back task data for OADualNoise group
b. File Description: Each file contains the following variables:
- groupScoreArray: contains response accuracy to the n-back task, where 1 is a correct response (press stimuli), 0 is a correct omission (do not press), -1 is an incorrect response (do not press), -2 is an incorrect omission (press)
- groupTimeArray: contains reaction time in seconds for responses
- Each variable is a struct with fields:
- "walk": a 125x10 matrix containing data from the baseline task
- "adapt": a 250x10 matrix containing data from the adaptation task
- in both matrices, each column is a subject and each row is a letter stimuli
3. Data generated using scripts below (not the original dataset; it is provided here as it takes a while to generate and it is needed to plot figures, but it can be generated de novo with the scripts and data provided here).
a. Bootstrapped data: Provided in a zipped folder "bootstraps.zip", it must be unzipped before running figures code.
b. Statistics: Provided in a zipped folder "statistics.zip", it must be unzipped before running figures code.
All scripts are in MATLAB (.m extension).
- bootstrap_subjects: creates 10000 samples of 10 subjects, which will be used by the other boostrap scripts
- bootstrap_motor: bootstraps 10000 samples of bins for step length asymmetry, limb excursion asymmetry, double support asymmetry
- bootstrap_cognitive: bootstraps 10000 samples of logistic fits of error rate to press and do not press stimuli of the n-back task, and linear fits of reaction time
- boostrap_doubleexp: bootstraps 1000 samples of double exponential fits to step length asymmetry in adaptation and readaptation
- statistics_motor: computes confidence intervals related to the motor data (step length asymmetry, limb excursion asymmetry, double support asymmetry)
- statistics_cognitive: computes confidence intervals related to the cognitive data (error rate with press and do not press stimuli, and reaction time)
- statistics_doubleexp: computes confidence intervals related to the double exponential fits of adaptation and readaptation step length asymmetry
- figure_2_5_6: creates figures 2, 5 and 6
- figure_3_7: creates figures 3 and 7
- figure_4_8: creates figures 4 and 8
- supfigure1: creates supplementary figure 1
- supfigure2: creates supplementary figure 2
- supfigure3: creates supplementary figure 3
- createallfigures: run this script to make figures 2 through 8 and suppl. figures 1 and 2
4. Helper functions:
- bssem: computes bootstrap standard error
- sem: computes standard error
- computeCI: computes confidence interval
- ccc: clears workspace and figures
- filldata, getaxis, getaxpos, getcolors, plotsproperties, setnox, setnoy: helper functions for figure-making
National Institutes of Health, Award: K12 HD055929
National Institutes of Health, Award: R03 HD104217
Magistro Family Foundation, Award: research grant
National Institutes of Health, Award: 5 R37 NS090610
National Institutes of Health, Award: R21 AG059184
National Institutes of Health, Award: R56 NS100528
National Institutes of Health, Award: R2 1NS120274
American Heart Association, Award: predoctoral fellowship