DOI: 10.5061/dryad.18931zcxm
title: 2-back task in split-belt adaptation

description: 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.
description: 1. Description of methods used for collection/generation of
 data: <br> 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: <br> 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.
description: All Usage Notes can be found in the README.txt file
 uploaded. DATA OVERVIEW 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). <br> 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.   SCRIPTS OVERVIEW
 All scripts are in MATLAB (.m extension). 1.
 Bootstraps: 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  2. Statistics:
 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 3.
 Figures: 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