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**Project Overview:** --------------------- This page contains data, analysis scripts, and experiment code associated with: Meidenbauer, K. L., Choe, K. W., Cardenas-Iniguez, C., Huppert, T. J., & Berman, M. G. (2021). Load-Dependent Relationships between Frontal fNIRS Activity and Performance: A Data-Driven PLS Approach. *NeuroImage, 117795*. Update log: * 1/28/2021 -- behavioral data analysis code, scripts, and results are updated from previous version to include analysis of RT data; slightly more documented version of Brain AnalyzIR toolbox analysis script (contains toolbox version info) uploaded. **Project Components** ---------------------- ### Data ### **Behavioral data** contains accuracy and reaction time data for each of the n-back conditions (including practice blocks) for all 68 participants as well as age and gender. Only 62 participants with usable fNIRS data are analyzed in the published work - see Rmarkdown analysis script (N-back_Accuracy_RT_analysis_usablefNIRS.Rmd) for which subjects to be removed from overall data frame (file updated 1.26.21) **fNIRS Raw data** contains the *.nirs files which are read into the Brain AnalyzIR toolbox (code: fNIRS_nback_analysis_20201218.m) **Supporting data** - *channels_ROIs.csv* lists the ROIs for each channel (source-detector pair), derived from the Brain AnalyzIR toolbox's depth map function (based on talairach daemon parcellation) - *nb_perf.csv* is the accuracy data in the format necessary for running behavioral PLS (stacked by condition first, then subject - see createmats4pls.m script for more details) ### Analysis Code ### **Behavioral Data Analysis Code** contains the Rmarkdown file (*N-back_Accuracy_RT_analysis_usablefNIRS.Rmd*) used to analyze accuracy and reaction time data (updated 1.26.21) by n-back level and produce the accuracy and RT boxplots **Brain AnalyzIR Toolbox Code** contains the analysis script (*fNIRS_nback_analysis_20201218.m*) for conducting task-based fNIRS analysis - Note: To run this script, you must first download the toolbox from the github site: - If using this code, please cite both this project AND the toolbox paper -- Santosa, H., Zhai, X., Fishburn, F., & Huppert, T. (2018). The NIRS Brain AnalyzIR Toolbox. Algorithms, 11(5), 73. - If planning to run PLS, the final section of this script outputs the subject-level activation stats needed to run the PLS analysis scripts below. **PLS Analysis Code** contains 2 Matlab scripts needed to run the behavioral PLS analysis - *createmats4pls.m* takes the subject-level activation beta values output from the final section of the fNIRS_nback_analysis.m script and creates .mat files needed to run behavioral PLS - *Run_BehavPLS_fnirs_nback.m* performs the behavioral PLS analysis using the files generated from createmats4pls.m and saves the output (*BehavPLS_fnirs_hbr_betas_Nback.mat*) - Note: to run PLS, first need to download the PLS code from ### Results ### **Behavioral Results** contains a pdf of the accuracy and RT analysis output created by the Rmarkdown file (*N-back_Accuracy_RT_analysis_usablefNIRS.Rmd*) **fNIRS Activation Results** contains group-level statistics for task-evoked change and contrasts - *ContrastStats.csv* contains statistics for all n-back contrasts (1-back vs. 2-back, 2-back vs. 3-back, and 1-back vs. 3-back) - *GroupStats.csv* contains the statistics for activation relative to baseline for each N-back level **PLS Results** contains the output from *Run_BehavPLS_fnirs_nback.m* - *BehavPLS_fnirs_hbr_betas_NBack.mat* has the latent variable (LV) probabilities, the boostrap ratios (bsrs) for each of the 43 channels (see *channels_ROIs.csv* for cross-reference of channel #s), and a results structure with more detailed PLS output information - *LV1_plot.fig* and *LV1_plot.png* are the figures created by the PLS analysis code for LV 1 ### N-back Experiment Code ### **** contains all files needed to run the n-back experiment - In its current form (as of 8/10/20), it contains the N-back task described in the paper as well as several elements related to a separate study aim (video and additional block of 3-back task). - It can be run with or without practice trials, and with or without fNIRS triggers. fNIRS triggers are sent using the lab-streaming layer (LSL) in the NIRx Aurora fNIRS acquisition software.
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