**Project Overview:**
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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*. https://doi.org/10.1016/j.neuroimage.2021.117795
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**
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### 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: https://github.com/huppertt/nirs-toolbox
- 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. https://doi.org/10.3390/a11050073
- 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 https://www.rotman-baycrest.on.ca/index.php?section=84
### 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 ###
**psychopy_to_share.zip** 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.
