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abcdcon_RScripts
scripts Compare just RHits for spatial univariate. Sep 8, 2017
vendor
.gitignore
README.md
config.yml.example
hc_transitions.yml.example
requirements.txt
snaplabels_forMatlab.txt

README.md

ABCDCon

Repository details

This repository contains the code used to collect data and generate analyses for the following paper:

Dimsdale-Zucker, H. R., Ritchey, M., Ekstrom, A. D., Yonelinas, A. P., Ranganath, C. (2017). CA1 and CA3 differentially support spontaneous retrieval of episodic contexts within human hippocampal subfields. bioRxiv. doi: https://doi.org/10.1101/142349

The scripts contained here were used to collect and analyze data using Matlab r2014b and R version 3.3.2 run in RStudio and have not been tested for compatibility with later versions or across operating systems. They are provided for the purposes of openness and transparency of the data generation and analysis process.

Note, these are the scripts I used at the time of data collection and analysis and I might suggest more efficient or cleaner coding solutions to the same problems now (so use at your own risk and please be kind if you find bugs).

Environment setup

Setting paths

  1. Copy the config.yml.example file to config.yml and edit the paths for the current machine.
cp config.yml.example config.yml
vim config.yml

Python

  1. Install python
  2. Install python requirements:
pip install -r requirements.txt

R

  • Required pacakges:
    • dplyr (>= 0.4.0)
    • ggplot2 (>= 1.0.1)
    • halle (>=0.5.6)
    • lme4(>= 1.1-11)
    • R.matlab (>= 3.2.0)
    • reshape2(>= 1.4.1)
    • stringr (>= 1.0.0)
    • tidyr (>= 0.2.0)
    • yaml (>= 2.1.13)

Outside functions and packages

Code from other sources has been included in the vendor directory. This is not code that I wrote and therefore is subject to the usage license and instructions of those authors. It is included here out of convenience for those trying to run the scripts in the current repository.

Scripts

Variables that are common across Matlab scripts will be set by scripts/mri_analyses/initialize_ABCDCon.m (This will need to be added to your Matlab path).

Data collection

  • These scripts are all contained in scripts/run_task
  • For the study, scripts were run in the following order:
    1. ABCDCon_contextEnc.m
    2. ABCDCon_objectEnc.m
    3. ABCDCon_objectRecog_MRI.m
    4. ABCDCon_locationRecog.m

Behavioral data analysis

These scripts can be found in abcdcon_RScripts. They are setup as an R package for ease of loading required packages and functions, but this is probably a non-standard use of an R package. You should open abcdcon.Rproj and then run scripts from here (this should set the current working directory correctly without using setwd()).

  1. load_ABCDCon.R
  2. analyze_ABCDCon.R

MRI: Univariate analyses

Assumes have already run load_ABCDCon.R, downloaded .zip MRI files, converted dicom images, set subject-specific folder names, commonized folder names (e.g., run1 instead of whatever the MRI scanner outputs), and run data quality assurance. Scripts assume they are being run relative to scripts/mri_analyses.

  1. Preprocess the data using preprocess.m
  2. Generate regressors for the first-level analyses using generate_regressors.m
  3. Run first-level analyses using first_level.m
  4. Put contrast images into MNI space using new_segment_write_deformations_batch.m
  5. Smooth the resultant wcon images using a 3mm kernal. (I set this up manually in the SPM GUI.)
  6. Run second-level (group) analyses using second_level_job.m

MRI: Multivariate analyses

Assumes you have already preprocessed, run QA (to generate spike regressors), and have traced ROIs.

  1. Generate single-trial regressors using RSA_generate_single_trial_regressors.m
  2. Estimate the single trial betas using RSA_single_trial_models_batch.m
  3. Identify outlier beta timepoints using RSA_beta_timeseries_graphs.m to visually identify the group threshold and then RSA_beta_timeseries_id_outliers.m to mark excluded betas on a subject-by-subject basis
  4. Gather the ROIs of interest ensuring that have them split so can look at body separate from head and tail. This involves extracting the ROIs of interest from the ASHS tracing files (RSA_extractROIs.m), combining subfields (RSA_combine_rois.py), and splitting along the long axis (RSA_split_anterior_posterior.m).
    • In order to split into head/body/tail, you must manually identify transitions. The slice numbers that define the subject-unique transitions are written out into files; the splitting scripts will assume the transitions file is in the <RAW_BEHAVIORAL> directory for each subject and are called s*_hc_transitions.yml. Use the hc_transitions.yml.example file for consistent formatting.
  5. Reslice the ROIs into EPI space (if using ASHS, these will be in T2 space) using RSA_reslice_t2_and_ROIs_batch.m
  6. Binarize the ROIs so they can be used as masks using RSA_binarize_ROIs_batch.m
  7. Generate trial labels
    • This is a convoluted system that should be re-implemented differently if these analysis scripts were re-generated from scratch. However, what I did was first extract trial IDs from the single trial betas using RSA_trial_ids_from_betas.m and then use decode_single_trial_labels.R to mark trials that needed to be excluded (e.g., outlier betas).
  8. Extract beta values for trials of interest using RSA_btwn_runs_exclude_outlier_trials.m
  9. Calculate the pattern similarity values of interest using pattern_similarity_no_outlier_trials_load_data_btwn_runs.R
  10. Analyze using mixed models with mixed_models.R

Supplemental analyses

  1. Use FIR to estimate shape of univariate response in each subfield (Supplemental Figure 3):
  2. Generate FIR regressors: control_analysis_RSA_generate_single_trial_regressors_FIR.m
  3. Estimate FIR models: control_analysis_first_level_FIR.m
  4. Extract beta values: control_analysis_FIR_to_GLM_extract_betas.m
  5. Plot the data: control_analysis_FIR_to_GLM_plot_betas.R
  6. Estimate univariate activity within each ROI (Supplemental Figure 4):
  7. Grab univariate activity by ROI: control_analysis_univariate_contrast_estimates_by_roi.m
  8. Plot: abcdcon_RScripts/control_analysis_summarize_univar_contrast_estimates_by_roi.R
  9. Look at distribution of beta values (Supplemental Figure 6)
  10. Get trial-wise mean betas: control_analysis_betas_by_ROI.m
  11. Plot distrubtions: control_analysis_plot_betas_by_ROI.m
  12. Plot individual pattern similarity data (Supplemental Figure 7): control_analysis_plot_PS_matrices.R
  13. Estimate pattern similarity in "control" ROIs (Supplemental Figure 8): control_analysis_mixed_models_other_ROIs.R
  14. Randomly select trials so that all conditions have equal bin sizes (Supplemental Figure 9): control_analysis_matched_trial_numbers.R
  15. Correlate pattern similarity with reaction times (Supplemental Figure 10):
  16. control_analysis_PS_RT_correlations.R
  17. Remove voxels of influence:
  18. Identify influential voxels: control_analysis_drop_voxels.R
  19. Remove these voxels from pattern matrices: control_analysis_remove_top_voxels.m
  20. Re-compute PS without these voxels: control_analysis_PS_truncated_voxels.R
  21. Run stats: control_analysis_PS_truncated_voxels_mixed_models.R