This repository contains the scripts necessary to preprocess, analyze, print and plot the results reported in the following paper:
Jangraw et al. (2023). "A Highly Replicable Decline in Mood During Rest and Simple Tasks." Nature Human Behaviour.
Code for preprocessing, fitting mixed effects models, and analysis and plotting of results will work from a conda environment. To get started, run the following commands:
conda create -p mooddrift -c ejolly -c conda-forge python=3.8.8 numpy=1.19.2 pandas=1.1.5 pytest=6.2.2 joblib=1.0.1 rpy2=3.4.3 matplotlib=3.3.4 seaborn=0.11.1 scikit-learn=0.24.1 numexpr=2.7.3 patsy=0.5.1 statsmodels=0.12.2 openpyxl=3.0.7 pymer4=0.7.3 r-psych=2.1.3 xlrd=2.0.1 r-mumin=1.43.17
conda activate ./mooddrift
git clone https://github.com/djangraw/MoodDrift.git
cd MoodDrift
pip install -e .
Data from the online participants described in the paper can be found in the following repository on the Open Science Framework: https://osf.io/km69z/. Some of our files are named "Passage of Time Dysphoria" instead of "Mood Drift" because this was our previous name for the phenomenon described in the paper.
After downloading and unzipping this file, the contents of the PassageOfTimeDysphoria_Data folder should be moved to the Data folder of this repository.
Data from the mobile app participants described in the paper are a subset of the dataset shared by Robb Rutledge's laboratory, now available at:
Rutledge, Robb B. (2021), Risky decision and happiness task: The Great Brain Experiment smartphone app, Dryad, Dataset, https://doi.org/10.5061/dryad.prr4xgxkk
After downloading and unzipping the dataset from the above repository, locate the file named Rutledge_GBE_risk_data.mat and copy it to the Data/PilotData folder of this repository before running the scripts described below.
If you need an environment for running the pytorch models, please open an issue and we'll work on adding this.
Data can be downloaded from the above locations to the Data folder of this repository. The scripts described below should then be run from the scripts folder (e.g. cd scripts;python ImportMmiData_May2020.py). Processed data files will be saved to the Data/OutFiles folder. Figures will be saved to the Figures folder.
We have broken down processing into 3 processing stages, outlined below.
Converts raw data into preprocessed files that combine across cohorts (or "batches") of participants. Run the following:
- ImportMmiData_May2020.py to import online adult cohorts
- ImportNimhMmiData.py to import online adolescent cohorts
- AssembleMmiBatches.py to combine across online cohorts for grouped analyses
- LoadRutledgeGbeData.py to import mobile app cohorts
- MakePytorchInputTable.py to format data for computational model fitting.
Fits the large-scale linear mixed effects (LME) model and the computational model described in the paper. The computational model scripts are computationally expensive and best run on a high-performance computing cluster. These scripts require considerable compute resources and pytorch which is not include in the conda environment built above. All outputs of these scripts are included in this repository thus they may be skipped if you just want to reproduce the figures. Executing the scripts in this section may require experience with pytorch and additional assistance from the authors :
- RunPymerOnCovidData_Aug2020.py to run the LME model. Must be run from a python environment with pymer4 installed.
- Tune_GBE_pytorch.py to tune the computational model's hyperparameters. Must have pytorch installed.
- Tune_GBE_pytorch_NoBetaT.py to do the same without the time-responsive beta_T term.
- Tune_GBE_pytorch.py to run the model with given hyperparameters. Must have pytorch installed.
- Run_GBE_pytorch_NoBetaT.py to do the same without the time-responsive beta_T term.
- CombineGbeConfirmResults.py to combine across computational model results for the confirmatory mobile app sub-cohorts, which were split apart to avoid memory errors.
Produces all the figures, tables, and printed results reported in the paper. Run the following:
- ProduceAllResults.py. Most scripts and functions in the Analysis folder will be called by this wrapper script.
- dep_effect.py. Checks whether depression-related results are driven by floor effects.
- TestControlHypotheses.py. Performs preregistered analyses on the follow-up "controls" dataset collected in 2021.
- TestTotalMwHypotheses.py. Performs modified versions of the preregistered analyses on the follow-up "mind-wandering" cohorts collected in 2021.
Cohorts were renamed after analysis to make them more intuitive in the paper. Here is a translation:
| Name in paper | Folder(s) in database |
|---|---|
| 15sRestBetween | Recovery1, RecoveryInstructed1 |
| 30sRestBetween | RecoveryInstructed1Freq0p5 |
| 7.5sRestBetween | RecoveryInstructed1Freq2 |
| 60sRestBetween | RecoveryInstructed1Freq0p25 |
| AlternateRating | Numbers |
| Expectation-7mRest | Expectation-7min |
| Expectation-12mRest | Expectation-12min |
| RestDownUp | RestDownUp |
| Daily-Rest-01 | Stability01-Rest |
| Daily-Rest-02 | Stability02-Rest |
| Weekly-Rest-01 | COVID01 |
| Weekly-Rest-02 | COVID02 |
| Weekly-Rest-03 | COVID03 |
| Adolescent-01 | RecoveryNimh |
| Adolescent-02 | RecoveryNimh |
| Visuomotor | Motion |
| Visuomotor-Feedback | MotionFeedback |
| RestAfterWins | Return1 |
| Daily-Closed-01 | Stability01-closed |
| Daily-Random-01 | Stability02-RandomVer2 |
| Activities | Activities |
| BoredomBeforeAndAfter | BoredomBeforeAndAfter |
| BoredomAfterOnly | BoredomAfterOnly |
| MwBeforeAndAfter | MwBeforeAndAfter |
| MwAfterOnly | MwAfterOnly |
| App-Exploratory | GbeExplore |
| App-Confirmatory | GbeConfirm |