# Does the heart forget? Modulation of cardiac activity induced by inhibitory control over emotional memories. Legrand, N., Etard, O., Vandevelde, A., Pierre, M., Viader, F., Clochon, P., Doidy, F., Peschanski, D., Eustache, F. & Gagnepain, P. (2018). Preprint version 3.0, doi: https://www.biorxiv.org/content/10.1101/376954v3 This repository contains data, scripts and Jupyter notebook needed to reproduce analyses from the preprint version of the paper. # Abstract >Effort to suppress past experiences from conscious awareness can lead to forgetting. It remains largely unknown whether emotions, including their physiological causes, are also impacted by such memory suppression. In two studies, we measured in healthy participants the aftereffect of suppressing negative memories on cardiac response. Results of Study 1 revealed that an efficient control of memories was associated with a long-term inhibition of the cardiac deceleration normally induced by disgusting stimuli. Attempts to suppress sad memories, on the opposite, aggravated cardiac response, an effect that was largely related to the inability to forget this specific material. In Study 2, we found using electroencephalography that a prominent neural marker of inhibitory control, a suppression of the 5-9 Hz frequency band, was related to the subsequent inhibition of the cardiac response. These results demonstrate that suppressing memories also influence the cardiac system, opening new avenues for treating intrusive memories. # Data Behavioral data from Study 1 (n=28) and Study 2 (n=24) are provided in `data/Emotion.csv`, `data/Recall.csv` and `data/Intrusions.csv`. Preprocessed ECG are provided in `ECG*.txt` files. # Code ## Notebooks Figures and statistical models can be reproduced via two Jupyter Notebook. * `Behavioral.ipynb` plots the behavioral results and implement the statistical models (require Rpy2). * `ECG.ipynb` gives additional details on the preprocessing steps of the ECG recording. ## Scripts ### Preprocessing * `1_run_filter.py` Set montage, filter raw data 1-30 Hz and average reference. * `2_run_epochs.py` Epoch and clean data with RANSAC. * `3_run_ica.py` Remove eye activity using ICA. * `4_run_autoreject.py` Reject/correct artifact with Autoreject. ### Time-frequency * `5_extract_tfr.py` Filter the epoched data (3-30 Hz, bin = 1 Hz). * `6_tf_statistcs.py` Average tf, plot and run permutation tests.