Computational and neural mechanisms of statistical pain learning
Suyi Zhang, Ben Seymour, Flavia Mancini
Mancini, F., Zhang, S. & Seymour, B. Computational and neural mechanisms of statistical pain learning. Nat Commun 13, 6613 (2022). https://doi.org/10.1038/s41467-022-34283-9
An older version of the paper: doi: https://doi.org/10.1101/2021.10.21.465270
Usage
The code in folder exp_code is used to generate the sequence of stimuli. The experiment is launched by the matlab function exp_MR_1500ms(sub,sess,stimCurrent,MR_state). See detailed comments inside the exp_MR_1500ms.m file.
For behavioural data analysis, the following directories contain code for specific use.
- data (behavioural data from fMRI sessions)
- model_fit (fit models to behavioural data)
- model_comparison (performs model comparison)
- model_gen (generate parametric modulators for fMRI analyses using fitted model parameters)
For imaging analysis,
- imaging (1st and 2nd level analysis scripts based on nipype)
- imaging_plot (result visualisation using nilearn)
Please change data paths and parameter settings within the scripts. The analysis code is written by Suyi Zhang.
The raw MRI data are available on OpenNeuro.
Requirements
To run the code for sequence generation, you will need:
- MATLAB
- Psychotoolbox 3
- a DAQ
- a stimulus generator
To run the code for behavioural analyses, you will need the following:
For imaging analyses, the required python packages are listed in requirements.txt. Nipype scripts are best run inside its docker/singularity container, a useful tutorial can be found here.