Functional Neuroimaging analysis of OCD clinical trial data derived from baseline analysis. Check our baseline paper in Brain: Naze et al. (2022)
Note: The statistical analysis from the Clinical outcomes section is in the R parent directory (R v4.1.3) and is independent of the python package described below.
Tested on Ubuntu 20.04 Linux-5.8.0
It is strongly advised to install the package in a new virtual environment using python 3.9:
pyenv install 3.9.16 OCDenv
pyenv activate OCDenv
Then from the root of the OCDbaseline source repository (where the setup.py is located), type
pip install -e .
This work is an extension of the OCD baseline analysis (Naze et al. (2022)), now applied to the clinical trial data before and after Transcranial Magnetic Stimulation (TMS).
The project is packaged in this repository and have dependencies from the baseline repo/package.
The code performs:
- functional connectivity analysis: to perform seed-to-voxel analysis of neuroimaging data (fMRI) active vs. sham.
- power analysis: to analyze fractional amplitude low-frequency fluctuations (fALFF) differences between active and sham.
- network-based statistics:
- visualizations: to project stimulation sites besides cortical surface.
The functional analysis assumes that fMRIPrep has already been run. Before running the following scripts, ensure that the path to the project directory
proj_diris correctly set in those scripts and that the output folderderivativeshas been generated from fMRIPrep with its adequate content.
To perform several preprocessing steps (denoising, filtering, global signal regression, scrubbing, etc.); from the HPC cluster, run the following PBS script
preprocessing/prep_seed-to-voxel.pbs
This calls preprocessing/post_fmriprep_denoising.py with a set of default preprocessing parameters. See this file for more details about the preprocessing pipeline and the fmripop package.
Then, it calls functional/seed_to_voxel_analysis.py to compute fronto-striatal brain correlations using seeds from Harrison et al. (2009) and used in Naze et al. (2022).
The analysis focuses:
- on the changes of functional connectivity between the nucleus accumbens (NAcc) and the stimulation site in the orbito-frontal cortex (OFC), before and after treatment.
- on the change in BOLD signal power within the stimulation site , before and after treatment.
To extract those using a 5mm radius and a full-width half maxima spatial smoothing of 8mm, plot the kernel density extimates of the outputs and run the statistical analysis (two-way mixed ANOVA), run:
python functional/seed_to_voxel_analysis.py --compute_voi_corr --compute_ALFF --stim_radius 5 --brain_smoothing_fwhm 8 --save_outputs --plot_pointplot --print_stats
The output should look like this (exact values may differ according to parameters):
To run the network-based statistics analysis for the main effect between sessions (i.e. paired t-test on FC pre vs. post TMS, collapsing groups), using a threshold of 3.5 and 5000 permutations:
python functional/seed_to_voxel_analysis.py --compute_nbs --nbs_session --nbs_paired --nbs_thresh 3.5 --n_perm 5000
To run the NBS analysis on the interaction (i.e. unpaired t-test on group difference, using "pre minus post TMS" within group contrast):
python functional/seed_to_voxel_analysis.py --compute_nbs --nbs_thresh 3.5 --n_perm 5000
To visualize the output of the NBS analysis as spheres which size represent the node degree, run python graphics/ct_visuals.py --plot_figs --plot_surface --show_roi_degree
This work is licensed under a Creative Commons Attribution 4.0 International License.
This code was contributed by Sebastien Naze for QIMR Berghofer in 2021-2022.
Australian NHMRC Grant number # GN2001283 and clinical trial # ACTRN12616001687482