The fMRI deconvolution toolbox is a Python package that has been developed on top of the fMRIsim module of BrainIAK. provide some additional information that will permit cognitive neuroscience researchers to develop optimal designs for many common experimental designs used in fMRI.
Currently, the toolbox only supports two conditions that are alternating in a sequence such as A-B-A-B-A-B... where A and B represent a cue and a target respectively, similiar to attention related fMRI designs (Hopfinger 2000, Kastner 1999).
A future release will include support for multiple stimuli in a trial similar to a event related trial by trial fMRI design.
Authors: Soukhin Das (UC Davis) & Weigang Yi (UC Davis), 2022.
This toolbox implements the methods proposed in paper:
Das S, Yi W, Ding M and Mangun GR (2023) Optimizing cognitive neuroscience experiments for separating event- related fMRI BOLD responses in non-randomized alternating designs. Front. Neuroimaging 2:1068616. doi: 10.3389/fnimg.2023.1068616
If it helps you, please kindly cite this paper. https://www.frontiersin.org/articles/10.3389/fnimg.2023.1068616/full
If you have git, you can fetch a local copy:
git clone https://github.com/soukhind2/deconv.git
Otherwise, you can download from here: Download Toolbox
To run jupyter notebooks, open a Terminal and cd to the toolbox directory and run:
python3 -m notebook --allow-root --nobrowser --ip=0.0.0.0
To install the dependencies, run:
pip -r install requirements.txt
To get started with the toobox, refer to the Jupyter notebooks provided.
Testing Different Parameter Combinations
Testing Different Null Ratio Combinations