This repository contains SPM-compatible MATLAB code for estimating inverse transformed encoding models (ITEM) based on first-level general linear models (GLMs) for functional magnetic resonance imaging (fMRI) data [1,2]. The ITEM approach allows trial-wise linear decoding of discrete experimental conditions (classification) or continuous modulator variables (reconstruction) from multivariate fMRI signals.
An ITEM analysis would usually proceed in two steps:
- Construct trial-wise design matrix AND estimate trial-wise response amplitudes using
ITEM_est_1st_lvl. - a) Classify discrete experimental conditions from trial-wise parameter estimates via
ITEM_dec_class(_SL)OR
b) Reconstruct continuous modulator variable from trial-wise parameter estimates viaITEM_dec_recon(_SL).
The functions ITEM_dec_class and ITEM_dec_recon are written for ROI-based ITEM analysis, whereas the functions ITEM_dec_class_SL and ITEM_dec_recon_SL serve searchlight-based ITEM analysis. Type help ITEM_fct_name for information on input parameters of these functions. You may also use the review function by typing ITEM_review and selecting an SPM.mat in order to check intermediate results at any time. The code in this repository references some functionality from the MACS toolbox [4]. If this toolbox is on the path, functions not starting by ITEM_ are not required.
This software is in beta testing. Future improvements will include a user interface via SPM's batch editor and a software manual. Preliminary documentation can be found in a preprint uploaded to bioRxiv [1] and a paper published in NeuroImage [2]. In case of questions on the methodology or issues with the toolbox, please contact the corresponding author [3].
[1] https://www.biorxiv.org/content/10.1101/610626v3
[2] https://www.sciencedirect.com/science/article/pii/S1053811919310407
[3] mailto:[email protected]
[4] https://github.com/JoramSoch/MACS