This toolbox (pronounced as "Max") evaluates general linear models (GLMs) for functional magnetic resonance imaging (fMRI) data estimated in Statistical Parametric Mapping (SPM). MACS includes classical, information-theoretic and Bayesian methods of model assessment previously applied to GLMs for fMRI as well as recent methodological developments of model selection [1] and model averaging [2] in fMRI data analysis [3].
This is MACS V1.3, also referred to as MACS R2018b, released on 31/12/2018. The developers intend to immediately commit bug fixes to this repository and provide a general update two times a year. A toolbox paper has been published in a peer-reviewed journal [3] and a toolbox manual is included in the repository [4].
To install the toolbox, it has to be downloaded and placed as a subdirectory "MACS" into the SPM toolbox folder. Upon starting SPM, batch modules for toolbox features can be accessed by clicking "SPM -> Tools -> MACS Toolbox" in the SPM batch editor [3, Fig. 3; 4, Fig. 1]. MACS is optimized for SPM12, but also compatible with SPM8.
The repository includes a number of sub-directories:
MACS_Examples: SPM batch editor job files for example analyses from the toolbox paper [3, Sec. 4]MACS_Pipelines: SPM template batches/script for cvBMS [1], cvBMA [2] and model space definitionMACS_Extensions: MATLAB scripts for toolbox extensions as described in the manual [4, Sec. 15]MACS_Manual: TEX and PDF file belonging to the latest version of the toolbox manual
[1] https://www.sciencedirect.com/science/article/pii/S1053811916303615
[2] https://www.sciencedirect.com/science/article/pii/S105381191730527X
[3] https://www.sciencedirect.com/science/article/pii/S0165027018301468
[4] https://github.com/JoramSoch/MACS/blob/master/MACS_Manual/Manual.pdf