an introduction to DMCC55B

It's hard to believe this is my first post of 2021! I've done a few updates to existing posts during the last few months, but not the detailed new ones I've been planning; hopefully this will be the first of a small "flurry" of new posts.

Here, I'm happy to announce the first big public release of Dual Mechanisms of Cognitive Control (DMCC) project data, "DMCC55B", together with a detailed description and example analyses. We've previously released smaller parts of the DMCC (DMCC13benchmark), which has been very useful for methods exploration, but isn't really enough data for detailed analysis of the task-related activity or individual differences. A wide range of analyses are possible with DMCC55B, and we hope the documentation both clear and detailed enough to make its use practical.

A few highlights: DMCC55B is data from 55 unrelated young adults. Each participant performed four cognitive control tasks (Stroop, AX-CPT, Cued Task-Switching, and Sternberg Working Memory) while undergoing moderately high-resolution fMRI scanning (MB4, 2.4 mm isotropic voxels, 1.2 s TR). Two runs of each task (one with AP encoding, the other PA), of approximately 12 minutes each (about 1.5 hours of task fMRI per person). There are also scores for the participants on 28 state and trait self-report questionnaires, as well as finger photoplethysmograph and respiration belt recordings collected during scanning.

This manuscript is intended to be the practical introduction to DMCC55B, providing details such as e.g., the file format of questionnaire data, the order in which task stimuli were presented, fMRI preprocessing (fmriprep output is included), and how Stroop responses were collected and scored. The manuscript also contains links and references to materials used for the main DMCC project (e.g., eprime task presentation scripts, code to extract reaction times from the Stroop audio recordings), which may be of interest or use to others in some cases, but likely only in a few specialized instances. A separate manuscript (preprint; accepted version) describes the wider DMCC project, including the theoretical basis for the task design. If some information is confusing or missing, please let me know!

Last, but most definitely not least, I want to highlight the "supplemental" accompanying DMCC55B. This designed to perform and summarize some standard behavioral and quality control analyses, with the intention of the files serving both as an introduction to working with DMCC data (e.g., how do I obtain the onset time of all AX trials?) and analysis tutorials (e.g., of a parcel-wise classification MVPA with surface data). Currently, the best introduction to this material is in the DMCC55B manuscript and the files themselves. The supplemental files are primarily knitr (R and LaTeX); they call afni functions (e.g., 3dTstat) directly when needed, and are entirely "untidy" base R, including base R graphics. (The last bit refers to the different "flavors" of R programming; I only rarely have need to visit the Tidyverse.)

 

 UPDATE 13 September 2021: Added a link to the published version of the DMCC overview manuscript.