Corresponding Schaefer2018 400x7 and 400x17 atlas parcels by number

My "default" cortical parcellation is the 400 parcels by 7 networks version of Schaefer2018. I like these parcellations because they're available in all the spaces we use (volume, fsaverage, fsLR (HCP)) and are independent from our analyses; "all parcellations are wrong, but some are useful".

The Schaefer parcellations come in several versions for each resolution, however, and a parcel described by its 7Networks number likely has a different 17Network number. Note that the parcel boundaries are the same for all same-resolution versions: there is only one set of 400 parcels, but which of those 400 parcels is #77 varies between the 7 and 17 network versions. This post describes how to translate parcel numbers between network versions, using a bit of (base) R code.

Logic: since there is only one set of parcels at each resolution, there is only one set of centroid coordinates at each resolution. Thus, we can match parcels across network orderings by centroids.

First, set sch.path to the location of your copy of the Schaefer2018 Parcellations directory and load the 7 and 17 network files:  (apologies for the wonky code formatting)

sch.path <- "/data/nil-bluearc/ccp-hcp/DMCC_ALL_BACKUPS/ATLASES/Schaefer2018_Parcellations/";

cen7.tbl <- read.csv(paste0(sch.path, "MNI/Centroid_coordinates/Schaefer2018_400Parcels_7Networks_order_FSLMNI152_1mm.Centroid_RAS.csv"));
cen17.tbl <- read.csv(paste0(sch.path, "MNI/Centroid_coordinates/Schaefer2018_400Parcels_17Networks_order_FSLMNI152_1mm.Centroid_RAS.csv"));

Next, make vectors for translating the 7Network number to the 17Network number (and the reverse):

x7to17 <- rep(NA, nrow(cen7.tbl));
x17to7 <- rep(NA, nrow(cen7.tbl));
for (i in 1:nrow(cen7.tbl)) { 
  x7to17[i] <- which(cen17.tbl$R == cen7.tbl$R[i] & cen17.tbl$A == cen7.tbl$A[i] & cen17.tbl$S == cen7.tbl$S[i]); 
  x17to7[i] <- which(cen7.tbl$R == cen17.tbl$R[i] & cen7.tbl$A == cen17.tbl$A[i] & cen7.tbl$S == cen17.tbl$S[i]); 
}

Now the vectors can be used to translate parcel numbers: parcel #77 in the 7Networks ordering is parcel #126 in the 17Networks ordering.

x7to17[77]   # [1] 126

cen7.tbl[77,]
#   ROI.Label                     ROI.Name   R   A  S
#77        77 7Networks_LH_DorsAttn_Post_9 -33 -46 41

cen17.tbl[126,]
#    ROI.Label                  ROI.Name   R   A  S
#126       126 17Networks_LH_ContA_IPS_5 -33 -46 41

x17to7[126]    # [1] 77

Note that the parcel's R A S coordinates are the same, but its name and label (number) vary between the two network versions.

OHBM 2023 links

I was a co-organizer (with Paul Taylor, Daniel Glen, and Richard Reynolds, all of afni fame) of the "Making Quality Control Part of Your Analysis: Learning with the FMRI Open QC Project" course at OHBM 2023. FMRI Open QC Project participants described how they approached the Project datasets, and we had several general QC discussions. Thanks to all participants and attendees! I hope we can keep making progress towards a common vocabulary for fMRI QC and encourage researchers to include it if they do not already.

The session was unfortunately not recorded live, though speakers submitted recordings in advance. OHBM is supposed to make all of these recordings available; in the meantime I've linked material already public.

• Brendan Williams, "Reproducible Decision Making for fMRI Quality Control"
• Céline Provins, "Quality control in functional MRI studies with MRIQC and fMRIPrep"
• Daniel Glen, "Quality control practices in FMRI analysis: philosophy, methods and examples using AFNI"
• Dan Handwerker, "The art and science of using quality control to understand and improve fMRI data"  slides
• Chao-Gan Yan, "Quality Control Procedures for fMRI in DPABI"
• Rasmus Birn, "Quality control in resting-state functional connectivity: qualitative and quantitative measures"
• Xin Di, "QC for resting-state and task fMRI in SPM"
• Jo Etzel, "Efficient evaluation of the Open QC task fMRI dataset"  video 
• Rebecca Lepping, "Quality Control in Resting-State fMRI: The Benefits of Visual Inspection"
• Francesca Morfini, "Functional Connectivity MRI Quality Control Procedures in CONN"

I also presented a poster, "Which Acquisition? Choosing protocols for task fMRI studies", #700. Here's some of the introduction and conclusion for an abstract. The test data is already public; the code isn't written up properly, but I could share if anyone is interested.

When planning a task fMRI study, one necessary choice is the acquisition sequence. Many are available, with recommendations varying with hardware, study population, brain areas of interest, task requirements, etc.; it is rare to have only one suitable option. Acquisition protocols for task studies can be difficult to evaluate, since metrics like tSNR are not specific to task-related activity. But task designs can make choosing easier, since there is a known effect to compare the candidate acquisition protocols against. 

The procedure illustrated here will rarely make the choice of acquisition completely unambiguous, but can indicate which to avoid, and give the experimenters confidence that the chosen sequence will produce usable data. After choosing the acquisition, more piloting should be performed with the study tasks to confirm that image quality and response clarity are sufficient and as expected.   

... it's taken me so long to finish this post (started August 2023!) that I'm publishing it without adding a proper explanation of the protocol-choosing logic. Take a look at the poster pdf, and please ask questions or otherwise nudge me for more information if you're interested.