US researchers: use extra caution with participant gender-related info

Last summer I wrote several times about pregnancy-related data suddenly becoming much more sensitive and potentially damaging to the participant if released. Unfortunately, now we must add transgender status, biological sex at birth (required, e.g., for GUID creation), gender identity, relationship status, and more to the list of data requiring extra care. 

The NIH Certificate of Confidentiality thankfully means that researchers can't be required to release data on something like someone's abortion or transgender history for criminal or other proceedings. We are responsible for ensuring that our data is handled and stored securely, so that it isn't accidentally (or purposely) shared and then possibly cause the participant harm. I suggest researchers review their data collection with an eye towards information that may be more sensitive now than it was in the past; is this information required? If so, consider how to store and use it securely and safely.

Also consider what you ask potential participants during screening (and how screening is done in general): people may not wish to answer screening questions about things like pregnancy or hormone treatments. If these possibly-sensitive questions must be asked, consider how to do so while minimizing potential discomfort or risk.

For example, one of our studies can't include pregnant people, so we must ask about pregnancy during the initial phone screenings. We used to ask potential participants about pregnancy separately, but then changed the script so that this sensitive question was in a list, and the participant is asked if any apply. This way, the participant doesn't have to state explicitly that they are pregnant, and the researcher doesn't have any specific notes, or even respond in a specific way (e.g., we don't want them to say something like "sorry Jane Doe, but you can't be in our study now because you're pregnant").

Here's the relevant part of the new screening script:

In order to determine your eligibility for our study, I need to ask you some questions. This will take about 15 minutes. 

Before we collect your demographic information, I will ready you a list of four exclusionary criteria. If any of these describe you, please answer yes after I read them all; if none apply, please answer no. You do not need to say which ones apply. 

• You are a non-native English speaker (you learned to speak English as an adult); 
• You are over the age of 45 or under the age of 18; 
• You are pregnant or breastfeeding; 
• You were born prematurely (before 37 weeks, or if twin, before 34 weeks) 

 Do any of these describe you? Yes (I am sorry, you do not qualify to be in our study) or No (continue with questions)

We switched to this "any of the above" style screening script for pregnancy last summer, and it has been working well. We recently reviewed our procedures again, and confirmed that we do not ask questions about sex or gender status or history. But if we did, we'd be looking closely about how exactly the questions were asked and responses recorded, with the aim of collecting the absolute minimum of information required.

bugfix for niftiPlottingFunctions.R, plot.volume()

If you use or adapted the my plot.volume() function from niftiPlottingFunctions.R, please update your code with the version released 30 March 2023. This post illustrates the bug (reversed color scaling for negative values) below the jump; please read it if you've used the function, and contact me with any questions. 

Huge thanks to Tan Nguyen for spotting and reporting the color discrepancy! 

reasonable motion censoring thresholds?

Recent participants have gotten me thinking (yet again) about the different types of motion during fMRI; causes and consequences. And more immediately practical: which motion censoring thresholds might be reasonable for particular tasks and analyses. 

I've long used (and recommended) FD > 0.9 as a censoring threshold for our event-related task fMRI studies (not functional correlation or resting state-type analyses). 0.9 is more lenient than many use for task fMRI; e.g., Siegel 2014: advise 0.5 FD for adults (which we have), 0.9 for kids or clinical populations. (I need to update a previous post; I'd misread Siegel's recommendations.)

Consider the following motion plot of a run from a recent participant, using my usual conventions (grey lines at one-minute intervals, frames along the x-axis); see this QC demonstration paper, the DMCC55B dataset descriptor, etc. for more explanation (and code). The lower panel shows FD, with the red line at the FD 0.9 censoring threshold, and red x marking censored frames; the grey horizontal line is at FD 0.5. 

I interpret this as a run with  minimal overt head motion, but pronounced apparent motion (from breathing, strongest in trans_y, consistent with the AP encoding direction). Zero frames are censored at an FD > 0.9 threshold (red line); 130 are censored at FD > 0.5 (grey line). There are 562 frames in the run, so 130/562 = 0.23 of the frames censored at FD 0.5, and we would drop the run at our usual criterion of < 20% censored frames.

Contrast the above plot with the following (marked with a 2 in the upper left); the same task, scanner, etc., but a different participant:

I'd characterize plot 2 as having more pronounced overt than apparent motion; there are oscillations in the trans_y, but these are dwarfed by the head motions, e.g., in the middle of the first minute. Looking at the censoring, 13 frames (corresponding to the largest overt head motions) are marked for censoring with FD 0.9; 40 are marked with FD 0.5, corresponding to more of the overt head motions. 40/562 = 0.07, well under the 20% censoring dropping threshold.

musings

To my eye, the 0.5 FD threshold is pretty reasonable in the second case, since it censors more of the overt head motion irregularities, and only those spikes. But for the first plot the 0.5 FD threshold seems far too aggressive: censoring part of every few breaths, 23% of the total frames. What do you think?

I hope to do some proper analyses of the impact of different amounts of apparent vs. overt motion on statistical analyses, but it is not a trivial problem, particularly with task entrainment. (Synchronizing breathing to task timing.)

As a final bit of food for thought, here are the tSNR and sd images for each of the two runs, without censoring (all 562 frames), after preprocessing, and with the same color scaling. The first strikes me as higher quality, despite the greater (> 0.5 FD) censoring. I believe apparent motion could have less of an impact on image quality than overt because the head is not actually moving, and so not creating the attendant magnetic disruptions; the differences are clear to the eye when viewing these types of runs as movies, but it's not clear how those differences translate to statistical analyses.