Updated checklist for fMRI acquisition methods reporting in the literature

This post updates the checklist that was presented back in January, 2013. The updated checklist is denoted Version 1.2. The main update is to include reporting for simultaneous multi-slice (SMS) (a.k.a. multi-band, MB) EPI.

Explanatory notes for parameter names appear in the lower portion of the post. Note that the present checklist was devised by considering typical fMRI experiments conducted on 1.5 T and 3 T scanners but the list should work reasonably well for 7 T sequences, too.

Please keep the comments and feedback coming. This is an ongoing, iterative process.

Download the checklist PDF here!

Release notes for Version 1.2

All changes from Version 1.1 have been highlighted in yellow, both on the list PDF and on the explanatory notes (below).

1. The "Spatial Encoding" parameter categories have been renamed "In-Plane Spatial Encoding" to better differentiate in-plane acceleration (e.g. GRAPPA) from slice dimension acceleration (SMS/MB).

2. When using slice dimension acceleration (i.e. SMS/MB), certain parameters that are listed as Supplemental for other EPI variants should be considered Essential. Specifically, it is suggested to report:

Matrix coil mode
Coil combination method

All the In-Plane Spatial Encoding parameters in the Supplemental category should be considered because there is a tendency to use SMS/MB to attain high spatial resolution, requiring long readout echo trains that can have higher distortion than found in typical EPI scans.

The In-plane reconstructed matrix parameter should be reported whenever partial Fourier sampling is used, as it often is for SMS/MB EPI.

All the RF & Contrast parameters in the Supplemental category should be reported because the shape, duration and amplitude of the excitation RF pulse are all integral components of the acceleration method.

The Shim routine should be reported if a non-standard shim is performed before SMS/MB EPI.

3. Pre-scan normalization has been added to the Supplemental section of RF & Contrast parameters. Large array coils produce strong receive field heterogeneity and the use of pre-scan normalization may improve the performance of post hoc motion correction.

Concomitant physiologic changes as potential confounds for BOLD-based fMRI: a checklist

Many thanks for all the feedback on the draft version of this post.

Main updates since the draft:

• Added DRIFTER to the list of de-noising methods
• Added a reference for sex differences in hematocrit and the effects on BOLD
• Added several medication classes, including statins, sedatives & anti-depressants
• Added a few dietary supplements, under Food

Please do continue to let me know about errors and omissions, especially new papers that get published. I'll gladly do future updates to this post.

UPDATES:

(Since this post release on 8th Dec, 2014.) 

17th Dec 2014: Update for cortisol, highlighted in yellow.
18th Dec 2014: Update for methylphenidate, atomoxetine & amphetamine, highlighted in orange.
19th Dec 2014: Update for oxytocin, highlighted in turquoise.
13th Jan 2015: Update for effects of the scanner itself, highlighted in green.
27th Feb 2015: Added a new reference, hematocrit effects on resting-state fMRI.
27th May 2016: Added new references on altitude, sleep, pharmacological fMRI (with morphine & alcohol).
1st Feb & 2nd Mar 2017: Added new references for flavonoids in foods, highlighted in red.
23rd May 2017: Added a new reference for the effect of carbon monoxide on BOLD.

______________________

A recent conversation on Twitter led to the suggestion that someone compile a list of physiological effects of concern for BOLD. That is, a list of potentially confounding physiological changes that could arise sympathetically in an fMRI experiment, such as altered heart rate due to the stress of a task, or that could exist as a systematic difference between groups. What follows is the result of a PubMed literature search (mostly just the abstracts) where I have tried to identify either recent review articles or original research that can be used as starting points for learning more about candidate effects. Hopefully you can then determine whether a particular factor might be of concern for your experiment.

This is definitely not a comprehensive list of all literature pertaining to all potential physiological confounds in fMRI, and I apologize if your very important contribution didn't make it into the post. Also, please note that I am not a physiologist so if I go seriously off piste in interpreting the literature, please forgive me and then correct my course. I would like to hear from you (comments below, or via Twitter) if I have omitted critical references or effects from the list, or if I have misinterpreted something. As far as possible I've tried to restrict the review to work in humans unless there was nothing appropriate, in which case I've included some animal studies if I think they are directly relevant. I'll try to keep this post up-to-date as new studies come out and as people let me know about papers I've missed.

A final caution before we begin. It occurs to me that some people will take this list as (further) proof that all fMRI experiments are hopelessly flawed and will use it as ammunition. At the other extreme there will be people who see this list as baseless scare-mongering. How you use the list is entirely up to you, but my intent is to provide cautious fMRI scientists with a mechanism to (re)consider potential physiologic confounds in their experiments, and perhaps stimulate the collection of parallel data that might add power to those experiments.

Getting into BOLD physiology

There are some good recent articles that introduce the physiological artifacts of prime concern. Tom Liu has reviewed neurovascular factors in resting-state functional MRI and shows how detectable BOLD signals arise from physiological changes in the first place. Kevin Murphy et al. then review some of the most common confounds in resting-state fMRI and cover a few ways these spurious signal changes can be characterized and even removed from data. Finally, Dan Handwerker et al. consider some of the factors causing hemodynamic variations within and, in particular, between subjects.

Once you start really looking into this stuff it can be hard not to get despondent. Think of the large number of potential manipulations as opportunities, not obstacles! Perhaps let The Magnetic Fields get you in the mood with their song, "I don't like your (vascular) tone." Then read on. It's a long list.