Multiband (aka simultaneous multislice) EPI validation in progress!

I am pleased to see a couple of presentations at next week's ISMRM conference in Salt Lake City dealing with some of the important validation steps that should be performed before multiband (MB) EPI (or simultaneous multislice (SMS) EPI if you prefer) is adopted for routine use by the neuroimaging community:

Characterization of Artifactual Correlation in Highly-Accelerated Simultaneous Multi-Slice (SMS) fMRI Acquisitions

Abstract #0410, ISMRM Annual Meeting, 2013.

Kawin Setsompop, Jonathan R. Polimeni, Himanshu Bhat, and Lawrence L. Wald

Simultaneous Multi-Slice (SMS) acquisition with blipped-CAIPI scheme has enabled dramatic reduction in imaging time for fMRI acquisitions, enabling high-resolution whole-brain acquisitions with short repetition times. The characterization of SMS acquisition performance is crucial to wide adoption of the technique. In this work, we examine an important source of artifact: spurious thermal noise correlation between aliased imaging voxels. This artifactual correlation can create undesirable bias in fMRI resting-state functional connectivity analysis. Here we provide a simple method for characterizing this artifactual correlation, which should aid in guiding the selection of appropriate slice- and inplane-acceleration factors for SMS acquisitions during protocol design.

(I also found this link to the full abstract.)

An Assessment of Motion Artefacts in Multi Band EPI for High Spatial and Temporal Resolution Resting State fMRI

Abstract #3275, ISMRM Annual Meeting, 2013.

Michael E. Kelly, Eugene P. Duff, Janine D. Bijsterbosch, Natalie L. Voets, Nicola Filippini, Steen Moeller, Junqian Xu, Essa S. Yacoub, Edward J. Auerbach, Kamil Ugurbil, Stephen M. Smith, and Karla L. Miller

Multiband (MB) EPI is a recent MRI technique that offers increased temporal and/or spatial resolution as well as increased temporal SNR due to increased temporal degrees-of-freedom (DoF). However, MB-EPI may exhibit increased motion sensitivity due to the combination of short TR with parallel imaging. In this study, the performance of MB-EPI with different acceleration factors was compared to that of standard EPI, with respect to subject motion. Although MB-EPI with 4 and 8 times acceleration exhibited some motion sensitivity, retrospective clean-up of the data using independent component analysis was successful at removing artefacts. By increasing temporal DoF, accelerated MB-EPI supports higher spatial resolution, with no loss in statistical significance compared to standard EPI. MB-EPI is therefore an important new technique capable of providing high resolution, temporally rich FMRI datasets for more interpretable mapping of the brain's functional networks.

The natural question to ask next occurs at the interface of these two topics: what about head motion-driven artifactual correlations between simultaneously excited slices? I am also curious to see how retrospective motion correction, e.g. affine registration algorithms, performs with MB-EPI that contains appreciable motion contamination. Is the "pre-processing" pipeline that we use for single-shot EPI appropriate for MB-EPI?

In-plane parallel imaging such as GRAPPA and SENSE were adopted for EPI-based fMRI experiments prematurely in my view, i.e. before full validations had been conducted. (Mea culpa. I was one of those beguiled by GRAPPA when I first saw it.) The failure modes - like motion sensitivity - hadn't been fully explored before a lot of us began employing the methods for their purported benefits. It would be nice if the failure modes of MB-EPI get a thorough workout before the neuroimaging community adopts it en masse. 

That said, I am still very excited that MB-EPI may offer the most significant performance boost for fMRI acquisition for more than a decade (since the introduction of scanners capable of EPI readout on all three gradient axes). But I continue to seek validation before recommending widespread adoption of MB-EPI (or any other method) and I look forward to seeing more reports such as these in the literature and online, prior to people using them in experiments to solve the brain.

Resting state fMRI confounds

(Thanks to Dave J. Hayes for tweeting the publication of these papers.)

Two new papers provide comprehensive reviews of some of the confounds to the acquisition, processing and interpretation of resting state fMRI data. In the paper, "Resting-state fMRI confounds and cleanup," Murphy, Birn and Bandettini consider in some detail many of the noise sources in rs-fMRI, especially those having a physiologic origin.

In "Overview of potential procedural and participant-related confounds for neuroimaging of the resting state," Duncan and Northoff review the effects that other circumstantial factors, such as the scanner's acoustic noise, subject instructions, subjects' emotional state, and caffeine might have on rs-fMRI studies. Without due consideration, some or all of these factors may inadvertently become experimental variables; the implications for inter-individual differences are considerable. (I've reviewed some of the issues concerning what we can permit subjects to do before and during rs-fMRI in this post.)

While we're on the subject of confounds in rs-fMRI - especially those with a motion component - another confound that motion introduces is a sensitivity to the receive field heterogeneity of the head coil. This problem gets worse the more channels the coil has, because the coil elements get smaller as the number of channels goes up. For an introduction to the issue see this arXiv paper; there will also be simulations of the effect for a 32-channel coil at the ISMRM conference in a couple of weeks' time. (See e-poster, abstract #3352.) The result is that spurious correlations and anti-correlations can result, necessitating some sort of clever sorting or de-noising scheme to distinguish them from "true" brain correlations. I mention it here because there is a common misconception in the field that applying a retrospective motion correction step fixes all motion-related artifacts. It doesn't. Nor does including all of the motion parameters as regressors in a model. Motion has some insidious ways in which it can modulate the MRI signal level, and it is high time that we, as a field, reconsider very carefully what we are doing for motion correction, and why.

Finally, I'll note in passing that slice timing correction may not be a good idea for rs-fMRI. It's been known since the correction was first proposed that it should interact a with a motion correction step. (The two corrections should be applied simultaneously, as one 4D space-time correction rather than a separate 3D space then time correction, or vice versa.) I don't have data to share just yet, but if anyone is wondering whether they should include STC in their rs-fMRI analysis, as they would do for event-related fMRI, then my advice is to skip it until someone can prove to you that it has no unintended consequences. (Demonstration of unintended consequences to follow eventually....)


References:

Resting state fMRI confounds and cleanup. K Murphy, RM Birn and PA Bandettini, NeuroImage Epub.
DOI: 10.1016/j.neuroimage.2013.04.001

Overview of potential procedural and participant-related confounds for neuroimaging of the resting state. NW Duncan and G Northoff, J. Psychiatry Neurosci. 2013, 38(2), 84-96.
PMID: 22964258
DOI: 10.1503/jpn.120059

Impressively rapid follow-ups to a published fMRI study

Alternative post title: Why blogs can be seriously useful in research.


Last week there was quite a lot of attention to an article published in PNAS by Aharoni et al. In their study they claimed that fMRI could be useful in predicting the likelihood of rearrest in a group of convicts up for parole:

"Identification of factors that predict recurrent antisocial behavior is integral to the social sciences, criminal justice procedures, and the effective treatment of high-risk individuals. Here we show that error-related brain activity elicited during performance of an inhibitory task prospectively predicted subsequent rearrest among adult offenders within 4 y of release (N = 96). The odds that an offender with relatively low anterior cingulate activity would be rearrested were approximately double that of an offender with high activity in this region, holding constant other observed risk factors. These results suggest a potential neurocognitive biomarker for persistent antisocial behavior."

The senior author, Kent Kiehl, was interviewed on National Public Radio on Friday morning. I heard it on my way into work. An NPR interview would suggest the media attention was widespread, although I haven't looked at this aspect specifically.

What I did notice, however, was that The Neurocritic came out with two quick posts (here and here) wherein he brought up a couple of interesting limitations of the study and even ran his own re-analysis of the data, the PNAS authors having been kind enough to make their data available publicly.

This afternoon, Russ Poldrack has followed up with his own analysis and interpretation of the study's data. I'll be honest, all the stats leaves me flat-footed. But I am very seriously impressed by the way the blogosphere, combined with shared data, has been able to poke and prod the original study's conclusions.

Why am I so enthused? Because the mainstream media (still) has the power to dominate the narrative in the public sphere, and it is especially important that specific criticisms can be leveled within the same news cycle, while the public might still be paying attention to the story. So, while I think it's highly unlikely that NPR will interview the senior author of the next study that finds there is no predictive use of fMRI for recidivism - we seem to have a serious positive results bias in science - maybe there's a slim chance that NPR will interview Russ about his follow-up analysis, just to balance the record. And if not, at least those in the field have the benefit of the post-publication peer review that blogs can offer.