Warning for MRI researchers: Unexpected clothes (e.g., yoga pants) may contain metal

Lululemon uses what it calls silverescent technology, according to information on some Lululemon clothing. The technology purports to stop odour-causing bacteria from embedding itself into the clothing.

But according to Alison Matthews David, an assistant professor at Ryerson University, silver or metal fibres can turn up in other brands but you may not know it.

“If you see the label, anti-microbial, in other words it kills microbes or bacteria, it mostly like does that with silver technology. Nano silver technology,” she said.

Jeanette Mumford's online crash course in fMRI stats starts TODAY

Looks great.

Between now and Labor Day weekend (September 4), I plan to have a cram session that covers approximately 3 weeks of a semester-long fMRI data analysis course I previously taught at UT. I’ve chosen these specific topics, because they relate to the questions I’m most frequently asked. The main topics include: linear regression overview, setting up group level design matrices (works for more imaging modalities than just fMRI), mixed models, collinearity, orthogonalization, mean centering regressors, parametrically modulated regressor set up and more!

Every MWF I’ll post a 10-20 minute video and I’ve split them up such that if you hit a topic you already know (e.g. you’re a matrix algebra expert), you can skip to the next video. Instead of zoning out during boring parts of a class, you can just skip ahead to the next part.

And also, good to see that Mumford is on twitter as @mumbrainstats.

ICA methods for motion correction in resting state fMRI

From the abstract:

Results demonstrated that ICA-AROMA, spike regression, scrubbing, and ICA-FIX similarly minimized the impact of motion on functional connectivity metrics. However, both ICA-AROMA and ICA-FIX resulted in significantly improved resting-state network reproducibility and decreased loss in tDoF compared to spike regression and scrubbing. In comparison to ICA-FIX, ICA-AROMA yielded improved preservation of signal of interest across all datasets.

Adaptive thresholding for reliable inference in single subject fMRI analysis

Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modeling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of total number of errors but also in terms of the trade-off between false negative and positive cluster error rates.