Tag Archives: functional magnetic resonance imaging (fMRI)

Brain scan variations

The Scientist is a magazine I do not feature here often enough. The latest issue (June 2020) features a May 20, 2020 opinion piece by Ruth Williams on a recent study about interpretating brain scans—70 different teams of neuroimaging experts were involved (Note: Links have been removed),

In a test of scientific reproducibility, multiple teams of neuroimaging experts from across the globe were asked to independently analyze and interpret the same functional magnetic resonance imaging dataset. The results of the test, published in Nature today (May 20), show that each team performed the analysis in a subtly different manner and that their conclusions varied as a result. While highlighting the cause of the irreproducibility—human methodological decisions—the paper also reveals ways to safeguard future studies against it.

Problems with reproducibility plague all areas of science, and have been particularly highlighted in the fields of psychology and cancer through projects run in part by the Center for Open Science. Now, neuroimaging has come under the spotlight thanks to a collaborative project by neuroimaging experts around the world called the Neuroimaging Analysis Replication and Prediction Study (NARPS).

Neuroimaging, specifically functional magnetic resonance imaging (fMRI), which produces pictures of blood flow patterns in the brain that are thought to relate to neuronal activity, has been criticized in the past for problems such as poor study design and statistical methods, and specifying hypotheses after results are known (SHARKing), says neurologist Alain Dagher of McGill University who was not involved in the study. A particularly memorable criticism of the technique was a paper demonstrating that, without needed statistical corrections, it could identify apparent brain activity in a dead fish.

Perhaps because of such criticisms, nowadays fMRI “is a field that is known to have a lot of cautiousness about statistics and . . . about the sample sizes,” says neuroscientist Tom Schonberg of Tel Aviv University, an author of the paper and co-coordinator of NARPS. Also, unlike in many areas of biology, he adds, the image analysis is computational, not manual, so fewer biases might be expected to creep in.

Schonberg was therefore a little surprised to see the NARPS results, admitting, “it wasn’t easy seeing this variability, but it was what it was.”

The study, led by Schonberg together with psychologist Russell Poldrack of Stanford University and neuroimaging statistician Thomas Nichols of the University of Oxford, recruited independent teams of researchers around the globe to analyze and interpret the same raw neuroimaging data—brain scans of 108 healthy adults taken while the subjects were at rest and while they performed a simple decision-making task about whether to gamble a sum of money.

Each of the 70 research teams taking part used one of three different image analysis software packages. But variations in the final results didn’t depend on these software choices, says Nichols. Instead, they came down to numerous steps in the analysis that each require a human’s decision, such as how to correct for motion of the subjects’ heads, how signal-to-noise ratios are enhanced, how much image smoothing to apply—that is, how strictly the anatomical regions of the brain are defined—and which statistical approaches and thresholds to use.

If this topic interests you, I strongly suggest you read Williams’ article in its entirety.

Here are two links to the paper,

Variability in the analysis of a single neuroimaging dataset by many teams. Nature DOI: https://doi.org/10.1038/s41586-020-2314-9 Published online: 20 May 2020 Check for updates

This first one seems to be a free version of the paper.

Variability in the analysis of a single neuroimaging dataset by many teams by R. Botvinik-Nezer, F. Holzmeister, C. F. Camerer, et al. (at least 70 authors in total) Nature 582, 84–88 (2020). DOI: https://doi.org/10.1038/s41586-020-2314-9 Published 20 May 2020 Issue Date 04 June 2020

This version is behind a paywall.