A strange characterization of fMRI

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Google Scholar has alerted me to a recent paper, Automatic Detection, Estimation, and Validation of Harmonic Components in Measured Power Spectra: All-in-One Approach that is to be published in IEEE Transactions on Instrumentation and Measurement. Finding the context where we were cited I read (page 1, first column):

In functional magnetic resonance imaging measurements,
one is interested in detecting tumor tissue based on a harmonic
analysis of the data [3]-[5]

There are two strangenesses here: First of all functional magnetic resonance imaging (fMRI) is not (primarily) interested in detecting tumor tissue. MRI without the ‘f’ might be interested in tumor detection. Second, harmonic analysis is not necessarily (and indeed rarely) used for fMRI analysis, and indeed it will be difficult (impossible?) with event-related fMRI. However, harmonic analysis by Fourier transform for image reconstruction is embedded in the MR-scanner.

The citation to our work (the “[5]”) goes to the “Hansen Harmonic Detector” (HHD) that Lars Kai Hansen came up with — a funny detector that can find harmonics on both sides of the Nyquist frequency. Coming from a classic signal processing background you might think that this is magic, but the approach “just” uses a linear model and Bayesian estimation with conjugate priors reaching a normal-inverse-gamma distribution. Keine hexerei.

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