The AstroStat Slog

[arxiv:0906.3662] The Statistical Analysis of fMRI Data by Martin A. Lindquist
Statistical Science, Vol. 23(4), pp. 439-464

This review paper offers some information and guidance of statistical image analysis for fMRI data that can be expanded to astronomical image data. I think that fMRI data contain similar challenges of astronomical images. As Lindquist said, collaboration helps to find shortcuts. I hope that introducing this paper helps further networking and collaboration between statisticians and astronomers.

List of similarities

• data acquisition: data read in frequency domain and image reconstruction via inverse Fourier transform. (To my naive eyes, It looks similar to Power Spectrum Analysis for cosmic microwave background (CMB) data).
• amplitudes or coefficients are cared and analyzed, not phase nor wavelets.
• understanding data:brain physiology or physics like cosmological models that describe data generating mechanism.
• limits in/trade-off between spatial and temporal resolution.
• understanding/modeling noise and signal.

These similarities seem common for statistically analyzing images from fMRI or telescopes. Notwithstanding, no astronomers can (or want) to carry out experimental design. This can be a huge difference between medical and astronomical image analysis. My emphasis is that because of these commonalities, strategies in preprocessing and data analysis for fMRI data can be shared for astronomical observations and vise versa. Some sloggers would like to check Section 6 that covers various statistical models and methods for spatial and temporal data.

I’d rather simply end this posting with the following quotes, saying that statisticians play a critical role in scientific image analysis.

There are several common objectives in the analysis of fMRI data. These include localizing regions of the brain activated by a task, determining distributed networks that correspond to brain function and making predictions about psychological or disease states. Each of these objectives can be approached through the application of suitable statistical methods, and statisticians play an important role in the interdisciplinary teams that have been assembled to tackle these problems. This role can range from determining the appropriate statistical method to apply to a data set, to the development of unique statistical methods geared specifically toward the analysis of fMRI data. With the advent of more sophisticated experimental designs and imaging techniques, the role of statisticians promises to only increase in the future.

A full spatiotemporal model of the data is generally not considered feasible and a number of short cuts are taken throughout the course of the analysis. Statisticians play an important role in determining which short cuts are appropriate in the various stages of the analysis, and determining their effects on the validity and power of the statistical analysis.