A very good article on The Statistical Analysis of fMRI Data Martin A. Lindquist avaiable for download.
Abstract. In recent years there has been explosive growth in the number
of neuroimaging studies performed using functional Magnetic Resonance
Imaging (fMRI). The field that has grown around the acquisition and analysis
of fMRI data is intrinsically interdisciplinary in nature and involves contributions
from researchers in neuroscience, psychology, physics and statistics,
among others. A standard fMRI study gives rise to massive amounts of noisy
data with a complicated spatio-temporal correlation structure. Statistics plays
a crucial role in understanding the nature of the data and obtaining relevant
results that can be used and interpreted by neuroscientists. In this paper we
discuss the analysis of fMRI data, from the initial acquisition of the raw data
to its use in locating brain activity, making inference about brain connectivity
and predictions about psychological or disease states. Along the way,
we illustrate interesting and important issues where statistics already plays a
crucial role.We also seek to illustrate areas where statistics has perhaps been
underutilized and will have an increased role in the future.
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There is new free course on Statistical Analysis of fMRI Data by Martin Lindquist, PhD, MSc from John Hopkins University
For you people eager to learn this is the chance to do it…
Go to this link and register for free…
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Some real cool innovation in medical imaging…
This is the webpage for the Statistical Methods and Applications for Research in Technology (SMART) working group of the Department of Biostatistics in the Bloomberg School of Public Health at Johns Hopkins University. The group was started by Ciprian Crainiceanu and Brian Caffo though it now boasts a core group from around the world
“.. work on Statistically principled methods for new technologies with special emphasis on brain imaging (e.g. fMRI, high resolution MRI, CT), wearable computing (e.g. hip accelerometers, heart monitors), and Biosignals (e.g. EEG, EKG, ECoG). The underlying principle is to develop methods that are automated, fast, scalable, and robust (AFSR.) Our analytic approaches are sometimes focused only on one subject, but typically we are investigating large populations observed at one or multiple time points.”
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