By Russell A. Poldrack
Sensible magnetic resonance imaging (fMRI) has turn into the most well-liked technique for imaging mind functionality. instruction manual for sensible MRI info research offers a accomplished and useful creation to the equipment used for fMRI facts research. utilizing minimum jargon, this booklet explains the ideas in the back of processing fMRI information, concentrating on the suggestions which are most typically utilized in the sector. This booklet presents history concerning the equipment hired by means of universal info research programs together with FSL, SPM, and AFNI. the various most recent state-of-the-art options, together with development class research, connectivity modeling, and resting nation community research, also are mentioned. Readers of this ebook, no matter if beginners to the sector or skilled researchers, will receive a deep and powerful wisdom of ways to hire fMRI research to invite medical questions and develop into extra subtle clients of fMRI research software program.
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Extra info for Handbook of Functional MRI Data Analysis
Example text
B10 , and c1 . . c10 ). Expansions to any order are possible, with a rapidly increasing number of parameters as the order increases; for example, a 12th order polynomial has a total of 1,365 parameters in three dimensions. Another nonlinear basis function set that is commonly encountered in fMRI data analysis is the discrete cosine transform (DCT) basis set, which historically was used in SPM (Ashburner & Friston, 1999), though more recently has been supplanted by spline basis functions. This basis set includes cosine functions that start at low frequencies (which change very slowly over the image) and increase in frequency.
11. In principle this form of interpolation should use information from every voxel in the image, since the sinc function extends to infinity. 10. An example of linear interpolation. Points a,b, c, and d are the original image locations, on a grid with 2 mm spacing. 75 mm translation to the left along the X dimension and downwards along the Y dimension. To determine the intensity of the image at point g, one first interpolates along one dimension; in this example, we interpolate along the X dimension, finding the values of points e and f .
In a comparison of many different cost functions for motion correction, Jenkinson et al. (2002) found that normalized correlation results in more accurate registration than several other cost functions, including least squares. It is the default cost function for motion correction in the FSL software package. , 2003) (which can be used for between- or within-modality registration) arises from the concept of entropy from information theory. Entropy refers to the amount of uncertainty or randomness that is present in a signal: N H= pi ∗ log i=1 1 pi N pi ∗ log(pi ) =− i=1 where pi is the probability of each possible value xi of the variable; for continuous variables, the values are grouped into N bins, often called histogram bins.