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The anatomical image was co-registered to the mean functional image using the mutual information criteria method and segmented and normalized to the International Consortium for Brain Mapping template using linear and non-linear deformations [30], [31].
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The pre-processing pipeline includes: a) re-orientating images to the standard (MNI) template, b) bias field correction, c) registration to the MNI template using both linear (FLIRT) and non-linear (FNIRT) registration tools and d) brain extraction.
Next, T1-coregistered volumes were normalized to an SPM T1- template (using 12 linear parameters and a set of non-linear cosine basis functions), and spatial smoothing was performed using an 8 mm FWHM Gaussian kernel.
The brain-extracted MRI images in original space were then transformed to match this study-specific template using both linear 12-parameter affine, and 5th order non-linear transformation using Automated Image Registration (AIR; http://bishopw.loni.ucla.edu/air5/) [31].ucla.edu/air5/
T1 images were normalized to the MNI template using affine linear registration followed by Gaussian smoothing (FWHM = 6 mm).
T1 magnetic resonance images were normalized to the MNI template using affine linear registration, followed by Gaussian smoothing (FWHM = 8 mm).
Since correlation is a linear measure, the high correlation between our scores and Farber's implies that one can transform Farber's mood template to our scale using linear transformation.
Structural brain images were bias-corrected, segmented into grey and white matter and CSF, and then normalized to a template in MNI space using linear and non-linear parameters.
Another set of 37 human proteins could be rescued by sequence optimisation and using linear templates for in vitro expression.
Standard curve was prepared for each vector using linear plasmid DNA template.
This 'best normalized' image acted as a template to which all other FA images were coregistered using linear affine transformations only.
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