Duke Neurogenetics Study MRI Protocol
Gray Matter Volume
Voxel-Based Morphometry to assess gray matter volume
T1-weighted images were obtained using a 3D Ax FSPGR BRAVO sequence with the following parameters: TR = 8.148 ms; TE = 3.22 ms; 162 axial slices; flip angle, 12°; FOV, 240 mm; matrix =256×256; slice thickness = 1 mm with no gap (voxel size 0.9375×0.9375×1 mm); and total scan time = 4 min and 13 s. Regional gray matter volumes were determined using the unified segmentation (Ashburner and Friston, 2005) and DARTEL normalization (Ashburner, 2007) modules in SPM12 (http://www.fil.ion.ucl.ac.uk/spm). Using this approach, individual T1-weighted images were segmented into gray, white, and CSF images then non-linearly registered to the existing IXI template of 550 healthy subjects averaged in standard Montreal Neurological Institute space (available with VBM8, http://dbm.neuro.uni-jena.de/vbm/). Subsequently, gray matter images were scaled by the Jacobian determinant of the transformation to preserve the total amount of signal from each region, and smoothed with an 8mm FWHM Gaussian kernel. The voxel size of processed images was 1.5×1.5×1.5 mm. A gray matter mask for subsequent analyses was created by thresholding the final stage (6th) IXI template at 0.1.
Spatially Unbiased Atlas Template (SUIT) of the Cerebellum and Brainstem
The Spatially Unbiased Infratentorial (SUIT) toolbox was used for cerebellar VBM (version 3.0, http://www.icn.ucl.ac.uk/motorcontrol/imaging/suit.htm) (Diedrichsen et al., 2009). For each subject, the Isolate function of the toolbox was used to create a mask of the cerebellum and generate gray and white matter segmentation maps. The masked segmentation maps were then normalized to the SUIT template with non-linear DARTEL normalization. The resulting cerebellar gray matter image was resliced into the SUIT atlas space, scaled by the Jacobian determinant of the transformation to preserve the total amount of signal from each region, and smoothed with a 4mm FWHM isotropic Gaussian kernel, a small kernel to preserve precision in the definition of cerebellar structures, in line with previous publications (D'Agata et al., 2011b). All images were visually inspected for quality.
References
Ashburner J, Friston KJ. 2005. Unified segmentation. Neuroimage 26: 839-851.
Ashburner J. 2007. A fast diffeomorphic image registration algorithm. Neuroimage 38: 95-113.
D'Agata, F., Caroppo, P., Boghi, A., Coriasco, M., Caglio, M., Baudino, B., Sacco, K., Cauda, F., Geda, E., Bergui, M., Geminiani, G., Bradac, G.B., Orsi, L., Mortara, P., 2011b. Linking coordinative and executive dysfunctions to atrophy in spinocerebellar ataxia 2 patients. Brain Struct. Funct. 216, 275-288.
Diedrichsen J, Balster JH, Flavell J, Cussans E, Ramnani N. A probabilistic MR atlas of the human cerebellum. Neuroimage 2009; 46: 39-46.
Arno Klein, Jesper Andersson, Babak A. Ardekani, John Ashburner, Brian Avants, Ming-Chang Chiang, Gary E. Christensen, D. Louis Collins, James Gee, Pierre Hellier, Joo Hyun Song, Mark Jenkinson, Claude Lepage, Daniel Rueckert, Paul Thompson, Tom Vercauteren, Roger P. Woods, J. John Mann, Ramin V. Parsey, Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration, NeuroImage, Volume 46, Issue 3, 1 July 2009, Pages 786-802, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2008.12.037. (http://www.sciencedirect.com/science/article/pii/S1053811908012974)