Supplementary Materials and Methods
MRI Sequence Parameters
3D T2-weighted FLAIR sequence: TR = 6000ms, TE = 403ms, TI = 2100ms, FOV =
256 x 222mm2, matrix size = 256 x 222, flip angle = 120°, ETL = 123, 176
contiguous sagittal 1mm thick slices.
3D T1-weighted sequence: TR = 1900ms, TE = 2.19ms, TI = 900ms, FOV = 256 x
222mm2, matrix size = 256 x 222, flip angle = 9°, ETL = 1, 176 contiguous sagittal
1mm thick slices.
Optic nerve FLAIR DTI sequence: 6 non-collinear diffusion gradients, TR = 3900ms,
TE = 79ms, b-value = 600s/mm2, FOV = 220 x 220mm2, matrix size = 168 x 168, and
13 contiguous 3.5mm thick coronal oblique slices. During scanning, subjects were
instructed to perform a fixation task during imaging to maintain the position of the
optic nerve.
Whole brain DTI sequence: 60 non-collinear diffusion encoding gradient images and
10 non-diffusion weighted images, TR = 7720ms, TE = 88ms, b-value = 1000s/mm2,
FOV = 240 x 240mm2, matrix size = 128 x 128, and 70 axial contiguous 2mm thick
slices.
After all imaging sequences, MR images were checked for excessive movement or
artefacts and repeated if necessary.
Whole brain DTI spatial normalisation
The procedure for DTI spatial normalisation involved first generating a study-specific
template from both control and patient DTI data followed by affine and nonlinear
registration of each subject’s DTI data to the template. A study-specific template was
generated using an iterative registraion procedure as required by the DTI registraion
software used (DTI-TK, http://www.nitrc.org/projects/dtitk) (1). To generate the
template the following method was used. (i) Each subject’s diffusion tensor images
(the six off-diagonal elements of the diffusion tensor [xx, yy, zz, xy, xz, yz]) were
affinely registered to a 3rd party template space (ICBM DTI-81) (2) and averaged to
compute a first-pass template. (ii) Subject space diffusion tensor images were affinely
registered to the first-pass template and the registered images averaged to compute a
second-pass template. (iii) The process described in (ii) was repeated to compute a
third-pass template. (iv) Subject space diffusion tensor images were affinely and
nonlinearly registered to the third-pass template and averaged to create a forth-pass
template. (v) The process described in (iv) was repeated a further two times to
compute the final template. Each subject’s original difusion tensor images were
normalised to the final template using an affine followed by nonlinear deformable
registration using DTI-TK.
Diffusion Tractography
The ROI mask image, containing significant voxels from the voxelwise linear
regression controlling for age and FA from the optic nerve and radiation was
transformed to each control subject’s native diffusion image space and used to seed a
probabilistic tractography algorithm (PROBTRACKX) (3) with 100,000 streamlines
from each ROI voxel. The resulting tract connectivity image values were normalised
by dividing by a robust maximum value (the absolute maximum was in all cases an
extreme value). The robust maximum was obtained by analysing the image intensity
histogram (1000 bins) for each subject’s data and selecting the highest intensity bin
containing ten or more voxels. The intensity normalised connectivity images were
then spatially normalised to template space using the nonlinear deformations
calculated for voxelwise analyses described above. The intensity and spatially
normalised connectivity images were then averaged to obtain a group connectivity
image.
Supplementary Table 1. Significant clusters for voxelwise partial regressions
between FA or MD and Sloan 100% visual acuity in patients’ affected eyes after
statistically controlling for age.
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