Computational protocol: Frontotemporal dementia with the C9ORF72 hexanucleotide repeat expansion: clinical, neuroanatomical and neuropathological features

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Protocol publication

[…] A majority of patients included in the University College London FTLD cohort have had at least one T1-weighted magnetic resonance brain volume acquired on either a 1.5T GE Signa scanner (General Electric) (256 × 256 matrix; 1.5 cm slice thickness) or a 3.0T Siemens Trio scanner (Siemens) (256 × 256 matrix; 1.1 cm slice thickness). Where available, volumetric brain MRI data for the C9ORF72 mutation cases were analysed using previously described semi-automated techniques and compared with the GRN and MAPT mutation groups.Whole-brain segmentation yielded a whole-brain region separated from CSF, dura and skull (). Scans were then transformed into standard space by registration to the Montreal Neurological Institute template and hemispheric volumes were calculated for each individual. In order to assess hemispheric asymmetry, a left/right asymmetry ratio was derived in each case by dividing the left hemispheric volume by the right hemispheric volume, as previously described (). Repeat scans were transformed into standard space and underwent an affine registration (12 degrees of freedom) onto the baseline scan. Volume change was calculated directly from registered scans using the brain boundary shift integral (). Brain boundary shift integral-derived whole-brain volume changes were expressed as an annualized volume change in ml per year. Volumetric data from a previously published cohort () of GRN [n = 4, mean interval 1.3 years, standard deviation (SD) 0.2] and MAPT (n = 6, 2.1 years, SD 1.0) mutation carriers (all values derived using the same methods) were compared against C9ORF72 mutation carriers using two-sample t-tests with unequal variance within STATA 10 (Statacorp).In order to determine patterns of brain atrophy at group level, volumetric MRI data from healthy controls and the C9ORF72, MAPT and GRN mutation groups were compared using voxel-based morphometry with the DARTEL toolbox of SPM8 ( running under Matlab 7.0 (Mathworks). Segmentation, modulation and smoothing of grey and white matter images were performed using default parameter settings. Final images were normalized to standard space. In order to reduce the effects of atrophy, analysis was performed over voxels within a consensus mask, which includes all voxels with intensity of >0.1 in 70% of subjects (). A multiple regression analysis was performed with voxel intensity modelled as a function of group membership with age, total intracranial volume, gender and scanner field strength included as nuisance covariates. Results were then overlaid on the MNI152 template brain for anatomical analysis. Total intracranial volume was calculated by summing together grey matter, white matter and CSF segmentations.Cortical thickness measurements were made using FreeSurfer v5.1 ( following previously described methods (; ). A surface-based Gaussian smoothing kernel of 20 mm full width at half-maximum was applied to reduce local variations in cortical thickness. Skull stripping used a locally generated brain mask, and FreeSurfer ventricular segmentations were added to the white matter mask to improve cortical segmentation. Between-group variations in cortical thickness were assessed using a vertex-by-vertex general linear model performed with SurfStat ( Regional differences in grey matter between control and disease groups were assessed using the same covariates as the voxel-based morphometry analysis. A significance threshold of P < 0.05 with false discovery rate correction was applied. Where this threshold was not met, percentage difference maps were used to illustrate trends in atrophy patterns.Alterations of white matter tract integrity were assessed with diffusion tensor imaging using an unbiased group-wise analysis technique. Three individuals underwent diffusion tensor imaging scanning on the same 3T scanner (64 direction single shot, spin-echo-echo planar imaging sequence, 55 contiguous slices). Tensor eigenvalues (λ1 = axial diffusivity, λ2 and λ3), fractional anisotropy and radial diffusivity (RD = λ2 + λ3) were extracted at each voxel using CAMINO (). Tensor fitted images were then imported for voxel-wise analysis within tract-based spatial statistic (TBSS v1.1) (). Voxel intensity within tracts was modelled on disease group membership with the age, gender and total intracranial volume included as nuisance covariates. Statistical analysis was implemented using the permutation-based (non-parametric) randomize tool within FSL.For all group-wise analyses, a significance threshold (P < 0.05) was applied following correction for multiple comparisons. Threshold-free cluster enhancement modulation was applied to diffusion tensor imaging results (). […]

Pipeline specifications

Software tools SPM, FreeSurfer
Application Neuroimaging analysis
Diseases Motor Neuron Disease, Frontotemporal Lobar Degeneration, Frontotemporal Dementia