Computational protocol: Functional and structural brain modifications induced by oculomotor training in patients with age-related macular degeneration

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Protocol publication

[…] For data analysis the S(statistical) P(parametric) M(mapping)8 (Wellcome Department of Imaging Neuroscience Group, London, UK; http://www.fil.ion.ucl.ac.uk/spm) software package was used. To investigate longitudinal changes in gray and white matter density due to fixation training we applied the Dartel toolbox (Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra) implemented in SPM8 (Ashburner, ). Before any further analysis the origin of the structural images was set to the anterior-posterior commissure manually. Within every subject its T1-weighted structural scans from the three sessions were spatially realigned to the first image with SPM8 (realignment options were: Quality 0.9, Separation 4, Smoothing (FWHM) 5, Num Pass Register to first and Interpolation 2nd degree B-spline). Then the new segment toolbox in SPM8 was used to generate gray matter-, white matter- and cerebral spinal fluid CSF-images from each one of the realigned structural scans using a very light bias regularization (0.0001) and a bias FWHM cutoff of 60 mm. No cleanup of the resulting images was performed to avoid the accidental remove of important information. The warping regularization parameter was set to 4, the affine registration was made to the ICBM space template - European brains and the sampling distance was set to 4. With the help of the DARTEL-Toolbox from SPM8, in a first step a group-template (patient or control, respectively) and corresponding flow-fields for every subject were calculated from the segmented images within the group control and patient. We decided to generate different separate templates for each group to avoid that group-specific differences are diminished during this process. The parameters for this setting were as follows: Regularization form: linear elastic energy, six outer iterations with the following respective settings for inner iterations, registration parameters, time steps and smoothing parameter: (3, [4, 2, 1e-6], 1, 16), (3, [2, 1, 1e-6], 1, 8), (3, [1, 0.5, 1e-6], 2, 4), (3, [0.5, 0.25, 1e-6], 4, 2), (3, [0.25, 0.125, 1e-6],16, 1), (3, [0.25, 0.125, 1e-6], 64, 0.5). The optimization settings were: 0.01 for the LM regularization, 3 cycles, and 3 iterations. The segmented images of each subject were then normalized to the MNI space based on the flow-fields and the corresponding group template that were generated in the first DARTEL step. This was done with the “Normalize to NMI Space” module of the DARTEL toolbox. The spatially normalized images were then modulated with the Jacobi determinants of the deformations to preserve the signal amount in the images. Finally, the resulting images were smoothed with an 8-mm FWHM Gaussian kernel.For statistical analysis two general linear models were constructed, for white and gray matter, respectively. Using the factorial design specification batch in SPM8, the “full factorial” design was selected in both cases, with the only factor being controls vs. patients. We assumed independence and unequal variance for both model specifications. Level one and level two in the design were the smoothed, normalized gray, and white matter likelihood maps for the controls and the patients, respectively, with each of the three sessions in ascending order. In an additional covariate we tried to capture the training effect by setting their values to “1”, “2”, and “3” for the first, second, and third sessions, while applying an overall mean centering for all subjects. Finally, for explicit masking we used the brainmask.nii image provided by SPM8. To assess group effects or differences one and two sample t-tests were conducted. Only clusters surviving a statistical threshold of less than 0.0001 (uncorrected for multiple comparisons) on voxel level and less than 0.05 [(F)false (D)discovery (R)rate corrected] on cluster level are supposed to be significant and are reported. Significant clusters are visualized on a volume-based standard brain from a single normal subject (ch2.nii.gz), using the software MRIcron (Rorden and Brett, ; http://www.nitrc.org/projects/mricron). […]

Pipeline specifications

Software tools SPM, MRIcron
Application Magnetic resonance imaging
Organisms Homo sapiens
Diseases Macular Degeneration