Cocozza S., et al. : ALTERATIONS OF FUNCTIONAL CONNECTIVITY OF THE MOTOR CORTEX IN FABRY'S DISEASE: AN RS-FMRI STUDY
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1 ALTERATIONS OF FUNCTIONAL CONNECTIVITY OF THE MOTOR CORTEX IN FABRY'S DISEASE: AN RS-FMRI STUDY SUPPLEMENTARY MATERIALS Sirio Cocozza, MD 1*, Antonio Pisani, MD, PhD 2, Gaia Olivo, MD 1, Francesco Saccà, MD 3, Lorenzo Ugga, MD 1, Eleonora Riccio, MD 2, Silvia Migliaccio, MD 2, Vincenzo Brescia Morra, MD 3, Arturo Brunetti, MD 1, Mario Quarantelli, MD 4#, Enrico Tedeschi, MD 1# 1 Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy 2 Department of Public Health, Nephrology Unit, University Federico II, Naples, Italy 3 Department of Neurosciences and Reproductive and Odontostomatological Sciences, University Federico II, Naples, Italy 4 Institute of Biostructure and Bioimaging, National Research Council, Naples, Italy # These authors share senior authorship.
2 MRI data acquisition FLAIR images were acquired with the following parameters: TR = 6000 ms; TE = 396 ms; TI = 2200 ms; Flip Angle = 120 ; voxel size = 1x1x1 mm3; number of slices = 160; sagittal orientation. Structural T1w volumes were acquired using a three-dimensional magnetization-prepared rapid gradient-echo sequence (MPRAGE; axial planes; TR=1900 ms; TE=3.4 ms; TI=900 ms; Flip Angle=9 ; voxel size=1x1x1 mm3; number of slices=160). The T2*-weighted volumes were acquired using an echo-planar imaging sequence (axial planes; TR=2500 ms; TE=40 ms; 64x64 acquisition matrix; 30 slices; voxel size=3x3x4 mm3 ; gap 1 mm; 200 time points; acquisition time 8'27''). Structural MRI data preprocessing To assess differences in GM volume between the two groups, normalized GM maps were obtained using the unified segmentation tool (1) coupled to DARTEL (2), as implemented in the Statistical Parametric Mapping (SPM8) software package ( using all the default parameters. GM maps were then modulated by the Jacobian determinants derived from the spatial normalization procedure to preserve the local GM volumes. GM volumes were then smoothed using a 6-mm FWHM isotropic Gaussian kernel to reduce confounding by individual variations in gyral anatomy, and to render the data more normally distributed as per the Gaussian random field model underlying the statistical process used for adjusting p-values. For each study, total intracranial volume was calculated on the non-normalized segmented volumes as the number of voxels where the sum of GM, WM and CSF probabilities exceeded 50%. RS-fMRI data preprocessing For the analysis of RS-fMRI data, pre-processing steps included the removal of the first five time points (to allow for instability of the initial MRI signal), leaving 195 time points, motion correction,
3 slice timing correction, and temporal despiking with a hyperbolic tangent squashing function to limit outlier values, followed by band-pass filtering (0.008 Hz < f < 0.09 Hz) and spatial smoothing (using a 6-mm Gaussian kernel). The motion correction procedure performed by SPM realigns the volumes of each study to the first one, iteratively finding the translation and rotation parameters that minimize a least-squares cost function derived from the voxel-by-voxel intensity difference from the reference image (3). This approach proved to be accurate in realigning fmri volumes for motion correction purposes (4). From the motion correction procedure, the mean displacement for each brain volume was computed as the root-mean-square (RMS) of the translation parameters at each time point. Studies with a mean relative RMS of 0.15 or higher (according to (5)) or with more than 1.5 mm displacement along or 1.5 degrees rotation around any axis were discarded. Furthermore, a "scrubbing" procedure was applied (6) to those time points that showed a framewise differential of signal intensity >9 z- values, to remove the effect of these time points, along with the preceding and the two following ones. Resulting data sets were then normalized to the standard Montreal Neurological Institute (MNI) EPI template and resampled to a voxel size of mm 3. All images were visually assessed case by case by an experienced operator, to evaluate the overall accuracy of the processing. For each subject, BOLD signal time course was calculated over the left (l_pcg) and right (r_pcg) Precentral gyri, as defined in the Automated Anatomical Labeling atlas (7). Corresponding correlation maps of the BOLD signal across the brain were generated, including in the model the time courses of WM and CSF signals, and the six parameters (translations and rotations along the X, Y and Z axes) of spatial transformation, as derived from the coregistration step.
4 Table e-1 Sex Age Mutation Residual Enzyme Activity Patient #1 M 55 A.288D 0.3 Patient #2 M 43 G740A 0.2 Patient #3 M 38 G740A 0.2 Patient #4 F 33 c.1133c>t 2.5 Patient #5 F 68 c.67t>g 3.4 Patient #6 M 49 p.r356w 1.8 Patient #7 M 34 c.67t>g 0.2 Patient #8 M 50 c.1066c>t 0 Patient #9 F 56 p.r356w 3.7 Patient #10 F 36 p.r356w 3.5 Patient #11 F 30 c.901c>g 3.9 Patient #12 F 25 A.288D 6.4 Patient #13 M 52 p.trip162x 3.1 Patient #14 F 26 c.1066c>t 4.3 Patient #15 M 56 c.1066c>t 2.7 Patient #16 M 54 c.1066c>t 0 Patient #17 M 47 c.1066c>t 2.3 Patient #18 F 51 c.1021dupg 1.9 Patient #19 F 45 c.1021dupg 2.3 Patient #20 F 28 c.1021dupg 4.4 Patient #21 M 43 c.1133c>t 0
5 Patient #22 F 59 c.1066c>t 2.1 Patient #23 F 46 p.r356w 4.7 Patient #24 F 20 IVS4+5G>T 4.7 Patient #25 F 49 IVS4+5G>T 2.7 Patient #26 M 45 IVS4+5G>T 0.6 Patient #27 F 35 c.680g>c 4.1 Patient #28 F 32 c.680g>c 2.9 Patient #29 F 62 c.680g>c 2.8 Patient #30 F 29 c.901c>g 0.2 Patient #31 F 57 c.901c>g 3 Patient #32 F 33 c.424t>c 6.3 Table e-1: List of mutations and residual enzyme activity for all FD patents included in the study.
6 References 1. Ashburner J, Friston KJ. Unified segmentation. Neuroimage Jul 1;26(3): Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage Oct 15;38(1): Friston KJ, Ashburner J, Frith CD, et al. Spatial registration and normalization of images. Human Brain Mapping. 1995;3(3): Ardekani BA, Bachman AH, Helpern JA. A quantitative comparison of motion detection algorithms in fmri. Magn Reson Imaging Sep;19(7): Van Dijk KR, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage Jan 2;59(1): Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage Feb 1;59(3): Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage Jan;15(1):
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