Computational protocol: Cortical Thickness, Surface Area and Subcortical Volume Differentially Contribute to Cognitive Heterogeneity in Parkinson’s Disease

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Protocol publication

[…] High-resolution structural MRI scans were obtained at the VUmc, using a GE Signa HDxt 3.0-Tesla MRI-scanner (General Electric, Milwaukee, Wisconsin, USA) with an 8-channel head coil. We acquired structural MRI data using a sagittal 3-dimensional gradient-echo T1-weighted sequence (256 x 256 matrix; field of view = 25cm; slice thickness = 1mm; voxel size = 1 x 0.98 x 0.98 mm; TR = 7.8 ms; TE = 3.0 ms; view angle = 12°). Image analysis was carried out with the stable version (v.5.3.0) of the FreeSurfer software ( [–]. In short, the procedure included: motion correction, intensity normalization, Talairach registration, skull stripping, segmentation of subcortical white matter, tessellation of the GM/white matter (WM) boundary, automated topology correction, and surface deformation. We used a 10 mm (full-width at half-maximum) Gaussian kernel to smooth maps. Finally, FreeSurfer created a surface 3D model of the cortex using intensity and continuity information. [...] A number of statistical tests was performed to assess between-group differences in structural measures. First, we performed a vertex-wise analysis of differences in CTh in FreeSurfer’s statistical program QDEC 1.5, using Monte Carlo-simulations with 10.000 iterations to correct for multiple comparisons and a cluster-wise p-value of .05 to display results. Second, surface (i.e. SA per parcellation) and volumetric analyses (i.e. sub-cortical volume estimates calculated by FreeSurfer, and the manually calculated volume estimate per cortical parcellation) were performed in SPSS 20.0 (SPSS, Chicago, IL, USA). For SA and cortical volume, we performed independent t-tests using the 68 parcellations (34 per hemisphere) as dependent variables, group as between-subject factor, and tGM volume as a nuisance variable []. Between-group differences in subcortical volume were investigated with the volume of the 23 automatically segmented subcortical regions as dependent variable, group as between-subject factor, and tGM as a nuisance variable. We applied a Bonferroni correction by dividing our p-value by the number of cortical areas per hemisphere (p < (.05/34) = ~.001) and by the number of sub-cortical structures per hemisphere (p < (.05/13) = ~.004) in order to correct for multiple comparisons. […]

Pipeline specifications

Software tools FreeSurfer, SPSS
Applications Miscellaneous, Magnetic resonance imaging
Organisms Homo sapiens
Diseases Parkinson Disease