Voxel-wise topographical correlation (r) of the PD, MSA and PSP-related brain networks.

*<p>p<0.05 after Bonferroni correction for multiple comparisons (3 comparisons: p<0.0167).</p><p>The p-value is empirically calculated based on the rank of r²-value in 1,000 simulations.</p

Regional differences of two covariance patterns.

<p>(<b>A</b>) Standard SPM analysis with paired t-test design for ON vs. OFF medication with 15 PD patients. (<b>B</b>) The PDRP derived from USA (off-medication) was subtracted from the PDRP derived from South Korea (on-medication). The resulting difference map is z-scored. Only the voxels that were reliable in permutation test were shown (p<0.05, 1,000 permutation). The topography of within-subject differences in medication status (A) was significantly correlated with between-group network differences (B) (r = 0.4228, p<0.001). Likewise, key regions of hypometabolism (e.g., M1, cingulate, cerebellum, putamen) and hypermetabolism (e.g., precuneus) were similarly shown.</p

Voxel-wise topographical correlation (r) of the PDRPs from 4 different countries.

*<p>p<0.05 after Bonferroni correction for multiple comparisons (6 comparisons: p<0.00833).</p><p>The p-value is empirically calculated based on the rank of r²-value in 1,000 simulations.</p

Schematic diagram of the simulation study.

<p>The stimulation was conducted to determine the Window size of Moran’s I that best reflected the inflated topological correlation of the two simulated networks. (<b>A</b>) 300 pseudo-random volume-pairs were generated, then box filters were applied to each volume with six different kernel sizes (3×3×3, 7×7×7, 11×11×11, 15×15×15, 19×19×19, 23×23×23). Then, the global Moran’s I of 1800 volume-pairs (300 original volume-pairs×6 different box filters) was estimated with varying window (W) size (3×3, 9×9, 15×15, 21×21, 27×27, 33×33, 45×45, 51×51, 57×57). The volume-pairs were then vector-transformed and tested for voxel-by-voxel Pearson’s correlation (topographical correlation). Multiple regression was utilized to test if the global Moran’s I significantly predicted the box-filtering-induced elevation of topographical correlation. The window size of the Moran’s I (W) that gave the best prediction of the topographical correlation from the global Moran’s I was identified using AIC. (<b>B–D</b>) The inflated topographical correlation was observed regardless of the W of Moran’s I while the best prediction resulted when the W of Moran’s I was 51 (lowest AIC).</p

The result of multiple regression: |r| = MI^*b1+Z^*B.

*<p>The lowest AIC value.</p><p>r: topographical correlation (Pearson’s correlation of the voxel weights of the two simulated patterns; MI: global Moran’s I; b1: coefficient of multiple regression of avgMI; Z: random effects dummy variables for 300 volume-pairs; B: coefficient for random effects; se: standard error of b1; AIC: Akaike Information Criteria for the whole model fit.</p