Connection-wise comparison reveals significantly reduced connections in 6-OHDA rats compared shams.

<p>Glass brain depicting edges and their associated nodes that showed significantly reduced functional connectivity in 6-OHDA rats in comparison to sham rats. The size of a node represents the number of abnormal connections associated with that node. Significance was assessed using non-parametric permutation tests (N = 10000). A p-value below 0.05 was considered significant (FWER corrected). The results shown were generated at a primary NBS threshold of t = 3.1 with p = 0.0425, FWER corrected. 6-OHDA rats N = 13; sham rats N = 11. Bold L marks the lesion side. R, contralateral side.</p

Global network measures of segregation and integration across different network sparsities in 6-OHDA rats and shams.

<p>(A) Mean clustering coefficient C normalized to a random reference network. (B) Mean characteristic path length L normalized to a random reference network. (C) Small-worldness S. (D) Number of modules. (E) Modularity Q. (F) Global efficiency. Data are expressed as mean ± SEM. 6-OHDA rats N = 13; sham rats N = 11.</p

Rat brain parcellation scheme showing 150 ROIs (75 on each hemisphere).

<p>Each bilateral ROI is labelled differently and overlaid onto coronal slices of the rat brain template.</p

Local differences on a nodal level in 6-OHDA rats compared to shams.

<p>Glass brains depicting nodes that showed significantly altered (A) node degree, (B) clustering coefficient and (C) node efficiency in 6-OHDA rats compared to shams. Nodes are represented as spheres and located in the glass brain according to their corresponding anatomical region’s centre mass. Blue spheres depict significantly lower, red spheres significantly higher values in 6-OHDA rats compared to shams. Significance was assessed using two-sample t-test by comparing the AUC of these network measures for each node across all network sparsities (0.05</p

White matter maps showing regions of significant decreased fractional anisotropy and increased mean diffusivity in MSA patients when compared to healthy controls and PD (Bonferroni corrected alpha = 0.0167).

<p>Background image corresponds to the mean fractional anisotropy image of all subjects in standard MNI152 space (radiological view). Fractional anisotropy white matter skeleton is represented by green voxels. Blue voxels represent regions of decreased FA and yellow voxels represent regions of increased MD in the PSP group.</p

White matter maps showing regions of significant correlation between mean diffusivity and measures of disease severity (Bonferroni corrected alpha = 0.0167).

<p>Top row coronal view, bottom row axial view. Background image corresponds to the mean fractional anisotropy image of all subjects in standard MNI152 space (radiological view). Fractional anisotropy white matter skeleton is represented by green voxels. Blue voxels represent regions of decreased FA and yellow voxels represent regions of increased MD in the PSP group.</p

White matter regions of fractional anisotropy changes between PSP, MSA, PD and HC.

<p>All results reported at p<0.05, TFCE and Bonferroni corrected (corrected alpha = 0.0167).</p><p>White matter regions of fractional anisotropy changes between PSP, MSA, PD and HC.</p

White matter regions of mean diffusivity changes between PSP, MSA, PD and HC.

<p>All results reported at p<0.05, TFCE and Bonferroni corrected (corrected alpha 0.0167).</p><p>White matter regions of mean diffusivity changes between PSP, MSA, PD and HC.</p

White matter maps showing regions of significant decreased fractional anisotropy and increased mean diffusivity in PSP patients when compared to healthy controls, PD and MSA (Bonferroni corrected alpha = 0.0167).

<p>Background image corresponds to the mean fractional anisotropy image of all subjects in standard MNI152 space (radiological view). Fractional anisotropy white matter skeleton is represented by green voxels. Blue voxels represent regions of decreased FA and yellow voxels represent regions of increased MD in the PSP group.</p

Multivariate discriminative weights computed using ordinal regression.

<p>For ordinal regression the weights can be interpreted as the projection of the data along the function space weight vector spanning CTL to MCI-s to MCI-s to AD. Note that the weights are symmetric across hemispheres. These weights are sensitive the spatial correlations in the data and therefore should not be interpreted in a univariate manner.</p