Fig. 4: PV-INs functional connectivity predicts pathology progression.

A Scatter plot on the principal component space showing the distribution of CNT and 6-OHDA mice on the first and second components. Unpaired t-test p < 0.0001. B First ten ranked features contributing to the PC1. C Average classification accuracy of the SVM classifier in distinguishing CNT and 6-OHDA mice using Leave-One-Out Cross-Validation (LOO-CV). The SVM was trained on different subsets of the dataset plus a bootstrap dataset as control, and accuracy was measured across all Leave-One-Out splits (Wilcoxon signed rank test (against chance: 0.5): All, p = 0.0039; Puncta, p = 0.0039; Lesion, p = 0.0039; Cylinder test, p = 0.0039; WF, p = 0.0391). D Heat map representing values from the correlation matrix (Pearson correlation) of analyzed parameters (bottom triangle), and the significance of each pairwise comparison (top triangle: black box indicates p < 0.05). E, F Scatter plot and linear regression detailing specific correlations depicted in (D). In particular, we show the correlation between wide-field data at 28DPL in the MOp-a_R with E performance in the cylinder test at 28DPL, and F PV density in MOp-pR (r and p values are indicated in the figures). All the FC data refer to FC at 28DPL. In blue, CNT n = 5 and in orange, 6-OHDA n = 4. Data are expressed as mean ± Standard Error of Mean. *p ≤ 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.