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Fig. 8 | Alzheimer's Research & Therapy

Fig. 8

From: Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer’s using machine learning

Fig. 8

A DKI estimates in three white matter ROIs (top row: corpus callosum (CC), middle row: cingulum (CG), and bottom row: fimbria of the hippocampus (Fi)). FA, fractional anisotropy; AxD/RD, axial/radial diffusivity; MK/AK/RK, mean/axial/radial kurtosis. Two-tailed t-test for inter-group comparison (red bars) and one-way ANOVA with Tukey-Cramer correction for within-group comparison across time (black and blue bars). ∗ : p < 0.05, ∗  ∗ : p < 0.01, ∗  ∗  ∗ : p < 0.001. + : outlier values (but not excluded from the analysis). B SHAP summary plots combining feature importance with feature effects based on DKI estimates. The position on the y-axis is determined by the feature and on the x-axis by the SHAP value. The color represents the value of the feature from low (blue) to high (red). The features are ordered according to their importance (top 10 displayed). Positive SHAP values lead the model to predict 1 (STZ) while negative ones lead the model to predict 0 (CTL)

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Alzheimer's Research & Therapy

ISSN: 1758-9193