Fig. 4: Groups, sex and macrosocial influences in brain-age gaps.
a,b, Violin plots for the distribution of prediction gaps for different groups and sex effects using (a) fMRI and (b) EEG datasets. Statistical comparisons were calculated using two-sided subsample permutation testing without multiple comparisons and with 5,000 algorithm iterations. c, Associations between macrosocial and disease disparity factors with brain-age gaps were assessed with a multi-method approach comprising SHAP values, feature importance (MDI) and permutation importance. Plots show the mean importance values for each method, along with their 99% CI, as well as the average R2 and Cohen’s f². *Features whose lower CI boundary does not cross zero. Shaded bars indicate significance across the three methods. We conducted a two-sided F-test to evaluate the overall significance of the regression models. The three models were significant: healthy controls LAC (R² = 0.37 (99% CI ±0.17), F² = 0.59 (99% CI ±0.21), r.m.s.e. = 6.9 (99% CI ±0.92), F = 138.78 (P < 1 × 10−15)); healthy controls non-LAC (R² = 0.41 (99% CI ±0.17), F² = 0.71 (99% CI ±0.21), r.m.s.e. = 6.57 (99% CI ±1.31), F = 135.91 (P < 1 × 10−15)) and total dataset (R² = 0.41 (99% CI ±0.12), F² = 0.71 (99% CI ±0.14), r.m.s.e. = 6.76 (99% CI ±0.89), F = 253.39 (P < 1 × 10−15)). The relevance of the features and their respective CI values are available in Supplementary Table 2. F, females; HC LAC, healthy controls from LAC; HC non-LAC, healthy controls from non-LAC; M, males. This figure was partially created with BioRender.com (fMRI and EEG devices).
