## 4 Discussion
For each CV (over 100 repetitions), they randomly undersampled the training set multiple times by selecting a fixed amount of samples for each diagnostic category from the training set. Then, a random forest model was trained within each CV round based on a grid search and nested k-fold stratified CV. The tuned model was tested on each sample of the test subject, and SHAP values were computed 100 times and averaged for each sample. Basically, in their first work, the authors trained the same model multiple times on different portions of training datasets [11], whereas, in the other study, they added hyperparameter tuning within the repetitions to select the best model given by the different subsets [37]. Our method allows the user to estimate representative SHAP values, separately for the training and test sets, in a repeated nCV setting, following a well-documented algorithm, which is accompanied by the source code. This will enable other researchers to apply the procedure in their own studies. It differs from the [PERSON] et al. approach [11; 37] for two main reasons: (i) we repeat the nested CV \(R\) times rather than generating repeated undersampled training set in a single nested CV; (ii) our proposed method allows the user to separately compute representative SHAP values for the training and test sets--coherently averaged, sample by sample, among folds and repetitions. This gives the user a stronger understanding of the average behavior of the model's interpretability in a very robust and popular validation setting (i.e., the repeated nested CV). Moreover, our method can be easily applied to simpler validation schemes, including repeated hold-out procedures and any machine learning model in a regression or classification task. Our results on individual age prediction using brain complexity features are consistent with previous findings [40]. We previously showed that a monotonic decrease in structural complexity (in terms of \(FD_{Auto\, fractalbrain}\)) of the cerebral cortex with age during almost all the lifespan [40] and, more recently, that cardiorespiratory fitness is positively associated with cortical gray matter complexity in the temporal lobe, a region which is particularly sensitive to normal and pathological aging [60]. In the present study, as expected, low values of brain complexity (\(FD_{Auto\, fractalbrain}\)) and cortical thickness give a positive contribution to the model output (individual age) in both children and adults (see the beeswarm plots in Figures 3-5). In other words, we confirmed that a lower brain complexity and cortical thinning are valuable predictors of older subjects in both a young and adult cohort. Moreover, our results suggest that our latest development of the fractal dimension (\(FD_{Auto\, fractalbrain}\)) [40] is more predictive of individual age compared to other implementations. This result strengthens the importance of the selection of the interval of spatial scales for an adequate characterization of the structural complexity of the cerebral cortex, which is especially fascinating when using ultra-high-field MRI [59]. Moreover,\(FD_{Auto\_fractalbrain}\) was the most impactful neuroimaging feature for predicting the age in children (NKI2 dataset) and the second most important feature for adults (ICBM dataset), with an impact on the model output close to that of the first top-ranking CT feature (see Figures 4 and 5). This result confirms the ability of the fractal dimension of the cerebral cortex, in addition to cortical thickness and gyrification, to characterize brain maturation and aging, as previously observed for neurodegeneration [69]. In addition, we showed that the global feature importance of the \(FD_{Auto\_fractalbrain}\) was consistently greater than that of the GI--a well-established index of the structural complexity of the human cortex. As expected, feature rankings were not consistently the same in the training and test sets.