## 4 Discussion
The two remaining features had mean feature importance across the five folds of the cross-validation of 0.10 and 0.13, respectively, and involved relationships between the right frontal interior orbital region and right insula (0.13 importance) and between the left hippocampus and right cerebellum (0.10 importance). These 'golden features' and correlations identified, which are based on patterns of resting state brain activity across neural networks, provide new insights in predicting touch discrimination in a cohort of stroke survivors in the first year post-stroke, as discussed below, and demonstrate the potential value of this machine learning approach. Although ANNs have shown great promise in many areas of ML, ANNs typically require larger amounts of data compared to classical ML models [61]. This is becauseANNs, especially deep learning models, have a high number of parameters that need to be optimised, and a large dataset helps prevent overfitting and allows the model to generalise well to unseen data [61]. In comparison, classical ML models, such as decision trees or linear regression, may perform reasonably well with smaller datasets, as they have fewer parameters and dependencies to learn. The architecture of the best-performing model type, LightGBM, is a machine learning library that provides algorithms which utilise a gradient boosting framework. This model essentially ignores a significant proportion of data with small gradients in favour of features with larger gradients, as these features play a more important role in prediction accuracy. Gradient-based one-side sampling (GOSS) can obtain quite an accurate estimation of the information gain with a much smaller data size. This approach is particularly useful in a situation like stroke studies when data amounts are likely to be restricted. The AutoML approach allows a thoroughness that would be difficult and time-consuming to achieve manually. This thorough and systematic testing of models and parameters, along with the creation of new features and dimensionality reduction, has resulted in a high-performing model that may not have been arrived at without using the AutoML approach. In addition to the discovery of this particular combination of model architecture and parameters, the time taken to arrive at a model was also significantly reduced compared to manually running each step of the analysis. The first 'golden feature' grouping pattern with highest mean feature importance (0.36), involved the difference between the strength of correlation between the right insula and right superior temporal lobe relative to the left cerebellum and vermis correlation. The insula and the medial temporal lobe are directly connected through white matter fibres, which connect the entire insular cortex with the temporal pole and the amygdala complex [62]. The large-scale connectivity of the insula cortex positions it to play an important role in processing and integrating internal and external multisensory stimuli. Further, distinct insula subregions are associated with particular neural networks (e.g., attentional and sensorimotor networks) [63], consistent with the importance of this connected region in the tactile discrimination of textured surfaces. The involvement of the right hemisphere is also consistent with the hypothesis that the right hemisphere plays a dominant role in tactile discrimination function and suggests the need for further systematic investigation. The left cerebellum and vermis (median portion of the cerebellum) were also associated and identified within this 'golden feature' combination. The cerebellum is involved in sensorimotor operations, cognitive tasks and affective processes [64]. In an earlier study of a different cohort of stroke survivors with somatosensory impairment, we found that clinical improvement in touch discrimination was associated with stronger correlations at 6 months between the contralesional thalamus and cerebellum [65].