## 4 Discussion  
ML approaches are providing new insights into complex systems and neuroscience [53]. Here, we interrogate and demonstrate the ability of a suite of machine learning approaches to identify patterns of brain activity associated with clinical outcomes in a relatively small cohort of stroke survivors. Using AutoML ([[https://mljar.com/automl/](https://mljar.com/automl/)]([https://mljar.com/automl/](https://mljar.com/automl/)), accessed on 5 August 2023), we compared six candidate approaches and applied algorithms. Both feature reduction and the identification of 'golden features' were involved, together with five-fold cross-validation. LightGBM, which utilises a gradient boosting framework [54], emerged as the best candidate for this stroke cohort proof-of-concept data set. In merging machine learning (ML) with stroke research, 'prediction' serves a distinct role. Unlike clinical predictions focused on prognoses, ML predictions evaluate how well models capture underlying data patterns--specifically, the relationship between brain network features and TDT scores post-stroke. The predictive performance of our models is not aimed at forecasting individual outcomes but at assessing the model's accuracy in reflecting complex data relationships, such as with resting state connectivity data. This is crucial for confidence in identifying significant 'golden features' in brain networks and understanding recovery mechanisms, which can inform targeted rehabilitation strategies. The goal is to harness these models as analytical tools, providing insights into stroke recovery's neural underpinnings. Such insights pave the way for interventions based on a nuanced understanding of post-stroke neural recovery, bridging the gap between ML and clinical applications. Thus, our findings, which focus on predictive accuracy, are a means to deepen our understanding of stroke recovery, guiding future rehabilitation efforts with data-driven precision. This marks a step towards innovative progress in stroke recovery research, leveraging ML to illuminate the complexities of neural impairment and recovery. The derivation of 'golden features' from basic correlation metrics mirrors the inherent complexity of brain networks themselves. Such networks are not merely linear or additive in nature but encompass interactions that might be compensatory, synergistic, or inhibitory [55; 56]. For example, a diminished functional connectivity between two brain regions post-stroke might be offset by enhanced connectivity in another part of the network, a dynamic interplay that 'golden features' are uniquely positioned to capture [57; 58]. Moreover, the value of 'golden features' extends beyond their descriptive power; they also hold prognostic significance. By identifying specific patterns of network disruption or reorganization, these features can predict clinical outcomes with greater accuracy than traditional measures [59]. This predictive capability not only informs the development of targeted therapeutic interventions but also facilitates a more personalised approach to stroke rehabilitation, tailoring treatments to the individual's unique neural landscape [60]. Two 'golden feature' patterns emerged based on the highest mean feature importance of the five folds of the cross-validation (0.36 and 0.21) for the LightGBM model. The brain regions associated involved the difference between the right insula and right superior temporal region correlation and area 3 of the left cerebellum and vermis correlation (0.36 importance), as well as the ratio between the right inferior temporal lobe and left cerebellum correlation and the left frontal inferior operculum and left supplementary motor area correlation (0.21 importance). Interestingly, the association between right insula and right superior temporal was based on the difference between the strength of these correlations.