## 4 Discussion
### Functional Interpretation of Hierarchical Clustering  
Our exploratory hierarchical clustering across three time points (R1: baseline, R2: mid-training, R3: post-training) provides novel insight into how high-intensity interval training (HIIT) drives a systemic reorganization of physiological relationships. In the HIIT group, variables did not remain in their initial clusters; instead, they regrouped into new functional clusters over time, as visualized by Sankey diagram flows of variable membership. Many variables that clustered together at R1 had diverged by R3, joining different clusters alongside variables from other domains. By contrast, the control group exhibited a far more stable clustering structure, with most variables staying in the same clusters from R1 to R3 and only minimal re-shuffling. This stark difference indicates that the HIIT intervention--but not standard training -perturbed the underlying network of physiological metrics, prompting it to \"rewire\" into a new configuration [11; 20]. While the observed shifts in cross-domain clustering were more pronounced in the HIIT group, we recognize that some pre-existing connectivity may be attributable to individual or maturational characteristics, and not exclusively to the intervention. Therefore, the interpretation of these shifts as HIIT-driven should be made cautiously, acknowledging that baseline coordination among systems could also play a role. These changes in cluster composition provide a functional portrait of training responsiveness. Classical training outcomes in the HIIT group were evident--for instance, we observed significant improvements in aerobic capacity (as reflected in VO2 max) and high-intensity performance, consistent with previous studies. Participants also reported lower perceived exertion for equivalent efforts post-HIIT (indicating improved exercise tolerance), and neuromuscular tests showed gains in power. However, beyond these expected domain-specific improvements, the clustering analysis demonstrates that HIIT altered how these improvements relate to each other across domains. The emergence of new cross-domain clusters at R3 suggests that the physiological systems of the HIIT-trained players began to coordinate in new ways [7; 8]. Essentially, HIIT not only improved individual capacities but also restructured the network of interactions among those capacities. This finding resonates with the concept of \"network-level\" adaptation: the training effect is seen not just in isolated variables, but in the architecture of their inter-relations. An increase in VO2 max (the cardiovascular domain) might reduce excessive acute lactate buildup (the metabolic domain) and moderate perceived fatigue (the cognitive/psychological domain) during intense exercise, thereby tying these domains closer together than before. Indeed, our HIIT group's final clusters often contained a mix of cardiovascular, metabolic, and perceptual variables, reflecting such integrated adaptation. No such reorganization was found in the controls; their physiological network appeared unchanged and compartmentalized, which aligns with the expectation that standard training of an equal volume but lower intensity mainly maintains the status quo. The control group's lack of cluster changes underscores that the network plasticity observed in the HIIT group was a targeted effect of the high-intensity stimulus rather than a generic time effect. **Limitations and Future research:** This investigation was conducted on a narrowly defined cohort of elite male adolescent soccer players, which imposes inherent limitations on the generalizability of the findings to broader populations, including female athletes, older individuals, or participants from non-elite or non-sporting backgrounds. While the four-week HIIT intervention elicited measurable physiological and cognitive adaptations, the relatively short duration may have constrained the full expression or long-term consolidation of network-level reorganization.