## 4 Discussion
By analyzing frequency-specific signal amplitudes in terms of skewness and kurtosis, the study described how these statistical features captured the variability of neural dynamics across the lifespan. Similarly, another study used resting-state EEG to predict Fugl-Meyer upper extremity motor scores in individuals with chronic stroke [50]. With functional connectivity estimated as the mean and maximum phase-locking value, the study found that maximum-based estimates achieved higher prediction accuracy [50]. These findings reinforce the importance of considering distribution tails when interpreting neural metrics. Characterizing the tails of the skewed distributions and focusing on their maximum values, skewness, or kurtosis falls into the domain of extreme value theory (EVT). In neural networks, EVT can be used to evaluate robustness against adversarial examples [51]. The extreme events help assess neural networks' capacity to resist extreme perturbations, thereby defining their robustness and capacity. In a study exploring the interaction between olfactory and visual systems in vertebrates, EVT was applied to model cross-modal sensory information integration [14]. This integration was mediated by extreme responses rather than average cell responses, suggesting that neural capacity is influenced by the ability to handle extreme sensory inputs. Cross-frequency coupling (CFC) in neural ensembles requires understanding how different oscillatory frequencies interact to enhance cognitive processes such as memory, perception, and attention [34]. CFC is a mechanism by which neural oscillations at different frequencies synchronize, facilitating information integration across neural populations [33]. This coupling is crucial for robust information storage and retrieval and for the modulation of cognitive functions [52]. Our study has limitations. We recognize that using the maximum value of cross-frequency coupling across brain regions to represent neural network capacity is a simplification. From a theoretical perspective, interpreting this peak, or the upper tail of its distribution, as a direct measure of neural capacity currently lacks strong theoretical grounding. CFC can occur through various mechanisms, including phase-phase and phase amplitude coupling, which are influenced by synaptic time scales, synaptic coupling, and neural population excitability [32]. Inhibitory neural mass models demonstrate that CFC can emerge from the collective dynamics of neural populations, with different dynamical regimes such as periodic and quasi-periodic oscillations [53]. Nonlinear interactions in neural fields, rather than specialized cells, can generate CFC, suggesting a universal mechanism across multi-scale systems [54]. Neuronal ensembles are considered modular units of neural circuits [55]. Neuronal ensembles represent and process information through coordinated activity patterns, which are influenced by factors such as the size of the ensemble, the diversity of neuronal responses, and the synaptic connections within the ensemble [55]. The capacity of a neural ensemble can be conceptualized in terms of its ability to encode, store, and retrieve patterns or information, which is influenced by the structure and dynamics of the neuronal ensemble. For example, cross-frequency coupling, particularly between theta and gamma oscillations, has been shown to increase the memory capacity of neural networks [56]. Other limitations should be acknowledged. First, although we analyzed a broad range of cognitive assessments available within the Cam-CAN dataset, our analyses did not incorporate all potentially influential variables. Factors such as detailed educational history, comprehensive health status indicators beyond gross clinical categories, and specific lifestyle variables (e.g., diet, physical activity) were not included. The inclusion of such covariates could further refine our models of brain-behaviour relationships and potentially reveal nuances in the observed effects or identify moderating influences.