###### Abstract  
The brain age gap (BAG), the divergence of an individual's neurobiologically predicted brain age from their chronological age, is a key indicator of brain health. While BAG can be derived from diverse brain metrics, its interpretation often polarizes between early-life trait influences and current state-dependent factors like cognitive decline. Here, we propose an integrative framework that moves beyond single summary statistics by considering the full distribution of brain metrics across regions or time. We distinguish between a neural system's \"baseline\" (typical values, e.g., mean) and its \"capacity\" (extreme values, e.g., maximum) within these distributions. To test this, we analyzed resting-state magnetoencephalography (MEG) from the Cam-CAN adult cohort, focusing on cross-frequency coupling (CFC) within functional MRI-defined networks. We derived network-specific CFC baseline (mean) and capacity (maximum) measures. Separate brain age prediction models were trained for each measure. The resulting BAGs (baseline-BAG and capacity-BAG) for each network were then correlated with cognitive performance on a picture priming task. Both baseline-BAG and capacity-BAG profiles showed associations with cognitive scores, with younger predicted brain age correlating with better performance. However, capacity-BAG exhibited more conclusive relationships, suggesting that metrics reflecting a neural system's peak operational ability (capacity) may better capture an individual's current cognitive state. These findings indicate that brain age models emphasizing neural capacity, rather than just baseline activity, could offer a more sensitive lens for understanding the state-dependent aspects of brain ageing. brain age; brain age gap; cross-frequency coupling; phase-amplitude coupling; magnetoencephalography; resting-state networks; brain-cognition associations; Cam-CAN study 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (  
encapsulate both enduring, early-life neurodevelopmental influences and more transient effects of current health or clinical deterioration [3]. Two primary, though not mutually exclusive, perspectives frame the interpretation of BAG: the \"state\" hypothesis and the \"trait\" hypothesis [4]. The state hypothesis posits that BAG reflects dynamic, potentially reversible brain function or structure changes linked to an individual's current physiological or pathological condition. Conversely, the trait hypothesis suggests that BAG is a more stable, enduring characteristic, possibly established early in life and reflecting long-term developmental trajectories or inherent predispositions. The state hypothesis suggests that an increased brain age gap may indicate brain pathology. For instance, studies have shown that individuals with schizophrenia exhibit a greater brain age gap, which correlates with negative symptom severity, suggesting that the brain age gap could serve as a biomarker for psychosis conversion in high-risk individuals [5]. Alzheimer's disease (AD) and mild cognitive impairment (MCI) are associated with an increased BAG, indicating accelerated brain ageing. This gap is linked to cognitive impairment and is a predictive marker for disease progression [6]. MRI models have identified higher than control-based brain age for diabetes, multiple sclerosis, mild cognitive impairment, and Alzheimer's dementia [7]. In another MRI study, neurologically older brains were associated with heavy episodic drinking, higher blood pressure, and higher blood glucose [8]. The trait hypothesis proposes that the brain age gap is a stable, individual characteristic rather than a reflection of transient physiological states [4]. Several studies support this hypothesis, suggesting that the brain age gap primarily reflects inherent individual differences rather than current health conditions or temporary influences [3; 4; 9]. EEG-based research provides evidence for the trait hypothesis.