We introduce ${\xi }$–${\alpha }$NET, a model of cortical activity that represents EEG aperiodic ($\xi$) and $\alpha$-rhythm ($\alpha$) components as Hida–Matérn processes constrained by anatomical connectivity and interareal conduction delays. This approach integrates the decomposition of spectral Granger causality and quantifies the lifespan trajectories of spectral processes. Using Bayesian inversion on cross-spectral rsEEG data from 1965 participants aged 5–100 (HarMNqEEG dataset), the model estimates cortical activity with high test-retest reliability, effective connectivity patterns and conduction delays. Given the approximate cortical hierarchy inferred from the inverted T1w/T2w myelination map, used as a proxy for feedforward and feedback organization, the aperiodic and ${\alpha }$ components reveal opposite directional networks across the lifespan: the aperiodic component is localized in the frontal cortex, whereas the ${\alpha }$ component is localized in the posterior cortex, with feedforward and feedback-directed connections, respectively. For both processes, the spectral parameters follow a nonlinear inverted U-shape lifespan trajectory. Finally, the model uniquely estimates global conduction delays, which are negatively correlated with $\alpha$ frequency and with independent cortical myelination (T1w/T2w) measures, consistent with a mechanistic link between conduction delays and $\alpha$-rhythm modulation.

Background: Reaction time (RT) is a fundamental measure of information processing speed in cognitive neuroscience and is influenced by both structural and functional brain properties. While prior studies have independently linked white matter microstructure and EEG alpha oscillations to cognitive performance, their joint contribution to distinct aspects of RT remains unclear. This study aims to investigate whether multimodal data can dissociate neural systems underlying cognitive and motor components of processing speed. Methods: We analyzed diffusion tensor imaging, resting-state individual EEG alpha peak frequency (IAF), demographic variables, and behavioral RT measures from a GO/NO-GO paradigm in 24 healthy adults from the Cuban Human Brain Mapping Project. Behavioral metrics included the mean, standard deviation and skewness of reaction times for simple and complex tasks. Sparse multiple canonical correlation analysis was applied to identify multivariate associations across modalities. Results: Two significant latent dimensions were identified. The first dimension linked bilateral fronto-temporal association tracts (SLF, IFOF, UNC) with complex RT performance, reflecting higher-order cognitive processing. The second dimension associated motor and interhemispheric tracts (CGC, CST, ILF, forceps major and minor) with intra-individual asymmetric variability (skewness) across tasks, indicating a motor-execution consistency system. IAF did not significantly contribute to either dimension. Sex showed strong associations with both components. Conclusions: Distinct white matter networks were associated with separable cognitive and motor aspects of processing speed, while resting-state alpha frequency did not show stable contributions with behavioral variability in this sample. IAF showed minimal contribution within the identified sparse multivariate dimensions. These findings highlight the importance of multimodal and multivariate approaches for understanding and potentially disentangling complex brain–behavior relationships.

Background: Reaction time (RT) is a fundamental measure of information processing speed in cognitive neuroscience and is influenced by both structural and functional brain properties. While prior studies have independently linked white matter microstructure and EEG alpha oscillations to cognitive performance, their joint contribution to distinct aspects of RT remains unclear. This study aims to investigate whether multimodal data can dissociate neural systems underlying cognitive and motor components of processing speed. Methods: We analyzed diffusion tensor imaging, resting-state EEG alpha peak frequency, demographic variables, and behavioral RT measures from a Go/No-Go paradigm in 24 healthy adults from the Cuban Human Brain Mapping Project. Behavioral metrics included the mean, standard deviation and skewness of reaction times for simple and complex tasks. Sparse multiple canonical correlation analysis was applied to identify multi-variate associations across modalities. Results: Two significant latent dimensions were identified (p< 0.05). The first dimension linked bilateral association tracts (SLF, IFOF, UNC) with complex RT performance, reflecting higher-order cognitive processing. The second dimension associated motor and interhemispheric tracts (CGC, CST, ILF, forceps major and minor) with intra-individual asymmetric variability (skewness) across tasks, indicating a motor-execution consistency system. EEG alpha peak frequency did not significantly contribute to either dimension. Sex showed strong associations with both components. Conclusions: Distinct white matter networks support separable cognitive and motor aspects of processing speed, while resting-state alpha frequency does not independently explain behavioral variability. These findings highlight the importance of multimodal and multivariate approaches for understanding and potentially disentangling complex brain–behavior relationships.