Bridging local and global dynamics: a biologically grounded model for cooperative and competitive interactions in the brain
Functional brain networks exhibit both cooperative and competitive interactions, yet existing models--assuming purely excitatory long-range coupling--fail to account for the widespread anti-correlations observed in fMRI. Starting from a laminar neural mass frame-work, where each mass comprises distinct slow (alpha-band) and fast (gamma-band) oscillatory pyramidal subpopulations (P1 and P2), we show how laminar-specific long-range excitatory projections across neural mass parcels can give rise to both cooperation and competition via cross-frequency envelope coupling. We demonstrate that homologous connections across parcels (e.g., P1[->]P1 or P2[->]P2) induce positive correlations between the infra-slow amplitude fluctuations of alpha band envelopes in each parcel, as well as in the simulated fMRI BOLD signals. Conversely, heterologous connections (P1[->]P2) induce negative correlations. We tested this mechanism by building personalized whole-brain models for a cohort of 60 subjects in two steps. First, we inferred signed inter-parcel generative effective connectivity directly from resting-state fMRI using regularized maximum-entropy (Ising) models. Then we connected laminar neural masses to simulate BOLD dynamics by implementing positive and negative Ising connections via homologous and heterologous projections, respectively. Ising-derived cooperative/competitive connectivity modeling faithfully reproduced both static and dynamic functional connectivity patterns, as well as gamma power-BOLD correlation and partial alpha power-BOLD anticorrelation-outperforming structurally constrained and cooperative-only variants. This further demonstrates that functional data alone suffices to infer individualized connectivity. Together, these results provide a biologically grounded mechanistic model on how long-range excitatory circuits and local cross-frequency interactions shape the balance of cooperation and competition in large-scale brain dynamics. HighlightsO_LIWe introduce a biologically grounded mechanism for brain-wide cooperation and competition, based on laminar-specific cross-frequency coupling (CFC) between alpha and gamma oscillations, mediated solely by excitatory long-range projections targeting different cortical layers. C_LIO_LIOur models can reproduce key empirical observations, including the negative correlation between alpha power and BOLD, the positive correlation between gamma power and BOLD, and the presence of local laminar cross-frequency interactions consistent with invasive and EEG findings. C_LIO_LIModels generate BOLD signals autonomously, without recourse to external stochastic inputs. C_LIO_LIWe introduce a method for personalized Ising modeling from Ising data using a sparsity (L1) constraint to infer signed connectivity from limited BOLD data. C_LIO_LIWe validate the proposed mechanism in a cohort of 60 subjects using subject-specific generative whole-brain models, which not only improve the replication of static functional connectivity but also accurately capture dynamic spatiotemporal brain state transitions. C_LIO_LIOur whole-brain models display biologically realistic local dynamics, with laminar neural mass models preserving plausible EEG-like alpha and gamma oscillations while aligning with large-scale BOLD patterns--offering a unifying framework that bridges microscale laminar physiology and macroscale functional connectivity. C_LI