Guasch-Morgades, M. | Search bioRxiv

bioRxiv2025

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

neuroscience↗

bioRxiv2025

Fast Interneuron Dysfunction in Laminar Neural Mass Model Reproduces Alzheimer's Oscillatory Biomarkers

Alzheimers disease (AD) is characterized by a progressive cognitive decline underpinned by disruptions in neural circuit dynamics. Early-stage AD is associated with cortical hyperexcitability, whereas later stages exhibit oscillatory slowing and hypoactivity, a progression observable in electrophysiological spectral characteristics. While previous studies have linked these changes to the dysfunction of fast-spiking parvalbumin-positive (PV) interneurons and neuronal loss associated with amyloid-beta (A{beta}) and hyperphos-phorylated tau (hp-{tau}) pathology, the precise mechanistic relationship between cellular and altered electrophysiology remains unclear. To study this relationship, we employed a Laminar Neural Mass Model that integrates excitatory and inhibitory neural populations within a biophysically informed columnar framework. The connectivity constant from PV cells to pyramidal neurons was gradually reduced to simulate the progressive neurotoxic effects of A{beta} oligomers. Other model parameters were systematically varied to compare with existing modeling literature and also to simulate the effects of hp-{tau}. All model predictions were compared to empirical M/EEG findings in the literature. Our simulations of PV interneuron dysfunction successfully reproduced the biphasic electrophysiological progression observed in AD: an early phase of hyperexcitability with increased gamma and alpha power, followed by oscillatory slowing and reduced spectral power. Alternative mechanisms and model parameters, such as increased excitatory drive, failed to replicate the observed biomarker trajectory. Additionally, to reconcile the hypoactivity and decreased firing rates observed in advanced AD stages, we combined the PV dysfunction model with a disruption of the pyramidal cell populations that reflects the neurotoxicity induced by hp-{tau}. Although this additional mechanism is not necessary to reproduce oscillatory changes in the isolated neural mass, it is crucial for aligning the model with evidence of reduced firing rates, metabolic activity, and cell loss and will enhance its applicability in future whole-brain modeling studies. These results support the hypothesis that at the local level, PV interneuron dysfunction is a primary driver of cortical electrophysiological alterations, while pyramidal neuron loss underlies later-stage severe hypoactivity. Our model provides a mechanistic framework for interpreting excitation-inhibition imbalance across AD progression, demonstrating the value of biophysically constrained models for interpreting electrophysiological biomarkers. Author summaryAlzheimers disease (AD) is not just a disorder of memory--it is a disease of brain networks. Before widespread neuronal loss occurs, the brain enters a state of hyperexcitability, which may contribute to disease progression. This hyperactivity is thought to arise from the selective dysfunction of inhibitory interneurons, particularly parvalbumin-positive (PV) interneurons, which play a crucial role in maintaining balanced brain activity. However, as the disease advances, a dramatic shift occurs, with neurons becoming progressively less active, leading to network breakdown. Understanding how this transition unfolds is essential for identifying new targets for early intervention. In this study, we developed a computational model of AD that represents the effects of amyloid-beta (A{beta}) oligomers and hyperphosphorylated tau (hp-{tau}) on neural circuits in a biologically meaningful way. Our model reproduces key features of M/EEG biomarkers, demonstrating that PV interneuron dysfunction leads to early hyperexcitability and the electrophysiological oscillatory changes characteristic of AD, while pyramidal cell pathology is necessary to drive later hypoactivity and network failure. By bridging molecular pathology mechanisms with mesoscale neural activity, our model provides a powerful tool for studying AD-related circuit dysfunction. It also highlights the importance of PV interneurons as a potential therapeutic target, paving the way for biologically informed interventions that could alter the course of the disease.

neuroscience↗