Paper List
-
Exactly Solvable Population Model with Square-Root Growth Noise and Cell-Size Regulation
This paper addresses the fundamental gap in understanding how microscopic growth fluctuations, specifically those with size-dependent (square-root) no...
-
Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
-
Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
-
MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
-
The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
-
A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
-
ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
-
DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
Universitat de les Illes Balears, Spain | Universitat Politècnica de Catalunya, Barcelona, Spain | Institut de Matemàtiques de la UPC - Barcelona Tech (IMTech), Barcelona, Spain | Centre de Recerca Matemàtica, Barcelona, Spain
The 30-Second View
IN SHORT: This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically detailed local dynamics and empirical anatomical connectivity.
Innovation (TL;DR)
- Methodology Introduces a next-generation neural mass model (NG-NMM) into a large-scale brain network framework, providing a more biophysically grounded and analytically tractable description of population-level gamma oscillations via the PING mechanism.
- Methodology Applies the Master Stability Function (MSF) formalism and Floquet theory to systematically analyze transverse instabilities of homogeneous states (both fixed points and limit cycles) in a high-dimensional (90-node) network, linking instability modes to emergent spatiotemporal patterns.
- Biology Demonstrates that the network coupling in NG-NMMs enables cross-frequency coupling (CFC), specifically generating gamma oscillations whose amplitude is modulated by slower rhythms—a phenomenon not possible in isolated nodes and highly relevant for cognitive functions like memory.
Key conclusions
- NG-NMMs exhibit a broader dynamical repertoire than classical models, including regions of bistability, period-doubling cascades, and deterministic chaos within the homogeneous manifold (e.g., positive Lyapunov exponents for I_ext^E ~10-10.5 at ε=12).
- Anatomical connectivity is crucial for inducing cross-frequency coupling, allowing the emergence of gamma oscillations (27-170 Hz) with amplitude modulated by slower rhythms, a key feature of brain dynamics.
- The system's rich spatiotemporal patterns (traveling waves, high-dimensional chaos) arise from transverse instabilities of homogeneous solutions, analytically predicted by the MSF and confirmed via Lyapunov exponent and frequency spectrum analysis.
Abstract: Understanding the dynamics of large-scale brain models remains a central challenge due to the inherent complexity of these systems. In this work, we explore the emergence of complex spatiotemporal patterns in a large scale-brain model composed of 90 interconnected brain regions coupled through empirically derived anatomical connectivity. An important aspect of our formulation is that the local dynamics of each brain region are described by a next-generation neural mass model, which explicitly captures the macroscopic gamma activity of coupled excitatory and inhibitory neural populations (PING mechanism). We first identify the system’s homogeneous states—both resting and oscillatory—and analyze their stability under uniform perturbations. Then, we determine the stability against non-uniform perturbations by obtaining dispersion relations for the perturbation growth rate. This analysis enables us to link unstable directions of the homogeneous solutions to the emergence of rich spatiotemporal patterns, that we characterize by means of Lyapunov exponents and frequency spectrum analysis. Our results show that, compared to previous studies with classical neural mass models, next-generation neural mass models provide a broader dynamical repertoire, both within homogeneous states and in the heterogeneous regime. Additionally, we identify a key role for anatomical connectivity in cross-frequency coupling, allowing for the emergence of gamma oscillations with amplitude modulated by slower rhythms. These findings suggest that such models are not only more biophysically grounded but also particularly well-suited to capture the full complexity of large-scale brain dynamics. Overall, our study advances the analytical understanding of emerging spatiotemporal patterns in whole-brain models.