Multiscale Modeling of Brain Function

The human brain is a quintessentially multiscale system, characterized by a complex hierarchy of interactions spanning several orders of magnitude in both space and time. From the rapid dynamics of ion channels and synaptic transmission at the molecular level to the integrated activity of microcircuits and large-scale cortical networks, brain function emerges through the orchestration of these disparate scales. Historically, computational models have often focused on a single level of abstraction, either detailed biophysical simulations or simplified top-down phenomenological models. However, it is increasingly recognized that understanding cognitive phenomena such as memory, perception, and the progression of neurological disorders requires a more holistic approach. Bridging the gap between microscopic mechanisms and macroscopic behavior remains one of the most formidable challenges in neuroscience. To truly decode how the brain processes information, we must develop robust frameworks that link these layers, revealing how local perturbations influence global brain states.The primary objective of this Research Topic is to showcase cutting-edge advancements in the development and application of multiscale models that provide a unified view of brain function. Current modeling efforts often suffer from "scale-separation," where the biological realism of the cellular level is lost when transitioning to network-level dynamics. This research topic focuses on contributions that address this problem, including work on mathematical frameworks, data-driven methods, and computational approaches. We seek to highlight work that goes beyond describing activity at multiple levels, focusing instead on the causal links between them. For instance, how do genetic mutations affecting single-channel kinetics manifest as altered oscillations in whole-brain EEG? By integrating diverse computational methodologies, ranging from compartmental modeling to graph theory and hybrid AI-neuroscience models, this collection aims to provide a roadmap for building more predictive and clinically relevant models of the healthy and diseased brain. Ultimately, we hope to facilitate a deeper dialogue between theoreticians and experimentalists to ensure multiscale models are grounded in empirical reality.We welcome papers that contribute to the theoretical and practical development of multiscale neuroscience. We particularly encourage submissions that emphasize the integration of experimental data, such as optogenetics, electrophysiology, or neuroimaging, with multiscale frameworks.Specific themes of interest include, but are not limited to:- Development of novel mathematical frameworks for bridging spatial and temporal scales in neural systems.- Models linking molecular signaling and synaptic plasticity to network-level learning and behavior.- Multiscale modeling of neurological and psychiatric disorders (e.g., epilepsy, Alzheimer’s, or schizophrenia).- Integration of neural dynamics across scales, from individual spikes to LFP, EEG, and fMRI signals.- Machine learning and AI-assisted methods for dimensionality reduction and parameter estimation in multiscale models.- Computational software, open-source tools, and platforms designed for multiscale simulation and reproducibility.- Cross-scale interactions between the nervous system and other physiological systems, such as neurovascular coupling. Topic editors: Eric Adriano Zizzi, Ranjit Kumar Upadhyay, Ursula van Rienen Source: https://www.frontiersin.org/research-topics/80414/multiscale-modeling-of-brain-function