Introduction to Multiscale Brain Modeling
Multiscale brain modeling represents one of the most dynamic and promising areas in neuroscience today. This field focuses on constructing models of the brain that integrate multiple scales, from the microscopic level of neuronal excitation to the macroscopic level of whole-brain functions and behaviors. Recent advancements in informatics and big data science have made these models increasingly feasible, offering new insights into both fundamental brain functions and neuropathological conditions.
Understanding Multiscale Brain Organization
Addressing the multiscale organization of the brain is crucial for comprehending its functional mechanisms. This understanding not only aids in answering complex neuropathological questions but also fosters the development of innovative technologies in artificial intelligence and healthcare. A comprehensive review of this topic is provided by Krejcar and Namazi, with additional insights from D’Angelo and Jirsa (2022) and Wang et al. (2024). The research primarily focuses on two areas: theoretical models of neuronal excitability and network oscillations, and models applied to Alzheimer’s disease research.
Innovative Theoretical Models
Galinsky and Frank propose an alternative framework to the traditional Hodgkin-Huxley model for action potentials in axons. Their theory of electric field wave propagation in anisotropic and inhomogeneous brain tissues addresses several limitations of the Hodgkin-Huxley model, such as its inability to explain extracellular spiking and efficient brain synchronization. Additionally, Pieramico et al. demonstrate the efficacy of Hidden Markov Models in analyzing neural activity time series, showing that Time-Delay Embedded Hidden Markov Models outperform Gaussian models in detecting brain states from synthetic data. Ghosh et al. introduce trainable networks of Hopf oscillators to model high-dimensional EEG signals across different sleep stages, marking a significant step toward creating large-scale, biologically inspired models of brain dynamics.
Applications in Alzheimer’s Disease Research
Two significant studies focus on Alzheimer’s disease. Fadel et al. present a model of functional connectivity changes in a mouse model, revealing patterns of hyperconnectivity associated with learning and memory. This model shows potential for early disease detection by identifying connectivity patterns linked to cognitive decline. Moravveji et al. conduct a sensitivity analysis of a mathematical model of Alzheimer’s disease progression, unveiling causal pathways and capturing the multifactorial nature of the disease. This analysis identifies key drivers of disease progression, offering insights into targeted therapeutic strategies.
Conclusion
The exploration of multiscale brain modeling is paving the way for groundbreaking advancements in neuroscience. By integrating various scales of brain function, researchers are not only enhancing our understanding of fundamental brain mechanisms but also developing innovative approaches to tackle complex neurological diseases like Alzheimer’s. As this field continues to evolve, it holds the promise of transforming both theoretical neuroscience and practical applications in medicine and technology.
🔗 **Fuente:** https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2026.1783885/full