Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models

Area of Science:

• Neuroscience
• Computational Neuroscience
• Medical Imaging

Background:

• Alzheimer's disease (AD) is a complex neurodegenerative disorder with challenging diagnostic methods.
• Traditional neuroscience models have advanced understanding from micro to macro scales.
• Recent advancements include large-scale brain dynamics models integrating multimodal neuroimaging data and neurodynamics theory.

Purpose of the Study:

• To review the application of biophysical large-scale brain dynamics models in Alzheimer's disease research.
• To explore how these models bridge anatomical structure and functional dynamics in AD.
• To highlight future directions for developing and analyzing AD models for clinical applications.

Main Methods:

• Utilizing biophysically informed, large-scale brain dynamics models.
• Integrating dual-driven multimodal neuroimaging data.
• Applying neurodynamics theory to model brain function at a macroscale.

Main Results:

• Large-scale brain dynamics models effectively explain macroscale neuroimaging biomarkers in AD.
• These models connect neuronal population disturbances to observable AD-related changes.
• The approach aids in understanding the brain mechanisms underlying Alzheimer's disease.

Conclusions:

• Biophysical large-scale brain dynamics models represent an emerging and promising approach for studying AD.
• These models are crucial for advancing the understanding of AD pathogenesis.
• Future development can facilitate virtual brain models for AD diagnosis, treatment, and clinical neuroscience advancement.