Designing Neural Dynamics: From Digital Twin Modeling to Regeneration

Area of Science:

• Systems Neuroscience
• Neurodegenerative Disease Research
• Computational Neuroscience

Background:

• Cognitive deterioration is characterized by rapid, global state transitions in neural networks, not linear regression.
• Understanding and controlling these transitions is a major challenge in neuroscience.
• This paper reframes cognitive decline as a transitional phase driven by molecular, vascular, and systemic factors.

Purpose of the Study:

• To present a conceptual reframing of cognitive decline as a transitional phase of complex neural networks.
• To integrate recent findings on factors destabilizing neural networks and leading to neurodegeneration.
• To propose new paradigms and therapeutic interventions for modulating neurodegenerative disease progression.

Main Methods:

• Integration of findings on glymphatic clearance, aquaporin-4 polarity, venous output, and neuroimmune signaling.
• Application of systems neuroscience paradigms: connectomics, network neuroscience, and critical transition theory.
• Utilizing artificial intelligence, machine learning, multi-omics, radiogenomics, and digital twin modeling for predictive biomarkers.

Main Results:

• Disruptions in key mechanisms destabilize neurophysiologic homeostasis, impacting memory, consciousness, and resilience.
• Exacerbating factors include epigenetic silencing, neurovascular decoupling, ECM remodeling, and metabolic collapse.
• Emerging predictive biomarkers and early warnings of system collapse are being identified.

Conclusions:

• Cognitive decline represents a translatable change of state, not irreversible degeneration.
• New paradigms offer tools to describe and predict state changes at the systems level.
• Precision neurointervention and regenerative medicine offer opportunities to prevent neurodegeneration progression, with ethical considerations.