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Designing Neural Dynamics: From Digital Twin Modeling to Regeneration.

Calin Petru Tataru1,2,3, Adrian Vasile Dumitru4, Nicolaie Dobrin2,5

  • 1Department of Opthamology, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania.

International Journal of Molecular Sciences
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

Cognitive decline involves rapid state transitions in neural networks, not linear regression. Understanding these critical transitions offers new avenues for neurodegenerative disease intervention.

Keywords:
AI-driven brain modelingadaptive neurocontrolastrocytic signalingattractor landscapesclosed-loop neuromodulationdigital twin neuroscienceepigenetic neurorepairglymphatic–venous couplingnonlinear brain dynamicssystems neuroengineering

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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.