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This study links brain aging and Alzheimer's risk to thermodynamic entropy, showing heteromodal cortex accumulates more disorder, suggesting selective vulnerability to neurodegeneration.

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Area of Science:

  • Computational neuroscience
  • Neurobiology
  • Biophysics

Background:

  • Aging and Alzheimer's disease risk correlate with increased entropy.
  • Neural networks adjust synaptic weights, a process requiring physical work (W) related to thermodynamic entropy (S) and temperature (T) via dS/dt = (dW/dt)/T.
  • This study models organic neural networks to predict brain vulnerability to age-associated neurodegeneration.

Purpose of the Study:

  • To investigate the relationship between thermodynamic entropy and selective brain vulnerability in aging.
  • To model information processing in hierarchical neural networks and its link to neurodegeneration.
  • To predict which brain regions are most susceptible to age-related changes based on entropy dynamics.

Main Methods:

  • Developed two feedforward hierarchical neural network models (columnar and convergent pyramidal) with parallel processing.
  • Simulated information processing across hierarchical layers representing sensory, unimodal, and heteromodal cortices.
  • Measured work proxies for weight adjustments, Lyapunov stability, and thermodynamic/informational entropies over 2000 iterations.

Main Results:

  • The topmost heteromodal cortex layer (Layer 3) exhibited the most significant and variable dynamics in both models.
  • Layer 3 showed substantial shifts in Lyapunov stability and began each cycle with the highest thermodynamic disorder (0.95 ± 0.18).
  • Informational entropy in Layer 3 increased over time, converging with the system's mean input probability (0.50 ± 0.04).

Conclusions:

  • The heteromodal cortex experiences greater thermodynamic entropy accumulation than sensory or unimodal cortices.
  • This increased entropy suggests selective vulnerability in heteromodal regions to age-related neurodegeneration.
  • Accumulated structural disorder from information processing demands may drive this vulnerability.