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Estimating the modulatory effects of nanoparticles on neuronal circuits using computational upscaling
Michael Busse1, David Stevens, Annette Kraegeloh
1Systems Neuroscience and Neurotechnology Unit, Saarland University, Faculty of Medicine, Neurocenter, and Saarland University of Applied Sciences, Homburg/Saarbruecken, Germany.
International Journal of Nanomedicine
|October 12, 2013
Summary
Coated silver nanoparticles (cAgNP) rapidly suppress neuronal sodium currents, altering firing patterns in entire neural networks. This study investigates nanoparticle effects on neural excitability and network signaling in silico.
Area of Science:
- Neuroscience
- Nanotechnology
- Computational Biology
Background:
- Nanomaterials are increasingly used in medicine, but their effects on neural systems are not well understood.
- Coated silver nanoparticles (cAgNP) are being explored for medical applications, necessitating investigation into their impact on neuronal function.
Purpose of the Study:
- To investigate the in vitro effects of cAgNP on the excitability of single neuronal cells.
- To integrate these findings into an in silico model to predict potential effects on neuronal circuits.
Main Methods:
- Patch clamp electrophysiology to measure neuronal excitability after cAgNP exposure.
- Hodgkin-Huxley model simulations to identify altered sodium current parameters.
- In silico modeling of thalamocortical circuits to predict network-level effects.
Main Results:
- Exposure to cAgNP rapidly suppressed sodium currents in vitro.
- Numerical simulations identified reduced sodium current amplitude as the primary effect of cAgNP.
- In silico network modeling demonstrated that local cAgNP application alters firing patterns across the entire neuronal circuit.
Conclusions:
- cAgNP primarily suppress neuronal sodium currents by reducing their amplitude.
- Local application of cAgNP can induce widespread changes in network activity, affecting the entire neuronal circuit.
