In Silico analysis of concentration and spatial distribution effects of magnetoelectric nanoparticles on peripheral nerve activation

The delivery of selective and specific neural stimulation is of paramount importance to enable effective interactions with the nervous system while reducing potential side effects. Neural interfaces play a key role in the communication between an external device and nervous tissue, and technological advances have been made in recent decades to optimize this interaction and thus improve the quality of life of patients. However, several challenges remain to be overcome, such as the trade-off between selectivity and invasiveness or the modulation of a specific stimulation that could mimic natural axonal spiking. In this context, emerging magnetoelectric nanoparticles (MENPs) could be designed as a possible solution to current limitations, allowing minimally invasive, highly selective, wireless and more natural neural stimulation. Among the variables that influence the interaction of MENPs with neural tissue, both the concentration and the distribution of nanoparticles play a key role, but appropriate studies and quantification of their effects are still lacking. Our study aims to fill this gap, moving an important step toward more realistic and informative modeling of the MENPs-nerve interaction. In addition, the great variability in the anatomy of peripheral nerves was considered by evaluating the stimulation capability of the nanoparticles while interacting with both myelinated and unmyelinated axons. A holistic computational framework was used, ranging from multiphysics characterization of the ME effect of a single nanoparticle to modeling the effects of the nanoparticle concentration and stochastic distribution to assess axonal electrical responses. Owing to their improved performance, nanorod-shaped MENPs (NRs) were selected as stimulation sources, and multiple nanoparticles were assumed to be randomly distributed in a small volume within the tissue. The results demonstrate the strong influence of both concentration and distribution on the stimulation capability of nanoparticles, with the stimulation capability increasing as the nanoparticle concentration increases. Specific axonal excitability strongly influences axonal responses, with the stimulation capability of MENPs varying greatly between myelinated and unmyelinated axons. This study makes a significant contribution to the foundational knowledge required for applying MENPs in peripheral nerve stimulation and provides quantitative evidence regarding how nanoparticle concentration and distribution influence MENP-nerve interactions.

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Publication Details

Journal
Bioelectronic Medicine
Published
2025-10-16
DOI
https://doi.org/10.1186/s42234-025-00187-4
Citations
1
Primary Topic
Conducting polymers and applications
Type
article
Field-Weighted Citation Impact
0.61
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In Silico analysis of concentration and spatial distribution effects of magnetoelectric nanoparticles on peripheral nerve activation

Emma Chiaramello, Serena Fiocchi, Marta Bonato, Marta Parazzini et al.
1 citations
Bioelectronic Medicine
Conducting polymers and applications
0.61
article

In Silico analysis of concentration and spatial distribution effects of magnetoelectric nanoparticles on peripheral nerve activation

Emma Chiaramello, Serena Fiocchi, Marta Bonato, Marta Parazzini, Valentina Galletta
article en
1 citations

Abstract

The delivery of selective and specific neural stimulation is of paramount importance to enable effective interactions with the nervous system while reducing potential side effects. Neural interfaces play a key role in the communication between an external device and nervous tissue, and technological advances have been made in recent decades to optimize this interaction and thus improve the quality of life of patients. However, several challenges remain to be overcome, such as the trade-off between selectivity and invasiveness or the modulation of a specific stimulation that could mimic natural axonal spiking. In this context, emerging magnetoelectric nanoparticles (MENPs) could be designed as a possible solution to current limitations, allowing minimally invasive, highly selective, wireless and more natural neural stimulation. Among the variables that influence the interaction of MENPs with neural tissue, both the concentration and the distribution of nanoparticles play a key role, but appropriate studies and quantification of their effects are still lacking. Our study aims to fill this gap, moving an important step toward more realistic and informative modeling of the MENPs-nerve interaction. In addition, the great variability in the anatomy of peripheral nerves was considered by evaluating the stimulation capability of the nanoparticles while interacting with both myelinated and unmyelinated axons. A holistic computational framework was used, ranging from multiphysics characterization of the ME effect of a single nanoparticle to modeling the effects of the nanoparticle concentration and stochastic distribution to assess axonal electrical responses. Owing to their improved performance, nanorod-shaped MENPs (NRs) were selected as stimulation sources, and multiple nanoparticles were assumed to be randomly distributed in a small volume within the tissue. The results demonstrate the strong influence of both concentration and distribution on the stimulation capability of nanoparticles, with the stimulation capability increasing as the nanoparticle concentration increases. Specific axonal excitability strongly influences axonal responses, with the stimulation capability of MENPs varying greatly between myelinated and unmyelinated axons. This study makes a significant contribution to the foundational knowledge required for applying MENPs in peripheral nerve stimulation and provides quantitative evidence regarding how nanoparticle concentration and distribution influence MENP-nerve interactions.

Bioelectronic MedicineVol. 11(1)
Institute of Electronics, Computer and Telecommunication Engineering (IT), Politecnico di Milano (IT)
Openalex Percentile: Top 29%
Conducting polymers and applications
0.61
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