A Multi-Scale Computational Framework for Evaluating Wireless Neuromodulation Feasibility via Magnetoelectric Nanoparticles.

Authors

Marta Bonato, Elric Zhang, Emma Chiaramello, Serena Fiocchi, Giulia Suarato, Valentin Gantenbein, Nathalia Cancino-Fuentes, Alejandro Suarez-Perez, Maria V Sanchez-Vives, Salvador Pané, Marta Parazzini

Published in

Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e77667. Sep 10, 2026. Epub Sep 10, 2026.

Abstract

Magnetoelectric nanoparticles (MENPs) represent a promising approach for wireless, minimally invasive neuromodulation with submillimetre precision. This study presents a multi-scale computational framework that bridges the gap from single-nanoparticle magnetoelectric properties to tissue-level electric field distributions, establishing a digital twin approach adaptable to different experimental platforms. The framework operates across three interconnected levels. (1). At the nanoscale, a 2D axisymmetric COMSOL model characterizes individual MENP response, integrating experimental data from cobalt ferrite cores (15-20 nm diameter) with barium titanate shells. Parametric analysis reveals that larger cores generate higher electric potentials due to increased magnetostrictive volume. (2). At the in vitro level, electromagnetic simulations replicate the experimental setup as a digital twin, establishing baseline field distributions and confirming that coil-induced electric fields alone remain below neuromodulation thresholds. (3). At the tissue scale, a dipole-based approach models collective MENP behavior at physiologically relevant concentrations (0.1% and 0.2% w/v). Results demonstrate that MENPs generate widespread electric fields exceeding 10 V/m (77%-92% tissue coverage) with localized hotspots surpassing 100 V/m, intensities sufficient for neural modulation and activation. The framework provides quantitative predictions for optimizing nanoparticle design and stimulation parameters, supporting the rational development of MENP-based neuromodulation strategies.

PMID:
42723170
Bibliographic data and abstract were imported from PubMed on 11 Sep 2026.

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