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Electrolytic-Microbubble Dynamics Delineate Safety Thresholds During Intracortical Microstimulation with Flexible Neural Interfaces

Preprint Created on 23 Jun 2026 bioRxiv

Intracortical microstimulation (ICMS) with ultraflexible neural electrodes enables low-threshold, chronically stable, and high-resolution modulation of neural circuits, providing a promising strategy for sensory restoration and closed-loop neuromodulation. However, the microscopic mechanisms delineating its safe and effective current range remain unclear. Here, we combine intravital two-photon (2P) imaging and electrophysiology in awake mice to examine the current-dependent neurovascular outcomes of charge-balanced stimulation via ultraflexible arrays. We observed gas bubbles formed along the electrode during ICMS, with bubble size increasing quadratically with current amplitude, consistent with a Faradaic bubble-growth model. Intravital 2P imaging reveals that at low-to-moderate currents (20~40 A), vascular leakage is small, spatially confined, and largely reversible, whereas higher currents ([≥]60 A) induce a sharp transition to extensive, field-dominated extravasation and secondary vessel disruption. This transition coincides with immediate, stimulus-locked motor responses and the onset of electrode degradation. Multiphysics simulations reproduce the observed nonlinear leakage-current relationship by incorporating gas bubble-induced electric field redistribution and voltage-dependent vessel wall permeability. The model indicates that gas bubbles act as local electric-field modulators, concentrating suprathreshold fields near the bubble boundary at lower currents while shielding more distant vessel segments; at higher currents, this confinement breaks down and the system enters a field-dominated damage regime. Collectively, these findings define a mechanistically informed safety window for ICMS with flexible neural interfaces and identify bubble-assisted vascular permeabilization as a key failure mode at high currents, crucial for the design of future bidirectional brain-computer interfaces and high-precision neuroprosthetic protocols.

Iliasov, A., Ma, H., Li, F., Chen, Z., Xu, M., Yu, C., Li, R., Wu, J., He, F.

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