On March 11th, the paper entitled: “A soft electrode array with reconfigurable hydrogel interfaces for high-fidelity neurophysiological monitoring during craniotomy” was published online in Proceedings of the National Academy of Sciences of the United States of America (PNAS). Ganguang Yang, Bo Pang, and Hangyu Gong are co-first authors. Professor Hao Wu, Academician Zhouping Yin, and Professor Changsheng Wu serve as corresponding authors.
In neurosurgery, craniotomy is a critical procedure for treating neurological disorders such as brain tumors and traumatic brain injuries, where precise intraoperative monitoring of cortical electrophysiological activity plays a key role in preventing neurological deficits and enabling accurate lesion resection. Although various commercial intracranial electrophysiological monitoring techniques have been developed, there are still several limitations including high modulus, elevated costs, and low signal acquisition quality. In recent years, flexible electronic devices have shown great promise in intracranial neural recording due to their excellent stretchability, mechanical compliance, and biocompatibility. However, current intraoperative neurophysiological monitoring during craniotomy faces several major technical challenges: (1) the wet and smooth surface of the brain makes it difficult to achieve robust adhesion between the device and brain interfaces, severely hindering stable signal acquisition; (2) brain tissue, characterized by complex contours, ultra-low modulus, and high fragility, exhibits a severe mechanical mismatch with existing monitoring devices, preventing conformal coupling; (3) the prolonged duration of craniotomy exposes electrode devices to an extreme intracranial environment, and repeated repositioning required during surgical manipulation often leads to rapid performance degradation and reduced signal quality, compromising sustainable high-fidelity neural signal recording. To address these challenges, the research team led by Professors Hao Wu and Academician Zhouping Yin has developed a reconfigurable hydrogel electrode array that is capable of sustainable, high-fidelity electrophysiological signal monitoring during craniotomy. This study proposes a solution-triggered reconfiguration strategy for hydrogel interface layers, which allows for the disassembly and replacement of degraded hydrogel sensing interfaces upon exposure to the solution, thereby enabling sustainably stable device/brain interfaces. Moreover, the soft electrode array, possessing excellent wet adhesion and anti-swelling properties, can form robust adhesion with moist brain tissue, significantly enhancing the stimulation and recording performance. By reconfiguring hydrogel interface layers, the proposed electrode array achieves high-quality and stable monitoring of somatosensory evoked potential (SSEP) signals, effectively mitigating motion artifacts during surgery while avoiding performance degradation caused by repeated electrode repositioning.
Design of the electrode array with reconfigurable hydrogel interfaces
In the animal model of traumatic brain injury (TBI), the proposed electrode array accurately captured the amplitude attenuation characteristics of SSEP signals, enabling rapid localization of injured brain regions. In nerve block experiments, compared with commercial electrodes, this electrode array before/after hydrogel interface reconfiguration could elicit higher-quality motor evoked potentials (MEPs) at lower stimulation currents, thereby enabling precise monitoring of the nerve block process and subsequent neurological recovery. Compared with existing monitoring technologies, this electrode array offers significant advantages in accurately assessing brain functional status and improving surgical safety during craniotomy, demonstrating broad application prospects in the field of neurosurgical monitoring.
SSEP monitoring by the soft electrode array
The above research work was supported by the National Natural Science Foundation of China (No. 52550004, 52188102) and the STAR Project by the School of Mechanical Science and Engineering of Huazhong University of Science and Technology.
Original link：https://doi.org/10.1073/pnas.2533145123
