journal article·2025-10-15

An Implantable System Design Based on Neuromorphic Memristor for Post-Craniotomy Intracranial Pressure Monitoring

Teng Wu, Kun Wang, Bai Sun, Zelin Cao, Junxiang Gu, Meirou Liang, Yifan Le, Fan Li, Jian Wang, Yifan Xiao, Mengna Wang, Longhui Fu, Kaikai Gao, Haoyuan Wang, Hui Ma, Xincheng Du, Jiajun Liu, Jianqiang Qu, Chang Liu, Guangdong Zhou, Jinyou Shao, Xianxia Yan

ACS Applied Materials & Interfaces· Volume 17· Issue 41
Publisher page

Abstract

Craniotomy, a complex neurosurgical intervention, carries significant risks of postoperative complications including intracerebral hemorrhage (ICH) and cerebral edema, causing elevated intracranial pressure (ICP) and life-threatening cerebral herniation. However, the current ventricular catheter ICP monitoring technologies pose risks of infection and hemorrhage, and restrict patient mobility during medical procedures. There is thus an urgent need to develop ICP monitoring technologies that simultaneously achieve sensitivity, safety, and portability. Memristors, with their integrated memory, sensing, and neuromorphic computing capabilities, offer a promising solution to traditional monitoring bottlenecks. In this work, we innovatively developed an Ag/WO 3 /MnO 2 /FTO-structured memristor and validated its pressure signal encoding capability in vitro via integration with a pressure sensor. A collagenase-induced ICH animal model was established to simulate postcraniotomy intracranial hypertension. Following model induction, the sensor-memristor system was implanted for intracranial pressure signal acquisition and encoding. The encoded signals were prospectively processed through a memristor-based logic circuit for noise reduction, and analyzed and classified via a memristive neural network. This study demonstrates the potential of implantable memristor-sensor system for postcraniotomy ICP monitoring and underscores its role in enhancing neurosurgical care, which also provides innovative insights for designing efficient, real-time, and low-power consumption implantable pressure monitoring devices for medical health monitoring.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · CCD and CMOS Imaging Sensors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Neurosurgery

The Second Affiliated Hospital of Xi’an Jiaotong University

Frontier Institute of Science and Technology, and Interdisciplinary Research Center of Frontier Science and Technology

Xi’an Jiaotong University

Micro-and Nano-Technology Research Center, State Key Laboratory for Manufacturing Systems Engineering

Department of Human Anatomy, Histology and Embryology and K.K. Leung Brain Research Centre

Fourth Military Medical University

Department of Neurosurgery, Xijing Hospital

Shaanxi University of Chinese Medicine

State Key Laboratory of Electromechanical Integrated Manufacturing of High-performance Electronic Equipment

Xidian University

Basic Medicine Experimental Teaching Center

Xi’an Jiaotong University Health Science Center

Department of Neurosurgery, Tangdu Hospital

Department of Anatomy, School of Medicine

Northwest University

Key Laboratory of Surgical Critical Care and Life Support (Xi’an Jiaotong University), Ministry of Education

College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab

Southwest University

Funding

Xi?an Jiaotong University · National Natural Science Foundation of China

References (58)

Show source-provided references
1. 10.1038/s41467-022-28298-510.1038/s41467-022-28298-5 ↗
2. 10.1016/j.accpm.2016.06.01210.1016/j.accpm.2016.06.012 ↗
3. 10.1002/admt.20210033910.1002/admt.202100339 ↗
4. 10.1038/s41582-023-00893-010.1038/s41582-023-00893-0 ↗
5. 10.1016/j.wneu.2019.10.10510.1016/j.wneu.2019.10.105 ↗
6. 10.1056/NEJMvcm180531410.1056/NEJMvcm1805314 ↗
7. 10.1021/acsami.9b1057510.1021/acsami.9b10575 ↗
8. 10.1021/acsnano.4c1848410.1021/acsnano.4c18484 ↗
9. 10.3390/bios1107021310.3390/bios11070213 ↗
10. 10.1002/adfm.20191071810.1002/adfm.201910718 ↗
11. 10.1002/adfm.20200375410.1002/adfm.202003754 ↗
12. 10.1002/adfm.20180824710.1002/adfm.201808247 ↗
13. 10.1038/s41551-023-01022-410.1038/s41551-023-01022-4 ↗
14. 10.1039/D2NH00536K10.1039/D2NH00536K ↗
15. 10.1002/smll.20230891810.1002/smll.202308918 ↗
16. 10.1021/acsami.4c0511210.1021/acsami.4c05112 ↗
17. 10.1021/acsami.2c1378010.1021/acsami.2c13780 ↗
18. 10.1002/smll.20220018510.1002/smll.202200185 ↗
19. 10.1002/advs.20210348410.1002/advs.202103484 ↗
20. 10.1002/adma.20230733410.1002/adma.202307334 ↗
21. 10.1021/acsami.2c1648110.1021/acsami.2c16481 ↗
22. 10.1039/D2NR05257A10.1039/D2NR05257A ↗
23. 10.1016/j.nanoen.2023.10854910.1016/j.nanoen.2023.108549 ↗
24. 10.1016/j.mtbio.2024.10116910.1016/j.mtbio.2024.101169 ↗
25. 10.1002/adfm.20240113210.1002/adfm.202401132 ↗
26. 10.1002/smll.20220787910.1002/smll.202207879 ↗
27. 10.1002/adma.20241165910.1002/adma.202411659 ↗
28. 10.1002/adma.20241565610.1002/adma.202415656 ↗
29. 10.1016/j.matdes.2024.11340610.1016/j.matdes.2024.113406 ↗
30. 10.1002/smtd.20200056610.1002/smtd.202000566 ↗
31. 10.1186/s12951-022-01296-x10.1186/s12951-022-01296-x ↗
32. 10.1016/j.biomaterials.2019.11946710.1016/j.biomaterials.2019.119467 ↗
33. 10.1016/j.bios.2020.11280010.1016/j.bios.2020.112800 ↗
34. 10.1016/j.bios.2019.11159810.1016/j.bios.2019.111598 ↗
35. 10.1016/j.nanoen.2025.11069810.1016/j.nanoen.2025.110698 ↗
36. 10.1109/TNB.2022.315222810.1109/TNB.2022.3152228 ↗
37. 10.1016/j.mtadv.2022.10029310.1016/j.mtadv.2022.100293 ↗
38. 10.1021/acs.nanolett.3c0435810.1021/acs.nanolett.3c04358 ↗
39. 10.1002/slct.20190415110.1002/slct.201904151 ↗
40. 10.1002/adfm20130185110.1002/adfm201301851 ↗
41. 10.1016/j.mtbio.2025.10193410.1016/j.mtbio.2025.101934 ↗
42. 10.1002/adfm.20180866710.1002/adfm.201808667 ↗
43. 10.3390/nano1219326510.3390/nano12193265 ↗
44. 10.1002/advs.20241314110.1002/advs.202413141 ↗
45. 10.1038/s41467-024-48908-810.1038/s41467-024-48908-8 ↗
46. 10.1038/s41467-025-56286-y10.1038/s41467-025-56286-y ↗
47. 10.1126/sciadv.abn775310.1126/sciadv.abn7753 ↗
48. 10.1038/s41928-025-01340-210.1038/s41928-025-01340-2 ↗
49. 10.1126/science.adi940510.1126/science.adi9405 ↗
50. 10.1126/science.adc993110.1126/science.adc9931 ↗
51. 10.1161/STROKEAHA.123.04594110.1161/STROKEAHA.123.045941 ↗
52. 10.1016/j.cej.2024.15806910.1016/j.cej.2024.158069 ↗
53. 10.1016/j.jneumeth.2014.01.03610.1016/j.jneumeth.2014.01.036 ↗
54. 10.1161/STROKEAHA.119.02665210.1161/STROKEAHA.119.026652 ↗
55. 10.1038/s41586-025-08807-410.1038/s41586-025-08807-4 ↗
56. 10.1084/jem.2020021310.1084/jem.20200213 ↗
57. 10.2147/DDDT.S50182310.2147/DDDT.S501823 ↗
58. 10.1126/science.adh772610.1126/science.adh7726 ↗