journal article·2025-12Open access

Long‐Term Stable Subdural Recordings Enabled by Fibrosis‐Resistant Hydrogel‐Integrated µECoG Arrays

Lin Chen, Hao Zhong, Linghao Wang, Liju Xu, Wanying Fan, Yongpeng Zhao, Huiling Zhang, Yang Shen, Kai Wu, Xin Fu, Jingxiang Guo, Ke Li, Dong Qiu, Ting Wu

Advanced Science· Volume 12· Issue 47

Abstract

Abstract The long‐term performance of microscale subdural arrays is often compromised by adverse tissue responses, leading to increased electrochemical impedance and signal deterioration during chronic applications. Here, the study presents an adhesive hydrogel‐integrated micro‐electrocorticography (aGel‐µECoG) array that improves mechanical compatibility and adhesion to brain tissue, effectively mitigating tissue responses and enabling stable, high‐performance electrical communication over days to months. The hydrogel consists of a hydrophilic polyvinyl alcohol and hydrophobic poly3‐(trimethoxysilyl) propyl methacrylate heteronetwork, serving as a biological and mechanical bridging to enable seamless, anti‐fibrotic, long‐term integration with brain tissue. The aGel‐µECoG achieves robust tissue adhesion (25.2 ± 3.8 kPa) with reversible and safe removal, enabling sutureless implantation. With an ultrathin 10‐µm ionic conductive hydrogel coating, the array exhibits high electrical fidelity, effectively preserving fine‐grained sub‐millimeter spatial resolution and maintaining unattenuated signal strength for functional cortical mapping. Compared to conventional µECoG arrays, aGel‐µECoG exhibits a 20‐fold reduction in acute‐phase impedance increases, attributed to significantly reduced neuroinflammation and fibrotic tissue formation, as confirmed by histological analyses. Long‐term recordings further reveal that aGel‐µECoG maintained 94.8% of the signal‐to‐noise ratio for steady‐state visually evoked potentials over 16 weeks, whereas uncoated µECoG arrays declined to 69.5%. These findings establish aGel‐µECoG as a durable and high‐performance neural interface.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Neuroscience and Neural Engineering · EEG and Brain-Computer Interfaces · Advanced Sensor and Energy Harvesting Materials

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

Affiliations

Beijing Institute for Brain Research Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 102206 China

Chinese Institute for Brain Research Beijing 102206 China

Beijing National Laboratory for Molecular Sciences, Laboratory of Polymer Physics and Chemistry CAS Research/Education Center for Excellence in Molecular Sciences Institute of Chemistry Chinese Academy of Sciences Beijing 100190 China

University of Chinese Academy of Sciences Beijing 100049 China

School of Basic Medical Sciences Capital Medical University Beijing 100069 China

Funding

National Natural Science Foundation of China

References (74)

Show source-provided references
1. 10.1093/brain/121.12.230110.1093/brain/121.12.2301 ↗
2. 10.1038/nature1191110.1038/nature11911 ↗
3. 10.1111/epi.1747210.1111/epi.17472 ↗
4. 10.1684/epd.2014.067510.1684/epd.2014.0675 ↗
5. 10.1038/s41586-023-06094-510.1038/s41586-023-06094-5 ↗
6. 10.1088/1741-2560/1/2/00110.1088/1741-2560/1/2/001 ↗
7. 10.1056/NEJMoa202754010.1056/NEJMoa2027540 ↗
8. 10.1109/TNSRE.2014.233354110.1109/TNSRE.2014.2333541 ↗
9. 10.3389/fnins.2023.126067510.3389/fnins.2023.1260675 ↗
10. 10.1016/j.brs.2013.02.00110.1016/j.brs.2013.02.001 ↗
11. 10.3390/s2101017810.3390/s21010178 ↗
12. 10.1016/j.neuron.2015.03.03710.1016/j.neuron.2015.03.037 ↗
13. 10.1016/S0165-0270(99)00160-010.1016/S0165-0270(99)00160-0 ↗
14. 10.1038/natrevmats.2017.7610.1038/natrevmats.2017.76 ↗
15. 10.1038/s41583-019-0140-610.1038/s41583-019-0140-6 ↗
16. 10.1038/nn.390510.1038/nn.3905 ↗
17. 10.1126/scitranslmed.abj144110.1126/scitranslmed.abj1441 ↗
18. 10.1101/2022.01.02.47465610.1101/2022.01.02.474656 ↗
19. 10.1016/j.biomaterials.2020.12017810.1016/j.biomaterials.2020.120178 ↗
20. 10.1038/s41467-024-44805-210.1038/s41467-024-44805-2 ↗
21. 10.1002/advs.20200376110.1002/advs.202003761 ↗
22. 10.1002/adma.20170652010.1002/adma.201706520 ↗
23. Fallegger F. · J. Vis. Exp. · 2023 · 193
24. 10.1016/j.snb.2023.13509910.1016/j.snb.2023.135099 ↗
25. 10.1038/s41565-023-01576-z10.1038/s41565-023-01576-z ↗
26. 10.1088/1741-2552/ac02dc10.1088/1741-2552/ac02dc ↗
27. 10.1093/braincomms/fcac12210.1093/braincomms/fcac122 ↗
28. 10.1038/s41467-023-42555-110.1038/s41467-023-42555-1 ↗
29. 10.1088/1741-2552/adaeed10.1088/1741-2552/adaeed ↗
30. 10.1038/nmat274510.1038/nmat2745 ↗
31. 10.1088/1741-2560/13/4/04601910.1088/1741-2560/13/4/046019 ↗
32. 10.1038/s41598-019-49772-z10.1038/s41598-019-49772-z ↗
33. 10.1021/cn500256e10.1021/cn500256e ↗
34. 10.1088/1741-2552/ac127e10.1088/1741-2552/ac127e ↗
35. 10.1021/acs.biochem.8b0012210.1021/acs.biochem.8b00122 ↗
36. 10.1002/adfm.20242271710.1002/adfm.202422717 ↗
37. 10.1038/s41928-024-01240-x10.1038/s41928-024-01240-x ↗
38. 10.1039/C8CS00595H10.1039/C8CS00595H ↗
39. 10.1038/s41586-023-06628-x10.1038/s41586-023-06628-x ↗
40. 10.1038/s41928-023-01023-w10.1038/s41928-023-01023-w ↗
41. 10.1038/s41586-024-07426-910.1038/s41586-024-07426-9 ↗
42. 10.1038/s41563-021-01051-x10.1038/s41563-021-01051-x ↗
43. 10.1126/sciadv.abc544210.1126/sciadv.abc5442 ↗
44. 10.1002/adma.20230781010.1002/adma.202307810 ↗
45. 10.1002/adma.20230707010.1002/adma.202307070 ↗
46. 10.1016/j.matt.2022.01.01210.1016/j.matt.2022.01.012 ↗
47. 10.1002/adma.20210533810.1002/adma.202105338 ↗
48. 10.1088/1741-2560/7/2/02600410.1088/1741-2560/7/2/026004 ↗
49. 10.1002/advs.20220007210.1002/advs.202200072 ↗
50. 10.1007/978-3-030-91855-2_1110.1007/978-3-030-91855-2_11 ↗
51. 10.1038/s41392-024-01852-x10.1038/s41392-024-01852-x ↗
52. 10.3389/fchem.2024.137679910.3389/fchem.2024.1376799 ↗
53. 10.1038/nbt.258010.1038/nbt.2580 ↗
54. 10.3390/ma1219316610.3390/ma12193166 ↗
55. 10.1088/1741-2560/11/1/01600610.1088/1741-2560/11/1/016006 ↗
56. 10.1038/ncomms625810.1038/ncomms6258 ↗
57. Ray S. · J. Neural Eng. · 2008 · 28
58. 10.1038/s41598-018-26457-710.1038/s41598-018-26457-7 ↗
59. 10.1038/nmat429010.1038/nmat4290 ↗
60. 10.3389/fnana.2017.0010210.3389/fnana.2017.00102 ↗
61. 10.1088/1741-2560/11/4/04601110.1088/1741-2560/11/4/046011 ↗
62. 10.1038/nbt.342810.1038/nbt.3428 ↗
63. 10.1038/s44287-024-00126-610.1038/s44287-024-00126-6 ↗
64. 10.1088/1741-2552/aae39d10.1088/1741-2552/aae39d ↗
65. 10.1088/1741-2552/ad570310.1088/1741-2552/ad5703 ↗
66. 10.1109/TBME.2009.201845710.1109/TBME.2009.2018457 ↗
67. 10.1088/2057-1976/aad71410.1088/2057-1976/aad714 ↗
68. 10.1038/s41467-023-39814-610.1038/s41467-023-39814-6 ↗
69. 10.1016/j.clinph.2005.05.01810.1016/j.clinph.2005.05.018 ↗
70. 10.1007/BF0236821910.1007/BF02368219 ↗
71. 10.1038/s41378-022-00366-210.1038/s41378-022-00366-2 ↗
72. 10.1002/admt.20220014310.1002/admt.202200143 ↗
73. 10.1039/C7TB01017F10.1039/C7TB01017F ↗
74. 10.5006/1.328060710.5006/1.3280607 ↗