Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Monolithic three-dimensional neural probes from deterministic rolling of soft electronics

Nature Electronics volume 8, pages 721–737 (2025)Cite this article

Subjects

Abstract

Cognition and behaviour rely on coordinated activity from neural circuits distributed across three dimensions. However, typical probes for recording neural activity in the brain are limited to two-dimensional interfacing due to the planar semiconductor fabrication process. Here we report a rolling-of-soft-electronics approach to create monolithic three-dimensional (3D) neural probes with high scalability and design flexibility. Compared with previous stacking or assembly methods, the approach directly transforms a planar device into a 3D probe by leveraging the softness of flexible electrodes. The electrode shanks are initially fabricated in a single plane and then connected to a flexible spacer. By varying the features of planar design, such as shank pitch and spacer layer thickness, the device can then be deterministically rolled into versatile 3D probe designs containing hundreds of electrodes. With the system, we demonstrate single-unit spike recording in vivo in rodent and non-human primate models. We also show that the probe can provide microscopy-like 3D spatiotemporal mapping of spike activities in the rodent visual cortex, with five-week-long recording stability and promising 3D decoding performance of visual orientation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: ROSE leads to monolithic 3D neural probes.
Fig. 2: The ROSE approach leverages conventional microelectronic design flexibility and is highly deterministic.
Fig. 3: Insertion dynamics of ROSE probes and robustness of aid-free insertion.
Fig. 4: Demonstration of high-yield, microscopy-like 3D recordings and their durability from ROSE probes in rodents in vivo.
Fig. 5: 3D ROSE probes facilitate the neural decoding of visual orientations in an awake mouse.
Fig. 6: Intracortical recording using large-scale ROSE probes results in high-yield SU spike detection in a rhesus monkey cortex.

Similar content being viewed by others

Data availability

Animal electrophysiology data that support the findings of this study are available via Zenodo at https://doi.org/10.5281/zenodo.15498935 (ref. 81). All other datasets generated during and/or analysed during the study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

The custom Python scripts used to analyse the neural signals and visualize the spike activities are available via GitHub at https://github.com/qiangy0819/ROSE_process.git. Code for extended analyses is available upon reasonable request.

References

  1. Lynn, C. W. & Bassett, D. S. The physics of brain network structure, function and control. Nat. Rev. Phys. 1, 318–332 (2019).

    Article  Google Scholar 

  2. Hong, G. & Lieber, C. M. Novel electrode technologies for neural recordings. Nat. Rev. Neurosci. 20, 330–345 (2019).

    Article  Google Scholar 

  3. Chung, J. E. et al. High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays. Neuron 101, 21–31.e25 (2019).

    Article  Google Scholar 

  4. Park, Y. et al. Three-dimensional, multifunctional neural interfaces for cortical spheroids and engineered assembloids. Sci. Adv. 7, eabf9153 (2021).

    Article  Google Scholar 

  5. Liu, L. D. et al. Accurate localization of linear probe electrode arrays across multiple brains. eNeuro 8, ENEURO.0241-21.2021 (2021).

  6. Cabrera, L., Sadle, C. & Purcell, E. Neuroethical considerations of high-density electrode arrays. Nat. Biomed. Eng. 3, 586–589 (2019).

    Article  Google Scholar 

  7. Maynard, E. M., Nordhausen, C. T. & Normann, R. A. The Utah intracortical electrode array: a recording structure for potential brain-computer interfaces. Electroencephalogr. Clin. Neurophysiol. 102, 228–239 (1997).

    Article  Google Scholar 

  8. Hoogerwerf, A. C. & Wise, K. D. A three-dimensional microelectrode array for chronic neural recording. IEEE Trans. Biomed. Eng. 41, 1136–1146 (1994).

    Article  Google Scholar 

  9. Bai, Q., Wise, K. D. & Anderson, D. J. A high-yield microassembly structure for three-dimensional microelectrode arrays. IEEE Trans. Biomed. Eng. 47, 281–289 (2000).

    Article  Google Scholar 

  10. Yao, Y., Gulari, M. N., Wiler, J. A. & Wise, K. D. A microassembled low-profile three-dimensional microelectrode array for neural prosthesis applications. J. Microelectromech. Syst. 16, 977–988 (2007).

    Article  Google Scholar 

  11. Du, J., Blanche, T. J., Harrison, R. R., Lester, H. A. & Masmanidis, S. C. Multiplexed, high density electrophysiology with nanofabricated neural probes. PLoS ONE 6, e26204 (2011).

    Article  Google Scholar 

  12. Merriam, S. M. E., Srivannavit, O., Gulari, M. N. & Wise, K. D. A three-dimensional 64-site folded electrode array using planar fabrication. J. Microelectromech. Syst. 20, 594–600 (2011).

    Article  Google Scholar 

  13. John, J., Li, Y., Zhang, J., Loeb, J. A. & Xu, Y. Microfabrication of 3D neural probes with combined electrical and chemical interfaces. J. Micromech. Microeng. 21, 105011 (2011).

    Article  Google Scholar 

  14. Shobe, J. L., Claar, L. D., Parhami, S., Bakhurin, K. I. & Masmanidis, S. C. Brain activity mapping at multiple scales with silicon microprobes containing 1,024 electrodes. J. Neurophysiol. 114, 2043–2052 (2015).

    Article  Google Scholar 

  15. Rios, G., Lubenov, E. V., Chi, D., Roukes, M. L. & Siapas, A. G. Nanofabricated neural probes for dense 3-D recordings of brain activity. Nano Lett. 16, 6857–6862 (2016).

    Article  Google Scholar 

  16. Barz, F. et al. Versatile, modular 3D microelectrode arrays for neuronal ensemble recordings: from design to fabrication, assembly, and functional validation in non-human primates. J. Neural Eng. 14, 036010 (2017).

    Article  Google Scholar 

  17. Wang, X. et al. Freestanding 3D mesostructures, functional devices, and shape-programmable systems based on mechanically induced assembly with shape memory polymers. Adv. Mater. 31, 1805615 (2019).

    Article  Google Scholar 

  18. Yan, Z. et al. Three-dimensional mesostructures as high-temperature growth templates, electronic cellular scaffolds, and self-propelled microrobots. Proc. Natl Acad. Sci. USA 114, E9455–E9464 (2017).

    Article  Google Scholar 

  19. Zhao, Z. et al. Ultraflexible electrode arrays for months-long high-density electrophysiological mapping of thousands of neurons in rodents. Nat. Biomed. Eng. 7, 520–532 (2023).

    Article  Google Scholar 

  20. Musk, E. An integrated brain-machine interface platform with thousands of channels. J. Med. Internet Res. 21, e16194 (2019).

    Article  Google Scholar 

  21. Obaid, A. et al. Massively parallel microwire arrays integrated with CMOS chips for neural recording. Sci. Adv. 6, eaay2789 (2020).

    Article  Google Scholar 

  22. Wang, P. et al. Direct-print three-dimensional electrodes for large-scale, high-density, and customizable neural interfaces. Preprint at bioRxiv https://doi.org/10.1101/2023.05.30.542925 (2023).

  23. Polikov, V. S., Tresco, P. A. & Reichert, W. M. Response of brain tissue to chronically implanted neural electrodes. J. Neurosci. Methods 148, 1–18 (2005).

    Article  Google Scholar 

  24. Chen, R., Canales, A. & Anikeeva, P. Neural recording and modulation technologies. Nat. Rev. Mater. 2, 16093 (2017).

    Article  Google Scholar 

  25. Sharafkhani, N. et al. Neural tissue-microelectrode interaction: brain micromotion, electrical impedance, and flexible microelectrode insertion. J. Neurosci. Methods 365, 109388 (2022).

    Article  Google Scholar 

  26. Lacour, S. P., Wagner, S., Huang, Z. & Suo, Z. Stretchable gold conductors on elastomeric substrates. Appl. Phys. Lett. 82, 2404–2406 (2003).

    Article  Google Scholar 

  27. Rogers, J. A., Someya, T. & Huang, Y. Materials and mechanics for stretchable electronics. Science 327, 1603–1607 (2010).

    Article  Google Scholar 

  28. Singh, S. et al. Modeling the insertion mechanics of flexible neural probes coated with sacrificial polymers for optimizing probe design. Sensors 16, 330 (2016).

    Article  Google Scholar 

  29. Yang, C., Xie, Y., Liu, S. & Sun, D. Force modeling, identification, and feedback control of robot-assisted needle insertion: a survey of the literature. Sensors 18, 561 (2018).

    Article  Google Scholar 

  30. Pomfret, R., Miranpuri, G. & Sillay, K. The substitute brain and the potential of the gel model. Ann. Neurosci. 20, 118–122 (2013).

    Article  Google Scholar 

  31. Lecomte, A., Descamps, E. & Bergaud, C. A review on mechanical considerations for chronically-implanted neural probes. J. Neural Eng. 15, 031001 (2018).

    Article  Google Scholar 

  32. Casanova, F., Carney, P. R. & Sarntinoranont, M. In vivo evaluation of needle force and friction stress during insertion at varying insertion speed into the brain. J. Neurosci. Methods 237, 79–89 (2014).

    Article  Google Scholar 

  33. Obaid, A. et al. Ultra-sensitive measurement of brain penetration mechanics and blood vessel rupture with microscale probes. Preprint at bioRxiv https://doi.org/10.1101/2020.09.21.306498 (2020).

  34. Le Floch, P. et al. 3D spatiotemporally scalable in vivo neural probes based on fluorinated elastomers. Nat. Nanotechnol. 19, 319–329 (2024).

    Article  Google Scholar 

  35. Zhao, S. et al. Tracking neural activity from the same cells during the entire adult life of mice. Nat. Neurosci. 26, 696–710 (2023).

    Article  Google Scholar 

  36. Seo, K. J. et al. A soft, high-density neuroelectronic array. NPJ Flex. Electron. 7, 40 (2023).

    Article  Google Scholar 

  37. Takeuchi, S., Ziegler, D., Yoshida, Y., Mabuchi, K. & Suzuki, T. Parylene flexible neural probes integrated with microfluidic channels. Lab Chip 5, 519–523 (2005).

    Article  Google Scholar 

  38. Kil, D. et al. Dextran as a resorbable coating material for flexible neural probes. Micromachines 10, 61 (2019).

    Article  Google Scholar 

  39. Cointe, C. et al. Scalable batch fabrication of ultrathin flexible neural probes using a bioresorbable silk layer. Microsyst. Nanoeng. 8, 21 (2022).

    Article  Google Scholar 

  40. Kozai, T. D. et al. Comprehensive chronic laminar single-unit, multi-unit, and local field potential recording performance with planar single shank electrode arrays. J. Neurosci. Methods 242, 15–40 (2015).

    Article  Google Scholar 

  41. Oke, O. O. et al. High‐frequency gamma oscillations coexist with low-frequency gamma oscillations in the rat visual cortex in vitro. Eur. J. Neurosci. 31, 1435–1445 (2010).

    Article  Google Scholar 

  42. Cody, P. A., Eles, J. R., Lagenaur, C. F., Kozai, T. D. & Cui, X. T. Unique electrophysiological and impedance signatures between encapsulation types: an analysis of biological Utah array failure and benefit of a biomimetic coating in a rat model. Biomaterials 161, 117–128 (2018).

    Article  Google Scholar 

  43. Black, B. J. et al. Chronic recording and electrochemical performance of Utah microelectrode arrays implanted in rat motor cortex. J. Neurophysiol. 120, 2083–2090 (2018).

    Article  Google Scholar 

  44. Nordhausen, C. T., Maynard, E. M. & Normann, R. A. Single unit recording capabilities of a 100 microelectrode array. Brain Res. 726, 129–140 (1996).

    Article  Google Scholar 

  45. Sahasrabuddhe, K. et al. The Argo: a high channel count recording system for neural recording in vivo. J. Neural Eng. 18, 015002 (2021).

    Article  Google Scholar 

  46. Deku, F. et al. Amorphous silicon carbide ultramicroelectrode arrays for neural stimulation and recording. J. Neural Eng. 15, 016007 (2018).

    Article  Google Scholar 

  47. Zhao, Z. et al. Parallel, minimally-invasive implantation of ultra-flexible neural electrode arrays. J. Neural Eng. 16, 035001 (2019).

    Article  Google Scholar 

  48. Fiáth, R. et al. A silicon-based neural probe with densely-packed low-impedance titanium nitride microelectrodes for ultrahigh-resolution in vivo recordings. Biosens. Bioelectron. 106, 86–92 (2018).

    Article  Google Scholar 

  49. Raducanu, B. C. et al. Time multiplexed active neural probe with 1,356 parallel recording sites. Sensors 17, 2388 (2017).

    Article  Google Scholar 

  50. Fang, H. et al. Capacitively coupled arrays of multiplexed flexible silicon transistors for long-term cardiac electrophysiology. Nat. Biomed. Eng. 1, 0038 (2017).

    Article  Google Scholar 

  51. Xu, W., Huang, X., Takagaki, K. & Wu, J.-Y. Compression and reflection of visually evoked cortical waves. Neuron 55, 119–129 (2007).

    Article  Google Scholar 

  52. Apollo, N. V. et al. Gels, jets, mosquitoes, and magnets: a review of implantation strategies for soft neural probes. J. Neural Eng. 17, 041002 (2020).

    Article  Google Scholar 

  53. Paxinos, G. & Watson, C. The Rat Brain in Stereotaxic Coordinates: Hard Cover Edition (Elsevier, 2006).

  54. Liu, X. et al. Stability of the interface between neural tissue and chronically implanted intracortical microelectrodes. IEEE Trans. Rehabil. Eng. 7, 315–326 (1999).

    Article  Google Scholar 

  55. McConnell, G. C. et al. Implanted neural electrodes cause chronic, local inflammation that is correlated with local neurodegeneration. J. Neural Eng. 6, 056003 (2009).

    Article  Google Scholar 

  56. Kozai, T. D., Jaquins-Gerstl, A. S., Vazquez, A. L., Michael, A. C. & Cui, X. T. Brain tissue responses to neural implants impact signal sensitivity and intervention strategies. ACS Chem. Neurosci. 6, 48–67 (2015).

    Article  Google Scholar 

  57. Franch, M. et al. Visuo-frontal interactions during social learning in freely moving macaques. Nature 627, 174–181 (2024).

    Article  Google Scholar 

  58. Schoonover, C. E., Ohashi, S. N., Axel, R. & Fink, A. J. Representational drift in primary olfactory cortex. Nature 594, 541–546 (2021).

    Article  Google Scholar 

  59. Gallego, J. A., Perich, M. G., Chowdhury, R. H., Solla, S. A. & Miller, L. E. Long-term stability of cortical population dynamics underlying consistent behavior. Nat. Neurosci. 23, 260–270 (2020).

    Article  Google Scholar 

  60. Wagner, M. J. et al. A neural circuit state change underlying skilled movements. Cell 184, 3731–3747.e21 (2021).

    Article  Google Scholar 

  61. Sponheim, C. et al. Longevity and reliability of chronic unit recordings using the Utah, intracortical multi-electrode arrays. J. Neural Eng. 18, 066044 (2021).

    Article  Google Scholar 

  62. Ghodrati, M., Alwis, D. S. & Price, N. S. Orientation selectivity in rat primary visual cortex emerges earlier with low‐contrast and high‐luminance stimuli. Eur. J. Neurosci. 44, 2759–2773 (2016).

    Article  Google Scholar 

  63. Girman, S. V., Sauvé, Y. & Lund, R. D. Receptive field properties of single neurons in rat primary visual cortex. J. Neurophysiol. 82, 301–311 (1999).

    Article  Google Scholar 

  64. Juavinett, A. L. & Callaway, E. M. Pattern and component motion responses in mouse visual cortical areas. Curr. Biol. 25, 1759–1764 (2015).

    Article  Google Scholar 

  65. Ohki, K. & Reid, R. C. Specificity and randomness in the visual cortex. Curr. Opin. Neurobiol. 17, 401–407 (2007).

    Article  Google Scholar 

  66. White, L. E. & Fitzpatrick, D. Vision and cortical map development. Neuron 56, 327–338 (2007).

    Article  Google Scholar 

  67. Fahey, P. G. et al. A global map of orientation tuning in mouse visual cortex. Preprint at bioRxiv https://doi.org/10.1101/745323 (2019).

  68. Stringer, C., Michaelos, M., Tsyboulski, D., Lindo, S. E. & Pachitariu, M. High-precision coding in visual cortex. Cell 184, 2767–2778.e15 (2021).

    Article  Google Scholar 

  69. Kamitani, Y. & Tong, F. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8, 679–685 (2005).

    Article  Google Scholar 

  70. Schneider, S., Lee, J. H. & Mathis, M. W. Learnable latent embeddings for joint behavioural and neural analysis. Nature 617, 360–368 (2023).

    Article  Google Scholar 

  71. Fiscella, M. et al. Visual coding with a population of direction-selective neurons. J. Neurophysiol. 114, 2485–2499 (2015).

    Article  Google Scholar 

  72. Liu, J. in Biomimetics Through Nanoelectronics: Development of Three Dimensional Macroporous Nanoelectronics for Building Smart Materials, Cyborg Tissues and Injectable Biomedical Electronics Ch. 5 (Springer, 2018).

  73. Rousche, P. J. et al. Flexible polyimide-based intracortical electrode arrays with bioactive capability. IEEE Trans. Biomed. Eng. 48, 361–371 (2001).

    Article  Google Scholar 

  74. Kim, B. J. et al. 3D parylene sheath neural probe for chronic recordings. J. Neural Eng. 10, 045002 (2013).

    Article  Google Scholar 

  75. Byun, D., Cho, S. J. & Kim, S. Fabrication of a flexible penetrating microelectrode array for use on curved surfaces of neural tissues. J. Micromech. Microeng. 23, 125010 (2013).

    Article  Google Scholar 

  76. Seo, K. J. et al. Transparent, flexible, penetrating microelectrode arrays with capabilities of single-unit electrophysiology. Adv. Biosyst. 3, 1800276 (2019).

    Article  Google Scholar 

  77. Wang, X. et al. A parylene neural probe array for multi-region deep brain recordings. J. Microelectromech. Syst. 29, 499–513 (2020).

    Article  Google Scholar 

  78. Shin, Y. et al. Array-wide uniform PEDOT: PSS electroplating from potentiostatic deposition. Biosens. Bioelectron. 261, 116418 (2024).

    Article  Google Scholar 

  79. Woeppel, K., Dhawan, V., Shi, D. & Cui, X. T. Nanotopography-enhanced biomimetic coating maintains bioactivity after weeks of dry storage and improves chronic neural recording. Biomaterials 302, 122326 (2023).

    Article  Google Scholar 

  80. Chung, J. E. et al. A fully automated approach to spike sorting. Neuron 95, 1381–1394.e6 (2017).

    Article  Google Scholar 

  81. Qiang, Y. 3D electrophysiology data in rodents and NHP brain. Zenodo https://doi.org/10.5281/zenodo.15498935 (2025).

Download references

Acknowledgements

We acknowledge support from NIH award nos. R21EY030710 and U01NS123668, NSF award no. 2347978 and funds from Dartmouth College. S. Wang acknowledges support from the NSF CAREER award no. CMMI-1847062 and the Oklahoma Center for Advancement of Science & Technology grant no. HR18-085. X.T.C. acknowledges NINDS R01NS136622, R01 NS102725 and U01 NS113279. C.C. is supported by an NIH/NINDS K99/R00 NS092972, NIH/NINDS R01 NS122969, the Brain and Behavior Research Foundation, the Moorman-Simon Interdisciplinary Career Development Professorship and the Whitehall Foundation. T.L.M. acknowledges financial support from NIH/NIA R01 AG068168 and NIH/NINDS R56 NS112207.

Author information

Author notes

  1. Wen Gu

    Present address: Lingang Laboratory, Shanghai, China

  2. Kyung Jin Seo

    Present address: Science Corporation, Alameda, CA, USA

  3. Pietro Artoni

    Present address: Takeda Pharmaceuticals, Cambridge, MA, USA

  4. These authors contributed equally: Yi Qiang, Wen Gu, Dongyeol Jang, Yieljae Shin, Delin Shi.

Authors and Affiliations

  1. Thayer School of Engineering, Dartmouth College, Hanover, NH, USA

    Yi Qiang, Dongyeol Jang, Yieljae Shin, Kyung Jin Seo, Gen Li, Jaehyeon Ryu, Tianyu Bai & Hui Fang

  2. Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA

    Wen Gu & Kyung Jin Seo

  3. Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

    Delin Shi, Vaishnavi Dhawan & Xinyan Tracy Cui

  4. Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA

    Delin Shi, Vaishnavi Dhawan & Xinyan Tracy Cui

  5. School of Mechanical & Aerospace Engineering, Oklahoma State University, Stillwater, OK, USA

    Sandra Vinnikova & Shuodao Wang

  6. Department of Neurological Surgery, Weill Cornell Medical College, New York, NY, USA

    Shiqiang Wu

  7. Department of Anesthesiology, Weill Cornell Medical College, New York, NY, USA

    Aditya Iyer & Jyun-you Liou

  8. Center for Life Science, Boston Children’s Hospital, Boston, MA, USA

    Pietro Artoni

  9. Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA

    Maria Medalla, Douglas L. Rosene, Tara L. Moore & Chandramouli Chandrasekaran

  10. Department of Chemical Engineering, Northeastern University, Boston, MA, USA

    Abigail N. Koppes

  11. Department of Bioengineering and Biology Engineering, Northeastern University, Boston, MA, USA

    Abigail N. Koppes & Ryan Koppes

  12. Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA

    Chandramouli Chandrasekaran

  13. McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Xinyan Tracy Cui

Authors
  1. Yi Qiang
    View author publications

    Search author on:PubMed Google Scholar

  2. Wen Gu
    View author publications

    Search author on:PubMed Google Scholar

  3. Dongyeol Jang
    View author publications

    Search author on:PubMed Google Scholar

  4. Yieljae Shin
    View author publications

    Search author on:PubMed Google Scholar

  5. Delin Shi
    View author publications

    Search author on:PubMed Google Scholar

  6. Kyung Jin Seo
    View author publications

    Search author on:PubMed Google Scholar

  7. Gen Li
    View author publications

    Search author on:PubMed Google Scholar

  8. Sandra Vinnikova
    View author publications

    Search author on:PubMed Google Scholar

  9. Shiqiang Wu
    View author publications

    Search author on:PubMed Google Scholar

  10. Aditya Iyer
    View author publications

    Search author on:PubMed Google Scholar

  11. Pietro Artoni
    View author publications

    Search author on:PubMed Google Scholar

  12. Jaehyeon Ryu
    View author publications

    Search author on:PubMed Google Scholar

  13. Tianyu Bai
    View author publications

    Search author on:PubMed Google Scholar

  14. Vaishnavi Dhawan
    View author publications

    Search author on:PubMed Google Scholar

  15. Maria Medalla
    View author publications

    Search author on:PubMed Google Scholar

  16. Douglas L. Rosene
    View author publications

    Search author on:PubMed Google Scholar

  17. Tara L. Moore
    View author publications

    Search author on:PubMed Google Scholar

  18. Abigail N. Koppes
    View author publications

    Search author on:PubMed Google Scholar

  19. Ryan Koppes
    View author publications

    Search author on:PubMed Google Scholar

  20. Jyun-you Liou
    View author publications

    Search author on:PubMed Google Scholar

  21. Chandramouli Chandrasekaran
    View author publications

    Search author on:PubMed Google Scholar

  22. Xinyan Tracy Cui
    View author publications

    Search author on:PubMed Google Scholar

  23. Shuodao Wang
    View author publications

    Search author on:PubMed Google Scholar

  24. Hui Fang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

H.F., S. Wang and X.T.C. supervised this work. W.G., K.J.S., Y.Q. and H.F. conceived the original concept of ROSE. W.G. and K.J.S. conducted the preliminary device optimization, fabrication and ROSE assembly. Y.Q. and T.B. designed and prototyped the MagMatrix board. Y.S., Y.Q. and J.R. further optimized the device design and fabrication for in vivo experiments. W.G., Y.Q., Y.S., G.L. and D.J. performed the device characterizations, mathematical modelling (offsets) and insertion dynamics testing. Y.Q., D.J., D.S. and W.G. performed the in vivo electrophysiology. Y.Q. and D.J. conducted the neural data processing and decoding analysis. D.J., D.S., S. Wu, A.I., J.-Y.L., D.L.R., M.M., T.L.M., A.N.K., R.K., P.A. and C.C. performed the animal surgeries and experiments. D.S. and V.D. performed the histological studies through immunohistochemistry and confocal fluorescence imaging. S.V., G.L., W.G. and S. Wang performed the structural and mechanical modelling of the ROSE shanks, including insertion force dynamics (S.V. and S. Wang), micromotion and twisting analysis (G.L.), and maximum principal strain (W.G.). Y.Q., W.G., D.S., K.J.S., D.J., Y.S., S. Wang and H.F. co‑wrote the manuscript. Y.Q., W.G. and D.S. prepared the figures. W.G. led the research in the original submission of the manuscript, and Y.Q. led the revision work. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Xinyan Tracy Cui, Shuodao Wang or Hui Fang.

Ethics declarations

Competing interests

H.F., W.G., Y.Q. and K.J.S. are inventors on patent application US 19/058,657 filed by Dartmouth College that covers the 3D ROSE probe technology reported in this manuscript. H.F., Y.Q. and K.J.S. are inventors on patent US 11417987B2 covering the MagMatrix connector technology reported in this manuscript. The other authors declare no competing interests.

Peer review

Peer review information

Nature Electronics thanks Joonsoo Jeong, Chi Hwan Lee and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–40, Tables 1–6 and Notes 1–8.

Supplementary Video 1 (download MOV )

ROSE probe insertion process and synchronized force dynamic.

Supplementary Video 2 (download MP4 )

3D SU firing map under one visual stimulation trial.

Source data

Source Data Fig. 1 (download XLSX )

Statistical source data.

Source Data Fig. 2 (download XLSX )

Statistical source data.

Source Data Fig. 3 (download XLSX )

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiang, Y., Gu, W., Jang, D. et al. Monolithic three-dimensional neural probes from deterministic rolling of soft electronics. Nat Electron 8, 721–737 (2025). https://doi.org/10.1038/s41928-025-01431-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41928-025-01431-0

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing