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A wireless and battery-less implant for multimodal closed-loop neuromodulation in small animals

Nature Biomedical Engineering volume 7, pages 1252–1269 (2023)Cite this article

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Abstract

Fully implantable wireless systems for the recording and modulation of neural circuits that do not require physical tethers or batteries allow for studies that demand the use of unconstrained and freely behaving animals in isolation or in social groups. Moreover, feedback-control algorithms that can be executed within such devices without the need for remote computing eliminate virtual tethers and any associated latencies. Here we report a wireless and battery-less technology of this type, implanted subdermally along the back of freely moving small animals, for the autonomous recording of electroencephalograms, electromyograms and body temperature, and for closed-loop neuromodulation via optogenetics and pharmacology. The device incorporates a system-on-a-chip with Bluetooth Low Energy for data transmission and a compressed deep-learning module for autonomous operation, that offers neurorecording capabilities matching those of gold-standard wired systems. We also show the use of the implant in studies of sleep–wake regulation and for the programmable closed-loop pharmacological suppression of epileptic seizures via feedback from electroencephalography. The technology can support a broader range of applications in neuroscience and in biomedical research with small animals.

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Fig. 1: Device concept, implantation and architecture.
Fig. 2: Characterization of the mechanical properties, wireless power transfer, and neurorecording and neuromodulation modalities.
Fig. 3: Validation of the wireless neurorecording functionality by pharmaceutical and auditory stimulation and comparison to a conventional tethered system.
Fig. 4: Open-field tests of rat locomotor behaviours, neurorecording in a freely behaving rat and chronic stability of neurorecording of a fully implanted device.
Fig. 5: Sleep–wake regulation studies in a freely behaving rat.
Fig. 6: Autonomous seizure management.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. Source data for Figs. 2, 3, 4e–h and 6 are provided with this paper. Source data for Figs. 4i,j and 5 and the raw data are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request.

Code availability

The custom code for analysing electroencephalography and electromyography data is available at https://github.com/wouyanglv/Wireless_EEG.

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Acknowledgements

We acknowledge financial support from the Querrey Simpson Institute for Bioelectronics at Northwestern University and the National Institute of Neurological Disorders and Stroke of the National Institutes of Health (grant number R44NS107142). This work made use of the NUFAB facility of Northwestern University’s NUANCE Center, which has received support from the Soft and Hybrid Nanotechnology Experimental Resource (grant number NSF ECCS­1542205), the Materials Research Science and Engineering Center (grant number DMR­1720139), the State of Illinois and Northwestern University. We acknowledge surgical and imaging work performed by the Developmental Therapeutics Core and the Center for Advanced Molecular Imaging at Northwestern University, which have received support from the Robert H Lurie Comprehensive Cancer Center (grant number NCI CCSG 773 P30 CA060553). From the US Army MRICD, we thank J. Abraham for his graphics assistance. C.H.G. was supported by the LUCI programme sponsored by the OASD R&E. K. Kwon acknowledges support by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT & Future Planning (MSIP); No. 2021R1F1A106387111, No. 2022R1C1C1010555, No. 2020R1A5A8018367 and BK21).

Author information

Author notes

  1. These authors contributed equally: Wei Ouyang, Wei Lu, Yamin Zhang.

Authors and Affiliations

  1. Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA

    Wei Ouyang, Wei Lu, Yamin Zhang, Jong Uk Kim, Yunyun Wu, Haiwen Luan, Stephen P. Lee, Changsheng Wu, Wubin Bai, Hedan Bai, Jacob Trueb, Yonggang Huang, Roozbeh Ghaffari, Anthony R. Banks, Cameron H. Good & John A. Rogers

  2. Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA

    Yiming Liu

  3. School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea

    Jong Uk Kim & Tae-il Kim

  4. Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA

    Haixu Shen, Yinsheng Lu, Jin Wang, Zhaoqian Xie, Yixin Wu, Wubin Bai, Hexia Guo, Surabhi R. Madhvapathy, Yonggang Huang & John A. Rogers

  5. Neurolux Inc., Northfield, IL, USA

    Keith Kilner, Stephen P. Lee, Yongjoon Yu, Maria Stanslaski, Roozbeh Ghaffari, Anthony R. Banks & Cameron H. Good

  6. Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA

    Yiyuan Yang, Zhaoqian Xie, Tzu-li Liu, Yonggang Huang & John A. Rogers

  7. US Army Research Laboratory, Aberdeen Proving Ground, MD, USA

    Amy J. Wegener, Justin A. Moreno & Cameron H. Good

  8. US Army Medical Research Institute of Chemical Defense, Aberdeen Proving Ground, MD, USA

    Amy J. Wegener, Justin A. Moreno & Cameron H. Good

  9. SURVICE Engineering, Belcamp, MD, USA

    Justin A. Moreno

  10. Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA

    Zhaoqian Xie & Yonggang Huang

  11. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea

    Sang Min Won

  12. School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

    Kyeongha Kwon

  13. Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Wubin Bai

  14. Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA

    Giuditta Monti & John A. Rogers

  15. Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA

    Jason Zhu

  16. Department of Chemistry, Northwestern University, Evanston, IL, USA

    Jason Zhu & John A. Rogers

  17. Developmental Therapeutics Core, Northwestern University, Evanston, IL, USA

    Elizabeth M. Higbee-Dempsey, Iwona Stepien & Nayereh Ghoreishi-Haack

  18. Center for Advanced Molecular Imaging, Northwestern University, Evanston, IL, USA

    Chad R. Haney

  19. Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University (SKKU), Suwon, Republic of Korea

    Tae-il Kim

  20. Department of Neurosciences, Medical University of South Carolina, Charleston, SC, USA

    Thomas C. Jhou

  21. Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA

    John A. Rogers

Authors
  1. Wei Ouyang
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  2. Wei Lu
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  3. Yamin Zhang
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  6. Haixu Shen
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  7. Yunyun Wu
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  8. Haiwen Luan
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  9. Keith Kilner
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  40. Cameron H. Good
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  41. John A. Rogers
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Contributions

J.A.R., C.H.G., T.C.J., A.R.B., R.G., W.O., W.L. and Y.Z. conceived the project and designed the research; W.O., W.L., Y.Z., T.C.J., C.H.G. and J.A.R. analysed the data and wrote the paper; W.O., W.L., S.P.L. and K. Kwon designed the electronics; W.O., W.L., J.T. and K. Kwon designed the firmware and software; W.O. designed the AI algorithms, Y.Z. and J.W. designed and manufactured the drug-delivery module; J.U.K., W.O., G.M. and T.K. designed and manufactured the soft epidural electrodes; W.O., W.L., Y. Liu, H.S. and Y. Yang designed the optogenetic probes; W.O., Y. Liu, Y. Lu and Yunyun Wu designed and manufactured the serpentine and twisted-pair interconnects; H.L., Z.X. and Y.H. conducted finite-element analyses; W.O., W.L., Y. Liu, J.U.K., H.S., Yunyun Wu, Y. Yang and S.M.W. developed the encapsulation method; W.O., W.L., Y.Z., Y. Liu, H.S., Yunyun Wu, Y. Lu, J.W., Y. Yang, Yixin Wu, C.W., W.B., H.G., T.L., H.B., G.M., J.Z., S.R.M., Y. Yu and M.S. fabricated the devices and tested them in animal models; C.H.G., T.C.J., A.J.W., J.A.M., K. Kilner, W.O., W.L., Y.Z., E.M.H.-D., I.S., N.G.-H. and C.R.H. designed the animal-model studies, and performed device implantation and computed tomography; Y. Liu, J.U.K. and H.S. contributed equally to this work.

Corresponding authors

Correspondence to Thomas C. Jhou, Cameron H. Good or John A. Rogers.

Ethics declarations

Competing interests

J.A.R. and A.R.B. are co-founders of NeuroLux Inc., which has potential commercial interest in the technology described in this work. C.H.G., R.G., S.P.L., K. Kilner, Y. Yu and M.S. are employees of NeuroLux Inc. The other authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Max Ortiz-Catalan, Silvestro Micera and Thomas Stieglitz 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.

Extended data

Extended Data Fig. 1 Power consumption profiles of the device under different configurations.

a, Testing scheme in which a sourcemeter is used as a power source instead of the NFC power. (b, c) Power profiles with the optogenetics module inactivated (b) without and (c) with supercapacitors. The supercapacitors effectively mitigate the power surges during BLE data transceiving (Tx/Rx). The tables summarize the current consumption in different operating stages. (d, e) Power profiles with the optogenetics module activated (d) without and (e) with supercapacitors. The supercapacitors mitigate power surges during active LEDs and active BLE Rx/Tx and are recharged during lower power consumptions. The tables summarize the current consumption in different operating stages.

Extended Data Fig. 2 Characterization of the wireless power transfer performance.

a, A device mounted on an agarose gel rat model for measuring the effect of rat rearing angle on the wireless power transfer and device survival time. b, Experiment setup simulating the rearing of a rat at the corner of the cage. c, Experiment setup simulating the rearing of a rat at the center of the cage. d, The power received by the device at different heights and locations of the cage under an input RF power of 2 W. e, The power received by the device when the rat rears with different angles at the center and corner of the cage under an input RF power of 2 W. f, Device survival time (that is device working time before it loses power to support its operation) at different rat rearing angles. The device is started normally and then the rat holds a rearing angle for an extended period of time at the center and corner of the cage under an input RF power of 2 W. g, The power received by the device at different heights and locations of the cage under an input RF power of 6 W. h, The power received by the device when the rat rears with different angles at the center and corner of the cage under an input RF power of 6 W. i, Device survival time at different rat rearing angles under an input RF power of 6 W.

Extended Data Fig. 3 Acute in vivo comparison of EEG signals measured by the PtBk-plated EEG electrodes and stainless steel screws.

a, EEG spectrograms during slow wave sleep and awake states in a rat show good correlation between the PtBk-plated EEG electrodes and stainless steel screws. (b-e) Comparison of time-domain EEG signals (left) and EEG power spectra (right) measured by of the PtBk-plated EEG electrodes and stainless steel screws during (b) awake, (c) slow wave sleep, (d) fatal plus onset, and (e) euthanized states and their correlations.

Extended Data Fig. 4 Benchtop validation of simultaneous optogenetic stimulation and electrical recording in PBS.

a, Experimental setup in which the device was submerged in PBS to simulate the electrical conduction of the body of a rat. The device performed simultaneous bilateral optical stimulation at 5 Hz (50% duty cycle) and electrical recording. The electrical signal was fed through PBS by a function generator that performed a cyclic frequency sweep (0.5 → 100 → 0.5 Hz) with a constant amplitude. b, Spectrogram showing recorded signal from the test. The signal showed clean frequency characteristics without interference from the μ-ILEDs, which would show as a horizontal band at 5 Hz if it existed. The video recording of the experiment is provided as Supplementary Video 1.

Extended Data Fig. 5 Characterization of drug-release rate.

a, UV-Vis spectra of different concentrations of Rhodamine B. b, Calibration curve of absorption at 550 nm v.s. concentration of Rhodamine B. c, UV-Vis spectra of PBS containing Rhodamine B released from the drug delivery vehicle at different time since electrolysis was triggered.

Extended Data Fig. 6 Additional data on validating the wireless EEG system against the conventional tethered system.

a, Baseline time-domain EEG data of rat #2 (used for pilocarpine stimulation study). b, Spectrograms of the baseline EEG of rat #2 measured by the tethered and wireless systems, respectively. c, The frequency spectra of EEG data measured by the tethered and wireless systems at the baseline and after baclofen stimulation. d, The frequency spectra of EEG data measured by the tethered and wireless systems at the baseline and after pilocarpine stimulation.

Extended Data Fig. 7 Validation of the wireless neurorecording functionality in its final intended, fully implanted configuration using the pilocarpine-induced seizure model.

a, EEG data measured by a fully implanted device in a rat under the stimulation of Pilocarpine (400 mg/kg). b, The corresponding frequency spectra of EEG at different conditions (baseline, drug onset, and seizure).

Extended Data Fig. 8 Characteristics of different sleep stages measured by the fully implanted, wireless device.

a, Time-domain data of EEG and EMG during wakefulness. b, Power spectrum of EEG during wakefulness showing medium powers in the delta band (0.5–4 Hz) and theta band (6–9 Hz). c, Time-domain data of EEG and EMG during NREM sleep. d, Power spectrum of EEG during NREM sleep showing a high power in the delta band (0.5–4 Hz) and a low power in the theta band (6–9 Hz). e, Time-domain data of EEG and EMG during REM sleep. f, Power spectrum of EEG during REM sleep showing a low power in the delta band (0.5–4 Hz) and a high power in the theta band (6–9 Hz).

Extended Data Fig. 9 Empirical mode decomposition of representative signals at different sleep stages.

a, EMD of EEG during wakefulness. b, Hilbert-Huang transform of EMD during wakefulness. c, EMD of EEG during NREM. d, Hilbert-Huang transform of EMD during NREM. e, EMD of EEG during rem. f, Hilbert-Huang transform of EMD during REM. The scale bar represents 100 μV.

Extended Data Fig. 10 Metrics of training the CNN model.

a, Training and validation loss. b, Training and validation accuracy.

Supplementary information

Supplementary Information

Supplementary notes, figures, tables and references.

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Supplementary Video

Benchtop validation of simultaneous optogenetic stimulation and electrical recording in phosphate buffered saline.

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Ouyang, W., Lu, W., Zhang, Y. et al. A wireless and battery-less implant for multimodal closed-loop neuromodulation in small animals. Nat. Biomed. Eng 7, 1252–1269 (2023). https://doi.org/10.1038/s41551-023-01029-x

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