Research Progress of Optogenetic Brain-Computer Interface
Meng Zhaoyang1, Pu Jiangbo2, Li Xiangning1, 3#*
1(Key Laboratory for Biomedical Photonics, Ministry of Education, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China) 2(Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China) 3(Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University,Sanya 572025, Hainan, China)
Abstract：As bidirectional communication systems between human brains and external devices, brain-computer interfaces (BCIs) have been widely employed in brain function enhancement, human-computer interaction and nerve rehabilitation. BCIs based on optogenetics remedy for the deficits of electrode stimulation in biological compatibility, stimulation accuracy and cell type specificity, thus becoming a hotspot in the research of neural engineering. In this review, we first described the application of optogenetic interfaces in animal experiments such as closed-loop control of brain activity, virtual sensation feedback, or brain-brain information channel. Subsequently, frontiers of novel integrated and miniaturized optogenetic interfaces were summarized. At last, we recapitulated the current challenges of optogenetic brain-computer interfaces, along with the prospects of optogenetic interface in multimodal brain activity monitoring and brain-computer intelligence.
孟兆阳, 蒲江波, 李向宁. 基于光遗传学技术的脑机接口研究进展[J]. 中国生物医学工程学报, 2024, 43(2): 204-213.
Meng Zhaoyang, Pu Jiangbo, Li Xiangning. Research Progress of Optogenetic Brain-Computer Interface. Chinese Journal of Biomedical Engineering, 2024, 43(2): 204-213.
[1] Luo Liqun. Architectures of neuronal circuits [J]. Science (New York, NY), 2021, 373(6559): eabg7285. [2] Mott MC, Gordon JA, Koroshetz WJ. The NIH BRAIN initiative: advancing neurotechnologies, integrating disciplines [J]. PLoS Biol, 2018, 16(11): e3000066. [3] Poo Muming, Du Jiulin, Ip NY, et al. China brain project: basic neuroscience, brain diseases, and brain-inspired computing [J]. Neuron, 2016, 92(3): 591-596. [4] Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain-computer interfaces for communication and control [J]. Clin Neurophysiol, 2002, 113(6): 767-791. [5] Flesher SN, Downey JE, Weiss JM, et al. A brain-computer interface that evokes tactile sensations improves robotic arm control [J]. Science (New York, NY), 2021, 372(6544): 831-836. [6] Cinel C, Valeriani D, Poli R. Neurotechnologies for human cognitive augmentation: current state of the art and future prospects [J]. Front Hum Neurosci, 2019, 13: 13. [7] Roelfsema PR, Denys D, Klink PC. Mind reading and writing: the future of neurotechnology [J]. Trends Cogn Sci, 2018, 22(7): 598-610. [8] Dubey A, Ray S. Cortical electrocorticogram (ECoG) is a local signal [J]. J Neurosci, 2019, 39(22): 4299-4311. [9] Collinger JL, Wodlinger B, Downey JE, et al. High-performance neuroprosthetic control by an individual with tetraplegia [J]. Lancet, 2013, 381(9866): 557-564. [10] Polikov VS, Tresco PA, Reichert WM. Response of brain tissue to chronically implanted neural electrodes [J]. J Neurosci Methods, 2005, 148(1): 1-18. [11] Campbell PK, Jones KE, Huber RJ, et al. A silicon-based, three-dimensional neural interface: manufacturing processes for an intracortical electrode array [J]. IEEE Trans Biomed Eng, 1991, 38(8): 758-768. [12] Nurmikko A. Challenges for large-scale cortical interfaces [J]. Neuron, 2020, 108(2): 259-269. [13] Xu Min, Chung Shinjae, Zhang Siyu, et al. Basal forebrain circuit for sleep-wake control [J]. Nature Neurosci, 2015, 18(11): 1641-1647. [14] Boyden ES, Zhang Feng, Bamberg E, et al. Millisecond-timescale, genetically targeted optical control of neural activity [J]. Nat Neurosci, 2005, 8(9): 1263-1268. [15] Sun Qingtao, Zhang Jianping, Li Anan, et al. Acetylcholine deficiency disrupts extratelencephalic projection neurons in the prefrontal cortex in a mouse model of Alzheimer′s disease [J]. Nat Commun, 2022, 13(1): 998. [16] Laverne G, Pesce J, Reynders A, et al. Cholinergic interneuron inhibition potentiates corticostriatal transmission in direct medium spiny neurons and rescues motor learning in parkinsonism [J]. Cell Rep, 2022, 40(1): 111034. [17] Kawano F, Okazaki R, Yazawa M, et al. A photoactivatable Cre-loxP recombination system for optogenetic genome engineering [J]. Nat Chem Biol, 2016, 12(12): 1059-1064. [18] De Santis R, Etoc F, Rosado-Olivieri EA, et al. Self-organization of human dorsal-ventral forebrain structures by light induced SHH [J]. Nat Commun, 2021, 12(1): 6768. [19] Bansal A, Shikha S, Zhang Yong. Towards translational optogenetics [J]. Nat Biomed Eng, 13 Jan, 2022 [Epub ahead of print]. [20] Nectow AR, Nestler EJ. Viral tools for neuroscience [J]. Nat Rev Neurosci, 2020, 21(12): 669-681. [21] Packer AM, Roska B, Hausser M. Targeting neurons and photons for optogenetics [J]. Nat Neurosci, 2013, 16(7): 805-815. [22] Kim D, Yokota T, Suzuki T, et al. Ultraflexible organic light-emitting diodes for optogenetic nerve stimulation [J]. Proc Natl Acad Sci USA, 2020, 117(35): 21138-21146. [23] Gong Xin, Mendoza-Halliday D, Ting JT, et al. An ultra-sensitive step-function opsin for minimally invasive optogenetic stimulation in mice and macaques [J]. Neuron, 2020, 107(1): 197. [24] Mattis J, Tye KM, Ferenczi EA, et al. Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins [J]. Nat Methods, 2011, 9(2): 159-172. [25] Won SM, Cai L, Gutruf P, et al. Wireless and battery-free technologies for neuroengineering [J]. Nat Biomed Eng, 8 Mar, 2021 [Epub ahead of print]. [26] El-Shamayleh Y, Horwitz GD. Primate optogenetics: progress and prognosis [J]. Proc Natl Acad Sci USA, 2019, 116(52): 26195-26203. [27] Kanta V, Pare D, Headley DB. Closed-loop control of gamma oscillations in the amygdala demonstrates their role in spatial memory consolidation [J]. Nat Commun, 2019, 10(1): 3970. [28] Zhang Qiaosheng, Hu S, Talay R, et al. A prototype closed-loop brain-machine interface for the study and treatment of pain [J]. Nat Biomed Eng, 21 Jun, 2021 [Epub ahead of print]. [29] Capogrosso M, Milekovic T, Borton D, et al. A brain-spine interface alleviating gait deficits after spinal cord injury in primates [J]. Nature, 2016, 539(7628): 284-288. [30] Abbasi A, Goueytes D, Shulz DE, et al. A fast intracortical brain-machine interface with patterned optogenetic feedback [J]. J Neural Eng, 2018, 15(4): 046011. [31] Pashaie R, Baumgartner R, Richner TJ, et al. Closed-loop optogenetic brain interface [J]. IEEE Trans Biomed Eng, 2015, 62(10): 2327-2337. [32] Soekadar SR, Witkowski M, Gomez C, et al. Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia [J]. Sci Robot, 2016, 1(1): eaag3296. [33] O′Doherty JE, Lebedev MA, Ifft PJ, et al. Active tactile exploration using a brain-machine-brain interface [J]. Nature, 2011, 479(7372): 228-231. [34] Yoo SS, Kim H, Filandrianos E, et al. Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains [J]. PLoS ONE, 2013, 8(4): e60410. [35] Pais-Vieira M, Chiuffa G, Lebedev M, et al. Building an organic computing device with multiple interconnected brains [J]. Sci Rep, 2015, 5:11869. [36] Nam CS, Traylor Z, Chen M, et al. Direct communication between brains: a systematic PRISMA review of brain-to-brain interface [J]. Front Neurorobot, 2021, 15: 656943. [37] Mashat MEM, Li Guangye, Zhang Dingguo. Human-to-human closed-loop control based on brain-to-brain interface and muscle-to-muscle interface [J]. Sci Rep, 2017, 7(1): 11001. [38] Jiang Linxing, Stocco A, Losey DM, et al. BrainNet: a multi-person brain-to-brain interface for direct collaboration between brains [J]. Sci Rep, 2019, 9(1): 6115. [39] Lu Lihui, Ren Yuqi, Yu Tao, et al. Control of locomotor speed, arousal, and hippocampal theta rhythms by the nucleus incertus [J]. Nat Commun, 2020, 11(1): 262. [40] Lu Lihui, Wang Ruiyu, Luo Minmin. An optical brain-to-brain interface supports rapid information transmission for precise locomotion control [J]. Sci China Life Sci, 2020, 63(6): 875-885. [41] Szonyi A, Sos KE, Nyilas R, et al. Brainstem nucleus incertus controls contextual memory formation [J]. Science, 2019, 364(6442): eaaw0445. [42] Chow BY, Han Xue, Dobry AS, et al. High-performance genetically targetable optical neural silencing by light-driven proton pumps [J]. Nature, 2010, 463(7277): 98-102. [43] Kleinlogel S, Feldbauer K, Dempski RE, et al. Ultra light-sensitive and fast neuronal activation with the Ca(2)+-permeable channelrhodopsin CatCh [J]. Nat Neurosci, 2011, 14(4): 513-518. [44] Bergs A, Schultheis C, Fischer E, et al. Rhodopsin optogenetic toolbox v2.0 for light-sensitive excitation and inhibition in Caenorhabditis elegans [J]. PLoS ONE, 2018, 13(2): e0191802. [45] Xu Huatai, Pan Feng, Yang Guang, et al. Choice of cranial window type for in vivo imaging affects dendritic spine turnover in the cortex [J]. Nat Neurosci, 2007, 10(5): 549-551. [46] Szarowski DH, Andersen MD, Retterer S, et al. Brain responses to micro-machined silicon devices [J]. Brain Res, 2003, 983(1-2): 23-35. [47] Ji Bowen, Guo Zhejun, Wang Minghao, et al. Flexible polyimide-based hybrid opto-electric neural interface with 16 channels of micro-LEDs and electrodes [J]. Microsyst Nanoeng, 2018, 4: 27. [48] Jia Yaoyao, Gong Yan, Weber A, et al. A mm-sized free-floating wireless implantable opto-electro stimulation device [J]. Micromachines (Basel), 2020, 11(6): 11060621. [49] Jia Yaoyao, Khan W, Lee B, et al. Wireless opto-electro neural interface for experiments with small freely behaving animals [J]. J Neural Eng, 2018, 15(4): 046032. [50] Kim TI, McCall JG, Jung YH, et al. Injectable, cellular-scale optoelectronics with applications for wireless optogenetics [J]. Science (New York, NY), 2013, 340(6129): 211-216. [51] Shupe LE, Miles FP, Jones G, et al. Neurochip3: an autonomous multichannel bidirectional brain-computer interface for closed-loop activity-dependent stimulation [J]. Front Neurosci, 2021, 15: 718465. [52] Gao Wei, Emaminejad S, Nyein HYY, et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis [J]. Nature, 2016, 529(7587): 509-514. [53] Tee BC, Chortos A, Berndt A, et al. A skin-inspired organic digital mechanoreceptor [J]. Science (New York, NY), 2015, 350(6258): 313-316. [54] Rus D, Tolley MT. Design, fabrication and control of soft robots [J]. Nature, 2015, 521(7553): 467-475. [55] Cortese AJ, Smart CL, Wang T, et al. Microscopic sensors using optical wireless integrated circuits [J]. Proc Natl Acad Sci USA, 2020, 117(17): 9173-9179. [56] Liu Xin, Lu Yichen, Iseri E, et al. A compact closed-loop optogenetics system based on artifact-free transparent graphene electrodes [J]. Front Neurosci, 2018, 12: 132. [57] Jiang Longlong, Xu Chenyin, Wu Xiaocheng, et al. Deep ultraviolet light stimulated synaptic transistors based on poly(3-hexylthiophene) ultrathin films [J]. ACS Appl Mater Interfaces, 2022, 14(9): 11718-11726. [58] Ji Bowen, Ge Chaofan, Guo Zhejun, et al. Flexible and stretchable opto-electric neural interface for low-noise electrocorticogram recordings and neuromodulation in vivo [J]. Biosens Bioelectron, 2020, 153: 112009. [59] Yokota T, Zalar P, Kaltenbrunner M, et al. Ultraflexible organic photonic skin [J]. Sci Adv, 2016, 2(4): e1501856. [60] Lee S, Sasaki D, Kim D, et al. Ultrasoft electronics to monitor dynamically pulsing cardiomyocytes [J]. Nat Nanotechnol, 2019, 14(2): 156-160. [61] Kozai TD, Catt K, Du Z, et al. Chronic in vivo evaluation of PEDOT/CNT for stable neural recordings [J]. IEEE Trans Biomed Eng, 2016, 63(1): 111-119. [62] Han M, Yildiz E, Kaleli HN, et al. Tissue-like optoelectronic neural interface enabled by PEDOT: PSS hydrogel for cardiac and neural stimulation [J]. Adv Healthc Mater, 2022, 11(8): e2102160. [63] Jiang Shan, Patel DC, Kim J, et al. Spatially expandable fiber-based probes as a multifunctional deep brain interface [J]. Nat Commun, 2020, 11(1): 6115. [64] Fan B, Li W. Miniaturized optogenetic neural implants: a review [J]. Lab Chip, 2015, 15(19): 3838-3855. [65] Adewole DO, Struzyna LA, Burrell JC, et al. Development of optically controlled "living electrodes" with long-projecting axon tracts for a synaptic brain-machine interface [J]. Sci Adv, 2021, 7(4): eaay5347. [66] Struzyna LA, Browne KD, Burrell JC, et al. Axonal tract reconstruction using a tissue-engineered nigrostriatal pathway in a rat model of Parkinson′s disease [J]. Int J Mol Sci, 2022, 23(22): 13985. [67] Ranjandish R, Schmid A. A review of microelectronic systems and circuit techniques for electrical neural recording aimed at closed-loop epilepsy control [J]. Sensors (Basel), 2020, 20(19): 5716. [68] ISO/IEC AWI 8663, Information technology-brain-computer interface-vocabulary [S]. [69] IEEE P2794, Standard for reporting of in vivo neural interface research [S]. [70] Naseer N, Hong KS. fNIRS-based brain-computer interfaces: a review [J]. Front Hum Neurosci, 2015, 9: 3. [71] Sahel JA, Boulanger-Scemama E, Pagot C, et al. Partial recovery of visual function in a blind patient after optogenetic therapy [J]. Nat Med, 2021, 27(7): 1223-1229. [72] Li Yuhang, Shi Xiaoting, Song Jizhou, et al. Erratum: thermal analysis of injectable, cellular-scale optoelectronics with pulsed power [J]. Proc Math Phys Eng Sci, 2013, 469(2157): 20130398. [73] Stujenske JM, Spellman T, Gordon JA. Modeling the spatiotemporal dynamics of light and heat propagation for in vivo optogenetics [J]. Cell Rep, 2015, 12(3): 525-534. [74] Bansal H, Pyari G, Roy S. Co-expressing fast channelrhodopsin with step-function opsin overcomes spike failure due to photocurrent desensitization in optogenetics: a theoretical study [J]. J Neural Eng, 2022, 19(2): 10. [75] Qiang Y, Artoni P, Seo KJ, et al. Transparent arrays of bilayer-nanomesh microelectrodes for simultaneous electrophysiology and two-photon imaging in the brain [J]. Sci Adv, 2018, 4(9): eaat0626. [76] Seo KJ, Artoni P, Qiang Y, et al. Transparent, flexible, penetrating microelectrode arrays with capabilities of single-unit electrophysiology [J]. Adv Biosyst, 2019, 3(3): e1800276. [77] Dizeux A, Gesnik M, Ahnine H, et al. Functional ultrasound imaging of the brain reveals propagation of task-related brain activity in behaving primates [J]. Nat Commun, 2019, 10(1): 1400. [78] Blaize K, Arcizet F, Gesnik M, et al. Functional ultrasound imaging of deep visual cortex in awake nonhuman primates [J]. Proc Natl Acad Sci USA, 2020, 117(25): 14453-14463. [79] Du Bing, Cheng Xiaomu, Duan Yiping, et al. fMRI brain decoding and its applications in brain-computer interface: a survey [J]. Brain Sci, 2022, 12(2): 228. [80] Moerel M, Yacoub E, Gulban OF, et al. Using high spatial resolution fMRI to understand representation in the auditory network [J]. Prog Neurobiol, 2021, 207: 101887. [81] Toi PT, Jang HJ, Min K, et al. In vivo direct imaging of neuronal activity at high temporospatial resolution [J]. Science (New York, NY), 2022, 378(6616): 160-168. [82] Wu Xiang, Zhu Xingjun, Chong P, et al. Sono-optogenetics facilitated by a circulation-delivered rechargeable light source for minimally invasive optogenetics [J]. Proc Natl Acad Sci USA, 2019, 116(52): 26332-26342. [83] Bakhshaee Babaroud N, Palmar M, Velea AI, et al. Multilayer CVD graphene electrodes using a transfer-free process for the next generation of optically transparent and MRI-compatible neural interfaces [J]. Microsyst Nanoeng, 2022, 8: 107. [84] Yamabe M, Horie K, Shiokawa H,et al. MC-SleepNet: large-scale sleep stage scoring in mice by deep neural networks[J]. Sci Rep, 2019, 9(1): 15793. [85] Sonwani E, Bansal U, Alroobaea R, Baqasah AM, Hedabou M. An artificial intelligence approach toward food spoilage detection and analysis[J]. Front Public Health, 2022, 9: 816226. [86] Gao Xiaorong, Wang Yijun, Chen Xiaogang, et al. Interface, interaction, and intelligence in generalized brain-computer interfaces [J]. Trends Cogn Sci, 2021, 25(8): 671-684.
