Advanced technologies for controlled delivery of light to targeted locations in biological tissues are essential to neuroscience research that applies optogenetics in animal models. Fully implantable, miniaturized devices with wireless control and power-harvesting strategies offer an appealing set of attributes in this context, particularly for studies that are incompatible with conventional fiber-optic approaches or battery-powered head stages. Limited programmable control and narrow options in illumination profiles constrain the use of existing devices. The results reported here overcome these drawbacks via two platforms, both with real-time user programmability over multiple independent light sources, in head-mounted and back-mounted designs. Engineering studies of the optoelectronic and thermal properties of these systems define their capabilities and key design considerations. Neuroscience applications demonstrate that induction of interbrain neuronal synchrony in the medial prefrontal cortex shapes social interaction within groups of mice, highlighting the power of real-time subject-specific programmability of the wireless optogenetic platforms introduced here.
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Raw data generated during the current study are available from the corresponding author on reasonable request. The data analyzed during the current study are available at https://github.com/A-VazquezGuardado/Real-time_control_Optogenetics. Source data are provided with this paper.
All computer code and customized software generated during and/or used in the current study are available at https://github.com/A-VazquezGuardado/Real-time_control_Optogenetics.
Bassett, D. S. & Sporns, O. Network neuroscience. Nat. Neurosci. 20, 353–364 (2017).
Klapoetke, N. C. et al. Independent optical excitation of distinct neural populations. Nat. Methods 11, 338–346 (2014).
Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G. & Deisseroth, K. Millisecond-timescale, genetically targeted optical control of neural activity. Nat. Neurosci. 8, 1263–1268 (2005).
Deisseroth, K. Optogenetics. Nat. Methods 8, 26–29 (2011).
Yizhar, O., Fenno, L. E., Davidson, T. J., Mogri, M. & Deisseroth, K. Optogenetics in neural systems. Neuron 71, 9–34 (2011).
Park, S. et al. One-step optogenetics with multifunctional flexible polymer fibers. Nat. Neurosci. 20, 612–619 (2017).
Montgomery, K. L. et al. Wirelessly powered, fully internal optogenetics for brain, spinal and peripheral circuits in mice. Nat. Methods 12, 969–974 (2015).
Shin, G. et al. Flexible near-field wireless optoelectronics as subdermal implants for broad applications in optogenetics. Neuron 93, 509–521 (2017).
Gutruf, P. & Rogers, J. A. Implantable, wireless device platforms for neuroscience research. Curr. Opin. Neurobiol. 50, 42–49 (2017).
Gutruf, P. et al. Fully implantable optoelectronic systems for battery-free, multimodal operation in neuroscience research. Nat. Electron. 1, 652–660 (2018).
Park, S. I. L. et al. Soft, stretchable, fully implantable miniaturized optoelectronic systems for wireless optogenetics. Nat. Biotechnol. 33, 1280–1286 (2015).
Gold, B. T. & Buckner, R. L. Common prefrontal regions coactivate with dissociable posterior regions during controlled semantic and phonological tasks. Neuron 35, 803–812 (2002).
Crossley, N. A. et al. Cognitive relevance of the community structure of the human brain functional coactivation network. Proc. Natl Acad. Sci. USA 110, 11583–11588 (2013).
Marlin, B. J., Mitre, M., D’amour, J. A., Chao, M. V. & Froemke, R. C. Oxytocin enables maternal behaviour by balancing cortical inhibition. Nature 520, 499–504 (2015).
Capelli, P., Pivetta, C., Soledad Esposito, M. & Arber, S. Locomotor speed control circuits in the caudal brainstem. Nature 551, 373–377 (2017).
Hitchcott, P. K., Quinn, J. J. & Taylor, J. R. Bidirectional modulation of goal-directed actions by prefrontal cortical dopamine. Cereb. Cortex 17, 2820–2827 (2007).
Tye, K. M. et al. Amygdala circuitry mediating reversible and bidirectional control of anxiety. Nature 471, 358–362 (2011).
Ma, T. et al. Bidirectional and long-lasting control of alcohol-seeking behavior by corticostriatal LTP and LTD. Nat. Neurosci. 21, 373–383 (2018).
Pashaie, R. et al. Optogenetic brain interfaces. IEEE Rev. Biomed. Eng. 7, 3–30 (2014).
Gunaydin, L. A. et al. Natural neural projection dynamics underlying social behavior. Cell 157, 1535–1551 (2014).
Yizhar, O. Optogenetic insights into social behavior function. Biol. Psychiatry 71, 1075–1080 (2012).
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).
Zhang, Y. et al. Experimental and theoretical studies of serpentine microstructures bonded to prestrained elastomers for stretchable electronics. Adv. Funct. Mater. 24, 2028–2037 (2014).
Scott, W. W. ASM Specialty Handbook: Copper and Copper Alloys (ASM International, 2001).
Lu, L. et al. Wireless optoelectronic photometers for monitoring neuronal dynamics in the deep brain. Proc. Natl Acad. Sci. USA 115, E1374–E1383 (2018).
Morales, M. & Margolis, E. B. Ventral tegmental area: cellular heterogeneity, connectivity and behaviour. Nat. Rev. Neurosci. 18, 73–85 (2017).
Lammel, S., Lim, B. K. & Malenka, R. C. Reward and aversion in a heterogeneous midbrain dopamine system. Neuropharmacology 76, 351–359 (2014).
Tsai, H.-C. et al. Phasic firing in dopaminergic neurons is sufficient for behavioral conditioning. Science 324, 1080–1084 (2009).
Kingsbury, L. et al. Correlated neural activity and encoding of behavior across brains of socially interacting animals. Cell 178, 429–446 (2019).
Kingsbury, L. & Hong, W. A multi-brain framework for social interaction. Trends Neurosci. 43, 651–666 (2020).
Yun, K., Watanabe, K. & Shimojo, S. Interpersonal body and neural synchronization as a marker of implicit social interaction. Sci. Rep. 2, 959 (2012).
Toppi, J. et al. Investigating cooperative behavior in ecological settings: an EEG hyperscanning study. PLoS ONE 11, e0154236 (2016).
Jia, Y. et al. A mm-sized free-floating wirelessly powered implantable optical stimulation device. IEEE Trans. Biomed. Circuits Syst. 13, 608–618 (2018).
Lee, S. Y. et al. 22.7 A programmable wireless EEG monitoring SoC with open/closed-loop optogenetic and electrical stimulation for epilepsy control. In 2019 IEEE International Solid-State Circuits Conference 372–374 (IEEE, 2019).
Montague, P. R. et al. Hyperscanning: simultaneous fMRI during linked social interactions. Neuroimage 16, 1159–1164 (2002).
Hasson, U., Ghazanfar, A. A., Galantucci, B., Garrod, S. & Keysers, C. Brain-to-brain coupling: a mechanism for creating and sharing a social world. Trends Cogn. Sci. 16, 114–121 (2012).
Liu, T. & Pelowski, M. A new research trend in social neuroscience: towards an interactive-brain neuroscience. Psych. J. 3, 177–188 (2014).
Zhang, W. & Yartsev, M. M. Correlated neural activity across the brains of socially interacting bats. Cell 178, 413–428 (2019).
Bazrafkan, S. & Kazemi, K. Modeling time resolved light propagation inside a realistic human head model. J. Biomed. Phys. Eng. 4, 49–60 (2014).
Stujenske, J. M., Spellman, T. & Gordon, J. A. Modeling the spatiotemporal dynamics of light and heat propagation for in vivo optogenetics. Cell Rep. 12, 525–534 (2015).
Aronov, D. & Fee, M. S. Analyzing the dynamics of brain circuits with temperature: design and implementation of a miniature thermoelectric device. J. Neurosci. Methods 197, 32–47 (2011).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Xiao, L., Priest, M. F., Nasenbeny, J., Lu, T. & Kozorovitskiy, Y. Biased oxytocinergic modulation of midbrain dopamine systems. Neuron 95, 368–384 (2017).
Xiao, L., Priest, M. F. & Kozorovitskiy, Y. Oxytocin functions as a spatiotemporal filter for excitatory synaptic inputs to VTA dopamine neurons. eLife 7, e33892 (2018).
Wu, M., Minkowicz, S., Dumrongprechachan, V., Hamilton, P. & Kozorovitskiy, Y. Ketamine rapidly enhances glutamate-evoked dendritic spinogenesis in medial prefrontal cortex through dopaminergic mechanisms. Biol. Psychiatry https://doi.org/10.1016/j.biopsych.2020.12.022 (2021).
Rodriguez, A. et al. ToxTrac: a fast and robust software for tracking organisms. Methods Ecol. Evol. 9, 460–464 (2018).
Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).
Broom, L. et al. A translational approach to capture gait signatures of neurological disorders in mice and humans. Sci. Rep. 7, 3225 (2017).
This work used the Northwestern University Micro/Nano Fabrication Facility, which is partially supported by the Soft and Hybrid Nanotechnology Experimental Resource (NSF ECCS-1542205), the Materials Research Science and Engineering Center (DMR-1720139), the State of Illinois and Northwestern University. This work was funded by NINDS R01NS106953 to R.W.G. and the Medical Scientist Training Program grant T32GM07200 and NINDS NRSA 5F31NS103472-02 to J.G.G.-R. Surgical and imaging work was performed by the Developmental Therapeutics Core and the Center for Advanced Molecular Imaging at Northwestern University, which are generously supported by NCI CCSG P30 CA060553 awarded to the Robert H. Lurie Comprehensive Cancer Center. Histology services were provided by the Northwestern University Mouse Histology and Phenotyping Laboratory, which is supported by NCI P30 CA060553 awarded to the Robert H. Lurie Comprehensive Cancer Center. From the US Army Medical Research Institute of Chemical Defense, we thank J. Abraham for his assistance with graphics, T. Shih for sharing his laboratory space and A. Collazo Martinez for laboratory support. C.H.G. is supported by the LUCI program, sponsored by the Basic Research Office, Office of Under Secretary of Defense for Research and Engineering. Y.K. is supported by the NIH (R01MH117111 and R01NS107539), a Rita Allen Foundation Scholar Award, the Searle Scholar Award and a Beckman Young Investigator Award. M.W. is supported as an affiliate fellow of the NIH (T32 AG20506), and S. Minkowicz is supported by the NSF GRFP (DGE-1842165). V.D. is a predoctoral fellow of the American Heart Association (19PRE34380056). Z.R.D. is supported by NIH DP2OD026143, a Whitehall Foundation grant and the Dana Foundation. Z.X. acknowledges support from the National Natural Science Foundation of China (grant no. 12072057) and Fundamental Research Funds for the Central Universities (grant no. DUT20RC(3)032). Y.H. acknowledges support from the NSF (CMMI1635443).
R.W.G., A.B. and J.A.R. are cofounders in a company, Neurolux, Inc., that offers related technology products to the neuroscience community.
Peer review information Nature Neuroscience thanks Avishek Adhikari, Ada Poon, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, The experimental platform includes the implanted device, transmission antenna, power distribution (PDC) box, and PC with user interface. The device is wirelessly programmed using a PC in a real-time manner through near field communication (NFC) control over the stimulation parameters. b, The NFC corresponds to an RF addressable memory chip supporting ISO15693 protocol. The microcontroller interfaces to the NFC memory via the I2C communication protocol. Up to four channels are supported by the microcontroller’s firmware, independently controlled by the end user. Each channel is filtered using a second order low–pass passive filter whose output is coupled by a high impedance voltage follower that drives the μ-ILED. The number of channels to be used depends on the type of implant: two for head mounted devices or four for back mounted devices. c, Simple voltage regulation circuit that implements a low dropout regulator (LDO) that passes current directly to the μ-ILEDs and resistor after rectification. The component bill of materials is also shown.
a, b, FEA simulations and photographs of 30% stretching of different head mounted devices. c, FEA simulations and photographs of these devices deformed into various configurations after implantation.
a, Cartoon representation of the timeline for monitoring device longevity and animal postoperative behavior. b, Routine testing of head mounted devices for 90 days (n = 5 animals). c, Normalized weight assessment for 7 postoperative days (POD) after implantation of head mounted device (two-way ANOVA Sidak’s multiple comparison test; POD1 P = 0.9998, POD2 P = 0.9491, POD3 P > 0.9999, POD4 P = 0.3731, POD5 P = 0.2038, POD6 P = 0.9966, POD7 P = 0.9966; n = 5 naïve & n = 5 device animals). d, Total distance traveled (P = 0.6905), e, Average speed (P = 0.5952), f, Distance in the outer zone (P = 0.6905), g, Distance in the inner zone (P > 0.9999), h, Time in the outer zone (P > 0.9999), i, Time in the inner zone (P = 0.5476). (d-i), Locomotion effects of implantation were assessed using the open field test and a variety of parameters were measured (two-tailed unpaired t-test, Mann Whitney test; n = 5 naïve & n = 5 device animals). j, Graphical representation of individual animal behavior, implanted animals (top row) versus naïve controls (bottom row) in the open field test. Outer and inner squares represent the two zones. All data are represented as mean ±s.e.m.
a, Astrocytic (GFAP) immunoreactivity surrounding the implantation site of an optical fiber (left) and a wireless probe (right). Scale bar, 200 µm. b-c, Same as (a), but for microgila (IBA1) and hemorrhage (hemoglobin subunit α). d, Summary data showing total astrocyte dense (GFAP) area at different distances from the edge of implantation. Two-way ANOVA, Sidak’s multiple comparisons test (Fiber vs Probe), 0-100 µm, P = 0.3477, 100-200 µm, P = 0.4931, 200-300 µm, P = 0.3285, 300-400 µm, p = 0.3022. n = 22 – 23 slices from 6 brains/group. e, Same as (d), but for microglia (IBA1). Two-way ANOVA, Sidak’s multiple comparisons test (Fiber vs Probe), 0-200 µm, P = 0.7692, 200-400 µm, P = 0.7414. n = 18 slices from 6 brains/group. f, Same as (d), but for hemorrhage (hemoglobin subunit α). Two-tailed unpaired t-test, P = 0.1054. n = 18 slices from 6 brains/group. g, Summary data show the ratio between measured brain damage and estimated brain damage, based on probe size. Two-tailed unpaired t-test ANOVA, P = 0.2191. n = 6 brains/group. h-i, H&E staining images show the morphology of the back tissues in mice that went through sham surgery or BM device implantation. Scale bar, 5 mm and 20 µm. j, Left, summary data showing average sacrospinalis myofiber size in BM implant and control mice. Two-tailed unpaired t-test, P = 0.6714. Right, cumulative frequency of myofiber size. n = 10 slices from 4 animals (Sham), n = 17 slices from 6 animals (Device). k, Summary data showing the thickness of dermis (left) and subcutaneous tissue (right) in mice after BM implantation of sham surgery. Two-tailed unpaired t-test, Dermis, P = 0.2650, Subcutaneous tissue, P = 0.9517. n = 16 slices from 4 animals (Sham), n = 24 slices from 6 animals (Device). l, Summary data for bone density in mice after sham surgery or BM implantation. Mann Whitney test, P = 0.0635. n = 4-5 animals/group. d-f, j, and k, white open circles: average value for each animal, grey filled circles: value for individual ROIs/brain slices.
a, Generic time diagram that depicts the dynamic parameters accessible via NFC programming. Each channel supports amplitude modulation with signal/carrier defined by period T1/T2 and duty cycle DC1/DC2, respectively. b, Voltage control implemented by amplitude modulation and its frequency interaction with a passive low-pass filter with cut-off frequency at fc. While the information signal contained in its low order harmonics, f01 < fc, passes almost unchanged, the high frequency carrier, f02 > fc, is filtered to its direct current component, which is proportional to the duty cycle of the carrier. c, Multichannel operation mode representation. This platform allows single channel operation addressed individually, dual operation of any arbitrary channel combination with two modes, in-phase and out-of-phase.
Extended Data Fig. 6 Thermal power, irradiance, illumination volume, and penetration depth of μ-ILEDs.
a, Maximum electrical and thermal power for a single blue μ-ILED (460 nm) as a function of RF power applied to the transmission antenna for HM and BM devices. b, c, d, Same measurements as reported in (a) for green (535 nm), orange (590 nm), and red (630 nm) μ-ILEDs respectively. e, Maximum electrical and optical irradiance for a single green μ-ILED (535 nm) as a function of RF power applied to the transmission antenna for HM and BM devices. f, Illumination volume and penetration depth as a function of optical irradiance from a green μ-ILED (535 nm; cutoff intensity 0.1 mW/mm2). g, h, Same measurements and simulations as reported in (e) and (f) respectively for an orange μ-ILED (590 nm). i, j, Same measurements and simulations as reported in (e) and (f) respectively for a red μ-ILED (630 nm).
a, Temperature change at the interface between the encapsulated μ-ILED and brain tissue as a function of operational irradiance of green μ-ILED (535 nm) and its duty cycle at 20 Hz frequency. b, c, Same simulation as reported in (a) for orange (590 nm) and red (630 nm) μ-ILEDs, respectively.
Extended Data Fig. 8 Bilateral burst wireless stimulation of midbrain dopaminergic neurons regulates place preference.
a, Schematic illustration of neonatal virus transduction in DATiCre animals. b, Schematic image of implanted position of bilateral wireless device posterior to the VTA. c, Left: sagittal brain section showing the expression of ChR2.EYFP in the VTA and the track of wireless probe. Right: Close up image of the VTA. Scale bar: 500 μm. d, Top: schematic showing the arena of real-time place preference (RTPP) and the stimulation area (blue). Bottom: burst pattern of wireless optogenetic stimulation. e, Example traces showing the tracks of positions in different test periods from one animal. Top: baseline condition without stimulation in the first and last 10 min of the testing session. Bottom: same period of test session, but with light stimulation (460 nm). f, Left: summary data showing the total time spent on the antenna side in baseline and stimulation conditions. Two-way ANOVA, Sidak’s multiple comparisons test (Baseline vs Stim), ChR2, P = 0.001, Fluorophore, P = 0.9962. Right: percentage of time spent on the antenna side in different testing period. Two-way ANOVA, Sidak’s multiple comparisons (Stim, ChR2 vs Fluorophore), 0 – 10 min, P = 0.0008, 10 – 20 min, P = 0.0005. N = 6 mice/group. All data are represented as mean ±s.e.m. ***P < 0.001. ns: not significant.
a, Image of viral expression of ChR2.mCherry and probe placement. Scale bar, 500 µm. b, Images of c-Fos immunoreactivity in ipsilateral mPFC, interconnected contralateral mPFC, and ipsilateral M1 as a control region. Scale bar, 20 µm. c, Summary data show average c-Fos intensity in individual mice (left), the distribution of c-Fos neuronal particle intensities (middle), and cumulative frequency of c-Fos particle intensities. RM one-way ANOVA, P = 0.0025, Sidak’s multiple comparisons test, ipsilateral (ip) mPFC vs ipsilateral M1, P = 0.0201, contralateral (con) mPFC vs ipsilateral M1, P = 0.0370. N = 4 mice/group. Data represent mean ±s.e.m.; dashed lines in the violin plot show quartiles and median. * P < 0.05.
a, Summary data show the percentage of time spent in social interaction for individual mice in dyads and triads, unpaired two-tailed t-test, P < 0.0001. N = 10 independent experiments (Dyads), N = 8 independent experiments (Triads). b, Left, Schematic illustrating simulation of subject proximity in dyads and triads. Right, estimated percent of time spent in proximity to another subject (distance < 3 mm) for simulated individual in dyads and triads. Unpaired two-tailed t-test, P < 0.0001. N = 30/group. c, Summary data show the proportion of time spent in social interactions within the synchronized pair, over the total social interaction time for each experiment. Unpaired two-tailed t-test, ChR2 vs Fluorophore, P = 0.0044. N = 10 independent experiments (ChR2), N = 8 independent experiments (Fluorophore). d, Summary data for non-social event durations for ChR2-expressing (left) and fluorophore control (right) mice. Two-way ANOVA, Sidak’s multiple comparisons test (main column effect), ChR2: A1 vs A2, P = 0.9991, A1 vs A3, P = 0.8996, A2 vs A3, P = 0.8420. Fluorophore: A1 vs A2, P = 0.7711, A1 vs A3, P = 0.9661, A2 vs A3, P = 0.9586. N = 10 independent experiments (ChR2), N = 8 independent experiments (Fluorophore). e, Schematic showing experimental design for 3 mouse social preference and real-time switching of synchronized pairs. f, Summary data for the total time spent engaged in social interactions for synchronized or desynchronized pairs across all test sessions. Two-way ANOVA, Sidak’s multiple comparisons test, ChR2 0 – 10 min, Sync vs Desync 1, P = 0.0198, Sync vs Desync 2, P = 0.0022, other comparisons, P > 0.1. N = 10 independent experiments (ChR2), N = 8 independent experiments (Fluorophore). Data represent mean ±s.e.m. in bar graphs; box and whisker plots show quartiles and median. * P < 0.05, **P < 0.01, ****P < 0.0001.
Supplementary Figs. 1–15 and Supplementary Tables 1–5.
Video of an HM device showing real-time control over optical intensity.
Video of an HM device showing real-time control over stimulation frequency.
Video of an HM device showing real-time control over pulse duration.
Video of an HM device showing real-time control over operation modes.
Video of a BM device showing real-time control over individual LEDs among four channels in unilateral operation mode.
Video of a BM device showing real-time control over paired LEDs among four channels in bilateral operation mode.
Video of multiple HM devices showing real-time individual control over on–off operation.
Video of multiple HM and BM devices showing real-time control over individual channels.
Video of HM devices showing real-time change from synchronized operation to desynchronized operation.
Video of a mouse pair during synchronized and desynchronized wireless optogenetic stimulation of the mPFC.
Video of HM devices showing real-time switching of synchronized and desynchronized paired stimulation.
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Yang, Y., Wu, M., Vázquez-Guardado, A. et al. Wireless multilateral devices for optogenetic studies of individual and social behaviors. Nat Neurosci 24, 1035–1045 (2021). https://doi.org/10.1038/s41593-021-00849-x