Patterned wireless transcranial optogenetics generates artificial perception

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patterned-wireless-transcranial-optogenetics-generates-artificial-perception
Patterned wireless transcranial optogenetics generates artificial perception

Data availability

Raw data generated during the present study are available from the corresponding authors upon reasonable request. The analyzed data are available at https://doi.org/10.5281/zenodo.14880024 (ref. 82). Data supporting the findings of this study are included within this paper and its Supplementary Information files. Source data are provided with this paper.

Code availability

All computer code and customized software generated during and/or used in the present study are available at https://doi.org/10.5281/zenodo.14880024 (ref. 82).

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Acknowledgements

We thank L. Butler for mouse colony management and F. Valero-Cuevas for meaningful insights and discussions. This work made use of the NUFAB facility of Northwestern University’s NUANCE Center, which has received support from the SHyNE Resource (National Science Foundation (NSF) ECCS-2025633), the International Institute for Nanotechnology and Northwestern’s Materials Research Science and Engineering Center program (NSF DMR-2308691). MicroCT imaging work was performed at the Northwestern University Center for Advanced Molecular Imaging (RRID: SCR_021192), generously supported by National Cancer Institute Cancer Center Support Grant P30 CA060553 awarded to the Robert H. Lurie Comprehensive Cancer Center. Microscopy analyses using Leica SP8 were performed at the Biological Imaging Facility at Northwestern University (RRID: SCR_017767), generously supported by the Chemistry for Life Processes Institute, the Northwestern University Office for Research, the Department of Molecular Biosciences and the Rice Foundation. This work was funded by the Querrey-Simpson Institute for Bioelectronics (M.W., Y.Y., A.I.E., A.V.-G., Y.W., J.G., L.Z., J.L., M.K., J.K., Y.H. and J.A.R.); National Institute of Neurological Disorders and Stroke (NINDS)/BRAIN Initiative 1U01NS131406 (Y.K. and J.A.R.); National Institute of Mental Health (NIMH) R01MH117111 (Y.K.); NINDS R01NS107539 (Y.K.); 2021 One Mind Nick LeDeit Rising Star Research Award (Y.K.); Kavli Exploration Award (Y.K.); Shaw Family Pioneer Award; Center for Reproductive Science, Feinberg School of Medicine (J.M.C.); NIMH R00MH120047 (L.P.); Simons Foundation grant 872599SPI (L.P.); Alfred P. Sloan Foundation grant SP-2022-19027 (L.P.); North Carolina State University Start-up Fund 201473-02139 (A.V.-G.); 2T32MH067564 (J.Z.); and the Christina Enroth-Cugell and David Cugell fellowship (M.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Mingzheng Wu, Yiyuan Yang, Jinglan Zhang, Andrew I. Efimov, Xiuyuan Li, Kaiqing Zhang.

Authors and Affiliations

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

    Mingzheng Wu, Yiyuan Yang, Andrew I. Efimov, Yue Wang, Jianyu Gu, Glingna Wang, Minsung Kim, Liangsong Zeng, Jiaqi Liu, Minkyu Lee, Jiheon Kang, Joanna L. Ciatti, Kaila Ting, Stephen Cheng, Anthony Banks, Cameron H. Good, Abraham Vázquez-Guardado, Yonggang Huang & John A. Rogers

  2. Department of Neurobiology, Northwestern University, Evanston, IL, USA

    Mingzheng Wu, Jinglan Zhang, Kevin L. Bodkin, Lauren H. Yoon, Sara N. Freda, Lucas Pinto & Yevgenia Kozorovitskiy

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

    Yiyuan Yang, Liangsong Zeng, Yonggang Huang & John A. Rogers

  4. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yiyuan Yang

  5. The N.1 Institute for Health, National University of Singapore, Singapore, Singapore

    Yiyuan Yang

  6. Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore

    Yiyuan Yang

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

    Andrew I. Efimov, Yue Wang & John A. Rogers

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

    Xiuyuan Li, Kaiqing Zhang, Haohui Zhang & Yonggang Huang

  9. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xiuyuan Li & Wenming Zhang

  10. State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian, China

    Kaiqing Zhang

  11. Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA

    Mohammad Riahi & Abraham Vázquez-Guardado

  12. Center for Advanced Self-Powered Systems of Integrated Sensors and Technologies (ASSIST), North Carolina State University, Raleigh, NC, USA

    Mohammad Riahi & Abraham Vázquez-Guardado

  13. Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, USA

    Glingna Wang

  14. Computation and Neural Systems, California Institute of Technology, Pasadena, CA, USA

    Lauren H. Yoon

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

    Joanna L. Ciatti, Yonggang Huang & John A. Rogers

  16. Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, CT, USA

    Xincheng Zhang, He Sun & Yi Zhang

  17. Center for Bio-Integrated Electronics, Northwestern University, Evanston, IL, USA

    Anthony Banks, Cameron H. Good & John A. Rogers

  18. Neurolux, Inc., Northfield, IL, USA

    Anthony Banks & Cameron H. Good

  19. Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Julia M. Cox & Lucas Pinto

  20. Department of Neurobiology, The University of Chicago, Chicago, IL, USA

    Julia M. Cox & Lucas Pinto

  21. Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, USA

    Yevgenia Kozorovitskiy

  22. Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    John A. Rogers

Authors

  1. Mingzheng Wu
  2. Yiyuan Yang
  3. Jinglan Zhang
  4. Andrew I. Efimov
  5. Xiuyuan Li
  6. Kaiqing Zhang
  7. Yue Wang
  8. Kevin L. Bodkin
  9. Mohammad Riahi
  10. Jianyu Gu
  11. Glingna Wang
  12. Minsung Kim
  13. Liangsong Zeng
  14. Jiaqi Liu
  15. Lauren H. Yoon
  16. Haohui Zhang
  17. Sara N. Freda
  18. Minkyu Lee
  19. Jiheon Kang
  20. Joanna L. Ciatti
  21. Kaila Ting
  22. Stephen Cheng
  23. Xincheng Zhang
  24. He Sun
  25. Wenming Zhang
  26. Yi Zhang
  27. Anthony Banks
  28. Cameron H. Good
  29. Julia M. Cox
  30. Lucas Pinto
  31. Abraham Vázquez-Guardado
  32. Yonggang Huang
  33. Yevgenia Kozorovitskiy
  34. John A. Rogers

Contributions

Conceptualization: M.W., Y.Y., Y.K. and J.A.R. Methodology: M.W., Y.Y., A.I.E., J.Z., A.V.-G., X.L., Y.K. and J.A.R. Theoretical simulations: X.L., K.Z., M.W., W.Z., A.V.-G. and Y.H. Investigation: M.W., Y.Y., J.Z., A.I.E., A.V.-G., X.L., K.Z., Y.W., K.L.B., J.G., M.R., G.W., M.K., L.Z., J.L., L.H.Y., H.Z., S.N.F., M.L., J.K., J.L.C., X.Z., H.S., K.T., S.C., Y.Z., A.B., C.H.G., J.M.C., L.P., Y.H., Y.K. and J.A.R. Software: A.V.-G., L.H.Y., Y.W., K.L.B. and M.R. Formal analysis: M.W., J.Z., X.L., K.Z., K.L.B. and L.H.Y. Validation: M.W., J.Z., A.I.E. and K.L.B. Data curation: J.Z., L.Z., L.H.Y., K.L.B. and A.I.E. Visualization: J.Z., M.W., Y.Y., A.I.E., X.L., K.Z., Y.W., J.G., J.L. and M.R. Supervision: Y.Y., Y.H., Y.K. and J.A.R. Funding acquisition: Y.H., Y.K. and J.A.R. Writing—original draft: M.W., Y.Y., J.Z., A.I.E., A.V.-G., X.L., K.Z., Y.K. and J.A.R. Writing—review and editing: M.W., Y.Y., J.Z., A.I.E., A.V.-G., X.L., K.Z., K.L.B., J.L., J.M.C., L.P., Y.H., Y.K. and J.A.R. M.W., Y.Y., J.Z., A.I.E., K.Z. and X.L. contributed equally to this work.

Corresponding authors

Correspondence to Yiyuan Yang, Abraham Vázquez-Guardado, Yonggang Huang, Yevgenia Kozorovitskiy or John A. Rogers.

Ethics declarations

Competing interests

J.A.R. and A.B. are co-founders in a company, Neurolux, Inc., that offers related technology products to the neuroscience community. C.H.G. is employed by Neurolux, Inc. The other authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Luis Carrillo-Reid 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.

Extended data

Extended Data Fig. 1 Device fabrication and assembly.

(a) Left, schematic illustration of the circuit base of the electronic module of the device using three-layer flexible printed circuit board (fPCB). Right, images of the fPCB for the electronic module. Scale bar: 5 mm. (b) Left, schematic illustration of the assembly of electronic components on the fPCB of the electronic module using hot-air soldering. Right, images of the electronic module after hot-air soldering of electronic components. (c) Left, schematic illustration of the circuit base of the FOD using three-layer fPCB. Right, magnified view of the soldering site for the μ-ILED with solder paste. Scale bar: 500 µm. (d) Left, schematic illustration of the FOD after hot-air soldering of the μ-ILEDs. Right, magnified view of the soldered μ-ILED. (e) Left, schematic illustration of the serpentine traces of the device using a five-layer fPCB. Right, image of the serpentine traces on the fPCB. Scale bar: 5 mm. (f) Left, image of the serpentine traces after laser ablation from the fPCB substrate. Right, scanning electron microscopic (SEM) image of the serpentine traces from lateral view. The experiment was repeated independently on 3 serpentine traces. Scale bar: 300 µm. (g) Schematic illustration of the final assembly of the electronic module, serpentine traces, and FOD. (h) Image of the final device after assembly. (i) Schematic illustration of the mold used for final silicone (Ecoflex 00-30) coating after parylene-C encapsulation. (j) Photograph of the device during mold casting of silicone. (k) Image of a fully encapsulated device ready for in vivo experiments.

Extended Data Fig. 2 Geometric optimization, numerical modeling, and experimental characterizations of serpentine interconnect.

(a) Left, geometric model of structures surrounding the serpentine interconnect. Right, calculation of the applied strain on the serpentine and equivalent strain on the Cu-based serpentine conductive traces. (b) Layered structure of the serpentine materials relative to the neutral mechanical plane. Left: 2-Cu-layer design; right: 3-Cu-layer design. (c) Equivalent strain distribution on copper traces with 13% applied strain. (d) When a certain strain is applied to the serpentine interconnect, a certain copper unit on the trace shows the highest equivalent strain among all units. Summary graph showing this highest equivalent strain versus applied strains. Equivalent strain for copper plastic deformation equals 0.3%. (e) Geometric parameters that affect the stretchability of the serpentine interconnects. (f) Serpentine design examples with different gap widths and radius. (g) Contour map summarizing the stretchability as a function of normalized gap width and radius. Stretchability: maximal applied strain without causing plastic deformation on copper-based conductive traces. (h) Average elongation of the gap when the serpentine interconnect is stretched. (i) Elongation of the gap results in principal strain on the elastomer, causing potential encapsulation defects. (j) Design 1 & 2: prototypes, not guided by numerical modeling; Design 3, optimized layer structure with preliminary modifications of gap width and connecting regions; Design 4, optimized layer structure and geometry guided by numerical modeling. (k) Equivalent strain on the Cu-based conductive traces versus applied strain on the serpentine interconnect for the four designs. Dashed line: Equivalent strain threshold for plastic deformation on the copper traces. (l) Finite element analysis (FEA) of equivalent strain on the optimized serpentine interconnect under stretching (left) and bending (middle). Right, the equivalent strain on the FOD when flexed to the curvature of the skull. Scale bar: 10 mm. (m) Schematic illustration of the benchtop validation of the modeling outcomes using cyclic stretching and bending tests of devices integrated on artificial skin immersed in saline. (n) Summary data showing normalized conductance loads on individual μ-ILEDs for 20k cycles in cyclic stretching and bending tests. n = 21 pairs of traces from 3 devices for design 1; n = 27 pairs of traces from 3 devices for design 2; n = 26 pairs of traces from 3 devices for design 3; n = 20 pairs of traces from 3 devices for design 4. Data are presented as mean ± s.e.m. (o) Summary data for stable cycles where the devices maintain constant resistance during stretching and bending. n = 3 devices. Dots represent individual devices. (p) Images showing devices in operation in the Bpod chamber, 32 days after implantation.

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Extended Data Fig. 3 Electronic modules for FOD with 2 × 4 configuration.

(a) Schematic illustrations showing electronic circuits for wireless power harvesting and voltage regulation. (b) Capacitor bank for energy storage and discharge. (c) Near-field communication module for real-time programming. (d) Micro-controller circuit for parameter (order, frequency, duty cycle) control. (e) Digital-to-analog converters for intensity modulation. (f) FOD. Essential components and input/output pins are labeled on the schematics.

Extended Data Fig. 4 Characterizations and validations for irradiance distribution profiles.

(a) Simulated magnetic-field-intensity distribution at the central plane of a behavioral cage (dimensions, 20 cm (length) × 14 cm (width)) with a double-loop antenna at heights of 3 cm and 6 cm. Scale bar: 10 cm. (b) Simulated magnetic-field-intensity distribution at different heights in the Bpod behavioral cage. (c) The total electrical power of the μ-ILED with respect to the primary antenna power. (d) The I-V characterization for the μ-ILED. (e) The optical power of the μ-ILED with respect to the input current. (f) Schematic illustration (left) and image (right) of the experimental setup for measuring light attenuation through the skull and brain tissue. (g) Simulated (left) and measured (right) results of light attenuation through the skull. A 60 µm layer of skull was used in the numerical model. A piece of thinned skull was used for measurement. (h) Illumination volume and penetration depth as a function of the input irradiance of the red μ-ILED (628 nm). Threshold intensity, 1 mW/mm2. (i) Contour map plot showing total overlap volume from 4 μ-ILED co-activation for different input optical power and intensity thresholds for opsin variants.

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Extended Data Fig. 5 Numerical and experimental assessments of heat accumulation to guide stimulation parameters.

(a) Left, image of micro-fabricated thermistor for measuring temperature; middle, schematic illustration of the experimental setup for measuring heat accumulation in the structures surrounding the optical-neural interface; right, image of experimental setup, with wires connected to the thermistors to collect resistance value. Scale bar: 500 µm. (b) Calibration curves of thermistors used in this study for measuring the temperature increase on the surface of skull (left), on the surface of brain (middle), and below the µ-ILED (right). Each dot represents one resistance measurement at one temperature measurement. The best-fit line represents the linear regression between resistance and temperature. n = 1 representative thermistor at each location (skull, brain, or µ-ILED). (c) Simulated (Top) and measured (bottom) results of temperature increase below the µ-ILED for 3 s μ-ILED operation. The temperature increases in a 20 × 300 × 300 µm3 volume, 100 µm below the μ-ILED surface, was output from the numerical model. The thermistor was manually placed and adhered to the μ-ILED surface, followed by a complete procedure of μ-ILED array fabrication. Measured data are presented as mean ± s.e.m. n = 3 technical replicates. Technical replicates are included to account for fluctuations in environmental temperature. (d) Left, simulated maximal temperature increases on the skull surface during μ-ILED operation with 10% duty cycle at varying frequencies. Right, same as left, but for varying duty cycles at 10 Hz. The finite element node with maximal temperature increase (max. node) was selected for plotting. (e) Same as (d), but for brain surface. (f) Left, measured temperature increases on the skull surface during μ-ILED operation with 10% duty cycle at varying frequencies. Right, same as left, but for varying duty cycles at 10 Hz. (g) Same as (f), but for brain surface. (h) Heatmap showing simulated heat production during a single μ-ILED operation in air at 100% duty cycle and 1.93 mW power for 10 s. Left, fPCB array patch without modification. Right, fPCB array patch coated with 100 µm Cu on the rear side. (i) Simulated temperature increases with or without device surface modification with 100 µm Cu.

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Extended Data Fig. 6 Opsin expression and surgical procedures.

(a) Representative images of ChrimsonR expression in the targeted cortical regions from one mouse. The experiment was repeated independently on 8 animals. (b) Heatmap showing the spread of viral expression across cortical regions. n = 8 animals. (c) Example image showing the distribution of tdT, vglut1, and vgat transcripts in cortical column of somatosensory cortex limb representation. L1, 1 to 100 μm, L2/3, 100 to 300 μm, L4, 300 to 400 μm, L5a, 400 to 500 μm, L5b, 500 to 700 μm, L6a, 700 to 900 μm. Scale bar, 25 μm. The experiment was repeated independently on 16 brain slices from 4 animals. (d) Schematic illustration of critical steps of surgical implantation.

Extended Data Fig. 7 Extracellular recording in ChrimsonR-negative mice and evoked local field potentials (LFPs) in ChrimsonR-positive mice.

(a) Schematic of in vivo extracellular electrophysiology recordings in ChrimsonR-negative mice. (b) Left, raw recording traces showing the electrical artifacts during the stimulation period at different distances (1,000-5,000 μm) from μ-ILED. Right, processed traces using a custom-modified version of the Estimation and Removal of Array Artifacts via Sequential Principal Components Regression (ERAASR) algorithm. Lines and shaded areas represent mean ± s.e.m. (c) Schematic of the in vivo extracellular electrophysiology recording setup for evoked LFPs in ChrimsonR positive mice. (d) Traces showing evoked LFPs averaged across all electrodes of the MEA at different distances from the μ-ILED with varying input optical power. Lines and shaded areas represent mean ± s.e.m. (e) Summary of evoked LFP magnitudes at different distances from the μ-ILED under varying input optical power levels. Lines represent the mean from all electrodes, and dots indicate individual electrode measurements. (f) Same as (e), but for latency to valley in LFP waveforms. Dots represent individual electrodes; lines indicate the mean.

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Extended Data Fig. 8 Operant learning performance, reaction time analysis, and open-field locomotion analysis.

(a) Number of sessions of 100 trials each across three levels of the task to reach the criterion of 80% success rate for animals expressing ChrimsonR (session median Level 1-12, Level 2-6, Level 3-12). One-way ANOVA, p = 0.5750, F (2, 28) = 0.5645; Sidak’s multiple comparisons test, Level 1 vs Level 2, p = 0.7258; Level 2 vs Level 3, p = 0.7596; Level 1 vs Level 3, p > 0.9999; Level 1, n = 11 animals; Level 2, n = 10 animals; Level 3, n = 10 animals. Data are presented as mean ± s.e.m. Dots represent individual animals. (b) Summary data showing the total training length to complete Levels 1-3 and the number of sessions per day. Dark orange, group average; pale orange, individual trajectories; symbols mark the day individual animals reached criterion. (c) Timeline from water restriction to the end of training. Open-field locomotion was measured at the start of water restriction, before device implantation, and 2, 5, and 10 days post-implantation. (d) Left, average movement speed in an open-field arena across days, one-way ANOVA, F (1.739, 5.217) = 1.632, p = 0.2773. Middle, same as left, but for acceleration, one-way ANOVA, F (2.154, 6.461) = 2.358, p = 0.1693. Right, same as left, but for exploration rate, one-way ANOVA, F (1.497, 4.491) = 3.271, p = 0.1354. Box plots show median (line), 25th and 75th percentiles (bounds of box), minimum and maximum values (whiskers). n = 4 animals. (e) Scatter plots showing reaction times in all trials for the Level 3 task as a function of total cortical distance between stimulated digits. (f) Summary data of cumulative distributions of reaction times for all animals.

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Extended Data Fig. 9 Spatiotemporal analysis of behavioral trajectories for individual animals.

(a) Line plots showing success rate as a function of spatial distance for 10 individual animals in the Level 3 task. The number of trials with correct or incorrect choices in each bin of spatial distance is plotted in the histogram. (b) Heatmap showing success rate for randomized non-target sequences grouped by specific stimulation locations at the first to the fourth stimulation digit in 10 ChrimsonR expressing animals. Red squares indicate the target stimulation sequence for each animal. (c) Two-sided Pearson’s correlation analysis of spatial distance and success rate based on stimulation digit for individual animals. Dashed lines indicate 95% confidence intervals.

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Extended Data Fig. 10 Cue-discrimination of varying spatiotemporal patterns.

(a) Left, schematic illustration of probing sequences with 75% (3 stimulation digits) and 50% similarity (2 stimulation digits) to the target sequences. Right, summary data showing the success rate of all probing sessions from all animals. 75% overlap, 0.6077 ± 0.0146; 50% overlap, 0.7312 ± 0.0203; Two-sided unpaired t-test, p < 0.0001. 75% overlap: n = 75 sessions from 5 animals; 50% overlap: n = 41 sessions from 5 animals. (b) Left, schematic illustration of probing experiments with reversed sequences. Right, summary data showing success rate of all probing sessions from all animals, 0.7347 ± 0.00338; One sample t-test vs 0.5, p < 0.0001; n = 15 sessions from 5 animals. (c) Schematic illustrating single site stimulation task for mice to discriminate neighboring stimulation either on the same hemisphere or on the contralateral hemisphere. (d) Left, example of target (stimulation on a single cortical region) and non-target stimulation on a neighboring site. Middle, summary data showing discrimination performance on neighboring stimulation sites. Ipsilateral, 0.6151 ± 0.0168; one sample t-test vs 0.5, p < 0.0001; n = 58 sessions from 5 animals. Contralateral, 0.7081 ± 0.0257; one sample t-test vs 0.5, p < 0.0001; n = 20 sessions from 3 animals; Two-sided unpaired t-test, ipsi vs contra, p = 0.0053. Right, summary data showing discrimination of ipsilateral neighboring stimulation, 1st column (motor vs limb), 0.6788 ± 0.0542; one sample t-test vs 0.5, p = 0.0132. 2nd column (limb vs trunk), 0.6147 ± 0.0471; one sample t-test vs 0.5, p = 0.0288. 3rd column (trunk vs visual), 0.6007 ± 0.0149; one sample t-test vs 0.5, p < 0.0001. One-way ANOVA, Sidak’s multiple comparisons test, 1st vs 2nd column, p = 0.5884; 1st vs 3rd column, p = 0.3287; 2nd vs 3rd column, p = 0.9792. (e) Left, example of a target and a non-target stimulation, where one digit was switched to a neighboring site. Middle, summary data showing no significant difference in discrimination performance when the switched neighboring digit was in the ipsilateral or contralateral hemisphere. Ipsilateral, 0.6132 ± 0.0239; one sample t-test vs 0.5, p < 0.0001; n = 24 sessions from 3 animals. Contralateral, 0.5935 ± 0.0249; one sample t-test vs 0.5, p = 0.0013; n = 20 sessions from 3 animals; Two-sided unpaired t-test, ipsi vs contra, p = 0.5742. Right, summary data showing discrimination performance when the switched digit was in the ipsilateral hemisphere, 1st column (motor vs limb), 0.5930 ± 0.0462; one sample t-test vs 0.5, p = 0.0786. 2nd column (limb vs trunk), 0.6600 ± 0.0620; one sample t-test vs 0.5, p = 0.0494. 3rd column (trunk vs visual), 0.6022 ± 0.0196; one sample t-test vs 0.5, p = 0.0008. One-way ANOVA, Sidak’s multiple comparisons test, 1st vs 2nd column, p = 0.6523; 1st vs 3rd column, p = 0.9978; 2nd vs 3rd column, p = 0.7640. (f) Left, example of a target stimulation for mice to discriminate the target against each individual digit within it. Right, summary data of performance, 1st stim. (target vs 1st digit of the target), 0.6893 ± 0.0196; one sample t-test vs 0.5, p < 0.0001. 2nd stim., 0.7911 ± 0.0703; one sample t-test vs 0.5, p = 0.0033. 3rd stim., 0.7600 ± 0.0428; one sample t-test vs 0.5, p = 0.0017. 4th stim., 0.9020 ± 0.0132; one sample t-test vs 0.5, p < 0.0001. One-way ANOVA, Sidak’s multiple comparisons test, 1st vs 2nd stim., p = 0.3006; 1st vs 3rd stim., p = 0.8439; 1st vs 4th stim., p = 0.0164; 2nd vs 3rd stim., p = 0.9991; 2nd vs 4th stim., p = 0.6932; 3rd vs 4th stim., p = 0.5151. (g) Left, example of a target for mice to discriminate the target against its first digit, with the initiation site across all cortical regions. Right, summary data of performance, motor (target initiates from the motor cortex), 0.7000 ± 0.0397; one sample t-test vs 0.5, p = 0.0005. somalimb, 0.6355 ± 0.0539; one sample t-test vs 0.5, p = 0.0031. somatrunk, 0.6625 ± 0.0309; one sample t-test vs 0.5, p < 0.0001. visual, 0.7497 ± 0.0339; one sample t-test vs 0.5, p < 0.0001. One-way ANOVA, Sidak’s multiple comparisons test, motor vs somalimb, p = 0.8672; motor vs somatrunk, p = 0.9840; motor vs visual, p = 0.9414; somalimb vs somatrunk, p = 0.9972; somalimb vs visual, p = 0.2442; somatrunk vs visual, p = 0.4324. (h) Left, example showing mice discriminate stimulations on the same site with different durations. Right, summary data of performance. 0.3 s (1.2 s target vs 0.3 s like-target), 0.7256 ± 0.0384; one sample t-test vs 0.5, p = 0.0004. 0.6 s, 0.7088 ± 0.0396; one sample t-test vs 0.5, p = 0.0012. 0.9 s, 0.6087 ± 0.0642; one sample t-test vs 0.5, p < 0.0001. One-way ANOVA, Sidak’s multiple comparisons test, 0.3 s vs 0.6 s, p = 0.9503; 0.3 s vs 0.9 s, p = 0.0003; 0.6 s vs 0.9 s, p = 0.0031. All data are presented as mean ± s.e.m. Dots represent individual sessions of 100 trials. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 for two-sided t-test and multiple comparisons. #p < 0.05, ##p < 0.01, ###p < 0.001, ####p < 0.0001 for one-sample t-test vs 0.5.

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Wu, M., Yang, Y., Zhang, J. et al. Patterned wireless transcranial optogenetics generates artificial perception. Nat Neurosci (2025). https://doi.org/10.1038/s41593-025-02127-6

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