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:

Forty-hertz light stimulation does not entrain native gamma oscillations in Alzheimer’s disease model mice

Nature Neuroscience volume 26, pages 570–578 (2023)Cite this article

Subjects

Abstract

There is a demand for noninvasive methods to ameliorate disease. We investigated whether 40-Hz flickering light entrains gamma oscillations and suppresses amyloid-β in the brains of APP/PS1 and 5xFAD mouse models of Alzheimer’s disease. We used multisite silicon probe recording in the visual cortex, entorhinal cortex or the hippocampus and found that 40-Hz flickering simulation did not engage native gamma oscillations in these regions. Additionally, spike responses in the hippocampus were weak, suggesting 40-Hz light does not effectively entrain deep structures. Mice avoided 40-Hz flickering light, associated with elevated cholinergic activity in the hippocampus. We found no reliable changes in plaque count or microglia morphology by either immunohistochemistry or in vivo two-photon imaging following 40-Hz stimulation, nor reduced levels of amyloid-β 40/42. Thus, visual flicker stimulation may not be a viable mechanism for modulating activity in deep structures.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Effect of acute 40-Hz visual simulation on Aβ load and microglia.
Fig. 2: Effect of chronic 7-d 40-Hz visual simulation.
Fig. 3: In vivo monitoring of plaques and microglia.
Fig. 4: A 40-Hz visual simulation does not engage native gamma oscillations.
Fig. 5: A 40-Hz flickering stimulation is aversive.

Similar content being viewed by others

Data availability

The data that support the main findings of this study are publicly available on the Buzsáki Lab web page (https://buzsakilab.com/wp/resources/). Supplementary data used from the Allen Brain Institute can be found at https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html. Source data are provided with this paper.

Code availability

The code used for this study was adapted from the buzcode repository (https://github.com/buzsakilab/buzcode).

References

  1. Llinás, R. R., Ribary, U., Jeanmonod, D., Kronberg, E. & Mitra, P. P. Thalamocortical dysrhythmia: a neurological and neuropsychiatric syndrome characterized by magnetoencephalography. Proc. Natl Acad. Sci. USA 96, 15222–15227 (1999).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Krook-Magnuson, E., Gelinas, J. N., Soltesz, I. & Buzsáki, G. Neuroelectronics and biooptics: closed-loop technologies in neurological disorders. JAMA Neurol. 72, 823–829 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Iaccarino, H. F. et al. Author correction: gamma frequency entrainment attenuates amyloid load and modifies microglia. Nature 562, E1 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Adaikkan, C. et al. Gamma entrainment binds higher-order brain regions and offers neuroprotection. Neuron 102, 929–943.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Martorell, A. J. et al. Multi-sensory gamma stimulation ameliorates Alzheimer’s-associated pathology and improves cognition. Cell 177, 256–271.e22 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shen, Q. et al. Gamma frequency light flicker regulates amyloid precursor protein trafficking for reducing β-amyloid load in Alzheimer’s disease model. Aging Cell 21, e13573 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Park, S. S. et al. Combined effects of aerobic exercise and 40-Hz light flicker exposure on early cognitive impairments in Alzheimer’s disease of 3×Tg mice. J. Appl. Physiol. 132, 1054–1068 (2022).

  8. Yao, Y. et al. Non-invasive 40-Hz light flicker ameliorates Alzheimer’s-associated rhythm disorder via regulating central circadian clock in mice. Front. Physiol. 11, 294 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Bero, A. W. et al. Neuronal activity regulates the regional vulnerability to amyloid-β deposition. Nat. Neurosci. 14, 750–756 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Chan, D. et al. Gamma frequency sensory timulation in probable mild Alzheimer’s dementia patients: results of a preliminary clinical trial. PLoS ONE 17, e0278412 (2022).

  11. Park, S. S. et al. Physical exercise during exposure to 40-Hz light flicker improves cognitive functions in the 3xTg mouse model of Alzheimer’s disease. Alzheimers Res. Ther. 12, 62 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zheng, L. et al. Rhythmic light flicker rescues hippocampal low gamma and protects ischemic neurons by enhancing presynaptic plasticity. Nat. Commun. 11, 3012 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Etter, G. et al. Optogenetic gamma stimulation rescues memory impairments in an Alzheimer’s disease mouse model. Nat. Commun. 10, 5322 (2019).

  14. Aronov, D., Nevers, R. & Tank, D. W. Mapping of a non-spatial dimension by the hippocampal-entorhinal circuit. Nature 543, 719–722 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Thompson, R. F. The neurobiology of learning and memory. Science 233, 941–947 (1986).

    Article  CAS  PubMed  Google Scholar 

  16. Tiitinen, H. et al. Selective attention enhances the auditory 40-Hz transient response in humans. Nature 364, 59–60 (1993).

    Article  CAS  PubMed  Google Scholar 

  17. Singer, A. C. et al. Noninvasive 40-Hz light flicker to recruit microglia and reduce amyloid beta load. Nat. Protoc. 13, 1850–1868 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Bolte, S. & Cordelières, F. P. A guided tour into subcellular colocalization analysis in light microscopy. J. Microsc. 224, 213–232 (2006).

    Article  CAS  PubMed  Google Scholar 

  19. Forner, S. et al. Systematic phenotyping and characterization of the 5xFAD mouse model of Alzheimer’s disease. Sci. Data 8, 270 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Thacker, J. S. et al. Unlocking the brain: a new method for western blot protein detection from fixed brain tissue. J. Neurosci. Methods 348, 108995 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Senzai, Y., Fernandez-Ruiz, A. & Buzsáki, G. Layer-specific physiological features and interlaminar interactions in the primary visual cortex of the mouse. Neuron 101, 500–513.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Einstein, M. C., Polack, P. O., Tran, D. T. & Golshani, P. Visually evoked 3–5 Hz membrane potential oscillations reduce the responsiveness of visual cortex neurons in awake behaving mice. J. Neurosci. 37, 5084–5098 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Nitzan, N., Swanson, R., Schmitz, D. & Buzsáki, G. Brain-wide interactions during hippocampal sharp wave ripples. Proc. Natl Acad. Sci. USA 119, e2200931119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Steinmetz, N. A., Zatka-Haas, P., Carandini, M. & Harris, K. D. Distributed coding of choice, action and engagement across the mouse brain. Nature 576, 266–273 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lyskov, E., Ponomarev, V., Sandström, M., Mild, K. H. & Medvedev, S. Steady-state visual evoked potentials to computer monitor flicker. Int. J. Psychophysiol. 28, 285–290 (1998).

    Article  CAS  PubMed  Google Scholar 

  26. Williams, P. E., Mechler, F., Gordon, J., Shapley, R. & Hawken, M. J. Entrainment to video displays in primary visual cortex of macaque and humans. J. Neurosci. 24, 8278–8288 (2004); erratum 24, 10034 (2004).

  27. Krolak-Salmon, P. et al. Human lateral geniculate nucleus and visual cortex respond to screen flicker. Ann. Neurol. 53, 73–80 (2003).

    Article  PubMed  Google Scholar 

  28. Lee, K. et al. Optimal flickering light stimulation for entraining gamma waves in the human brain. Sci. Rep. 11, 16206 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Jing, M. et al. An optimized acetylcholine sensor for monitoring in vivo cholinergic activity. Nat. Methods 17, 1139–1146 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Oliva, A., Fernández-Ruiz, A., Fermino de Oliveira, E. & Buzsáki, G. Origin of gamma frequency power during hippocampal sharp-wave ripples. Cell Rep. 25, 1693–1700.e4 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Duecker, K., Gutteling, T. P., Herrmann, C. S. & Jensen, O. No evidence for entrainment: endogenous gamma oscillations and rhythmic flicker responses coexist in visual cortex. J. Neurosci. 41, 6684–6698 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wang, X. J. & Buzsáki, G. Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J. Neurosci. 16, 6402–6413 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fernández-Ruiz, A. et al. Gamma rhythm communication between entorhinal cortex and dentate gyrus neuronal assemblies. Science 372, eabf3119 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Berman, S. M., Greenhouse, D. S., Bailey, I. L., Clear, R. D. & Raasch, T. W. Human electroretinogram responses to video displays, fluorescent lighting, and other high frequency sources. Optom. Vis. Sci. 68, 645–662 (1991).

    Article  CAS  PubMed  Google Scholar 

  35. Herrmann, C. S. Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena. Exp. Brain Res. 137, 346–353 (2001).

    Article  CAS  PubMed  Google Scholar 

  36. Fylan, F. & Harding, G. F. The effect of television frame rate on EEG abnormalities in photosensitive and pattern-sensitive epilepsy. Epilepsia 38, 1124–1131 (1997); erratum 39, 453 (1998).

  37. Gur, M. & Snodderly, D. M. A dissociation between brain activity and perception: chromatically opponent cortical neurons signal chromatic flicker that is not perceived. Vis. Res. 37, 377–382 (1997).

    Article  CAS  PubMed  Google Scholar 

  38. Ghose, G. M. & Freeman, R. D. Oscillatory discharge in the visual system: does it have a functional role? J. Neurophysiol. 68, 1558–1574 (1992).

    Article  CAS  PubMed  Google Scholar 

  39. Martin, P. R. & Teoh, H. J. Effects of visual stimuli and a stressor on head pain. Headache 39, 705–715 (1999).

    Article  CAS  PubMed  Google Scholar 

  40. Gray, C. M. & Singer, W. Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl Acad. Sci. USA 86, 1698–1702 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Britten, K. H., Shadlen, M. N., Newsome, W. T. & Movshon, J. A. Responses of neurons in macaque MT to stochastic motion signals. Vis. Neurosci. 10, 1157–1169 (1993).

    Article  CAS  PubMed  Google Scholar 

  42. Pesaran, B. et al. Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nat. Neurosci. 21, 903–919 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Stern, Y. Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurol. 11, 1006–1012 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Moe, K. E., Vitiello, M. V., Larsen, L. H. & Prinz, P. N. Symposium: cognitive processes and sleep disturbances: sleep/wake patterns in Alzheimer’s disease: relationships with cognition and function. J. Sleep Res. 4, 15–20 (1995).

    Article  CAS  PubMed  Google Scholar 

  45. Mander, B. A. et al. Prefrontal atrophy, disrupted NREM slow waves and impaired hippocampal-dependent memory in aging. Nat. Neurosci. 16, 357–364 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lucey, B. P. et al. Associations between β-amyloid kinetics and the β-amyloid diurnal pattern in the central nervous system. JAMA Neurol. 74, 207–215 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kastanenka, K. V. et al. Frequency-dependent exacerbation of Alzheimer’s disease neuropathophysiology. Sci. Rep. 9, 8964 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Kastanenka, K. V. et al. Optogenetic restoration of disrupted slow oscillations halts amyloid deposition and restores calcium homeostasis in an animal model of Alzheimer’s disease. PLoS ONE 12, e0170275 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Giovannetti, E. A. et al. Restoring memory by optogenetic synchronization of hippocampus oscillations in an Alzheimer’s disease mouse model. Preprint at bioRxiv https://doi.org/10.1101/363820 (2018).

  50. Wu, J. W. et al. Neuronal activity enhances tau propagation and tau pathology in vivo. Nat. Neurosci. 19, 1085–1092 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Venturino, A. et al. Microglia enable mature perineuronal nets disassembly upon anesthetic ketamine exposure or 60-Hz light entrainment in the healthy brain. Cell Rep. 36, 109313 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ismail, R. et al. The effect of 40-Hz light therapy on amyloid load in patients with prodromal and clinical Alzheimer’s disease. Int. J. Alzheimers Dis. 30, 6852303 (2018).

    Google Scholar 

  53. Clarke, D., Crombag, H. S. & Hall, C. N. An open-source pipeline for analysing changes in microglial morphology. Open Biol. 11, 210045 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Baik, S. H., Kang, S., Son, S. M. & Mook-Jung, I. Microglia contributes to plaque growth by cell death due to uptake of amyloid β in the brain of Alzheimer’s disease mouse model. Glia 64, 2274–2290 (2016).

    Article  PubMed  Google Scholar 

  55. Yang, G., Pan, F., Parkhurst, C. N., Grutzendler, J. & Gan, W. B. Thinned-skull cranial window technique for long-term imaging of the cortex in live mice. Nat. Protoc. 5, 201–208 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pachitariu, M., Steinmetz, N., Kadir, S., Carandini, M. & Harris K. D. Fast and accurate spike sorting of high-channel count probes with KiloSort. in Advances in Neural Information Processing Systems 29 (eds Lee, D. et al.) (NIPS, 2016).

  57. Petersen, P. C., Siegle, J. H., Steinmetz, N. A., Mahallati, S. & Buzsáki, G. CellExplorer: a framework for visualizing and characterizing single neurons. Neuron. 109, 3594–3608.e2 (2021).

  58. Zar, J. H. Biostatistical Analysis 4th edn (Prentice Hall, 1999).

  59. Zhang, Y. et al. Cholinergic suppression of hippocampal sharp-wave ripples impairs working memory. Proc. Natl Acad. Sci. USA 118, e2016432118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank D. Adler, M. Valero, E. M. Sigurdsson, the Experimental Pathology research core at NYU and Life Canvas for their experimental support. This work was supported by NRSA grant no. 5TL1TR001447-07 (M.S.), a DFG Walter Benjamin fellowship (grant no. NI 2057/1-1) (N.N.), the Alzheimer’s Association grant no. AARFD-17-533584 (A.M.-A.), grant no. R01 AG075840 (M.J.S.), the Fisher Center for Alzheimer’s Research Foundation (M.J.S.) NIH grants no. MH122391 and no. U19 NS107616 (G.B.).

Author information

Authors and Affiliations

  1. Neuroscience Institute, Langone Medical Center, New York University, New York, NY, USA

    Marisol Soula, Yiyao Zhang, Annika Dhingra, Noam Nitzan & György Buzsáki

  2. Department of Physiology and Neuroscience, Langone Medical Center, New York University, New York, NY, USA

    Alejandro Martín-Ávila & György Buzsáki

  3. Skirball Institute of Biomolecular Medicine, Langone Medical Center, New York University, New York, NY, USA

    Alejandro Martín-Ávila & Wen-Biao Gan

  4. Department of Neurology and Psychiatry, Langone Medical Center, New York University, New York, NY, USA

    Martin J. Sadowski & György Buzsáki

  5. Department of Biochemistry and Molecular Pharmacology, Langone Medical Center, New York University, New York, NY, USA

    Martin J. Sadowski

Authors
  1. Marisol Soula
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alejandro Martín-Ávila
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yiyao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Annika Dhingra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Noam Nitzan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Martin J. Sadowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wen-Biao Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. György Buzsáki
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.S., M.J.S., W.-B.G. and G.B. designed the research. M.S., Y.Z. and A.M.-A. performed the research. M.S., A.D., Y.Z. and N.N. analyzed the data. G.B. and M.S. wrote the paper.

Corresponding author

Correspondence to György Buzsáki.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks the anonymous reviewers 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 Histological analysis.

a, b) Heatmaps of Aβ plaque load in various coronal sections in 5xFAD mice after sham (a) and 40-Hz flicker (b) stimulation. c) Representative coronal sections of Aβ expression (LifeCanvas Technologies, Cambridge, MA 02141). Was replicated in n = 2 mice. d) Posterior visual cortex section (marked by red dash lines), used for quantification of V1. e) Group difference (medians, interquartile ranges, maxima and minima) of the percent area occupied by plaques after sham (n = 11 mice) and 40-Hz flicker (n = 13 mice) stimulation (ns, not significant by Wilcoxon test: p = 0.12). f) Cortex plaque load variability among six cohorts (mean + /− s.d; two-sided Wilcoxon test; p = 0.915, p = 0.0368, p = 0.879, p = 0.988, p = 0.0878, p = 0.379; ns, not significant).

Source data

Extended Data Fig. 2 ELISA analysis by groups.

a, b) Group difference (medians, interquartile ranges, maxima and minima) Aβ40 peptide levels in the (a) hippocampus and (b) V1 separated by sex (ns, not significant; two-sided Wilcoxon test). (n = 7 male and n = 5 female 40-Hz treated; n = 7 male and n = 4 female non-treated controls) c, d) Group difference (medians, interquartile ranges, maxima and minima) Aβ42 peptide levels in the (c) hippocampus (p = 0.38; p = 1;p = 0.19; p = 0.19 two-sided Wilcoxon Test) and (d) V1 (p = 0.02; p = 0.46;p = 0.68; p = 0.73; two-sided Wilcoxon Test) separated by sex and ELISA test. To test the reliability and consistency of the ELISA method, we performed two separated analyses for Aβ42 from the same aliquot samples on different days separated by one week (sample 1, sample2). The corresponding aliquots in the two tests are connected by lines. (n = 7 male and n = 5 female 40-Hz treated; n = 7 male and n = 4 female non-treated controls) e) The correlation between the two ELISA tests in c and d. Two conclusions may be drawn from this repeated analysis. First, the two tests were correlated strongly and significantly (R = 0.78). Second, the values of the second ELISA test were, on average, lower. This latter observation suggests that some epitope degradation of the sample may take place with time. These findings points to further potential sources of variability across studies, which may have different delays between tissue processing and ELISA Analysis.

Source data

Extended Data Fig. 3 Allen Brain Institute 4-Hz light response.

a) Peristimulus time histograms showing the responses of individual neurons (bottom panels) and the average (mean ± s.e.m.) response per area, from primary visual cortex (VISp), the 3 major hippocampal areas (DG, dentate gyrus), the thalamus (TH) and midbrain (MB). b) Anatomical locations of 35,910 units included in the dataset, color coded according to their modulation index. c) Box and whisker plot (medians, interquartile ranges, maxima and minima) showing the distributions of modulation index in the different areas p = 0, p = 0, p = 0, p = 0, p = 0, p = 4.9e-14, p = 0.013,p = 0.999,p = 0.970, p = 8.97e-19, p = 0.0006, p = 0.0434, p = 0.940, p = 0.359, p = 0.962 for VISp-CA1, VISp-CA3, VISp-DG, VISp-TH, VISp-MB, CA1-CA3, CA1-DG, CA1-TH, CA1-MB, CA3-DG, CA3-TH, CA3-MB, DG-TH,DG-MB,TH-MB, respectively; KW; *p < 0.05, **p < 0.01, ***p < 0.001. d) Bar graph shows the fractions of significantly modulated cells per area (VISp: 1597/3439; CA1: 1225/5833; CA3:145/835; TH:2329/6249; MB: 638/1908) (n = 49 mice).

Source data

Extended Data Fig. 4 Electrophysiological methods.

a) Relationship between electrode impedance and 40-Hz power in the hippocampus. Note that artifactual 40-Hz power can occur at high impedance sites. b) Power spectra in a high impedance channel in the hippocampus during 40-Hz stimulation and no-stimulation epochs (10 s on, 10 s off). Note large peak at 40-Hz during stimulation. c) Example artifacts at a high-impedance channels (red traces) in the hippocampus show 40-Hz when the light is turned on. d) Comparison (medians, interquartile ranges, maxima and minima) of the two methods we used to quantify phase modulation of spikes: bootstrap and Rayleigh’s methods (Example from the hippocampus; ns, nonsignificant p = 0.193; two-sided Wilcoxon test). Vector length distribution. Vertical red lines separate nonsignificant and significant events. (59 sessions in 15 mice). e) Significant difference (medians, interquartile ranges, maxima and minima) in the vector lengths of neurons statistically modulated by 40-Hz in different brain regions. Note that despite very few CA1 and EC neurons show significant phase-locking to 40-Hz stimuli (Fig. 2), the few that do show comparable vector lengths in all three structures (***p < 0.001; V1:p = 3.12e-41;CA1: p = 1.6e-30; EC: p = 0.239; two-sided Wilcoxon test) (V1 = 14 sessions in 5 mice, EC = 7 sessions in 3 mice, CA1 = 59 sessions in 15 mice). f) Example raster plots of significantly modulated putative interneurons in CA1 (red) and EC (orange) regions. g) Separation of putative pyramidal cells from interneurons using spike duration and burst index. Bar graphs show the fraction of significantly modulated putative pyramidal cells and interneurons.

Source data

Extended Data Fig. 5 Firing rates changes during stead state driving at 40-Hz stimulation.

Firing rates of all neurons during onset and offset of 40-Hz trains every 10 second for the visual cortex (a; two-sided paired t-test; p = 0.0066), hippocampus (b; two-sided paired t-test; p = 0.2581), and entorhinal cortex (c; two-sided paired t-test; p = 0.2629), respectively. Bin size= 0.005 s. 40-Hz modulated cells are marked by color circles.

Source data

Extended Data Fig. 6 Aversive response to 40-Hz flicker and control Ach.

a) Time spent in the 40-Hz compartment versus in the compartment with continuous light (n = 14 mice). b) Ach response to continuous white light (mean + /− s.e.m; p = 0.59, two-tailed paired t-test; n = 5 mice). Note lack of sustained activity, in contrast to the sustained Ach activation with 40-Hz flickering light (Fig. 4).

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Heatmaps of Aβ plaque load in the whole brain of a 5xFAD mouse and a 40-Hz flicker light-treated mouse (method: LifeCanvas Technologies, Cambridge, MA 02141).

Representative video of two-photon z-stack before 40-Hz flicker and after 1 h of 40-Hz flicker stimulation. Bottom: RGB fluorescence scale in real time for both conditions. Green: Methoxy-X04. Red: Microglia.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

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

Soula, M., Martín-Ávila, A., Zhang, Y. et al. Forty-hertz light stimulation does not entrain native gamma oscillations in Alzheimer’s disease model mice. Nat Neurosci 26, 570–578 (2023). https://doi.org/10.1038/s41593-023-01270-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-023-01270-2

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