Individualizing repetitive visual stimulation to enhance neuromodulation
DOI: Pending DOI
Synopsis
Growing evidence supports the view that repetitive visual stimulation (RVS) has potential as a noninvasive neuromodulation technique. Although RVS may also engage other neural mechanisms, this project focuses primarily on the long-term potentiation-like effects it exerts on the visual cortex. Our primary goal in this project is to investigate whether stronger effects can be obtained by individualizing the RVS frequency. Earlier work showed that the largest potentiating effects of RVS occur for frequencies near 10 Hz. Because the human visual cortex has strong intrinsic oscillations in this range, we hypothesized that matching the RVS frequency to a participant’s intrinsic oscillation (their Individual Preferred Frequency; IPF) could maximize the neuromodulatory effect. Initial behavioral findings support our hypothesis; participants’ contrast sensitivity was higher after RVS at their IPF than after RVS at IPF + 2 Hz. This supports the view that individualizing RVS parameters enhances its neuromodulatory effects.
Background
Recent interest in 40 Hz visual stimulation has increased interest in repetitive visual stimulation (RVS) as a neuromodulation technique [1,2]. RVS has been a longstanding technique to study steady-state visually evoked potentials [3,4,5] (SSVEPs). When a visual stimulus is flickering at a constant frequency, populations of neurons respond. Based on activity-dependent plasticity [6,7,8,9,10], RVS enhances the response and may lead to long term changes if exposed for a longer period. However, there are many parameters to consider when utilizing RVS, such as stimulation frequency. In our work, we have found that RVS near 10 Hz often results in potentiation. Combining this with the well-established finding that most individuals have a dominant oscillation near 10 Hz in the visual cortex[11,12,13,14] leads to our hypothesis that RVS with a frequency that matches an individual’s dominant intrinsic oscillation could lead to the strongest potentiation.
Methods
The preliminary analysis reported here includes data obtained from 25 healthy participants (M/F: 15/10) with an age range of 31.08.
Using 256 channel high-density EEG, we first determined participants’ resting individual preferred frequency (rIPF; the frequency between 5 to 15 Hz that generates a peak in the power spectrum during eyes-open resting state). First, we averaged the signal over eight occipital channels around Oz. Then, we performed a time-frequency analysis using complex Morlet wavelets [15]. Since the signal is nonstationary, we divided the signal into one-second epochs, and removed any epoch that had unusually large, artifactual, broadband power. For each epoch, we estimated the peak frequency using the MATLAB function findpeaks and, finally, we defined rIPF as the peak frequency that occurred most frequently across the resting state session. To assess the stability of the rIPF, we performed a Pearson’s correlation test to compare the rIPF calculated at the beginning and end of a session, as well as across sessions.
To extract the steady-state neural responses to visual flicker, we estimated power spectra per epoch and channel via multitaper spectral analysis and used a used a peak-finding algorithm within a window around the visual stimulation frequency to determine the peak power of the neural response (signal). We then computed the background power as the mean power at frequency bins neighboring the peak (noise). From these, we computed the signal-to-noise ratio (SNR) as the ratio of the signal to the background noise power.
To assess behavioral changes, participants completed a visual detection task before and after receiving RVS. Participants were presented with a light patch that appeared at one of four quadrants on the screen and were instructed to press a button corresponding to the location of the patch (a 4 alternative forced choice). The contrast of the patch varied across trials using a Bayesian adaptive method to estimate subjects’ contrast detection threshold. We quantified reaction time as the time between the button press and the patch’s appearance on the screen.
Results
The rIPF was highly stable within subjects, suggesting that it is a trait-like property. We confirmed that most participants had the highest SNRs for 10 Hz RVS (from a range of [5 10 20 40] Hz). In addition, we found that participants whose rIPF was close to 10 Hz also had higher SNRs at 10Hz. Most importantly, we found that contrast detection thresholds were lower (i.e., improved) after RVS at their rIPF compared to RVS at rIPF + 2 Hz.
Discussion
RVS offers a simple and non-invasive neuromodulation technique. Current research has shown promising applications for Alzheimer's [1] and amblyopia [2]. Our findings suggest that individualization of RVS parameters can help to improve the neuromodulatory effects.
Disclosures
Research is supported by the NIGMS of the National Institutes of Health under award number 5T32GM140951NL, and the Eye Institute of the National Institutes of Health (R01EY032744).
References
1. Iaccarino, H. F., Singer, A. C., Martorell, A. J., Rudenko, A., Gao, F., Gillingham, T. Z., Mathys, H., Seo, J., Kritskiy, O., Abdurrob, F., Adaikkan, C., Canter, R. G., Rueda, R., Brown, E. N., Boyden, E. S., & Tsai, L.-H. (2016). Gamma frequency entrainment attenuates amyloid load and modifies microglia. Nature, 540(7632), 230–235. https://doi.org/10.1038/nature20587
2. Montey, K. L., Eaton, N. C., & Quinlan, E. M. (2013). Repetitive visual stimulation enhances recovery from severe amblyopia. Learning & Memory, 20(6), 311–317. https://doi.org/10.1101/lm.030361.113
3. Ding, J., Sperling, G., & Srinivasan, R. (2006). Attentional modulation of SSVEP power depends on the network tagged by the flicker frequency. Cerebral Cortex, 16(7), 1016–1029. https://doi.org/10.1093/cercor/bhj044
4. Regan, D. (1966). Some characteristics of average steady-state and transient responses evoked by modulated light. Electroencephalography and Clinical Neurophysiology, 20(3), 238–248. https://doi.org/10.1016/0013-4694(66)90088-5
5. Sakurada, T., Kawase, T., Komatsu, T., & Kansaku, K. (2015). Use of high-frequency visual stimuli above the critical flicker frequency in a SSVEP-based BMI. Clinical Neurophysiology, 126(10), 1972–1978. https://doi.org/10.1016/J.CLINPH.2014.12.010
6. Bliss, T. v, & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. The Journal of Physiology, 232(2), 331–356. https://doi.org/10.1113/jphysiol.1973.sp010273
7. Clapp, W. C., Hamm, J. P., Kirk, I. J., & Teyler, T. J. (2012). Translating long-term potentiation from animals to humans: a novel method for noninvasive assessment of cortical plasticity. Biological Psychiatry, 71(6), 496–502. https://doi.org/10.1016/j.biopsych.2011.08.021
8. Monte-Silva, K., Kuo, M.-F., Hessenthaler, S., Fresnoza, S., Liebetanz, D., Paulus, W., & Nitsche, M. A. (2013). Induction of late LTP-like plasticity in the human motor cortex by repeated non-invasive brain stimulation. Brain Stimulation, 6(3), 424–432. https://doi.org/10.1016/j.brs.2012.04.011
9. Ross, R. M., McNair, N. A., Fairhall, S. L., Clapp, W. C., Hamm, J. P., Teyler, T. J., & Kirk, I. J. (2008). Induction of orientation-specific LTP-like changes in human visual evoked potentials by rapid sensory stimulation. Brain Research Bulletin, 76(1–2), 97–101. https://doi.org/10.1016/j.brainresbull.2008.01.021
10. Teyler, T. J., Hamm, J. P., Clapp, W. C., Johnson, B. W., Corballis, M. C., & Kirk, I. J. (2005). Long‐term potentiation of human visual evoked responses. European Journal of Neuroscience, 21(7), 2045–2050. https://doi.org/10.1111/j.1460-9568.2005.04007.x
11. Bollimunta, A., Chen, Y., Schroeder, C. E., & Ding, M. (2008). Neuronal mechanisms of cortical alpha oscillations in awake-behaving macaques. Journal of Neuroscience, 28(40), 9976–9988. https://doi.org/10.1523/JNEUROSCI.2699-08.2008
12. Grandy, T. H., Werkle‐Bergner, M., Chicherio, C., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2013). Peak individual alpha frequency qualifies as a stable neurophysiological trait marker in healthy younger and older adults. Psychophysiology, 50(6), 570–582. https://doi.org/10.1111/psyp.12043
13. Halgren, M., Ulbert, I., Bastuji, H., Fabó, D., Erőss, L., Rey, M., Devinsky, O., Doyle, W. K., Mak-McCully, R., Halgren, E., Wittner, L., Chauvel, P., Heit, G., Eskandar, E., Mandell, A., & Cash, S. S. (2019). The generation and propagation of the human alpha rhythm. Proceedings of the National Academy of Sciences of the United States of America, 116(47), 23772–23782. https://doi.org/10.1073/pnas.1913092116
14. Lopes, F. H., Silva, D. A., & Storm Van Leeuwen, W. (1977). TIlE CORTICAL SOURCE OF THE ALPHA RHYTHM. In ~reurosci’ence Letters (Vol. 6).
15. Cohen, M. X. (2019). A better way to define and describe Morlet wavelets for time-frequency analysis. NeuroImage, 199, 81–86. https://doi.org/10.1016/j.neuroimage.2019.05.048