Modulation of Cortical Oscillations by Low-Frequency Direct Cortical Stimulation Is State-Dependent

Sankaraleengam Alagapan, Stephen L Schmidt, Jérémie Lefebvre, Eldad Hadar, Hae Won Shin, Flavio Frӧhlich, Sankaraleengam Alagapan, Stephen L Schmidt, Jérémie Lefebvre, Eldad Hadar, Hae Won Shin, Flavio Frӧhlich

Abstract

Cortical oscillations play a fundamental role in organizing large-scale functional brain networks. Noninvasive brain stimulation with temporally patterned waveforms such as repetitive transcranial magnetic stimulation (rTMS) and transcranial alternating current stimulation (tACS) have been proposed to modulate these oscillations. Thus, these stimulation modalities represent promising new approaches for the treatment of psychiatric illnesses in which these oscillations are impaired. However, the mechanism by which periodic brain stimulation alters endogenous oscillation dynamics is debated and appears to depend on brain state. Here, we demonstrate with a static model and a neural oscillator model that recurrent excitation in the thalamo-cortical circuit, together with recruitment of cortico-cortical connections, can explain the enhancement of oscillations by brain stimulation as a function of brain state. We then performed concurrent invasive recording and stimulation of the human cortical surface to elucidate the response of cortical oscillations to periodic stimulation and support the findings from the computational models. We found that (1) stimulation enhanced the targeted oscillation power, (2) this enhancement outlasted stimulation, and (3) the effect of stimulation depended on behavioral state. Together, our results show successful target engagement of oscillations by periodic brain stimulation and highlight the role of nonlinear interaction between endogenous network oscillations and stimulation. These mechanistic insights will contribute to the design of adaptive, more targeted stimulation paradigms.

Conflict of interest statement

The authors declare no competing financial interests.

Figures

Fig 1. Simple Static Model Explains State-Dependence.
Fig 1. Simple Static Model Explains State-Dependence.
(A) The endogenous oscillation is denoted as a pure sine wave, and the amplitude of the sine wave represents the strength of oscillation in the three states studied here. Dotted line denotes the threshold above which stimulation produces a change (denoted by arrow). When the change produced by stimulation is below the threshold, no change in the oscillation is observed. However, when the threshold is crossed, stimulation produces a response that decays with time. The bands denote the phases of the oscillation at which stimulation produces a change in the oscillation. For stronger oscillations (eyes-closed), the phases at which stimulation produces change are minimal. For very weak oscillations (task-engaged), stimulation produces change at all phases. (B) The function used to model the stimulation response. The stimulation response is modeled as the linear convolution between the stimulation pulse and this response function. (C) Convolution between stimulation pulses and stimulation response function. (D) Example traces illustrating the model behavior. The red lines at the bottom denote the timing of the stimulation pulses. The gray line denotes the stimulation response that is added to the oscillation waveform (black dashed line) to produce the stimulation effect (orange solid line). (E) Example traces produced using the model described in (A). In the eyes-closed state, stimulation-induced changes are minimal. In the eyes-open state, the stimulation-induced change is still constrained by the endogenous frequency. In the task-engaged state, stimulation produces change at all phases resulting in entrainment. Black dashed line represents the endogenous oscillation. Red solid line represents the waveform resulting from the addition of the stimulation waveform to the endogenous oscillation. (F) Top: Effect of varying endogenous oscillation strength. The change in power at the endogenous frequency increases until a certain limit and then decreases when the strength of oscillation relative to stimulation strength is high (violet line). The power at stimulation frequency (green line) decreases with increasing oscillation strength as stimulation effect is observed in a restricted range of phases. Bottom: Effect of varying stimulation strength for a given oscillation strength. As stimulation strength increases, the power at oscillation frequency increases until the strength relative to oscillation strength is high enough to cause increase in power at stimulation frequency. Beyond this, the increase in power at oscillation frequency is minimal, and power at stimulation frequency increases monotonically. The data shown in this figure are available online: http://dx.doi.org/10.5281/zenodo.45811
Fig 2. Computational Model Explains Outlasting Effects…
Fig 2. Computational Model Explains Outlasting Effects of Periodic Stimulation.
(A) Schematic of computational model that includes the different components and interactions among them. The region of the cortex being stimulated is denoted by a gray circle encompassing excitatory and inhibitory neurons. The model neurons exhibit reciprocal as well as recurrent connections. The cortico-thalamic and cortico-cortical interactions are also modeled to be reciprocal. (B) Membrane potential observed from excitatory neurons in the model shows task-dependent differences in stimulation effect. During the eyes-closed state (blue trace), a strong oscillation is observed in the alpha frequency range. Stimulation onset does not alter the dynamics significantly. In the eyes-open state (magenta trace), an oscillation is still observed in the alpha frequency range. However, the strength is decreased compared to the eyes-closed state. Stimulation onset causes the amplification of this oscillation, which persists after stimulation offset and then slowly decays. In the task-engaged state (yellow trace), no strong oscillation is observed due to the external inputs that model task-related input. In this state, stimulation causes the network to oscillate at the stimulation frequency, which persists and decays in the epoch after stimulation. (C) Spectral analysis of membrane potential reveals state-dependent effect of stimulation. Refer to S1 Fig for dynamics in the absence of cortico-thalamic and cortico-cortical interactions. The data shown in this figure are available online: http://dx.doi.org/10.5281/zenodo.45811
Fig 3. Electrode Locations and Artifact Suppression.
Fig 3. Electrode Locations and Artifact Suppression.
(A) Surface model of an atlas brain showing locations of electrodes over the parietal regions in each of the three patients. Signals measured from only these electrodes were used in the analysis. Refer to S2 Fig for the stimulation electrodes and electrodes over other regions. (B) Schematic of stimulation waveform used. Stimulation consisted of one biphasic pulse 400 μs in duration every 100 ms for 5 s. (C) Stimulation artifacts in representative sample traces from four electrodes (top). Enlarged portion denoted by the black line (bottom). Artifacts appear as periodic sharp deflections with stereotyped waveforms. (D) Traces from the same four electrodes as in (B) after the artifact suppression procedure. The artifacts are suppressed compared to the signal amplitude. (E) Spectrum of the fourth waveform in (B) showing peaks at 10 Hz and harmonics of 10 Hz corresponding to artifact waveform. Top: Full frequency range. Bottom: Zoom-in on low frequencies. (F) Spectra of the same waveform after artifact suppression confirming the effectiveness of the algorithm. The only remaining exogenous peak is at 60 Hz, caused by electric line noise.
Fig 4. State-Dependent Modulation by Periodic Stimulation.
Fig 4. State-Dependent Modulation by Periodic Stimulation.
(A) Power spectra in the epochs before stimulation (black trace), during stimulation (orange trace), and after stimulation (blue trace) for the three participants during the different states. Participant P001’s spectra showed no appreciable change in the eyes-closed state in the endogenous frequency band (violet shaded region) and minimal change in the stimulation frequency band (green shaded region) in eyes-closed state during stimulation. However, there was a change in dominant oscillation frequency from endogenous frequency to stimulation frequency in the task-engaged state. P005 showed no change in power at endogenous frequency in the eyes-closed state, while there was an increase in the eyes-open state. Power at stimulation frequency was decreased in both states. P008 showed an increase in both states in the endogenous and stimulation frequencies. (B) Mean modulation indices at the endogenous frequency (left) and the stimulation frequency (right) in the epochs during stimulation (top) and after stimulation (bottom) for each of the three participants. Bars denote standard error of mean. * denotes statistical significance (p < 0.05) from a paired t test. The data shown in this figure are available online: http://dx.doi.org/10.5281/zenodo.45811

References

    1. Buzsaki G, Draguhn A. Neuronal oscillations in cortical networks. Science. 2004;304(5679):1926–9. Epub 2004/06/26. 10.1126/science.1099745 .
    1. Berger H. Uber das Elektrenkephalogramm des Menschen. Archiv für Psychiatrie und Nervenkrankheiten. 1929;87(1):527–70.
    1. Pfurtscheller G, Stancak A Jr., Neuper C. Event-related synchronization (ERS) in the alpha band—an electrophysiological correlate of cortical idling: a review. International journal of psychophysiology: official journal of the International Organization of Psychophysiology. 1996;24(1–2):39–46. .
    1. Klimesch W. alpha-band oscillations, attention, and controlled access to stored information. Trends in cognitive sciences. 2012;16(12):606–17. 10.1016/j.tics.2012.10.007
    1. Sauseng P, Klimesch W, Heise KF, Gruber WR, Holz E, Karim AA, et al. Brain oscillatory substrates of visual short-term memory capacity. Current biology: CB. 2009;19(21):1846–52. 10.1016/j.cub.2009.08.062 .
    1. Romei V, Gross J, Thut G. On the role of prestimulus alpha rhythms over occipito-parietal areas in visual input regulation: correlation or causation? J Neurosci. 2010;30(25):8692–7. 10.1523/JNEUROSCI.0160-10.2010 .
    1. Thut G, Veniero D, Romei V, Miniussi C, Schyns P, Gross J. Rhythmic TMS causes local entrainment of natural oscillatory signatures. Curr Biol. 2011;21(14):1176–85. Epub 2011/07/05. 10.1016/j.cub.2011.05.049
    1. Zaehle T, Rach S, Herrmann CS. Transcranial alternating current stimulation enhances individual alpha activity in human EEG. PLoS ONE. 2010;5(11):e13766 10.1371/journal.pone.0013766
    1. Helfrich RF, Schneider TR, Rach S, Trautmann-Lengsfeld SA, Engel AK, Herrmann CS. Entrainment of brain oscillations by transcranial alternating current stimulation. Current biology: CB. 2014;24(3):333–9. 10.1016/j.cub.2013.12.041 .
    1. Cecere R, Rees G, Romei V. Individual differences in alpha frequency drive crossmodal illusory perception. Current biology: CB. 2015;25(2):231–5. 10.1016/j.cub.2014.11.034
    1. Uhlhaas PJ, Singer W. Neuronal dynamics and neuropsychiatric disorders: toward a translational paradigm for dysfunctional large-scale networks. Neuron. 2012;75(6):963–80. 10.1016/j.neuron.2012.09.004 .
    1. Uhlhaas PJ, Singer W. Abnormal neural oscillations and synchrony in schizophrenia. Nature reviews Neuroscience. 2010;11(2):100–13. 10.1038/nrn2774 .
    1. Woltering S, Jung J, Liu Z, Tannock R. Resting state EEG oscillatory power differences in ADHD college students and their peers. Behavioral and brain functions: BBF. 2012;8:60 10.1186/1744-9081-8-60
    1. George MS. Transcranial magnetic stimulation for the treatment of depression. Expert review of neurotherapeutics. 2010;10(11):1761–72. 10.1586/ern.10.95 .
    1. Fröhlich F. Experiments and models of cortical oscillations as a target for noninvasive brain stimulation. Progress in brain research. 2015;222:41–73. 10.1016/bs.pbr.2015.07.025 Epub 2015 Aug 28.
    1. Leuchter AF, Cook IA, Jin Y, Phillips B. The relationship between brain oscillatory activity and therapeutic effectiveness of transcranial magnetic stimulation in the treatment of major depressive disorder. Frontiers in human neuroscience. 2013;7:37 10.3389/fnhum.2013.00037
    1. Leuchter AF, Cook IA, Feifel D, Goethe JW, Husain M, Carpenter LL, et al. Efficacy and Safety of Low-field Synchronized Transcranial Magnetic Stimulation (sTMS) for Treatment of Major Depression. Brain stimulation. 2015;8(4):787–94. 10.1016/j.brs.2015.05.005 .
    1. Frohlich F. Tuning out the Blues—Thalamo-Cortical Rhythms as a Successful Target for Treating Depression. Brain stimulation. 2015. 10.1016/j.brs.2015.07.040 .
    1. Ali MM, Sellers KK, Frohlich F. Transcranial alternating current stimulation modulates large-scale cortical network activity by network resonance. J Neurosci. 2013;33(27):11262–75. 10.1523/JNEUROSCI.5867-12.2013 .
    1. Kutchko KM, Frohlich F. Emergence of metastable state dynamics in interconnected cortical networks with propagation delays. PLoS Comput Biol. 2013;9(10):e1003304 10.1371/journal.pcbi.1003304
    1. Reato D, Rahman A, Bikson M, Parra LC. Low-intensity electrical stimulation affects network dynamics by modulating population rate and spike timing. J Neurosci. 2010;30(45):15067–79. 10.1523/JNEUROSCI.2059-10.2010
    1. Frohlich F, McCormick DA. Endogenous electric fields may guide neocortical network activity. Neuron. 2010;67(1):129–43. 10.1016/j.neuron.2010.06.005
    1. Schmidt SL, Iyengar AK, Foulser AA, Boyle MR, Frohlich F. Endogenous cortical oscillations constrain neuromodulation by weak electric fields. Brain stimulation. 2014;7(6):878–89. 10.1016/j.brs.2014.07.033
    1. Hughes SW, Crunelli V. Thalamic mechanisms of EEG alpha rhythms and their pathological implications. The Neuroscientist: a review journal bringing neurobiology, neurology and psychiatry. 2005;11(4):357–72. 10.1177/1073858405277450 .
    1. Hughes SW, Lorincz M, Cope DW, Blethyn KL, Kekesi KA, Parri HR, et al. Synchronized oscillations at alpha and theta frequencies in the lateral geniculate nucleus. Neuron. 2004;42(2):253–68. .
    1. Lorincz ML, Kekesi KA, Juhasz G, Crunelli V, Hughes SW. Temporal framing of thalamic relay-mode firing by phasic inhibition during the alpha rhythm. Neuron. 2009;63(5):683–96. 10.1016/j.neuron.2009.08.012
    1. Neuling T, Rach S, Herrmann CS. Orchestrating neuronal networks: sustained after-effects of transcranial alternating current stimulation depend upon brain states. Frontiers in human neuroscience. 2013;7:161 10.3389/fnhum.2013.00161
    1. Vossen A, Gross J, Thut G. Alpha Power Increase After Transcranial Alternating Current Stimulation at Alpha Frequency (alpha-tACS) Reflects Plastic Changes Rather Than Entrainment. Brain stimulation. 2015;8(3):499–508. 10.1016/j.brs.2014.12.004
    1. Struber D, Rach S, Neuling T, Herrmann CS. On the possible role of stimulation duration for after-effects of transcranial alternating current stimulation. Front Cell Neurosci. 2015;9:311 10.3389/fncel.2015.00311
    1. Ojemann G, Ojemann J, Lettich E, Berger M. Cortical language localization in left, dominant hemisphere. An electrical stimulation mapping investigation in 117 patients. J Neurosurg. 1989;71(3):316–26. 10.3171/jns.1989.71.3.0316 .
    1. Gallentine WB, Mikati MA. Intraoperative electrocorticography and cortical stimulation in children. Journal of clinical neurophysiology: official publication of the American Electroencephalographic Society. 2009;26(2):95–108. 10.1097/WNP.0b013e3181a0339d .
    1. Klimesch W, Sauseng P, Hanslmayr S. EEG alpha oscillations: the inhibition-timing hypothesis. Brain research reviews. 2007;53(1):63–88. 10.1016/j.brainresrev.2006.06.003 .
    1. Jensen O, Mazaheri A. Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Frontiers in human neuroscience. 2010;4:186 10.3389/fnhum.2010.00186
    1. Mathewson KE, Lleras A, Beck DM, Fabiani M, Ro T, Gratton G. Pulsed out of awareness: EEG alpha oscillations represent a pulsed-inhibition of ongoing cortical processing. Frontiers in psychology. 2011;2:99 10.3389/fpsyg.2011.00099
    1. Thut G, Schyns PG, Gross J. Entrainment of perceptually relevant brain oscillations by non-invasive rhythmic stimulation of the human brain. Frontiers in psychology. 2011;2:170 10.3389/fpsyg.2011.00170
    1. Helfrich RF, Knepper H, Nolte G, Struber D, Rach S, Herrmann CS, et al. Selective modulation of interhemispheric functional connectivity by HD-tACS shapes perception. PLoS Biol. 2014;12(12):e1002031 10.1371/journal.pbio.1002031
    1. Feurra M, Pasqualetti P, Bianco G, Santarnecchi E, Rossi A, Rossi S. State-dependent effects of transcranial oscillatory currents on the motor system: what you think matters. J Neurosci. 2013;33(44):17483–9. 10.1523/JNEUROSCI.1414-13.2013 .
    1. Parvizi J, Jacques C, Foster BL, Witthoft N, Rangarajan V, Weiner KS, et al. Electrical stimulation of human fusiform face-selective regions distorts face perception. J Neurosci. 2012;32(43):14915–20. 10.1523/JNEUROSCI.2609-12.2012
    1. Parvizi J, Rangarajan V, Shirer WR, Desai N, Greicius MD. The will to persevere induced by electrical stimulation of the human cingulate gyrus. Neuron. 2013;80(6):1359–67. 10.1016/j.neuron.2013.10.057
    1. Rauschecker AM, Dastjerdi M, Weiner KS, Witthoft N, Chen J, Selimbeyoglu A, et al. Illusions of visual motion elicited by electrical stimulation of human MT complex. PLoS ONE. 2011;6(7):e21798 10.1371/journal.pone.0021798
    1. Koubeissi MZ, Bartolomei F, Beltagy A, Picard F. Electrical stimulation of a small brain area reversibly disrupts consciousness. Epilepsy & behavior: E&B. 2014;37:32–5. 10.1016/j.yebeh.2014.05.027 .
    1. Cervenka MC, Corines J, Boatman-Reich DF, Eloyan A, Sheng X, Franaszczuk PJ, et al. Electrocorticographic functional mapping identifies human cortex critical for auditory and visual naming. NeuroImage. 2013;69:267–76. 10.1016/j.neuroimage.2012.12.037
    1. Borchers S, Himmelbach M, Logothetis N, Karnath HO. Direct electrical stimulation of human cortex—the gold standard for mapping brain functions? Nature reviews Neuroscience. 2012;13(1):63–70. 10.1038/nrn3140 .
    1. Keller CJ, Honey CJ, Megevand P, Entz L, Ulbert I, Mehta AD. Mapping human brain networks with cortico-cortical evoked potentials. Philosophical transactions of the Royal Society of London Series B, Biological sciences. 2014;369(1653). 10.1098/rstb.2013.0528
    1. Kunieda T, Yamao Y, Kikuchi T, Matsumoto R. New Approach for Exploring Cerebral Functional Connectivity: Review of Cortico-cortical Evoked Potential. Neurol Med Chir (Tokyo). 2015;55(5):374–82. 10.2176/nmc.ra.2014-0388 .
    1. Matsumoto R, Nair DR, LaPresto E, Bingaman W, Shibasaki H, Luders HO. Functional connectivity in human cortical motor system: a cortico-cortical evoked potential study. Brain: a journal of neurology. 2007;130(Pt 1):181–97. 10.1093/brain/awl257 .
    1. Matsumoto R, Nair DR, LaPresto E, Najm I, Bingaman W, Shibasaki H, et al. Functional connectivity in the human language system: a cortico-cortical evoked potential study. Brain: a journal of neurology. 2004;127(Pt 10):2316–30. 10.1093/brain/awh246 .
    1. Entz L, Toth E, Keller CJ, Bickel S, Groppe DM, Fabo D, et al. Evoked effective connectivity of the human neocortex. Human brain mapping. 2014;35(12):5736–53. 10.1002/hbm.22581 .
    1. Keller CJ, Bickel S, Entz L, Ulbert I, Milham MP, Kelly C, et al. Intrinsic functional architecture predicts electrically evoked responses in the human brain. Proceedings of the National Academy of Sciences of the United States of America. 2011;108(25):10308–13. 10.1073/pnas.1019750108
    1. Sun FT, Morrell MJ. Closed-loop neurostimulation: the clinical experience. Neurotherapeutics. 2014;11(3):553–63. 10.1007/s13311-014-0280-3
    1. Hallett M. Transcranial magnetic stimulation: a primer. Neuron. 2007;55(2):187–99. 10.1016/j.neuron.2007.06.026 .
    1. Walker JE. Power spectral frequency and coherence abnormalities in patients with intractable epilepsy and their usefulness in long-term remediation of seizures using neurofeedback. Clinical EEG and neuroscience. 2008;39(4):203–5. .
    1. Luck SJ, Vogel EK. The capacity of visual working memory for features and conjunctions. Nature. 1997;390(6657):279–81. 10.1038/36846 .
    1. Kemp B, Varri A, Rosa AC, Nielsen KD, Gade J. A simple format for exchange of digitized polygraphic recordings. Electroencephalography and clinical neurophysiology. 1992;82(5):391–3. .
    1. Wagenaar DA, Potter SM. Real-time multi-channel stimulus artifact suppression by local curve fitting. Journal of neuroscience methods. 2002;120(2):113–20. .
    1. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magnetic resonance imaging. 2012;30(9):1323–41. 10.1016/j.mri.2012.05.001
    1. Iglesias JE, Liu CY, Thompson PM, Tu Z. Robust brain extraction across datasets and comparison with publicly available methods. IEEE transactions on medical imaging. 2011;30(9):1617–34. 10.1109/TMI.2011.2138152 .
    1. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage. 2006;31(3):1116–28. 10.1016/j.neuroimage.2006.01.015 .
    1. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL, et al. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage. 2011;54(1):313–27. 10.1016/j.neuroimage.2010.07.033
    1. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, et al. Automated Talairach atlas labels for functional brain mapping. Human brain mapping. 2000;10(3):120–31. .
    1. Wilson HR, Cowan JD. Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J. 1972;12(1):1–24. 10.1016/S0006-3495(72)86068-5
    1. Amari S. Dynamics of pattern formation in lateral-inhibition type neural fields. Biological cybernetics. 1977;27(2):77–87. .

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