Closed-Loop Deep Brain Stimulation for Refractory Chronic Pain

Prasad Shirvalkar, Tess L Veuthey, Heather E Dawes, Edward F Chang, Prasad Shirvalkar, Tess L Veuthey, Heather E Dawes, Edward F Chang

Abstract

Pain is a subjective experience that alerts an individual to actual or potential tissue damage. Through mechanisms that are still unclear, normal physiological pain can lose its adaptive value and evolve into pathological chronic neuropathic pain. Chronic pain is a multifaceted experience that can be understood in terms of somatosensory, affective, and cognitive dimensions, each with associated symptoms and neural signals. While there have been many attempts to treat chronic pain, in this article we will argue that feedback-controlled 'closed-loop' deep brain stimulation (DBS) offers an urgent and promising route for treatment. Contemporary DBS trials for chronic pain use "open-loop" approaches in which tonic stimulation is delivered with fixed parameters to a single brain region. The impact of key variables such as the target brain region and the stimulation waveform is unclear, and long-term efficacy has mixed results. We hypothesize that chronic pain is due to abnormal synchronization between brain networks encoding the somatosensory, affective and cognitive dimensions of pain, and that multisite, closed-loop DBS provides an intuitive mechanism for disrupting that synchrony. By (1) identifying biomarkers of the subjective pain experience and (2) integrating these signals into a state-space representation of pain, we can create a predictive model of each patient's pain experience. Then, by establishing how stimulation in different brain regions influences individual neural signals, we can design real-time, closed-loop therapies tailored to each patient. While chronic pain is a complex disorder that has eluded modern therapies, rich historical data and state-of-the-art technology can now be used to develop a promising treatment.

Keywords: affective; chronic pain; closed-loop stimulation; cognitive; control theory; deep brain stimulation; neuropathic pain; somatosensory.

Figures

Figure 1
Figure 1
The Kanizsa triangle can be used to represent a multidimensional framework for pain. Pain is an underlying state made apparent by three types of observable symptoms (somatosensory, affective, and cognitive). Therapies which selectively address a single facet of pain risk misinterpreting aspects of symptoms (the “shape” of the symptoms) outside of the context of the larger pathology. The optimal way to “break” the pain state might lie in modulation (or “re-orienting”) the facets of pain rather than trying to suppress them (adapted from https://commons.wikimedia.org/wiki/File:Kanizsa_triangle.svg; Accessed on March 14, 2018).
Figure 2
Figure 2
Block Diagram schematics of closed-loop control systems. (A) Classical block diagram of a single-input, single-output negative feedback control system, where the measured output of the system is compared to a reference signal via a closed-loop, to modify the system output and minimize error [adapted from (Orzetto)]. (B) Example block diagram of a multi-input, multi-output closed-loop DBS system where a pain signal derived from biomarkers is compared to a reference signal via a feedback loop. Multi-regional stimulation is triggered to bring the system closer into the reference state. The red box highlights elements of an open-loop paradigm. aAvailable online at: https://upload.wikimedia.org/wikipedia/commons/2/24/Feedback_loop_with_descriptions.svg (Accessed Nov 30, 2017).
Figure 3
Figure 3
Pain related brain regions. Key brain regions related to somatosensory (blue), affective (green), and cognitive (orange) pain processing. Only regions of interest have been included for clarity.
Figure 4
Figure 4
A multidimensional state space framework can be used to characterize pain states, reference states, and goals of DBS paradigms. (A) A state space representing neural activity can be defined along the multiple dimensions of pain: somatosensory, affective and cognitive. For simplicity, a pain state is represented as a single red zone in the upper right corner, with defined threshold boundaries (dashed red line). The reference (pain-free) state is any region outside the red zone. The dynamics of neural activity that underlie transition from a pain-free state toward a pain state are shown as neural trajectories (black arrows). During constant baseline pain, there is a self-sustaining neural trajectory confined to the pain state (spiral arrow). (B) Different paradigms of DBS accomplish different goals. Tonic, open-loop DBS aims to maintain neural activity in a constant pain-free state (blue arrow). Abortive, patient-triggered or sensor-triggered DBS aims to push neural representations out of the pain state into the reference state (purple arrows). Closed-loop DBS will ideally deflect neural activity well before entering a pain-state (green arrows).

References

    1. Adams J. E., Hosobuchi Y., Fields H. L. (1974). Stimulation of internal capsule for relief of chronic pain. J. Neurosurg. 41, 740–744. 10.3171/jns.1974.41.6.0740
    1. Apkarian A. V., Bushnell M. C., Treede R.-D., Zubieta J.-K. (2005). Human brain mechanisms of pain perception and regulation in health and disease. Eur. J. Pain 9, 463–463. 10.1016/j.ejpain.2004.11.001
    1. Ashwin P., Coombes S., Nicks R. (2016). Mathematical frameworks for oscillatory network dynamics in neuroscience. J. Math. Neurosci. 6:2. 10.1186/s13408-015-0033-6
    1. Babiloni C., Brancucci A., Del Percio C., Capotosto P., Arendt-Nielsen L., Chen A. C. N., et al. . (2006). Anticipatory electroencephalography alpha rhythm predicts subjective perception of pain intensity. J. Pain Off. J. Am. Pain Soc. 7, 709–717. 10.1016/j.jpain.2006.03.005
    1. Ballantine H. T., Cassidy W. L., Flanagan N. B., Marino R. (1967). Stereotaxic anterior cingulotomy for neuropsychiatric illness and intractable pain. J. Neurosurg. 26, 488–495. 10.3171/jns.1967.26.5.0488
    1. Ben-Menachem E., Mañon-Espaillat R., Ristanovic R., Wilder B. J., Stefan H., Mirza W., et al. . (1994). Vagus nerve stimulation for treatment of partial seizures: 1. a controlled study of effect on seizures. First International Vagus Nerve Stimulation Study Group. Epilepsia 35, 616–626. 10.1111/j.1528-1157.1994.tb02482.x
    1. Boccard S. G. J., Pereira E. A. C., Aziz T. Z. (2015). Deep brain stimulation for chronic pain. J. Clin. Neurosci. 22, 1537–1543. 10.1016/j.jocn.2015.04.005
    1. Boccard S. G. J., Prangnell S. J., Pycroft L., Cheeran B., Moir L., Pereira E. A. C., et al. . (2017). Long-Term results of deep brain stimulation of the anterior cingulate cortex for neuropathic pain. World Neurosurg. 106, 625–637. 10.1016/j.wneu.2017.06.173
    1. Bokil H., Andrews P., Kulkarni J. E., Mehta S., Mitra P. (2010). Chronux: a platform for analyzing neural signals. J. Neurosci. Methods 192, 146–151. 10.1016/j.jneumeth.2010.06.020
    1. Boord P., Siddall P. J., Tran Y., Herbert D., Middleton J., Craig A. (2008). Electroencephalographic slowing and reduced reactivity in neuropathic pain following spinal cord injury. Spinal Cord 46, 118–123. 10.1038/sj.sc.3102077
    1. Brocker D. T., Swan B. D., So R. Q., Turner D. A., Gross R. E., Grill W. M. (2017). Optimized temporal pattern of brain stimulation designed by computational evolution. Sci. Transl. Med. 9:eaah3532. 10.1126/scitranslmed.aah3532
    1. Brown J. A., Pilitsis J. G. (2005). Motor cortex stimulation for central and neuropathic facial pain: a prospective study of 10 patients and observations of enhanced sensory and motor function during stimulation. Neurosurgery 56, 290–297. 10.1227/01.NEU.0000148905.75845.98
    1. Bushnell M. C., Ceko M., Low L. A. (2013). Cognitive and emotional control of pain and its disruption in chronic pain. Nat. Rev. Neurosci. 14:502. 10.1038/nrn3516
    1. Buzsáki G., Anastassiou C. A., Koch C. (2012). The origin of extracellular fields and currents–EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420. 10.1038/nrn3241
    1. CDC (2016). Wide-Ranging Online Data for Epidemiologic Research. Atlanta, GA: WONDER.
    1. Chen Z., Zhang Q., Tong A. P. S., Manders T. R., Wang J. (2017). Deciphering neuronal population codes for acute thermal pain. J. Neural Eng. 14:036023. 10.1088/1741-2552/aa644d
    1. Churchland M. M., Yu B. M., Ryu S. I., Santhanam G., Shenoy K. V. (2006). Neural variability in premotor cortex provides a signature of motor preparation. J. Neurosci. 26, 3697–3712. 10.1523/JNEUROSCI.3762-05.2006
    1. Coffey R. J. (2001). Deep brain stimulation for chronic pain: results of two multicenter trials and a structured review. Pain Med. Malden Mass 2, 183–192. 10.1046/j.1526-4637.2001.01029.x
    1. Coghill R. C., McHaffie J. G., Yen Y.-F. (2003). Neural correlates of interindividual differences in the subjective experience of pain. Proc. Natl. Acad. Sci. U.S.A. 100, 8538–8542. 10.1073/pnas.1430684100
    1. Colgin L. L., Denninger T., Fyhn M., Hafting T., Bonnevie T., Jensen O., et al. . (2009). Frequency of gamma oscillations routes flow of information in the hippocampus. Nature 462, 353–357. 10.1038/nature08573
    1. Cunningham J. P., Yu B. M. (2014). Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 17, 1500–1509. 10.1038/nn.3776
    1. Dejerine J., Roussy G. (1906). Le syndrome thalamique. Rev. Neurol. Paris 14, 521–532.
    1. Ezzyat Y., Kragel J. E., Burke J. F., Levy D. F., Lyalenko A., Wanda P., et al. . (2017). Direct brain stimulation modulates encoding states and memory performance in humans. Curr. Biol. 27, 1251–1258. 10.1016/j.cub.2017.03.028
    1. Flint R. D., Lindberg E. W., Jordan L. R., Miller L. E., Slutzky M. W. (2012). Accurate decoding of reaching movements from field potentials in the absence of spikes. J. Neural Eng. 9:046006. 10.1088/1741-2560/9/4/046006
    1. Foltz E. L., White L. E. (1962). Pain “Relief” by Frontal Cingulumotomy. J. Neurosurg. 19, 89–100. 10.3171/jns.1962.19.2.0089
    1. Goense J., Bohraus Y., Logothetis N. K. (2016). fMRI at high spatial resolution: implications for BOLD-Models. Front. Comput. Neurosci. 10:66. 10.3389/fncom.2016.00066
    1. Gross J., Schnitzler A., Timmermann L., Ploner M. (2007). Gamma oscillations in human primary somatosensory cortex reflect pain perception. PLOS Biol. 5:e133. 10.1371/journal.pbio.0050133
    1. Hastie T., Tibshirani R., Friedman J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd. Edn. New York, NY: Springer-Verlag; Available online at: (Accessed Feb. 26, 2018).
    1. Hemptinne C., de Swann N. C., Ostrem J. L., Ryapolova-Webb E. S., Luciano M. S., Galifianakis N. B., et al. . (2015). Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in Parkinson's disease. Nat. Neurosci. 18, 779–786. 10.1038/nn.3997
    1. Hochberg L. R., Serruya M. D., Friehs G. M., Mukand J. A., Saleh M., Caplan A. H., et al. . (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171. 10.1038/nature04970
    1. Hosobuchi Y., Adams J. E., Rutkin B. (1973). Chronic thalamic stimulation for the control of facial anesthesia dolorosa. Arch. Neurol. 29, 158–161. 10.1001/archneur.1973.00490270040005
    1. Hosomi K., Seymour B., Saitoh Y. (2015). Modulating the pain network—neurostimulation for central poststroke pain. Nat. Rev. Neurol. 11, 290–299. 10.1038/nrneurol.2015.58
    1. Hsieh H. L., Shanechi M. M. (2016). Multiscale brain-machine interface decoders, in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Orlando, FL: ), 6361–6364.
    1. Huster R. J., Debener S., Eichele T., Herrmann C. S. (2012). Methods for simultaneous EEG-fMRI: an introductory review. J. Neurosci. 32, 6053–6060. 10.1523/JNEUROSCI.0447-12.2012
    1. Jazayeri M., Afraz A. (2017). Navigating the neural space in search of the neural code. Neuron 93, 1003–1014. 10.1016/j.neuron.2017.02.019
    1. Johansen J. P., Fields H. L. (2004). Glutamatergic activation of anterior cingulate cortex produces an aversive teaching signal. Nat. Neurosci. 7, 398–403. 10.1038/nn1207
    1. Johansen J. P., Fields H. L., Manning B. H. (2001). The affective component of pain in rodents: direct evidence for a contribution of the anterior cingulate cortex. Proc. Natl. Acad. Sci. U.S.A. 98, 8077–8082. 10.1073/pnas.141218998
    1. Karamintziou S. D., Custódio A. L., Piallat B., Polosan M., Chabardès S., Stathis P. G., et al. . (2017). Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: a computational approach. PLoS ONE 12:e0171458. 10.1371/journal.pone.0171458
    1. Keifer O. P., Riley J. P., Boulis N. M. (2014). Deep brain stimulation for chronic pain. Neurosurg. Clin. N. Am. 25, 671–692. 10.1016/j.nec.2014.07.009
    1. Kucyi A., Davis K. D. (2015). The dynamic pain connectome. Trends Neurosci. 38, 86–95. 10.1016/j.tins.2014.11.006
    1. Kumar R. (2002). Methods for programming and patient management with deep brain stimulation of the globus pallidus for the treatment of advanced Parkinson's disease and dystonia. Mov. Disord. 17, S198–S207. 10.1002/mds.10164
    1. Kuo P.-C., Chen Y.-T., Chen Y.-S., Chen L.-F. (2017). Decoding the perception of endogenous pain from resting-state MEG. Neuroimage 144, 1–11. 10.1016/j.neuroimage.2016.09.040
    1. Lefaucheur J.-P., Drouot X., Cunin P., Bruckert R., Lepetit H., Créange A., et al. . (2009). Motor cortex stimulation for the treatment of refractory peripheral neuropathic pain. Brain J. Neurol. 132, 1463–1471. 10.1093/brain/awp035
    1. Lempka S. F., Malone D. A., Hu B., Baker K. B., Wyant A., Ozinga J. G., et al. . (2017). Randomized clinical trial of deep brain stimulation for poststroke pain. Ann. Neurol. 81, 653–663. 10.1002/ana.24927
    1. Levy R., Deer T. R., Henderson J. (2010). Intracranial neurostimulation for pain control: a review. Pain Physic. 13, 157–165.
    1. Li L., Liu X., Cai C., Yang Y., Li D., Xiao L., et al. . (2016a). Changes of gamma-band oscillatory activity to tonic muscle pain. Neurosci. Lett. 627, 126–131. 10.1016/j.neulet.2016.05.067
    1. Li L., Wang H., Ke X., Liu X., Yuan Y., Zhang D., et al. (2016b). Placebo analgesia changes alpha oscillations induced by tonic muscle pain: EEG frequency analysis including data during pain evaluation. Front. Comput. Neurosci. 10:45 10.3389/fncom.2016.00045
    1. Lieberman M. D., Eisenberger N. I. (2015). The dorsal anterior cingulate cortex is selective for pain: results from large-scale reverse inference. Proc. Natl. Acad. Sci. U.S.A. 112, 15250–15255. 10.1073/pnas.1515083112
    1. Lima M. C., Fregni F. (2008). Motor cortex stimulation for chronic pain: systematic review and meta-analysis of the literature. Neurology 70, 2329–2337. 10.1212/01.wnl.0000314649.38527.93
    1. Liu C.-C., Ohara S., Franaszczuk P. J., Crone N. E., Lenz F. A. (2011). Attention to painful cutaneous laser stimuli evokes directed functional interactions between human sensory and modulatory pain-related cortical areas. Pain 152, 2781–2791. 10.1016/j.pain.2011.09.002
    1. Louppe J.-M., Nguyen J.-P., Robert R., Buffenoir K., de Chauvigny E., Riant T., et al. . (2013). Motor cortex stimulation in refractory pelvic and perineal pain: report of two successful cases. Neurourol. Urodyn. 32, 53–57. 10.1002/nau.22269
    1. Mante V., Sussillo D., Shenoy K. V., Newsome W. T. (2013). Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84. 10.1038/nature12742
    1. Melzack R. (1999). From the gate to the neuromatrix. Pain 82, S121–S126. 10.1016/S0304-3959(99)00145-1
    1. Melzack R., Casey K. (1968). Sensory, motivational and central control determinants of pain: a new conceptual model, in The Skin Senses (Tallahassee, FL: ).
    1. Moont R., Crispel Y., Lev R., Pud D., Yarnitsky D. (2012). Temporal changes in cortical activation during distraction from pain: a comparative LORETA study with conditioned pain modulation. Brain Res. 1435, 105–117. 10.1016/j.brainres.2011.11.056
    1. Nauta W. J. (1958). Hippocampal projections and related neural pathways to the midbrain in the cat. Brain J. Neurol. 81, 319–340. 10.1093/brain/81.3.319
    1. Nevian T. (2017). The cingulate cortex: divided in pain. Nat. Neurosci. 20, 1515–1517. 10.1038/nn.4664
    1. Ohara S., Crone N. E., Weiss N., Lenz F. A. (2006). Analysis of synchrony demonstrates ‘pain networks’ defined by rapidly switching, task-specific, functional connectivity between pain-related cortical structures. Pain 123, 244–253. 10.1016/j.pain.2006.02.012
    1. Orsborn A. L., So K., Dangi S., Carmena J. M. (2013). Comparison of neural activity during closed-loop control of spike- or LFP-based brain-machine interfaces, in 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) (San Diego, CA: ), 1017–1020.
    1. Ossipov M. H., Dussor G. O., Porreca F. (2010). Central modulation of pain. J. Clin. Invest. 120, 3779–3787. 10.1172/JCI43766
    1. Pandarinath C., Nuyujukian P., Blabe C. H., Sorice B. L., Saab J., Willett F. R., et al. . (2017). High performance communication by people with paralysis using an intracortical brain-computer interface. Elife 6:e18554. 10.7554/eLife.18554
    1. Papez J. (1937). A proposed mechanism of emotion. Arch. Neurol. Psychiatry 38, 725–743. 10.1001/archneurpsyc.1937.02260220069003
    1. Parvizi J., Rangarajan V., Shirer W. R., Desai N., Greicius M. D. (2013). The will to persevere induced by electrical stimulation of the human cingulate gyrus. Neuron 80, 1359–1367. 10.1016/j.neuron.2013.10.057
    1. Ploner M., Gross J., Timmermann L., Pollok B., Schnitzler A. (2006). Pain suppresses spontaneous brain rhythms. Cereb. Cortex. 16, 537–540. 10.1093/cercor/bhj001
    1. Ploner M., Sorg C., Gross J. (2017). Brain rhythms of pain. Trends Cogn. Sci. 21, 100–110. 10.1016/j.tics.2016.12.001
    1. Rabinovich M. I., Afraimovich V. S., Bick C., Varona P. (2012). Information flow dynamics in the brain. Phys. Life Rev. 9, 51–73. 10.1016/j.plrev.2011.11.002
    1. Radons G., Becker J. D., Dülfer B., Krüger J. (1994). Analysis, classification, and coding of multielectrode spike trains with hidden Markov models. Biol. Cybern. 71, 359–373. 10.1007/BF00239623
    1. Rainville P., Duncan G. H., Price D. D., Carrier B., Bushnell M. C. (1997). Pain affect encoded in human anterior cingulate but not somatosensory cortex. Science 277, 968–971. 10.1126/science.277.5328.968
    1. Reddan M. C., Wager T. D. (2017). Modeling Pain Using fMRI: From Regions to Biomarkers. Neurosci. Bull. 34, 208–215. 10.1007/s12264-017-0150-1
    1. Romanelli P., Heit G. (2004). Patient-controlled deep brain stimulation can overcome analgesic tolerance. Stereotact. Funct. Neurosurg. 82, 77–79. 10.1159/000077404
    1. Santaniello S., Fiengo G., Glielmo L., Grill W. M. (2011). Closed-Loop control of deep brain stimulation: a simulation study. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 15–24. 10.1109/TNSRE.2010.2081377
    1. Sarnthein J., Stern J., Aufenberg C., Rousson V., Jeanmonod D. (2006). Increased EEG power and slowed dominant frequency in patients with neurogenic pain. Brain 129, 55–64. 10.1093/brain/awh631
    1. Schmidt S., Naranjo J. R., Brenneisen C., Gundlach J., Schultz C., Kaube H., et al. . (2012). Pain ratings, psychological functioning and quantitative EEG in a controlled study of chronic back pain patients. PLoS ONE 7:e31138. 10.1371/journal.pone.0031138
    1. Schultz D. M., Webster L., Kosek P., Dar U., Tan Y., Sun M. (2012). Sensor-driven position-adaptive spinal cord stimulation for chronic pain. Pain Physic. 15, 1–12.
    1. Schulz E., May E. S., Postorino M., Tiemann L., Nickel M. M., Witkovsky V., et al. . (2015). Prefrontal gamma oscillations encode tonic pain in humans. Cereb. Cortex 25, 4407–4414. 10.1093/cercor/bhv043
    1. Schulz E., Zherdin A., Tiemann L., Plant C., Ploner M. (2012). Decoding an individual's sensitivity to pain from the multivariate analysis of EEG data. Cereb. Cortex 22, 1118–1123. 10.1093/cercor/bhr186
    1. Shealy C. N. (1969). Dorsal column electrohypalgesia. Headache 9, 99–102. 10.1111/j.1526-4610.1969.hed0902099.x
    1. Shenoy K. V., Sahani M., Churchland M. M. (2013). Cortical control of arm movements: a dynamical systems perspective. Annu. Rev. Neurosci. 36, 337–359. 10.1146/annurev-neuro-062111-150509
    1. Shirvalkar P. R., Rapp P. R., Shapiro M. L. (2010). Bidirectional changes to hippocampal theta–gamma comodulation predict memory for recent spatial episodes. Proc. Natl. Acad. Sci. U.S.A. 107, 7054–7059. 10.1073/pnas.0911184107
    1. Smith A. C., Brown E. N. (2003). Estimating a State-Space model from point process observations. Neural Comput. 15, 965–991. 10.1162/089976603765202622
    1. So K., Dangi S., Orsborn A. L., Gastpar M. C., Carmena J. M. (2014). Subject-specific modulation of local field potential spectral power during brain–machine interface control in primates. J. Neural Eng. 11:026002. 10.1088/1741-2560/11/2/026002
    1. Spooner J., Yu H., Kao C., Sillay K., Konrad P. (2007). Neuromodulation of the cingulum for neuropathic pain after spinal cord injury. J. Neurosurg. 107, 169–172. 10.3171/JNS-07/07/0169
    1. Stavisky S. D., Kao J. C., Nuyujukian P., Ryu S. I., Shenoy K. V. (2015). A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes. J. Neural Eng. 12:036009. 10.1088/1741-2560/12/3/036009
    1. Stern J., Jeanmonod D., Sarnthein J. (2006). Persistent EEG overactivation in the cortical pain matrix of neurogenic pain patients. Neuroimage 31, 721–731. 10.1016/j.neuroimage.2005.12.042
    1. Sun F. T., Morrell M. J. (2014). The RNS system: responsive cortical stimulation for the treatment of refractory partial epilepsy. Expert Rev. Med. Devices 11, 563–72. 10.1586/17434440.2014.947274
    1. Swann N. C., de Hemptinne C., Miocinovic S., Qasim S., Ostrem J. L., Galifianakis N. B., et al. . (2018). Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease. J. Neurosurg. 128, 605–616. 10.3171/2016.11.JNS161162
    1. Swann N. C., Hemptinne C., de Miocinovic S., Qasim S., Wang S. S., Ziman N., et al. . (2016). Gamma oscillations in the hyperkinetic state detected with chronic human brain recordings in Parkinson's Disease. J. Neurosci. 36, 6445–6458. 10.1523/JNEUROSCI.1128-16.2016
    1. Tasker R. R. (1990). Thalamotomy. Neurosurg. Clin. N. Am. 1, 841–864.
    1. Tort A. B. L., Komorowski R., Eichenbaum H., Kopell N. (2010). Measuring Phase-Amplitude coupling between neuronal oscillations of different frequencies. J. Neurophysiol. 104, 1195–1210. 10.1152/jn.00106.2010
    1. Tu Y., Zhang Z., Tan A., Peng W., Hung Y. S., Moayedi M., et al. . (2016). Alpha and gamma oscillation amplitudes synergistically predict the perception of forthcoming nociceptive stimuli. Hum. Brain Mapp. 37, 501–514. 10.1002/hbm.23048
    1. Villemure C., Bushnell C. M. (2002). Cognitive modulation of pain: how do attention and emotion influence pain processing? Pain 95, 195–199. 10.1016/S0304-3959(02)00007-6
    1. Volkmann J., Herzog J., Kopper F., Deuschl G. (2002). Introduction to the programming of deep brain stimulators. Mov. Disord. 17, S181–S187. 10.1002/mds.10162
    1. Wager T. D., Atlas L. Y., Lindquist M. A., Roy M., Woo C.-W., Kross E. (2013). An fMRI-Based neurologic signature of physical pain. N. Engl. J. Med. 368, 1388–1397. 10.1056/NEJMoa1204471
    1. Wang J.-Y., Luo F., Chang J.-Y., Woodward D. J., Han J.-S. (2003). Parallel pain processing in freely moving rats revealed by distributed neuron recording. Brain Res. 992, 263–271. 10.1016/j.brainres.2003.08.059
    1. Whitty C. W. M., Duffield J. E., Tow P. M., Cairns H. (1952). Anterior cingulectomy in the treatment of mental disease. Lancet 259, 475–481. 10.1016/S0140-6736(52)90051-2
    1. Wycis H. T., Spiegel E. A. (1949). Thalamotomy and mesencephalothalamotomy; neuro-surgical aspects, including treatment of pain. N. Y. State. J. Med. 49, 2275–2277.
    1. Xiao Y., Peña E., Johnson M. D. (2016). Theoretical optimization of stimulation strategies for a directionally segmented deep brain stimulation electrode array. IEEE Trans. Biomed. Eng. 63, 359–371. 10.1109/TBME.2015.2457873
    1. Yang Y., Shanechi M. M. (2016). Generalized Binary Noise Stimulation Enables Time-Efficient Identification of Input-Output Brain Network Dynamics, in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Orlando, FL: ), 1766–1769.
    1. Zhang Z. G., Hu L., Hung Y. S., Mouraux A., Iannetti G. D. (2012). Gamma-Band oscillations in the primary somatosensory cortex—a direct and obligatory correlate of subjective pain intensity. J. Neurosci. 32, 7429–7438. 10.1523/JNEUROSCI.5877-11.2012
    1. Zubieta J.-K., Smith Y. R., Bueller J. A., Xu Y., Kilbourn M. R., Jewett D. M., et al. . (2001). Regional mu opioid receptor regulation of sensory and affective dimensions of pain. Science 293, 311–315. 10.1126/science.1060952

Source: PubMed

Szponzorok és közreműködők

Egészségi állapot

Kábítószer-beavatkozások

3
Iratkozz fel