Brain–machine interfaces from motor to mood

Article metrics

Abstract

Brain–machine interfaces (BMIs) create closed-loop control systems that interact with the brain by recording and modulating neural activity and aim to restore lost function, most commonly motor function in paralyzed patients. Moreover, by precisely manipulating the elements within the control loop, motor BMIs have emerged as new scientific tools for investigating the neural mechanisms underlying control and learning. Beyond motor BMIs, recent work highlights the opportunity to develop closed-loop mood BMIs for restoring lost emotional function in neuropsychiatric disorders and for probing the neural mechanisms of emotion regulation. Here we review significant advances toward functional restoration and scientific discovery in motor BMIs that have been guided by a closed-loop control view. By focusing on this unifying view of BMIs and reviewing recent work, we then provide a perspective on how BMIs could extend to the neuropsychiatric domain.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: BMIs create closed-loop control systems.
Fig. 2: Motor BMIs for functional restoration and scientific discovery.
Fig. 3: Steps toward realizing mood BMIs for functional restoration and scientific discovery.

References

  1. 1.

    Green, A. M. & Kalaska, J. F. Learning to move machines with the mind. Trends Neurosci. 34, 61–75 (2011).

  2. 2.

    Orsborn, A. L. & Pesaran, B. Parsing learning in networks using brain-machine interfaces. Curr. Opin. Neurobiol. 46, 76–83 (2017).

  3. 3.

    Shenoy, K. V. & Carmena, J. M. Combining decoder design and neural adaptation in brain-machine interfaces. Neuron 84, 665–680 (2014).

  4. 4.

    Shanechi, M. M. Brain-machine interface control algorithms. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 1725–1734 (2017).

  5. 5.

    Golub, M. D., Chase, S. M., Batista, A. P. & Yu, B. M. Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control. Curr. Opin. Neurobiol. 37, 53–58 (2016).

  6. 6.

    Moxon, K. A. & Foffani, G. Brain-machine interfaces beyond neuroprosthetics. Neuron 86, 55–67 (2015).

  7. 7.

    Hoang, K. B., Cassar, I. R., Grill, W. M. & Turner, D. A. Biomarkers and stimulation algorithms for adaptive brain stimulation. Front. Neurosci. 11, 564 (2017).

  8. 8.

    Widge, A. S. et al. Treating refractory mental illness with closed-loop brain stimulation: Progress towards a patient-specific transdiagnostic approach. Exp. Neurol. 287, 461–472 (2017).

  9. 9.

    Sani, O. G. et al. Mood variations decoded from multi-site intracranial human brain activity. Nat. Biotechnol. 36, 954–961 (2018).

  10. 10.

    Yang, Y., Connolly, A. T. & Shanechi, M. M. A control-theoretic system identification framework and a real-time closed-loop clinical simulation testbed for electrical brain stimulation. J. Neural Eng. 15, 066007 (2018).

  11. 11.

    Provenza, N. R. et al. The case for adaptive neuromodulation to treat severe intractable mental disorders. Front. Neurosci. 13, 152 (2019).

  12. 12.

    Whiteford, H. A. et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 382, 1575–1586 (2013).

  13. 13.

    Rush, A. J. et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am. J. Psychiatry 163, 1905–1917 (2006).

  14. 14.

    Mayberg, H. S. et al. Deep brain stimulation for treatment-resistant depression. Neuron 45, 651–660 (2005).

  15. 15.

    Lozano, A. M. et al. Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression. Biol. Psychiatry 64, 461–467 (2008).

  16. 16.

    Schlaepfer, T. E. et al. Deep brain stimulation to reward circuitry alleviates anhedonia in refractory major depression. Neuropsychopharmacology 33, 368–377 (2008).

  17. 17.

    Malone, D. A. Jr. et al. Deep brain stimulation of the ventral capsule/ventral striatum for treatment-resistant depression. Biol. Psychiatry 65, 267–275 (2009).

  18. 18.

    Schlaepfer, T. E., Bewernick, B. H., Kayser, S., Mädler, B. & Coenen, V. A. Rapid effects of deep brain stimulation for treatment-resistant major depression. Biol. Psychiatry 73, 1204–1212 (2013).

  19. 19.

    Dougherty, D. D. et al. A randomized sham-controlled trial of deep brain stimulation of the ventral capsule/ventral striatum for chronic treatment-resistant depression. Biol. Psychiatry 78, 240–248 (2015).

  20. 20.

    Holtzheimer, P. E. et al. Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. Lancet Psychiatry 4, 839–849 (2017).

  21. 21.

    Riva-Posse, P. et al. Defining critical white matter pathways mediating successful subcallosal cingulate deep brain stimulation for treatment-resistant depression. Biol. Psychiatry 76, 963–969 (2014).

  22. 22.

    Rao, V. R. et al. Direct electrical stimulation of lateral orbitofrontal cortex acutely improves mood in individuals with symptoms of depression. Curr. Biol. 28, 3893–3902.e4 (2018).

  23. 23.

    Sitaram, R. et al. Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci. 18, 86–100 (2017).

  24. 24.

    Kupfer, D. J., Frank, E. & Phillips, M. L. Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet 379, 1045–1055 (2012).

  25. 25.

    Ochsner, K. N., Silvers, J. A. & Buhle, J. T. Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion. Ann. NY Acad. Sci. 1251, E1–E24 (2012).

  26. 26.

    Gross, J. J. Emotion regulation: current status and future prospects. Psychol. Inq. 26, 1–26 (2015).

  27. 27.

    Etkin, A., Büchel, C. & Gross, J. J. The neural bases of emotion regulation. Nat. Rev. Neurosci. 16, 693–700 (2015).

  28. 28.

    Yuste, R. et al. Four ethical priorities for neurotechnologies and AI. Nature 551, 159–163 (2017).

  29. 29.

    Drevets, W. C. Neuroimaging and neuropathological studies of depression: implications for the cognitive-emotional features of mood disorders. Curr. Opin. Neurobiol. 11, 240–249 (2001).

  30. 30.

    Williams, L. M. Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation. Depress. Anxiety 34, 9–24 (2017).

  31. 31.

    Shadmehr, R., Smith, M. A. & Krakauer, J. W. Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci. 33, 89–108 (2010).

  32. 32.

    Krakauer, J. W. & Mazzoni, P. Human sensorimotor learning: adaptation, skill, and beyond. Curr. Opin. Neurobiol. 21, 636–644 (2011).

  33. 33.

    Haith, A.M. & Krakauer, J.W. Model-based and model-free mechanisms of human motor learning. in Progress in Motor Control (eds. Richardson, M. J., Riley, M. A. & Shockley, K.) 1–21 (Springer, 2013).

  34. 34.

    Linden, D. E. The challenges and promise of neuroimaging in psychiatry. Neuron 73, 8–22 (2012).

  35. 35.

    Hsieh, H.-L., Wong, Y. T., Pesaran, B. & Shanechi, M. M. Multiscale modeling and decoding algorithms for spike-field activity. J. Neural Eng. 16, 016018 (2019).

  36. 36.

    Stavisky, S. D., Kao, J. C., Nuyujukian, P., Ryu, S. I. & Shenoy, K. V. A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes. J. Neural Eng. 12, 036009 (2015).

  37. 37.

    Taylor, D. M., Tillery, S. I. H. & Schwartz, A. B. Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002).

  38. 38.

    Gilja, V. et al. A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15, 1752–1757 (2012).

  39. 39.

    Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012).

  40. 40.

    Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564 (2013).

  41. 41.

    Orsborn, A. L. et al. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82, 1380–1393 (2014).

  42. 42.

    Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 1142–1145 (2015).

  43. 43.

    Shanechi, M. M., Orsborn, A. L. & Carmena, J. M. Robust brain-machine interface design using optimal feedback control modeling and adaptive point process filtering. PLoS Comput. Biol. 12, e1004730 (2016).

  44. 44.

    Shanechi, M. M. et al. Rapid control and feedback rates enhance neuroprosthetic control. Nat. Commun. 8, 13825 (2017).

  45. 45.

    Shanechi, M. M. et al. A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design. PLoS One 8, e59049 (2013).

  46. 46.

    Willett, F. R., Suminski, A. J., Fagg, A. H. & Hatsopoulos, N. G. Improving brain-machine interface performance by decoding intended future movements. J. Neural Eng. 10, 026011 (2013).

  47. 47.

    Cunningham, J. P. et al. A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces. J. Neurophysiol. 105, 1932–1949 (2011).

  48. 48.

    Abbaspourazad, H., Hsieh, H. L. & Shanechi, M. M. A multiscale dynamical modeling and identification framework for spike-field activity. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1128–1138 (2019).

  49. 49.

    Wang, C. & Shanechi, M. M. Estimating multiscale direct causality graphs in neural spike-field networks. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 857–866 (2019).

  50. 50.

    Bighamian, R., Wong, Y.T., Pesaran, B. & Shanechi, M.M. Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks. J. Neural Eng. https://doi.org/10.1088/1741-2552/ab225b (2019).

  51. 51.

    Carmena, J. M. et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1, E42 (2003).

  52. 52.

    Moritz, C. T., Perlmutter, S. I. & Fetz, E. E. Direct control of paralysed muscles by cortical neurons. Nature 456, 639–642 (2008).

  53. 53.

    Ganguly, K. & Carmena, J. M. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 7, e1000153 (2009).

  54. 54.

    Wander, J. D. et al. Distributed cortical adaptation during learning of a brain-computer interface task. Proc. Natl Acad. Sci. USA 110, 10818–10823 (2013).

  55. 55.

    Ganguly, K., Dimitrov, D. F., Wallis, J. D. & Carmena, J. M. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 14, 662–667 (2011).

  56. 56.

    Chase, S. M., Kass, R. E. & Schwartz, A. B. Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. J. Neurophysiol. 108, 624–644 (2012).

  57. 57.

    Hwang, E. J., Bailey, P. M. & Andersen, R. A. Volitional control of neural activity relies on the natural motor repertoire. Curr. Biol. 23, 353–361 (2013).

  58. 58.

    Koralek, A. C., Costa, R. M. & Carmena, J. M. Temporally precise cell-specific coherence develops in corticostriatal networks during learning. Neuron 79, 865–872 (2013).

  59. 59.

    Sadtler, P. T. et al. Neural constraints on learning. Nature 512, 423–426 (2014).

  60. 60.

    Golub, M. D. et al. Learning by neural reassociation. Nat. Neurosci. 21, 607–616 (2018).

  61. 61.

    Gulati, T., Guo, L., Ramanathan, D. S., Bodepudi, A. & Ganguly, K. Neural reactivations during sleep determine network credit assignment. Nat. Neurosci. 20, 1277–1284 (2017).

  62. 62.

    Todorov, E. & Jordan, M. I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002).

  63. 63.

    Golub, M. D., Yu, B. M. & Chase, S. M. Internal models for interpreting neural population activity during sensorimotor control. eLife 4, e10015 (2015).

  64. 64.

    Suminski, A. J., Tkach, D. C., Fagg, A. H. & Hatsopoulos, N. G. Incorporating feedback from multiple sensory modalities enhances brain-machine interface control. J. Neurosci. 30, 16777–16787 (2010).

  65. 65.

    O’Doherty, J. E. et al. Active tactile exploration using a brain-machine-brain interface. Nature 479, 228–231 (2011).

  66. 66.

    Klaes, C. et al. A cognitive neuroprosthetic that uses cortical stimulation for somatosensory feedback. J. Neural Eng. 11, 056024 (2014).

  67. 67.

    Ethier, C., Oby, E. R., Bauman, M. J. & Miller, L. E. Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature 485, 368–371 (2012).

  68. 68.

    Shanechi, M. M., Hu, R. C. & Williams, Z. M. A cortical-spinal prosthesis for targeted limb movement in paralysed primate avatars. Nat. Commun. 5, 3237 (2014).

  69. 69.

    Bouton, C. E. et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature 533, 247–250 (2016).

  70. 70.

    Knudsen, E. B. & Moxon, K. A. Restoration of hindlimb movements after complete spinal cord injury using brain-controlled functional electrical stimulation. Front. Neurosci. 11, 715 (2017).

  71. 71.

    Ajiboye, A. B. et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. Lancet 389, 1821–1830 (2017).

  72. 72.

    Vaz, A. P., Inati, S. K., Brunel, N. & Zaghloul, K. A. Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory. Science 363, 975–978 (2019).

  73. 73.

    Yang, Y., Sani, O., Chang, E. F. & Shanechi, M. M. Dynamic network modeling and dimensionality reduction for human ECoG activity. J. Neural Eng. 16, 056014 (2019).

  74. 74.

    Kirkby, L. A. et al. An amygdala-hippocampus subnetwork that encodes variation in human mood. Cell 175, 1688–1700.e14 (2018).

  75. 75.

    McIntyre, C. C. & Hahn, P. J. Network perspectives on the mechanisms of deep brain stimulation. Neurobiol. Dis. 38, 329–337 (2010).

  76. 76.

    Stefanescu, R. A., Shivakeshavan, R. G. & Talathi, S. S. Computational models of epilepsy. Seizure 21, 748–759 (2012).

  77. 77.

    Liu, J., Khalil, H. K. & Oweiss, K. G. Model-based analysis and control of a network of basal ganglia spiking neurons in the normal and parkinsonian states. J. Neural Eng. 8, 045002 (2011).

  78. 78.

    Santaniello, S., Fiengo, G., Glielmo, L. & Grill, W. M. Closed-loop control of deep brain stimulation: a simulation study. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 15–24 (2011).

  79. 79.

    Yang, Y. & Sani, O.G. Sellers, K., Chang, E.F. & Shanechi, M.M. A novel framework for dynamic modeling of brain-network response to electrical stimulation. Computational and Systems Neuroscience (Cosyne) abstr. II-64 http://cosyne.org/cosyne18/Cosyne2018_program_book.pdf (2018).

  80. 80.

    Yang, Y. et al. Developing a personalized closed-loop controller of medically-induced coma in a rodent model. J. Neural Eng. 16, 036022 (2019).

  81. 81.

    Linden, D. E. et al. Real-time self-regulation of emotion networks in patients with depression. PLoS One 7, e38115 (2012).

  82. 82.

    Scheinost, D. et al. Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity. Transl. Psychiatry 3, e250 (2013).

  83. 83.

    Young, K. D. et al. Randomized clinical trial of real-time fMRI amygdala neurofeedback for major depressive disorder: effects on symptoms and autobiographical memory recall. Am. J. Psychiatry 174, 748–755 (2017).

  84. 84.

    Keynan, J. N. et al. Limbic activity modulation guided by functional magnetic resonance imaging-inspired electroencephalography improves implicit emotion regulation. Biol. Psychiatry 80, 490–496 (2016).

  85. 85.

    LaConte, S. M., Peltier, S. J. & Hu, X. P. Real-time fMRI using brain-state classification. Hum. Brain Mapp. 28, 1033–1044 (2007).

  86. 86.

    Sitaram, R. et al. Real-time support vector classification and feedback of multiple emotional brain states. Neuroimage 56, 753–765 (2011).

  87. 87.

    Yang, Y. & Shanechi, M. M. An adaptive and generalizable closed-loop system for control of medically induced coma and other states of anesthesia. J. Neural Eng. 13, 066019 (2016).

  88. 88.

    Yang, Y., Chang, E.F. & Shanechi, M.M. Dynamic tracking of non-stationarity in human ECoG activity. in Conf. Proc. IEEE Eng. Med. Biol. Soc. 1660–1663 (IEEE, 2017).

  89. 89.

    Hsieh, H.-L. & Shanechi, M. M. Optimizing the learning rate for adaptive estimation of neural encoding models. PLoS Comput. Biol. 14, e1006168 (2018).

  90. 90.

    Calhoon, G. G. & Tye, K. M. Resolving the neural circuits of anxiety. Nat. Neurosci. 18, 1394–1404 (2015).

  91. 91.

    Dayan, E. & Cohen, L. G. Neuroplasticity subserving motor skill learning. Neuron 72, 443–454 (2011).

  92. 92.

    Zavala, B. et al. Cognitive control involves theta power within trials and beta power across trials in the prefrontal-subthalamic network. Brain 141, 3361–3376 (2018).

  93. 93.

    Rudebeck, P. H. & Murray, E. A. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron 84, 1143–1156 (2014).

  94. 94.

    Kim, S.-P., Simeral, J. D., Hochberg, L. R., Donoghue, J. P. & Black, M. J. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J. Neural Eng. 5, 455–476 (2008).

  95. 95.

    Eden, U. T., Frank, L. M., Barbieri, R., Solo, V. & Brown, E. N. Dynamic analysis of neural encoding by point process adaptive filtering. Neural Comput. 16, 971–998 (2004).

  96. 96.

    Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P. & Brown, E. N. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J. Neurophysiol. 93, 1074–1089 (2005).

  97. 97.

    Kao, J. C. et al. Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nat. Commun. 6, 7759 (2015).

  98. 98.

    Geller, E. B. et al. Brain-responsive neurostimulation in patients with medically intractable mesial temporal lobe epilepsy. Epilepsia 58, 994–1004 (2017).

  99. 99.

    Meidahl, A. C. et al. Adaptive deep brain stimulation for movement disorders: the long road to clinical therapy. Mov. Disord. 32, 810–819 (2017).

  100. 100.

    Shanechi, M. M. et al. Neural population partitioning and a concurrent brain-machine interface for sequential motor function. Nat. Neurosci. 15, 1715–1722 (2012).

Download references

Acknowledgements

I thank O. G. Sani and Y. Yang in the Shanechi lab for their helpful feedback and contributions. This work was supported in part by the following to M.M.S.: Army Research Office (ARO) under contract W911NF-16-1-0368 as part of the collaboration between US DOD, UK MOD and UK Engineering and Physical Research Council (EPSRC) under the Multidisciplinary University Research Initiative (MURI); Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number W911NF-14-2-0043 issued by the ARO contracting office in support of DARPA’s SUBNETS program; Office of Naval Research (ONR) Young Investigator Program (YIP) under contract N00014-19-1-2128; National Science Foundation (NSF) CAREER Award CCF-1453868; and US National Institutes of Health (NIH) BRAIN grant R01-NS104923. The views, opinions, and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the US Government.

Author information

Correspondence to Maryam M. Shanechi.

Ethics declarations

Competing interests

The author declares no competing financial interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shanechi, M.M. Brain–machine interfaces from motor to mood. Nat Neurosci 22, 1554–1564 (2019) doi:10.1038/s41593-019-0488-y

Download citation