Mind control as a guide for the mind

Abstract

The human brain is a complex network that supports mental function. The nascent field of network neuroscience applies tools from mathematics to neuroimaging data in the hope of shedding light on cognitive function. A critical question arising from these empirical studies is how to modulate a human brain network to treat cognitive deficits or enhance mental abilities. While historically a number of tools have been employed to modulate mental states (such as cognitive behavioural therapy and brain stimulation), theoretical frameworks to guide these interventions—and to optimize them for clinical use—are fundamentally lacking. One promising and as yet under-explored approach lies in a subdiscipline of engineering known as network control theory. Here, we posit that network control fundamentally relates to mind control, and that this relationship highlights important areas for future empirical research and opportunities to translate knowledge into practical domains. We clarify the conceptual intersection between neuroanatomy, cognition, and control engineering in the context of network neuroscience. Finally, we discuss the challenges, ethics, and promises of mind control.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Control theory.
Figure 2: Network control.
Figure 3: Brain control.
Figure 4: Neural codes and cognition.

References

  1. 1

    Thaler, R. H., Sunstein, C. R. & Balz, J. P. in The Behavioral Foundations of Public Policy (ed. Shafir, E. ) Ch. 25 (Princeton Univ. Press, 2013).

    Google Scholar 

  2. 2

    Kailath, T. Linear Systems (Prentice-Hall, 1980).

    Google Scholar 

  3. 3

    Liu, Y.-Y., Slotine, J.-J. & Barabási, A.-L. Controllability of complex networks. Nature 473, 167–173 (2011).

    CAS  Article  Google Scholar 

  4. 4

    Gu, S. et al. Controllability of structural brain networks. Nat. Commun. 6, 8414 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5

    Muldoon, S. F. et al. Stimulation-based control of dynamic brain networks. PLoS Comput. Biol. 12, e1005076 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. 6

    Humayun, M. et al. Recent results from second sight's Argus® II retinal prosthesis study. Invest. Ophthalmol. Vis. Sci. 54, 349–349 (2013).

    Google Scholar 

  7. 7

    Arts, R. A., George, E. L., Stokroos, R. J. & Vermeire, K. Review: cochlear implants as a treatment of tinnitus in single-sided deafness. Curr. Opin. Otolaryngol. Head Neck Surg. 20, 398–403 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  8. 8

    Schiff, S. J. Neural Control Engineering (MIT Press, 2012).

    Google Scholar 

  9. 9

    Figee, M. et al. Deep brain stimulation restores frontostriatal network activity in obsessive-compulsive disorder. Nat. Neurosci. 16, 386–387 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  10. 10

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11

    Hallett, M. Transcranial magnetic stimulation and the human brain. Nature 406, 147–150 (2000).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12

    Bassett, D. S. & Bullmore, E. Small-world brain networks. Neuroscientist 12, 512–523 (2006).

    PubMed  Article  PubMed Central  Google Scholar 

  13. 13

    Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  14. 14

    Bullmore, E. T. & Bassett, D. S. Brain graphs: graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 7, 113–140 (2011).

    PubMed  Article  PubMed Central  Google Scholar 

  15. 15

    Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F. & Bassett, D. S. Optimally controlling the human connectome: the role of network topology. Sci. Rep. 6, 30770 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16

    Pasqualetti, F., Zampieri, S. & Bullo, F. Controllability metrics, limitations and algorithms for complex networks. IEEE Trans. Control Network Syst. 1, 40–52 (2014).

    Article  Google Scholar 

  17. 17

    Lee, E. B. & Markus, L. Foundations of Optimal Control Theory (Krieger Publishing Company, 1967).

    Google Scholar 

  18. 18

    Stigen, T., Danzl, P., Moehlis, J. & Netoff, T. Controlling spike timing and synchrony in oscillatory neurons. BMC Neurosci. 12, P223 (2011).

    PubMed Central  Article  Google Scholar 

  19. 19

    Nabi, A. & Moehlis, J. Single input optimal control for globally coupled neuron networks. J. Neural Eng. 8, 065008 (2011).

    PubMed  Article  PubMed Central  Google Scholar 

  20. 20

    Sarma, S. V. et al. Using point process models to compare neural spiking activity in the subthalamic nucleus of Parkinson's patients and a healthy primate. IEEE Trans. Biomed. Eng. 57, 1297–1305 (2010).

    PubMed  Article  PubMed Central  Google Scholar 

  21. 21

    Greenberg, B. D. et al. Three-year outcomes in deep brain stimulation for highly resistant obsessive–compulsive disorder. Neuropsychopharmacology 31, 2384–2393 (2006).

    PubMed  Article  PubMed Central  Google Scholar 

  22. 22

    Gu, S. et al. Optimal trajectories of brain state transitions. NeuroImage 148, 305–317 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23

    Sporns, O., Tononi, G. & Kötter, R. The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  24. 24

    Miniussi, C., Harris, J. A. & Ruzzoli, M. Modelling non-invasive brain stimulation in cognitive neuroscience. Neurosci. Biobehav. Rev. 37, 1702–1712 (2013).

    PubMed  Article  PubMed Central  Google Scholar 

  25. 25

    Brunoni, A. R. et al. Clinical research with transcranial direct current stimulation (tDCS): challenges and future directions. Brain Stimul. 5, 175–195 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  26. 26

    Teneback, C. C. et al. Changes in prefrontal cortex and paralimbic activity in depression following two weeks of daily left prefrontal TMS. J. Neuropsychiatry Clin. Neurosci. 11, 426–435 (2015).

    Google Scholar 

  27. 27

    Nitsche, M. tDCS/tACS and plasticity. Clin. Neurophysiol. 127, e32 (2016).

    Article  Google Scholar 

  28. 28

    Ruths, J. & Ruths, D. Control profiles of complex networks. Science 343, 1373–1376 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  29. 29

    Medaglia, J. D., Lynall, M.-E. & Bassett, D. S. Cognitive network neuroscience. J. Cogn. Neurosci. 27, 1471–1491 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  30. 30

    Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).

    CAS  Article  Google Scholar 

  31. 31

    Dosenbach, N. U. et al. Distinct brain networks for adaptive and stable task control in humans. Proc. Natl Acad. Sci. USA 104, 11073–11078 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. 32

    Watanabe, T., Masuda, N., Megumi, F., Kanai, R. & Rees, G. Energy landscape and dynamics of brain activity during human bistable perception. Nat. Commun. 5, 4765 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33

    Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215 (2002).

    CAS  Article  Google Scholar 

  34. 34

    Sestieri, C., Corbetta, M., Spadone, S., Romani, G. L. & Shulman, G. L. Domain-general signals in the cingulo-opercular network for visuospatial attention and episodic memory. J. Cogn. Neurosci. 26, 551–568 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  35. 35

    Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98, 676–682 (2001).

    CAS  Article  Google Scholar 

  36. 36

    Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L. & Raichle, M. E. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc. Natl Acad. Sci. USA 103, 10046–10051 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  37. 37

    Fox, M. D. & Raichle, M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  38. 38

    Bassett, D. S. & Gazzaniga, M. S. Understanding complexity in the human brain. Trends Cogn. Sci. 15, 200–209 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  39. 39

    Panzeri, S., Brunel, N., Logothetis, N. K. & Kayser, C. Sensory neural codes using multiplexed temporal scales. Trends Neurosci. 33, 111–120 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  40. 40

    Sutskever, I., Vinyals, O. & Le, Q. V. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems (eds Ghahramani, Z., Welling, M. et al.) 3104–3112 (MIT Press, 2014).

    Google Scholar 

  41. 41

    Merchant, H., Harrington, D. L. & Meck, W. H. Neural basis of the perception and estimation of time. Annu. Rev. Neurosci. 36, 313–336 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. 42

    Stolier, R. M. & Freeman, J. B. Neural pattern similarity reveals the inherent intersection of social categories. Nat. Neurosci. 19, 795–797 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  43. 43

    Bassett, D. S., Yang, M., Wymbs, N. F. & Grafton, S. T. Learning-induced autonomy of sensorimotor systems. Nat. Neurosci. 18, 744–751 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44

    Calhoun, V. D., Miller, R., Pearlson, G. & Adalı, T. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84, 262–274 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45

    Shamir, M. Emerging principles of population coding: in search for the neural code. Curr. Opin. Neurobiol. 25, 140–148 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46

    Zaslaver, A. et al. Hierarchical sparse coding in the sensory system of Caenorhabditis elegans. Proc. Natl Acad. Sci. USA 112, 1185–1189 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47

    Lisman, J. E. & Jensen, O. The theta-gamma neural code. Neuron 77, 1002–1016 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48

    Miura, K., Mainen, Z. F. & Uchida, N. Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron 74, 1087–1098 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49

    Ainsworth, M. et al. Rates and rhythms: a synergistic view of frequency and temporal coding in neuronal networks. Neuron 75, 572–583 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  50. 50

    Csiszar, I. & Körner, J. Information Theory: Coding Theorems for Discrete Memoryless Systems (Cambridge Univ. Press, 2011).

    Google Scholar 

  51. 51

    Pressley, M. & Hilden, K. in Handbook of Child Psychology Vol. 2 (eds Damon, W. & Lerner, R. M. ) Ch. 12 (Wiley, 2006).

    Google Scholar 

  52. 52

    Van Gelder, T. What might cognition be, if not computation? J. Philos. 92, 345–381 (1995).

    Article  Google Scholar 

  53. 53

    Sowell, E. R. et al. Mapping cortical change across the human life span. Nat. Neurosci. 6, 309–315 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  54. 54

    Draganski, B. et al. Temporal and spatial dynamics of brain structure changes during extensive learning. J. Neurosci. 26, 6314–6317 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  55. 55

    Lan, M. J., Chhetry, B. T., Liston, C., Mann, J. J. & Dubin, M. Transcranial magnetic stimulation of left dorsolateral prefrontal cortex induces brain morphological changes in regions associated with a treatment resistant major depressive episode: an exploratory analysis. Brain Stimul. 9, 577–583 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  56. 56

    Betzel, R. F. & Bassett, D. S. Multi-scale brain networks. NeuroImage (in the press).

  57. 57

    Karamintziou, S. et al. Design of a novel closed-loop deep brain stimulation system for Parkinson's disease and obsessive-compulsive disorder. In Proc. 7th International IEEE/EMBS Conference on Neural Engineering 860–863 (IEEE, 2015).

    Google Scholar 

  58. 58

    Reis, J. et al. Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proc. Natl Acad. Sci. USA 106, 1590–1595 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  59. 59

    Snowball, A. et al. Long-term enhancement of brain function and cognition using cognitive training and brain stimulation. Curr. Biol. 23, 987–992 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60

    Deisseroth, K. Optogenetics. Nat. Methods 8, 26–29 (2011).

    CAS  Article  Google Scholar 

  61. 61

    Barker, A. T., Jalinous, R. & Freeston, I. L. Non-invasive magnetic stimulation of human motor cortex. Lancet 325, 1106–1107 (1985).

    Article  Google Scholar 

  62. 62

    Silvanto, J., Muggleton, N. & Walsh, V. State-dependency in brain stimulation studies of perception and cognition. Trends Cogn. Sci. 12, 447–454 (2008).

    PubMed  Article  PubMed Central  Google Scholar 

  63. 63

    Fox, M. D. et al. Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proc. Natl Acad. Sci. USA 111, E4367–E4375 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  64. 64

    George, M. S. & Aston-Jones, G. Noninvasive techniques for probing neurocircuitry and treating illness: vagus nerve stimulation (VNS), transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). Neuropsychopharmacology 35, 301–316 (2010).

    PubMed  Article  PubMed Central  Google Scholar 

  65. 65

    Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 11, 1664 (2015).

    Article  CAS  Google Scholar 

  66. 66

    Kozai, T. D. Y. et al. Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces. Nat. Mater. 11, 1065–1073 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67

    Grosenick, L., Marshel, J. H. & Deisseroth, K. Closed-loop and activity-guided optogenetic control. Neuron 86, 106–139 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68

    Mullen, T. R. et al. Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans. Biomed. Eng. 62, 2553–2567 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  69. 69

    Zotev, V., Phillips, R., Yuan, H., Misaki, M. & Bodurka, J. Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage 85, 985–995 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  70. 70

    Sitaram, R. et al. Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci. 18, 86–100 (2016).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  71. 71

    Florin, E., Bock, E. & Baillet, S. Targeted reinforcement of neural oscillatory activity with real-time neuroimaging feedback. Neuroimage 88, 54–60 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  72. 72

    Kalman, R. E. A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35–45 (1960).

    Article  Google Scholar 

  73. 73

    Tarokh, M. Measures for controllability, observability and fixed modes. IEEE Trans. Automat. Contr. 37, 1268–1273 (1992).

    Article  Google Scholar 

  74. 74

    Norman, K. A., Polyn, S. M., Detre, G. J. & Haxby, J. V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10, 424–430 (2006).

    Article  Google Scholar 

  75. 75

    Kopell, N. J., Gritton, H. J., Whittington, M. A. & Kramer, M. A. Beyond the connectome: the dynome. Neuron 83, 1319–1320 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76

    Motter, A. E. Networkcontrology. Chaos 25, 097621 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  77. 77

    Tang, E. & Bassett, D. S. Control of dynamics in brain networks. Preprint at https://arxiv.org/abs/1701.01531 (2017).

  78. 78

    Sotero, R. C., Trujillo-Barreto, N. J., Iturria-Medina, Y., Carbonell, F. & Jimenez, J. C. Realistically coupled neural mass models can generate EEG rhythms. Neural Comput. 19, 478–512 (2007).

    PubMed  Article  PubMed Central  Google Scholar 

  79. 79

    Brown, E., Moehlis, J. & Holmes, P. On the phase reduction and response dynamics of neural oscillator populations. Neural Comput. 16, 673–715 (2004).

    PubMed  Article  PubMed Central  Google Scholar 

  80. 80

    Kilpatrick, Z. P. in Encyclopedia of Computational Neuroscience (eds Jaeger, D. & Jung, R. ) 3159–3163 (Springer, 2015).

    Google Scholar 

  81. 81

    Denslow, S., Lomarev, M., George, M. S. & Bohning, D. E. Cortical and subcortical brain effects of transcranial magnetic stimulation (TMS)-induced movement: an interleaved TMS/functional magnetic resonance imaging study. Biol. Psychiatry 57, 752–760 (2005).

    PubMed  Article  PubMed Central  Google Scholar 

  82. 82

    Groppa, S. et al. Subcortical substrates of TMS induced modulation of the cortico-cortical connectivity. Brain Stimul. 6, 138–146 (2013).

    PubMed  Article  PubMed Central  Google Scholar 

  83. 83

    Nowzari, C., Preciado, V. M. & Pappas, G. J. Analysis and control of epidemics: a survey of spreading processes on complex networks. IEEE Control Syst. 36, 26–46 (2016).

    Google Scholar 

  84. 84

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

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  85. 85

    Zhao, S. & Iyengar, R. Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu. Rev. Pharmacol. Toxicol. 52, 505 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86

    Rosenbaum, D. A. The Cinderella of psychology: the neglect of motor control in the science of mental life and behavior. Am. Psychol. 60, 308 (2005).

    PubMed  Article  PubMed Central  Google Scholar 

  87. 87

    Frank, M. J., Samanta, J., Moustafa, A. A. & Sherman, S. J. Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism. Science 318, 1309–1312 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  88. 88

    Churchland, P. S. & Sejnowski, T. J. Neural Representation and Neural Computation (MIT Press, 1989).

    Google Scholar 

  89. 89

    Hamilton, R., Messing, S. & Chatterjee, A. Rethinking the thinking cap ethics of neural enhancement using noninvasive brain stimulation. Neurology 76, 187–193 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  90. 90

    Shamoo, A. E. & Resnik, D. B. Responsible Conduct of Research (Oxford Univ. Press, 2009).

    Google Scholar 

  91. 91

    Bronstein, J. M. et al. Deep brain stimulation for Parkinson disease: an expert consensus and review of key issues. Arch. Neurol. 68, 165–165 (2011).

    PubMed  Article  PubMed Central  Google Scholar 

  92. 92

    Pinsker, M., Amtage, F., Berger, M., Nikkhah, G. & van Elst, L. T. in Stereotactic and Functional Neurosurgery 47–51 (Springer, 2013).

    Google Scholar 

  93. 93

    Been, G., Ngo, T. T., Miller, S. M. & Fitzgerald, P. B. The use of tDCS and CVS as methods of non-invasive brain stimulation. Brain Res. Rev. 56, 346–361 (2007).

    PubMed  Article  PubMed Central  Google Scholar 

  94. 94

    Krause, B., Márquez-Ruiz, J. & Kadosh, R. C. The effect of transcranial direct current stimulation: a role for cortical excitation/inhibition balance? Front. Hum. Neurosci. 7, 602 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  95. 95

    Sarkar, A., Dowker, A. & Kadosh, R. C. Cognitive enhancement or cognitive cost: trait-specific outcomes of brain stimulation in the case of mathematics anxiety. J. Neurosci. 34, 16605–16610 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. 96

    Iuculano, T. & Kadosh, R. C. The mental cost of cognitive enhancement. J. Neurosci. 33, 4482–4486 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97

    Bertsekas, D. P. Dynamic Programming and Optimal Control Vol. 1 (Athena Scientific, 1995).

    Google Scholar 

  98. 98

    Wear, S. & Moreno, J. D. Informed consent: patient autonomy and physician beneficence within clinical medicine. HEC Forum 6, 323–325 (Springer, 1994).

    Google Scholar 

  99. 99

    Lentz, J., Kennett, M., Perlmutter, J. & Forrest, A. Paving the way to a more effective informed consent process: recommendations from the clinical trials transformation initiative. Contemp. Clin. Trials 49, 65–69 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  100. 100

    McCabe, S. E., Knight, J. R., Teter, C. J. & Wechsler, H. Non-medical use of prescription stimulants among US college students: prevalence and correlates from a national survey. Addiction 100, 96–106 (2005).

    PubMed  Article  PubMed Central  Google Scholar 

  101. 101

    Sahakian, B. & Morein-Zamir, S. Professor's little helper. Nature 450, 1157–1159 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  102. 102

    Fox, D. Brain buzz. Nature 472, 156–159 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  103. 103

    Wilson, R. A. & Lenart, B. A. in Handbook of Neuroethics 423–439 (Springer, 2015).

    Google Scholar 

  104. 104

    Riis, J., Simmons, J. P. & Goodwin, G. P. Preferences for enhancement pharmaceuticals: the reluctance to enhance fundamental traits. J. Consum. Res. 35, 495–508 (2008).

    Article  Google Scholar 

  105. 105

    Olson, E. T. The Human Animal: Personal Identity Without Psychology (Oxford Univ. Press, 1999).

    Google Scholar 

  106. 106

    Singer, P. Practical Ethics (Cambridge Univ. Press, 2011).

    Google Scholar 

  107. 107

    Dennett, D. C. Elbow Room: The Varieties of Free Will Worth Wanting (MIT Press, 2015).

    Google Scholar 

  108. 108

    Brodmann, K. Vegleichende Lokalisationslehre der Grosshirnde (Barth, 1909).

Download references

Acknowledgements

The authors thank J. Gold, E. Karuza and R. Betzel for helpful comments and discussion regarding this work. J.D.M. acknowledges support from the Office of the Director at the National Institutes of Health through grant number 1-DP5-OD-021352-01. D.S.B. acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory and the Army Research Office through contract numbers W911NF-10-2-0022 and W911NF-14-1-0679, the National Institute of Health (2-R01-DC-009209-11, 1R01HD086888-01, R01-MH107235, R01-MH107703, R01MH109520, 1R01NS099348 and R21-M MH-106799), the Office of Naval Research, and the National Science Foundation (BCS-1441502, CAREER PHY-1554488, BCS-1631550, and CNS-1626008). W.S.-A. acknowledges support from the John Templeton Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.

Author information

Affiliations

Authors

Contributions

J.D.M. wrote the manuscript. P.Z. and W.S.-A. provided feedback and contributed substantially to the conceptualization and editing of ‘The ethics of brain control’. D.S.B. contributed organizational and conceptual input and editing throughout the manuscript.

Corresponding author

Correspondence to John D. Medaglia.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Medaglia, J., Zurn, P., Sinnott-Armstrong, W. et al. Mind control as a guide for the mind. Nat Hum Behav 1, 0119 (2017). https://doi.org/10.1038/s41562-017-0119

Download citation

Further reading

Search

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