Abstract
Neuropsychiatric disorders are a leading cause of disability worldwide. In our research project we are developing brain-computer interface technology to decode mood states that determine appropriate stimulation parameters for real-time therapy.
Equal contribution: Yuxiao Yang, Omid G. Sani
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
P. Ahmadipour, Y. Yang, E.F. Chang, M.M. Shanechi, Adaptive tracking of human ECoG network dynamics. J. Neural Eng. (2020). https://doi.org/10.1088/1741-2552/abae42
D.D. Dougherty 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(4), 240–248 (2015). https://doi.org/10.1016/j.biopsych.2014.11.023
W.C. Drevets, Neuroimaging and neuropathological studies of depression: implications for the cognitive-emotional features of mood disorders. Curr. Opin. Neurobiol. 11(2), 240–249 (2001). https://doi.org/10.1016/S0959-4388(00)00203-8
P. Ekkekakis, The Measurement of Affect, Mood, and Emotion: A Guide for Health-Behavioral Research (Cambridge University Press, New York, 2013)
P.E. Holtzheimer et al., Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. Lancet Psychiatry 4(11), 839–849 (2017). https://doi.org/10.1016/S2215-0366(17)30371-1
D.J. Kupfer, E. Frank, M.L. Phillips, Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet 379(9820), 1045–1055 (2012). https://doi.org/10.1016/S0140-6736(11)60602-8
A.M. Lozano, H.S. Mayberg, P. Giacobbe, C. Hamani, R.C. Craddock, S.H. Kennedy, Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression. Biol. Psychiatry 64(6), 461–467 (2008). https://doi.org/10.1016/j.biopsych.2008.05.034
D.A. Malone et al., Deep brain stimulation of the ventral capsule/ventral striatum for treatment-resistant depression. Biol. Psychiatry 65(4), 267–275 (2009). https://doi.org/10.1016/j.biopsych.2008.08.029
H.S. Mayberg et al., Deep brain stimulation for treatment-resistant depression. Neuron 45(5), 651–660 (2005). https://doi.org/10.1016/j.neuron.2005.02.014
“2018 National Survey of Drug Use and Health (NSDUH).” (2018). Accessed 11 Apr 2020 [Online]. Available: https://www.samhsa.gov/data/release/2018-national-survey-drug-use-and-health-nsduh-releases
A.J. Rush et al., Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am. J. Psychiatry 163(11), 1905–1917 (2006). https://doi.org/10.1176/ajp.2006.163.11.1905
O.G. Sani, Y. Yang, M.B. Lee, H.E. Dawes, E.F. Chang, M.M. Shanechi, Mood variations decoded from multi-site intracranial human brain activity. Nat. Biotechnol. 36, 954 (2018). https://doi.org/10.1038/nbt.4200
T.E. Schlaepfer et al., Deep brain stimulation to reward circuitry alleviates anhedonia in refractory major depression. Neuropsychopharmacology 33(2), 368–377 (2008). https://doi.org/10.1038/sj.npp.1301408
T.E. Schlaepfer, B.H. Bewernick, S. Kayser, B. Mädler, V.A. Coenen, Rapid effects of deep brain stimulation for treatment-resistant major depression. Biol. Psychiatry 73(12), 1204–1212 (2013). https://doi.org/10.1016/j.biopsych.2013.01.034
M.M. Shanechi, Brain–machine interfaces from motor to mood. Nat. Neurosci. 22(10), 1554–1564 (2019). https://doi.org/10.1038/s41593-019-0488-y
H.A. Whiteford et al., Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 382(9904), 1575–1586 (2013). https://doi.org/10.1016/S0140-6736(13)61611-6
A.S. Widge et al., Treating refractory mental illness with closed-loop brain stimulation: progress towards a patient-specific transdiagnostic approach. Exp. Neurol. 287, 461–472 (2017). https://doi.org/10.1016/j.expneurol.2016.07.021
Y. Yang, A.T. Connolly, M.M. Shanechi, A control-theoretic system identification framework and a real-time closed-loop clinical simulation testbed for electrical brain stimulation. J. Neural Eng. 15(6), 066007 (2018). https://doi.org/10.1088/1741-2552/aad1a8
Y. Yang, O.G. Sani, E.F. Chang, M.M. Shanechi, Dynamic network modeling and dimensionality reduction for human ECoG activity. J. Neural Eng. 16(5), 056014 (2019). https://doi.org/10.1088/1741-2552/ab2214
Y. Yang, P. Ahmadipour, M.M. Shanechi, Adaptive latent state modeling of brain network dynamics with real-time learning rate optimization. J. Neural Eng. (2020). https://doi.org/10.1088/1741-2552/abcefd
Y. Yang, S. Qiao, O.G. Sani, J.I. Sedillo, B. Ferrentino, B. Pesaran, M.M. Shanechi, Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation. Nat. Biomed. Eng. (2021). https://doi.org/10.1038/s41551-020-00666-w
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Yang, Y., Sani, O.G., Lee, M.B., Dawes, H.E., Chang, E.F., Shanechi, M.M. (2021). Developing a Closed-Loop Brain-Computer Interface for Treatment of Neuropsychiatric Disorders Using Electrical Brain Stimulation. In: Guger, C., Allison, B.Z., Tangermann, M. (eds) Brain-Computer Interface Research. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-60460-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-60460-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60459-2
Online ISBN: 978-3-030-60460-8
eBook Packages: EngineeringEngineering (R0)