Maryam Shanechi

Maryam M. Shanechi is a neuroengineer. She studies ways of decoding the brain's activity to control brain-machine interfaces. She was honored as one of MIT Technology Review's Innovators under 35 in 2014 and one of the Science News 10 scientists to watch in 2019. She is Assistant Professor and Viterbi Early Career Chair in Electrical and Computer Engineering at the Viterbi School of Engineering, and a member of the Neuroscience Graduate Program at the University of Southern California.

Maryam M. Shanechi
Born1981 (age 38–39)
Iran
AwardsMIT Technology Review's Innovators Under 35

NSF CAREER Award

ONR Young Investigator Award

Science News 10 Scientists to Watch

Popular Science Brilliant 10
Academic background
Alma materUniversity of Toronto, MIT
ThesisReal-time brain-machine interface architectures : neural decoding from plan to movement (2011)

Early life and career

Shanechi was born in Iran and moved to Canada with her family when she was 16.[1][2] She received her bachelor's degree in engineering from the University of Toronto in 2004. She then moved to MIT, where she completed her master's degree in electrical engineering and computer science in 2006 and her PhD in 2011.[3] She completed a postdoc at Harvard Medical School before moving to the University of California, Berkeley, in 2012. She held a faculty position at Cornell University, before moving to the University of Southern California, where she is currently an assistant professor and Viterbi Early Career Chair within the USC Viterbi School of Engineering.[1][3][4][2]

Research

While pursuing her graduate degree at MIT, Shanechi became interested in decoding the brain, the idea of reading out the original meaning from brain signals. She developed an algorithm to determine where a monkey wanted to point the cursor on a screen based on the animal's brain activity.[1][5] She later improved upon her work by including high-rate decoding, meaning the decoding happened over a few milliseconds, rather than every 100 milliseconds, which is the standard for traditional methods.

In 2013 she developed a brain decoding method that could help automatically control the amount of anesthesia that is to be administered to a patient.[6][7] Her team, which included colleagues from Massachusetts General Hospital and Massachusetts Institute of Technology was able to control the depth of the medically-induced coma in rodents automatically based on their brain activity.[6][7][8][9]

Shanechi is also interested in the application of neural decoding algorithms to psychiatric disorders, such as PTSD and depression.[2][10][11] Her research team developed a method to decipher the mood of a person from their brain activity.[12][13] They measured the brain activity of seven patients who had electrodes implanted in their brain to monitor epilepsy.[11] The patients answered questions about their mood while the electrodes were implanted. Using the data about the mood and the brain activity, Shanechi's lab was able to match the two together and decipher which brain activity was related to which mood.[11][12] The paper on this work was awarded the 3rd prize in the International BCI Awards.[14] In the future, Shanechi wants to develop this technique in order to stimulate the brain automatically when a change in mood is detected.[1][15]

Awards

Selected publications

Shanechi's publications include:

  • Shanechi, Maryam; Hu, Rollin; Marissa, Powers; Wornell, Gregory; Brown, Emery; Williams, Ziv (2012). "Neural population partitioning and a concurrent brain-machine interface for sequential motor function". Nature Neuroscience. 15 (12): 1715–1722. doi:10.1038/nn.3250. PMC 3509235. PMID 23143511.
  • Shanechi, Maryam M.; Chemali, Jessica J.; Liberman, Max; Solt, Ken; Brown, Emery N. (2013-10-31). "A Brain-Machine Interface for Control of Medically-Induced Coma". PLOS Computational Biology. 9 (10): e1003284. Bibcode:2013PLSCB...9E3284S. doi:10.1371/journal.pcbi.1003284. ISSN 1553-7358. PMC 3814408. PMID 24204231.
  • Shanechi, Maryam M.; Orsborn, Amy L.; Moorman, Helene G.; Gowda, Suraj; Dangi, Siddharth; Carmena, Jose M. (2017). "Rapid control and feedback rates enhance neuroprosthetic control". Nature Communications. 8 (1): 13825. Bibcode:2017NatCo...813825S. doi:10.1038/ncomms13825. ISSN 2041-1723. PMC 5227098. PMID 28059065.
  • Sani, Omid G.; Yang, Yuxiao; Lee, Morgan B.; Dawes, Heather E.; Chang, Edward F.; Shanechi, Maryam M. (2018). "Mood variations decoded from multi-site intracranial human brain activity". Nature Biotechnology. 36 (10): 954–961. doi:10.1038/nbt.4200. ISSN 1546-1696. PMID 30199076.
  • Shanechi, Maryam M. (2019). "Brain-machine interfaces from motor to mood" Nature Neuroscience. 22 (10): 1554–1564. doi.org/10.1038/s41593-019-0488-y
gollark: (and replace `total -= 254./3.;` accordingly, obviously)
gollark: Make `total` into an int. Replace `total += 254./3.;` with `total = min(2, max(0, total + 1))` or something, if the arduinos' weird language has that. Do `analogWrite(LED, total * 85)`. QED.
gollark: Make the total an integer from 0 to 2 or something and enforce this, then multiply by 85 in the analogWrite bit.
gollark: The main issue is that data is just *data*, and can't corrupt itself in some way if you do stuff wrong or enforce timeouts, only the programs operating on it can (and generally do).
gollark: Basically, if someone copies the relevant data elsewhere, to a system without your time limits, you can't enforce them without it actually being computationally hard.

References
  1. "Maryam Shanechi designs machines to read minds". Science News. 2019-10-02. Retrieved 2019-11-22.
  2. "Maryam Shanechi | Innovators Under 35". MIT Technology Review. Retrieved 2019-11-22.
  3. "USC - Viterbi School of Engineering - Viterbi Faculty Directory". viterbi.usc.edu. Retrieved 2019-11-22.
  4. "ECE Seminar Series: Maryam M. Shanechi, of the University of Southern California". today.iit.edu. Retrieved 2019-11-22.
  5. Shanechi, Maryam M.; Hu, Rollin C.; Powers, Marissa; Wornell, Gregory W.; Brown, Emery N.; Williams, Ziv M. (2012). "Neural population partitioning and a concurrent brain-machine interface for sequential motor function". Nature Neuroscience. 15 (12): 1715–1722. doi:10.1038/nn.3250. ISSN 1546-1726. PMC 3509235. PMID 23143511.
  6. "Brain-machine interface allows anesthesia control". Cornell Chronicle. Retrieved 2019-11-22.
  7. Lewis, Tanya (2013-11-01). "Brain-Machine Interface Puts Anesthesia on Autopilot". msnbc.com. Retrieved 2019-11-22.
  8. Shanechi, Maryam M.; Chemali, Jessica J.; Liberman, Max; Solt, Ken; Brown, Emery N. (2013-10-31). "A Brain-Machine Interface for Control of Medically-Induced Coma". PLOS Computational Biology. 9 (10): e1003284. Bibcode:2013PLSCB...9E3284S. doi:10.1371/journal.pcbi.1003284. ISSN 1553-7358. PMC 3814408. PMID 24204231.
  9. Yang, Yuxiao; Lee, Justin T; Guidera, Jennifer A; Vlasov, Ksenia Y; Pei, JunZhu; Brown, Emery N; Solt, Ken; Shanechi, Maryam M (2019-06-01). "Developing a personalized closed-loop controller of medically-induced coma in a rodent model". Journal of Neural Engineering. 16 (3): 036022. doi:10.1088/1741-2552/ab0ea4. ISSN 1741-2560. PMID 30856619.
  10. Waltz, Emily. "The Mood Ring of Algorithms Could Zap Your Brain to Help You Feel Better". IEEE Spectrum: Technology, Engineering, and Science News. Retrieved 2019-11-22.
  11. "Brain-zapping implants that fight depression inch closer to reality". Science News. 2019-02-10. Retrieved 2019-11-22.
  12. Sani, Omid G.; Yang, Yuxiao; Lee, Morgan B.; Dawes, Heather E.; Chang, Edward F.; Shanechi, Maryam M. (2018). "Mood variations decoded from multi-site intracranial human brain activity". Nature Biotechnology. 36 (10): 954–961. doi:10.1038/nbt.4200. ISSN 1546-1696. PMID 30199076.
  13. "Tracking brain waves to decode mood could help fight depression". New Atlas. 2018-09-11. Retrieved 2019-11-22.
  14. "2019". BCI Award. Retrieved 2019-11-22.
  15. Shanechi, Maryam M. (2019-09-24). "Brain–machine interfaces from motor to mood". Nature Neuroscience. 22 (10): 1554–1564. doi:10.1038/s41593-019-0488-y. ISSN 1097-6256. PMID 31551595.
  16. "Brilliant 10: Maryam Shanechi Decodes The Brain To Unlock Its Potential". Popular Science. Retrieved 2019-11-22.
  17. "USC - Viterbi School of Engineering - Brain, Meet Machine". viterbi.usc.edu. Retrieved 2019-11-22.
  18. "NSF Award Search: Award#1453868 - CAREER: Generalizable, Robust, and Closed-Loop Brain-Machine Interface Control Architectures". www.nsf.gov. Retrieved 2019-11-23.
  19. "2019 Young Investigator Award Recipients".
  20. "USC Viterbi scholar to lead research on brain-machine interfaces". USC News. 2016-04-18. Retrieved 2019-11-23.
  21. "13 U of T Engineering alumni and students honoured at 2019 EAN Awards". U of T Engineering News. 2019-11-08. Retrieved 2019-11-23.
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