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. 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. More recently, the Shanechi Lab has developed novel methods that can dissociate those dynamics in neural activity that are most predictive of behavior and can significantly improve decoding. Her lab has also developed methods that can simultaneously use multiple spatiotemporal scales of neural measurements to model their relationships and improve decoding. 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. 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. Shanechi is also interested in the application of neural decoding algorithms to psychiatric disorders, such as
PTSD and
depression. Her research team developed a method to decipher the mood of a person from their brain activity. They measured the brain activity of seven patients who had electrodes implanted in their brain to monitor
epilepsy. The paper on this work was awarded the 3rd prize in the International BCI Awards. Her lab has also developed a stochastic stimulation and modeling approach that can predict the response of multi-regional brain networks implicated in neuropsychiatric disorders to ongoing deep brain stimulation (DBS). In the future, Shanechi wants to develop these techniques in order to stimulate the brain automatically when a change in mood is detected. == Awards ==