Decoding human brain signals could help treat depression
Findings from teams at the University of Southern California (USC) and UC San Francisco (UCSF) may lead to the discovery of better treatments for debilitating mood and anxiety disorders.
Their study, published in Nature Biotechnology, is another step towards the creation of closed-loop therapies using brain stimulation for people living with these disorders.
Assistant professor Maryam Shanechi of the Ming Hsieh Department of Electrical Engineering and the neuroscience graduate programme at USC led the development of the decoding technology, while Edward Chang, professor of neurological surgery at UCSF, led the human implantation and data collection effort.
Monitoring brain signals
The team recruited seven human volunteers among a group of epilepsy patients who already had intracranial electrodes inserted in their brain to monitor the location of seizures.
The electrodes recorded large-scale brain signals across multiple days at UCSF, while the participants recorded their moods using a questionnaire at regular intervals. Shanechi and her students, Omid Sani and Yuxiao Yang, used that data to develop a novel decoding technology that could predict mood variations over time from the brain signals in each human subject.
Shanechi said that decoding the human mood presents “a unique computational challenge” as it is represented across multiple sites within the brain. She added: “This challenge is made more difficult by the fact that we don’t have a full understanding of how these regions coordinate their activity to encode mood, and that mood is inherently difficult to assess.
“To solve this challenge, we needed to develop new decoding methodologies that incorporate neural signals from distributed brain sites while dealing with infrequent opportunities to measure moods.”
In each of the 24 questions in the patient questionnaire, the individuals were asked to ‘rate how you feel now’ by tapping one of seven buttons. The buttons represented a continuum between a pair of negative and positive mood state descriptors.
Researchers were then able to uncover patterns of brain signals that matched the moods reported by the study participants. Using this knowledge, they created a decoder that would independently recognise the patterns of signals corresponding to a certain mood. Once the researchers created it, the decoder measured the brain signals alone to predict mood variations in each patient over multiple days.
A new frontier for managing depression and anxiety?
The team believes the findings could support the development of new closed-loop brain stimulation therapies. In particular, the researchers are hoping these therapies could help patients for whom some treatments are no longer working.
Shanechi said: “Our goal is to create a technology that helps clinicians obtain a more accurate map of what is happening in a depressed brain at a particular moment in time, and a way to understand what the brain signal is telling us about mood. This will allow us to obtain a more objective assessment of mood over time to guide the course of treatment for a given patient.”
She added that the new decoding system might be useful to create other closed-loop systems for conditions such as addiction and chronic pain.
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