Researchers decode the mood of human cerebral signals

In developing a new decoding technology, a team of engineers and physicians from the University of Southern California and the University of San Francisco discovered how to decode mood variations from neural signals. nowadays.

Their study, published in Nature Biotechnologyis a milestone in the creation of new closed-loop therapies that use brain stimulation to treat debilitating mood disorders and anxiety in millions of patients who do not respond to current treatments.

Maryam Shanechi, an assistant professor and Viterbi Chair in Ming Hsieh's Department of Electrical Engineering and USC's Post Graduate Program in Neuroscience, led the development of decoding technology. The researchers supported the Defense Advanced Research Projects Agency's SUBNETS program to develop new biomedical technologies for the treatment of intractable neurological diseases.

The team recruited seven human volunteers from a group of epilepsy patients who already had intracranial electrodes inserted into their brains for standard clinical monitoring to locate their seizures. Large-scale brain signals were recorded from these electrodes in volunteers for several days at UCSF, while they also intermittently reported their mood for help. a questionnaire. Shanechi and his students, Omid Sani and Yuxiao Yang, used this data to develop a new decoding technology that can predict mood changes over time from brain signals in every human subject, an unachievable goal at this time. day.

"The mood is represented on several sites of the brain rather than in localized regions, which explains why the decoding mode presents a unique IT challenge," Shanechi said. "This challenge is made more difficult by the fact that we do not fully understand how these regions coordinate their activity to encode mood and that this mood is inherently difficult to evaluate.To meet this challenge, we needed to develop new decoding methodologies that incorporate neural signals from distributed brain sites while facing rare opportunities to measure moods. "

To build the decoder, Shanechi and the team of researchers analyzed cerebral signals recorded from intracranial electrodes in the seven human volunteers. Raw cerebral signals were recorded continuously across distributed brain regions, while patients self-reported their moods using a tablet questionnaire.

In each of the 24 questions, the patient was asked to "evaluate his current state" by pressing one of the 7 buttons of a continuum between a pair of mood state descriptors. negative and positive ("depressed" and "happy", for example). A higher score corresponds to a more positive state of mind.

Using their methodology, researchers were able to discover patterns of cerebral signals corresponding to the declared moods. They then used this knowledge to build a decoder that would independently recognize patterns of signals corresponding to a certain mood. Once the decoder was built, he measured brain signals alone to predict mood changes in each patient for several days.

The USC / UCSF team believes their findings could support the development of novel closed-loop brain stimulation therapies for mood and anxiety disorders.

Data from the 2016 National Survey of Drug Use and Health revealed that 16.2 million adults in the United States (approximately 6.7% of all US adults) had at least one episode major depressive. Treatments such as selective serotonin reuptake inhibitors (SSRIs) may be effective in some patients, but not all.

According to the STAR * D study funded by the National Institutes of Health – the longest study evaluating depression treatments – nearly 33% of patients with major depression do not respond to treatment (over 5.3 million people in the United States). In June 2018, the Centers for Disease Control and Prevention reported that suicide was on the rise in the United States.

For millions of treatment-resistant patients, alternative therapies can be effective. For example, human imaging studies using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have suggested that several brain regions intervene in depression and that therapies brain stimulation in which a region depressive symptoms. Although open loop cerebral stimulation treatments are promising, more accurate and effective treatment may require a closed-loop approach in which objective monitoring of mood over time indicates how the stimulation is administered.

According to Shanechi, for clinical practitioners, a powerful decoding tool would clearly define, in real time, the network of brain regions that support emotional behavior.

"Our goal is to create a technology that helps clinicians get a more accurate map of what's happening in a depressed brain at a given time and a way to understand what the brain signal tells us about the mood. more objective evaluation of mood over time to guide the treatment of a given patient, "said Shanechi. "For example, if we know the mood at a given time, we can use it to decide if, or how, electrical stimulation should be administered to the brain at that time to regulate the debilitating emotional extremes. the possibility of new personalized therapies for neuropsychiatric disorders such as depression and anxiety in millions of people who do not respond to traditional treatments. "

Shanechi explained that the new decoding technology could also be extended to develop closed-loop systems for other neuropsychiatric conditions such as chronic pain, drug addiction or post-traumatic stress disorder whose neural correlates are not localized. regions of the brain, and whose behavioral assessment is difficult and therefore not frequently available.

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This research was funded in part by the Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement No. W911NF-14-2-0043, issued by the US Department of Defense's Defense Research Projects Agency (DARPA). Contracts The opinions, opinions and / or conclusions expressed are those of the authors and should not be construed as representing the views or official policies of the Department of Defense or the US Government.

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