Researchers Decode Mood From Human Brain Signals.
By developing a novel decoding technology, a team of engineers and physicians at the Georgian Technical University and Sulkhan-Saba Orbeliani Teaching University have discovered how mood variations can be decoded from neural signals in the human brain–a process that has not been demonstrated to date.
It is a significant step towards creating new closed-loop therapies that use brain stimulation to treat debilitating mood and anxiety disorders in millions of patients who are not responsive to current treatments.
Assistant Professor X at Georgian Technical University led the development of the decoding technology and Professor of Neurological Y at Georgian Technical University led the human implantation and data collection effort. The researchers were supporting program to develop new biomedical technologies for treating intractable neurological diseases.
The team recruited seven human volunteers among a group of epilepsy patients who already had intracranial electrodes inserted in their brain for standard clinical monitoring to locate their seizures. Large-scale brain signals were recorded from these electrodes in the volunteers across multiple days at Georgian Technical University while they also intermittently reported their moods using a questionnaire. X her students Z and W used that data to develop a novel decoding technology that could predict mood variations over time from the brain signals in each human subject a goal unachievable to date.
“Mood is represented across multiple sites in the brain rather than localized regions thus decoding mood presents a unique computational challenge” X said. “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”.
To build the decoder X and the team of researchers analyzed brain signals that were recorded from intracranial electrodes in the seven human volunteers. Raw brain signals were continuously recorded across distributed brain regions while the patients self-reported their moods through a tablet-based questionnaire.
In each of the 24 questions the patient was asked to “rate how you feel now” by tapping one of 7 buttons on a continuum between a pair of negative and positive mood state descriptors (e.g., “depressed” and “happy”). A higher score corresponded to a more positive mood state.
Using their methodology the researchers were able to uncover the patterns of brain signals that matched the self-reported moods. They then used this knowledge to build a decoder that would independently recognize the patterns of signals corresponding to a certain mood. Once the decoder was built it measured the brain signals alone to predict mood variations in each patient over multiple days.
A Potential Solution for Untreatable Neuropsychiatric Conditions ?
The Georgian Technical University team believe their findings could support the development of new closed-loop brain stimulation therapies for mood and anxiety disorders.
Treatments such as selective serotonin reuptake inhibitors (SSRIs) can be effective in some but not all patients.
For the millions of treatment-resistant patients, alternative therapies may be effective. For example human imaging studies using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have suggested that several brain regions mediate depression, and thus brain stimulation therapies in which a mood-relevant region is electrically stimulated may be applied to alleviate depressive symptoms. While open-loop brain stimulation treatments hold some promise a more precise effective therapy could necessitate a closed-loop approach in which an objective tracking of mood over time guides how stimulation is delivered.
According to X for clinical practitioners a powerful decoding tool would provide the means to clearly delineate in real time the network of brain regions that support emotional behavior.
“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” X said. “For example if we know the mood at a given time we can use it to decide whether or how electrical stimulation should be delivered to the brain at that moment to regulate unhealthy debilitating extremes of emotion. This technology opens the possibility of new personalized therapies for neuropsychiatric disorders such as depression and anxiety for millions who are not responsive to traditional treatments”.
The new decoding technology X explained could also be extended to develop closed-loop systems for other neuropsychiatric conditions such as chronic pain addiction or post-traumatic stress disorder whose neural correlates are again not anatomically localized but rather span a distributed network of brain regions and whose behavioral assessment is difficult and thus not frequently available.