Biology

Tracking brain waves to decode mood could help fight depression

A new technique may be able to decode brain waves to infer what mood a patient is in, which could eventually be used to better time treatments of depression and anxiety
A new technique may be able to decode brain waves to infer what mood a patient is in, which could eventually be used to better time treatments of depression and anxiety

We all know that our moods can be mysterious, sometimes shifting seemingly at random and not necessarily dependent on what we're doing. Unfortunately, even neuroscience in general is stumped by what's going on in the brain when we feel joy or sadness. In an effort to peek inside our moody minds, a team of researchers in California has developed a technique to read brain signals and infer what mood a patient may be in, which could lead to new treatments for depression and anxiety.

The brain is a complicated beast, so it can be hard to tell which bits are responsible for which functions – a task not made any easier by the fact many functions aren't isolated to specific regions but involve various areas across the brain. Emotions are one example of this, making it extremely difficult to read and understand them.

"Mood is represented across multiple sites in the brain rather than localized regions, thus decoding mood presents a unique computational challenge," says Maryam Shanechi, co-lead author of the study. "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 begin tackling that problem, the study analyzed neural signals from across the brains of participants. To get the best data possible, the researchers studied a group of seven patients who already had electrodes implanted in their brains to help monitor epileptic seizures. The patients periodically reported their mood using a tablet-based questionnaire, selecting one of seven spots along 24 sliding scales of opposite emotions (like depressed and happy).

Over time, the researchers matched up the reported moods with the brain signal readings at that time to build up a profile of what each mood looked like. Using that, they then created a "decoder" that would be able to read the brain waves of a patient and predict what they may be feeling.

The system could have applications beyond reading emotions – it might help bring unruly ones under control. Already, studies have been looking into treating disorders like anxiety and depression by electrically stimulating the brain. This new decoder system could pair up with that technology to monitor when a person really needs a zap to feel better, and where it should be targeted.

"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," says Shanechi. "This will allow us to obtain a more objective assessment of mood over time to guide the course of treatment for a given patient. 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."

Of course, it's still very early days for this kind of work. Emotions are notoriously fickle, and the brain is so complex that it's difficult to know for sure which signals are correlating with them. Intracranial electrodes are pretty invasive too, and a tiny sample size of seven people is hardly enough to declare the case closed. Still, it's an interesting first step.

The research was published in the journal Nature Biotechnology.

Source: University of Southern California

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