Understanding Brain Activity Through Behavior
Maryam Shanechi, the Sawchuk Chair in Electrical and Computer Engineering and founding director of the USC Center for Neurotechnology, along with her team, has developed a groundbreaking AI algorithm that separates brain patterns related to specific behaviors. This development, which could significantly advance brain-computer interfaces and uncover new brain patterns, was recently published in Nature Neuroscience.
As you are reading this, your brain is juggling multiple behaviors. You might be reaching for a cup of coffee, reading aloud, or even noticing a slight hunger. Each of these activities—whether it’s arm movement, speech, or internal states like hunger—creates complex and intertwined electrical signals in the brain. One of the biggest challenges researchers face is distinguishing the brain patterns responsible for a single behavior, such as moving an arm, from all the others happening simultaneously.
The Challenge of Brain-Computer Interfaces
This distinction is critical for developing brain-computer interfaces (BCIs), particularly those designed to restore movement in paralyzed individuals. These patients cannot communicate their intentions to their muscles, but BCIs can decode planned movements directly from brain activity. The decoded signals can then control external devices like robotic arms or computer cursors, restoring functionality.
The DPAD Algorithm: A Breakthrough in Brain Pattern Analysis
Shanechi and her former Ph.D. student, Omid Sani, now a research associate in her lab, tackled this challenge by creating a new AI algorithm called DPAD, which stands for Dissociative Prioritized Analysis of Dynamics. “Our AI algorithm dissociates the brain patterns that encode a specific behavior, such as arm movement, from all other ongoing brain activity,” Shanechi explained. “This allows us to decode movements from brain activity more accurately than previous methods, which improves the functionality of BCIs. Additionally, our method can uncover new brain patterns that might have otherwise gone unnoticed.”
A critical component of the DPAD algorithm is its ability to prioritize learning patterns related to the behavior of interest first, as Sani described: “The algorithm identifies and focuses on behavior-related patterns during the training of a deep neural network. After that, it learns the remaining brain patterns, ensuring they do not obscure or interfere with the key behavior-related signals.” The flexibility of neural networks allows the algorithm to describe a wide range of brain patterns.
Expanding Beyond Movement: Potential for Mental Health Applications
While DPAD currently focuses on movement, it holds the potential to decode mental states such as pain or depression. This could revolutionize the treatment of mental health conditions by monitoring patients’ symptom states in real-time, providing precise feedback to tailor therapies to their specific needs.
“We are very excited to explore how our method could track mental health symptom states,” Shanechi noted. “This advancement could lead to BCIs designed not only for movement disorders and paralysis but also for mental health conditions.”
The DPAD algorithm marks a significant step forward in decoding brain activity for both physical and mental health applications. By separating complex brain patterns, this technology could pave the way for more accurate, responsive BCIs and potentially unlock new treatments for a variety of neurological conditions.
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