Study Finds Consistent Brain Patterns Across Different Tasks

By separating these patterns from the influence of visual inputs, Maryam Shanechi and her colleagues have created a novel machine learning technique that uncovers unexpectedly consistent internal brain patterns in a variety of subjects. The research has been published in the National Academy of Sciences Proceedings.  
Our brain must process information when engaging in a variety of routine movement actions, such reaching for a book. This information is frequently visual, such as recognizing the location of the book. The internal processing of this information by our brain is then required to synchronize our muscle movements and complete the movement. 
However, how can our brain’s millions of neurons accomplish such a task? To answer this question, it is necessary to examine the patterns of collective neuronal activity while separating the contribution of input from the intrinsic (also known as internal) activities of the neurons, whether these processes are movement relevant.  
That’s what Shanechi, Parsa Vahidi, her Ph.D. student, and Omid Sani, a research associate in her lab, accomplished by creating a novel machine-learning technique that considers sensory input as well as movement behavior when modeling neuronal activity. “Prior methods for analyzing brain data have either considered neural activity and input but not behavior, or considered neural activity and behavior but not input,” Shanechi stated.  
“We created a technique that can extract hidden brain patterns while taking into account all three signals: input, behavior, and neural activity. This made it possible for us to distinguish between intrinsic and input-related neural patterns and identify which patterns were associated with and were not with movement behavior.  
Using this technique, Shanechi and her colleagues examined three publicly accessible datasets in which three separate participants completed one of two unique movement tasks: either a cursor was moved consecutively to random positions on a computer screen, or it was moved over a grid. 
“When using methods that did not consider all three signals, the patterns found in neural activity of these three subjects looked different,” Vahidi stated. However, the scientists discovered a strikingly consistent hidden pattern from the brain activity of all three participants important to movement when they applied the novel method to consider all three signals. Even though the tasks completed by the three subjects differed, there was nevertheless a commonality.  
“In addition to revealing this new consistent pattern, the method also improved the prediction of neural activity and behavior compared to when all three signals were not considered during machine learning, as in prior work,” Sani stated. “The new method enables researchers to more accurately model neural and behavioral data by accounting for various measured inputs to the brain, such as sensory inputs as in this work, electrical or optogenetic stimulation, or even input from different brain areas.”  
We can gain a better understanding of how our brains execute motions based on input from the outside environment by utilizing this strategy and the identified pattern. Furthermore, by optimizing external inputs like deep brain stimulation therapy, this method can aid in the development of future brain-computer interfaces that regulate abnormal brain patterns in disorders like major depression by modeling the effect of input and separating out intrinsic patterns that are behavior-relevant.  
“We are excited about how this algorithm could facilitate both scientific discoveries and the development of future neurotechnologies for millions of patients with neurological or neuropsychiatric disorders,” Shanechi stated. 

Journal Reference  

Parsa Vahidi et al, Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior, Proceedings of the National Academy of Sciences (2024). DOI: 10.1073/pnas.2212887121.