Newswise — Using a new model for brain activity, Indiana University computational neuroscience researchers Maria Pope, Richard Betzel, and Olaf Sporns have been exploring striking bursts in activity in the human mind that have never been studied before. These bursts could be used as biomarkers for conditions like ADHD, schizophrenia, depression, and dementia.
The IU research team found that short bursts in brain activity are ongoing events and occur regardless of brain activity. In the course of a 10-minute brain scan, these events will occur roughly 10 to 20 times, each lasting for just a few seconds, the researchers found.
“People didn’t realize that brain regions communicate with each other in brief, intense moments. These moments are punctuated by the events,” stated Olaf SPorns , who is Distinguished Professor and Robert H. Shaffer chair in the College of Arts and Sciences Department of Psychological and Brain Sciences of IU Bloomington.
“Now we can see them. We’ve chosen those moments to build a picture of the brain regions that link up during these events .”
To begin exploring the mysterious workings of these events, the team created a computational model. Maria Pope ,, a graduate student at Sporns’ laboratory and dual Ph.D. candidate both in neuroscience and informatics led the team in building a computational model that replicated these connections. To create synthetic MRI signals using mathematical equations, the model was placed in a similar state to the resting brain.
The model displayed burst-like events, similar to those in human brain recordings.
The paper outlining the model and describing how it compares to the real brain was published in the November 16 issue of the Proceedings of the National Academy of Sciences.
” The model shows that these events are controlled by the brain’s structure network,” Pope stated. They are linked to the brain’s physical structure .”
More specifically, they originate in brain regions and clusters of neurons that are densely interconnected and briefly light up together. Sporns likened the pattern to an orchestra playing music.
” There are moments when the orchestra is unified and there’s a common theme. They are not just playing a single note for 10 minutes. Sporns explained that there are moments when coordinated activity dominates, while other times it might be less. Our model can replicate this ebb-and-flow of coordination, which we see also in the brain. Different brain regions can combine in different ways. Sporns said that the new model’s result could be a game-changer.
“Functional connectivity is a key focus of brain disorder research and has been linked to depression, schizophrenia and dementia. Sporns stated that brain simulations have been used in clinical applications to model lesions and diseases for many years. “This new model gives us a better lens through which to look at the brain, to see more clearly what goes on under both normal and abnormal conditions.”
The researchers are now delving further into why the human brain employs these brief bursts of activity.
” Perhaps the brain developed this kind of activity to be beneficial. Pope stated that something about the structure and timing of events could be helpful to the brain. “For example, many kinds of networked systems have to do occasional system updates or resets, taking some kind of globally useful information and communicating it to the rest of the system.”
Answers to these questions may have implications not only for understanding the brain, but also for the study of neural networks and artificial intelligence.
“A clearer mapping of structure and function at the individual level could have implications for how we diagnose neurological disease and lead to personalized treatment and intervention,” said Betzel, professor in the College of Arts and Sciences Department of Psychological and Brain Sciences.
The study was funded by a National Science Foundation grant, “NRT” (NSF 1735095) Maria Pope and a National Science Foundation grant, “NCS-FO” (NSF 2023985) Richard Betzel and Olaf Sporns). Makoto Fukushima from the Division of Information Science at the Graduate School of Science and Technology and the Data Science Center at Nara Institute of Science and Technology in Nara (Nara, Japan) and Center for Information and Neural Networks at National Institute for Information and Communications Technology Suita, Osaka Japan are other co-authors.
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