Computational Model Suggests Explanation for Correlation between Gamma Oscillation and Cognitive Process
Selective Attention Increases Oscillatory Brain Wave Activity
MEDFORD/SOMERVILLE, Mass- Ever since the invention of electroencephalography (EEG), it has been known that electrical activity in the brain is often oscillatory. Different frequencies of oscillation – electrical wave patterns - are associated with different mental states and activities.
Gamma oscillations are relatively fast frequencies (30-90Hz) associated with sensory processing, short term memory and attention. Researchers in neuroscience have accumulated experimental evidence that links gamma oscillations with the brain's capacity to focus on one stimulus while it filters out others.
In a new study published in the November issue of the "Proceedings of the National Academy of Sciences," co-authors Christoph Börgers, professor of mathematics at Tufts University, and Nancy Kopell and Steven Epstein of Boston University's Department of Mathematics and Center for Biodynamics, present a computational model to attempt to explain the link between attention and gamma oscillations.
"The goal of work of this kind is to understand what, physically, happens in the brain when a person or animal pays attention to one thing and filters out others," Börgers explained. "This is of course a question of great intrinsic interest, but we also hope that at some point in the future, a clearer understanding of these processes may contribute to the medical treatment of attention disorders."
The researchers' mathematical model simulates the interplay between excitatory neurons and a class of inhibitory neurons called fast-spiking interneurons in the brain's cortical network when multiple visual stimuli are presented. Excitatory neurons transmit and amplify signals while inhibitory neurons work to inhibit and refine them. Both have a role in the generation of brain waves. The researchers simulated a network populated by 160 excitatory cells and 40 interneurons.
In the presence of multiple stimuli, the model suggested that interneurons become over-excited, which suppresses the activity of the excitatory neurons and prevents the network from oscillating at gamma frequency. Moreover, the absence of oscillation further increases the suppressive effect of the interneurons.
The model suggests a different effect when the brain directs attention to one stimulus. Here, the fast-spiking interneurons are inhibited by a second, different class of inhibitory neurons excited by a substance called acetylcholine. This allows the emergence of gamma oscillations. The gamma oscillations diminish the interneurons' ability to dampen the excitatory neurons that respond to the stimulus but strengthen the interneurons' ability to suppress excitatory neurons responding to distracting stimuli.
The research was funded by grants from the National Science Foundation and the National Institutes of Health as part of the Collaborative Research in Computational Neuroscience Program.
About Tufts University
Tufts University, located on three Massachusetts campuses in Boston, Medford/Somerville, and Grafton, and in Talloires, France, is recognized among the premier research universities in the United States. Tufts enjoys a global reputation for academic excellence and for the preparation of students as leaders in a wide range of professions. A growing number of innovative teaching and research initiatives span all Tufts campuses, and collaboration among the faculty and students in the undergraduate, graduate and professional programs across the university's schools is widely encouraged.
