Beta frequency rhythms (15-29 Hz) are prominent signatures of brain activity that can be measured using electro/magnetoencephalography (EEG/MEG). High beta activity is associated with inhibited tactile perception, and can be modulated through selective attention Beta has been shown to exist as transient events, rather than a continuous oscillation. These events have a stereotyped waveform shape, each lasting around 150ms with a prominent trough lasting 50ms, which is the period of beta. A novel finding in our lab is a lower frequency oscillation that appears to be phase locked to the beta event. We are interested in analyzing features of this waveform oscillation, to obtain insight about the underlying neurophysiology surrounding beta events. Traditional signal analysis methods are based on the Fourier transform, which models neural data as sums of component sinusoids. Here, our aim is to identify non-sinusoidal characteristics of MEG data collected by our lab. To do this, we employed a tool developed by the Voytek lab at UCSD. that quantifies oscillatory features in the time domain on a cycle-by-cycle basis. Through fitting this data analysis method to our data, we were able to obtain waveform feature outputs from averaged and individual trial data. Based on our preliminary results, quantifying the period of each cycle in the grand average signal across subjects reveals that as the phase-locked oscillation approaches the beta event, the frequency appears to increase from the alpha (8-12 Hz) to the beta range (15-29 Hz).The opposite pattern is observed following the beta event. Using this analysis, we are interested in examining if there are differences across subjects or experimental conditions that predict tactile perception or shifts in attention. If so, we can use our lab’s computational neural modeling framework (HNN) to study the neurophysiological source of these differences, providing an unprecedented link between novel measures of human information processing and their underlying circuit level generators.
Jayaram, Rahul,
"Characterizing Beta Event-Locked Oscillations in Human Electrophysiology Data"
(2020).
Summer Research Symposium.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:1139281/
Each year, Brown University showcases the research of its undergraduates at the Summer Research Symposium. More than half of the student-researchers are UTRA recipients, while others receive funding from a variety of Brown-administered and national programs and fellowships and go …
