Meditation: Project Dhyaan
Project Dhyaan: The goal of this project is to study the effect of various meditative practices on stimulus-induced gamma oscillations (30-80 Hz) recorded using electroencephalogram (EEG). EEG often shows rhythmic patterns at various frequency bands such as alpha (8-12 Hz) and gamma, which have been linked to different behavioral states and have been shown to be modulated by meditation. However, researchers so far have mainly focused on endogenous brain waves. Gamma waves, which have been heavily linked with high-level cognition, can also be induced by the presentation of certain visual stimuli. Further, these stimulus-induced-gamma waves weaken with age and mental disorders such as Alzheimer’s Disease (AD). Our main goal is to study whether/how meditation affects stimulus-induced gamma.
In collaboration with Dr. J Patrick Mayo (University of Pittsburgh), who had recorded spikes and LFP data from area V4 of monkeys while they were engaged in an attentional task as part of his postdoctoral work, we found that for a single electrode, attention was more discriminable using high frequency (>30 Hz) LFP power than spikes, which is expected because LFP represents a population signal and therefore is expected to be less noisy than spikes. However, previous studies have shown that when multiple electrodes are used, spikes can outperform LFPs. Surprisingly, in our study, spikes did not outperform LFPs when discriminability was computed using multiple electrodes, even though the LFP activity was highly correlated across electrodes compared with spikes. These results constrain the spatial scale attention operates over and highlight the usefulness of LFPs in studying attention (Prakash et al., 2021, Cerebral Cortex). We hypothesize that the superior performance of LFPs compared to spikes is because the spatial extent of attentional focus is comparable to LFP (Prakash et al., 2022, Current Opinion in Neurobiology).
Prerviously, we studied the relationship between selective attention, gamma rhythm, and a neural mechanism called normalization, which has been recently associated with attention. For example, we found that the effect of normalization and attention are highly correlated across neurons in MT (Ni, Ray and Maunsell, 2012, Neuron), and gamma power was correlated with the strength of normalization, independent of attentional load (Ray, Ni and Maunsell, 2013, PLoS Biology).