Associate Professor

Centre For Neuroscience

Associate Faculty in Electrical Engineering

Indian Institute of Science

Our senses convey rich and detailed information about the external world, but we can selectively attend to some details while ignoring others. This capacity for selective attention is critical for survival and essential for complex tasks. Our lab studies the mechanisms of selective attention in two species (humans and non-human primates) and at four “levels” of recordings: spikes, local field potential (LFP), electrocorticogram (ECoG) and electroencephalogram (EEG) (see Figure below). In particular, we focus on a brain rhythm called gamma (30-80 Hz), which is modulated by attentional load and is also highly dependent on the properties of visual stimuli, and can provide useful clues about the network architecture. This cross-scale, cross-species recording setup is also used for brain-computer interfacing applications and for diagnosis of mental disorders.

Research Summary

Different recording scales in humans and non-human primates

1.      Microelectrode recordings from monkeys: This allows us to record activity of single neurons using microelectrode arrays. We also get a second “level” called local field potential (LFP), which represents the local activity of a few thousand neurons, by low-pass filtering the signal.

2.       EEG from humans: Complex behaviours such as attention are studied at a coarse “network” level using EEG, in which electrodes are placed on the scalp.

3.   EEG from monkeys: We have modified our recording system that is used for microelectrode recordings to simultaneously record EEG from monkeys as well.

4.       Electrocorticogram (ECoG) from monkeys: ECoG is obtained from macro-electrodes placed directly on the brain, and is typically recorded from humans undergoing epilepsy treatment. We have collaborated with companies that make microelectrodes (Blackrock Microsystems) and ECoG arrays (Ad-Tech Medical Instrumentation) to design a hybrid electrode grid that includes both. We are therefore able to simultaneously record from four scales in monkeys (spikes, LFP, ECoG and EEG).

 

Projects (along with references to recent publications)

1.    Neural mechanisms of selective attention

We study 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).

2.    Stimulus dependence of gamma oscillations

Gamma oscillations induced in the visual cortex are highly dependent on the properties of the visual stimulus, such as its size, spatial frequency, contrast etc. We study how gamma power and centre frequency changes as the stimulus properties are modified, and from that try to understand the properties of the underlying network. For example, we found that presenting large (full screen) gratings induces two gamma oscillations, one between 40-70 Hz (“fast gamma”) that appears to be involved in local processing and a slower gamma between 25-40 Hz that is more global (Dinavahi*, Shirhatti*, Ravishankar* and Ray, Journal of Neuroscience, in press; * indicates joint authorship). Similarly, We found that gamma rhythm is highly colour tuned, such that  presentation of long wavelength (red-ish) hues generate gamma rhythm of very large magnitude (Shirhatti and Ray, under review).

3.    Neural basis of LFP, ECoG and EEG

Our setup allows a thorough investigation of the properties of macro-signals such as LFP, ECoG and EEG, since we have simultaneous access to spikes from ~100 microelectrodes as well. For example, we are using flickering visual stimuli to study the neural basis of steady state visual evoked potential (SSVEP) in EEG, and to study whether LFPs represent inputs coming in a particular visual area or the outputs (Salelkar, Somasekhar and Ray, under Review). We also investigated the spatial spread of different brain signals, and found that the spatial spread of LFP is band-pass (Dubey and Ray, 2016, Journal of Neurophysiology), and the spatial spread of ECoG is remarkably local (Dubey and Ray, 2017, SfN; in prep).  

4.    Signal Processing Techniques

Bridging brain signals across scales requires mathematical and signal processing tools (such as power spectral density and coherence), which are often studied only at one level and their limitations are not well understood at other levels. To build links across scales, we need to thoroughly investigate the properties of such tools. For example, we have shown that a popular metric of phase consistency called coherence is also modulated by amplitude correlations between the signals (Srinath and Ray, 2014, Journal of Neurophysiology), and using reference schemes such as average referencing can change the phase differences between two signals by 90 degrees (Shirhatti, Borthakur and Ray, 2016, Neural Computation). We have developed a publically available toolbox to study brain signals using a technique called Matching Pursuit (Subhash Chandran, Mishra*, Shirhatti* and Ray, 2016, Journal of Neuroscience; * indicates joint authorship), which can better resolve transients as well as rhythms present in a signal, and used Matching Pursuit to determine the duration of gamma rhythm accurately (Subhash Chandran, Seelamantula and Ray, 2017, Journal of Neurophysiology).

In addition, we are also expanding our research to the following areas: (1) building detailed biophysical models to better understand brain signals at various scales, (2) clinical applications using gamma rhythm as a diagnostic tool and (3) brain-machine interfacing applications.

See our poster for the Wellcome-DBT India Alliance end report that has a summary of completed and ongoing projects here.