Biology subjects

Gulati, D.

Publications and source records attributed to Gulati, D..

3 recordsLinked to original sources

bioRxiv2025

Slow-varying normalization explains auditory steady-state masking interactions in human EEG

The inherent ability of sensory neurons to entrain to modulations in the temporal structure of an auditory stimulus gives rise to the auditory steady-state response (ASSR). Simultaneous presentation of multiple acoustic stimuli by frequency tagging them to generate ASSRs at different frequencies is routinely employed for hearing threshold determination and cognitive studies. However, the nature of ASSR interactions as a function of competing modulation frequencies in the absence of overt behaviour and the underlying neural mechanisms have not been well studied. Such interactions have previously been studied with visual stimuli that generate steady-state visually evoked potentials (SSVEPs) and explained using a normalization model. Here, we tested whether similar interactions are observed in the auditory domain, and if so, can be explained using a similar model. We played sinusoidally amplitude-modulated stimuli simultaneously modulated at two frequencies while we recorded a 64-channel EEG from subjects who passively listened to these sounds with closed eyes. We used multiple modulation frequencies and depths to characterize the ASSR modulation masking response profile. We observed that modulation frequencies closer to each other suppress the ASSR strongly than frequencies that are farther apart, similar to the interaction observed in the visual domain using SSVEPs. The observed suppression was captured by a slow-varying normalization model, which was initially used to explain SSVEP interactions. We obtained a band-pass shaped suppression profile with a low-pass cutoff that matched to that observed for SSVEP interactions. Our findings highlight the universality of the normalization model in accounting for masking interactions across modalities.

neuroscience↗

bioRxiv2025

Slow-varying normalization explains diverse temporal frequency masking interactions in the macaque primary visual cortex

Neurons in the primary visual cortex (V1) respond non-linearly with the presentation of multiple stimuli, which has been explained by a normalization model where the excitatory drive is divided by the summed activity of a large neuronal population. While recent studies have suggested that normalization could be time and frequency-dependent, neural mechanisms underlying this dependence remain unknown. Steady-state visually evoked potentials (SSVEPs), which are produced by presenting flickering or counterphasing visual stimuli, serve as a robust tool to probe these underlying mechanisms by leveraging frequency-specific tagging of concurrently presented stimuli. We presented two overlapping counterphasing grating stimuli (plaids), either parallelly or orthogonally, at multiple contrasts and temporal frequencies and recorded spikes, local field potential, and electrocorticogram from V1 of bonnet macaques while they passively fixated. We also recorded electroencephalogram (EEG) activity. The resulting SSVEPs exhibited complicated dynamics - with "low-pass" and "band-pass" suppression profiles for orthogonal and parallel plaids, respectively. Importantly, these dynamics were conserved across scales - from spiking activity to EEG. Surprisingly, adding a simple low-pass filter in the normalization signal sufficiently explained these diverse effects. Our results present a simple mechanism to explain the spectro-temporal dynamics of normalization. These insights may also aid in designing and interpreting SSVEP-based cognitive and EEG-based brain-computer interfacing (EEG-BCI) studies.

neuroscience↗

bioRxiv2024

Auditory and visual gratings elicit distinct gamma responses

Sensory stimulation is often accompanied by fluctuations at high frequencies (>30Hz) in brain signals. These could be "narrowband" oscillations in the gamma band (30-70 Hz) or non-oscillatory "broadband" high-gamma (70-150 Hz) activity. Narrowband gamma oscillations, which are induced by presenting some visual stimuli such as gratings and have been shown to weaken with healthy aging and the onset of Alzheimers Disease, hold promise as potential biomarkers. However, since delivering visual stimuli is cumbersome as it requires head stabilization for eye tracking, an equivalent auditory paradigm could be useful. Although simple auditory stimuli have been shown to produce high-gamma activity, whether specific auditory stimuli can also produce narrowband gamma oscillations is unknown. We tested whether auditory ripple stimuli, which are considered an analogue to visual gratings, could elicit narrowband oscillations in auditory areas. We recorded 64-channel EEG from male and female (18 each) subjects while they either passively fixated on the monitor while viewing static visual gratings, or listened to stationary and moving ripples, played using loudspeakers, with their eyes open or closed. We found that while visual gratings induced narrowband gamma oscillations with suppression in the alpha band (8-12Hz), auditory ripples did not produce narrowband gamma but instead elicited very strong broadband high-gamma response and suppression in the beta band (14-26Hz). Even though we used equivalent stimuli in both modalities, our findings indicate that the underlying neuronal circuitry may not share ubiquitous strategies for stimulus processing. Significance statementIn the visual cortex, gratings can induce robust narrowband gamma oscillations (30-70Hz). These visual stimulus-induced oscillations can further be used as a biomarker for diagnosing neuronal disorders. However, tasks used to elicit these oscillations are challenging for elderly subjects, and therefore, we tested if we could use auditory stimuli instead. We hypothesized that auditory ripple stimuli, which are analogous to visual gratings, may elicit these narrowband oscillations. We found that ripples induce a broadband high-gamma response (70-150Hz) in human EEG, unlike visual gratings that produce robust gamma. Thus, the underlying neural circuitry in the two areas may not be canonical.

neuroscience↗