Biology subjects

Rangarajan, V.

Publications and source records attributed to Rangarajan, V..

4 recordsLinked to original sources

bioRxiv2025

High frequency broadband activity detected noninvasively in infants distinguishes wake from sleep states

High frequency broadband activity (HFB; 70-150 Hz) indexes local brain activity. It is predominantly studied using invasive measures due to signal drop off from skull attenuation. We hypothesized that HFB is detectable in infants noninvasively through fontanelles and thin skull that have not fully developed. We analyzed scalp electroencephalography (EEG) data during wake and sleep states in 19 channels from 18 infants (1-4 months, both sexes). At the group level, linear mixed-effects models revealed greater HFB power in wake versus sleep states in midline and central channels near fontanelles, as well as in occipital channels over thin skull. These differences were detected with 90% reliability using as few as 25 seconds of data per state in as few as 10 subjects. On the individual level, linear mixed-effects models revealed the same wake > sleep effect with a mean reliability of 60% when using at least 50 seconds of data per state. These findings establish that noninvasive HFB detection in infants is not only possible at sites where the skull has not fully developed, but sufficiently robust to enable systematic investigation of early cognitive development.

neuroscience↗

bioRxiv2021

Neurophysiological evidence for cognitive map formation during sequence learning

Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individuals estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.

neuroscience↗

bioRxiv2019

Diverse temporal dynamics of repetition suppression revealed by intracranial recordings in human ventral temporal cortex

Repeated stimulus presentations commonly produce decreased neural responses - a phenomenon known as repetition suppression (RS) or adaptation - in ventral temporal cortex (VTC) in humans and nonhuman primates. However, the temporal features of RS in human VTC are not well understood. To fill this gap in knowledge, we utilized the precise spatial localization and high temporal resolution of electrocorticography (ECoG) from 9 human subjects implanted with intracranial electrodes in VTC. Subjects viewed non-repeated and repeated images of faces with long-lagged intervals and many intervening stimuli between repeats. We report three main findings: (i) robust RS occurs in VTC for activity in high-frequency broadband (HFB), but not lower frequency bands, (ii) RS of the HFB signal is associated with lower peak magnitude, lower total responses, and earlier peak responses, and (iii) RS effects occur early within initial stages of stimulus processing and persist for the entire stimulus duration. We discuss these findings in the context of early and late components of visual perception, as well as theoretical models of repetition suppression.

neuroscience↗

bioRxiv2018

Fast temporal dynamics and causal relevance of face processing in the human temporal cortex

Recordings with a large number of intracranial electrodes in eight neurosurgical subjects offered a unique opportunity to examine the fast temporal dynamics of face processing simultaneously across a relatively large extent of the human temporal cortex (TC). Measuring the power of slow oscillatory bands of activity ({theta}, , {beta}, and {gamma}) as well as High-Frequency Broadband (HFB, 70-177 Hz) signal, we found that the HFB showed the strongest univariate and multivariate changes in response to face compared to non-face stimuli. Using the HFB signal as a surrogate marker for local cortical engagement, we identified recording sites with selective responses to faces that were anatomically consistent across subjects and responded with graded strength to human, mammal, bird, and marine animal faces. Importantly, the most face selective sites were located more posteriorly and responded earlier than those with less selective responses to faces. Using machine learning based methods, we demonstrated that a sparse model focusing on information from the human face selective sites performed as well as, or better than, anatomically distributed models of face processing when discriminating faces from non-faces stimuli. Lastly, we identified the posterior fusiform (pFUS) site as causally the most relevant node for inducing distortion of face perception by direct electrical stimulation. Our findings support the notion of face information being processed first in the most selective sites - that are anatomically discrete and localizable within individual brains and anatomically consistent across subjects - which is then distributed in time to less selective anterior temporal sites within a time window that is too fast to be detected by current neuroimaging methods. The new information about the fast spatio-temporal dynamics of face processing across multiple sites of the human brain provides a new common ground for unifying the seemingly contradictory modular and distributed models of face processing in the human brain.

neuroscience↗