Biology subjects

Bhaskaran-Nair, K.

Publications and source records attributed to Bhaskaran-Nair, K..

4 recordsLinked to original sources

bioRxiv2024

Temperature-dependent predation predicts a more reptilian future

Vertebrate diversity increases toward the tropics, but the extent to which this pattern varies with thermoregulatory strategy is unknown. A strong divergence, if confirmed, would imply a global restructuring of vertebrate communities with temperature. Here we present evidence for a novel latitudinal and thermal gradient of comparative vertebrate diversity. Synthesizing over 34,000 terrestrial species distributions, we observe a two-orders-of-magnitude shift in comparative richness with temperature, from endothermic dominance in temperate habitats and high elevations, toward parity with ectotherms in the lowland tropics. Next, we provide experimental support for an underlying thermal gradient of predation. Using machine vision tracking in over 4,500 endotherm-ectotherm predation trials, we show that thermally-mediated differences in performance favor endotherm predators in colder conditions and yield theoretically predicted outcomes, including strike count, distance traveled, and time to capture ectotherm prey. Finally, we integrate theory and data to forecast future patterns of diversity, revealing that as the world gets warmer, it will become increasingly reptilian.

ecology↗

bioRxiv2023

Protosequences in human cortical organoids model intrinsic states in the developing cortex

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human and murine brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human and murine brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

neuroscience↗

bioRxiv2023

Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not the activity of individual neurons.

The homeostatic regulation of neuronal activity is essential for robust computation; key set-points, such as firing rate, are actively stabilized to compensate for perturbations. From this perspective, the disruption of brain function central to neurodegenerative disease should reflect impairments of computationally essential set-points. Despite connecting neurodegeneration to functional outcomes, the impact of disease on set-points in neuronal activity is unknown. Here we present a comprehensive, theory-driven investigation of the effects of tau-mediated neurodegeneration on homeostatic set-points in neuronal activity. In a mouse model of tauopathy, we examine 27,000 hours of hippocampal recordings during free behavior throughout disease progression. Contrary to our initial hypothesis that tauopathy would impact set-points in spike rate and variance, we found that cell-level set-points are resilient to even the latest stages of disease. Instead, we find that tauopathy disrupts neuronal activity at the network-level, which we quantify using both pairwise measures of neuron interactions as well as measurement of the networks nearness to criticality, an ideal computational regime that is known to be a homeostatic set-point. We find that shifts in network criticality 1) track with symptoms, 2) predict underlying anatomical and molecular pathology, 3) occur in a sleep/wake dependent manner, and 4) can be used to reliably classify an animals genotype. Our data suggest that the critical set-point is intact, but that homeostatic machinery is progressively incapable of stabilizing hippocampal networks, particularly during waking. This work illustrates how neurodegenerative processes can impact the computational capacity of neurobiological systems, and suggest an important connection between molecular pathology, circuit function, and animal behavior.

neuroscience↗

bioRxiv2022

Transcriptomic cell type structures in vivo neuronal activity across multiple time scales.

SUMMARYCell type is hypothesized to be a key determinant of the role of a neuron within a circuit. However, it is unknown whether a neurons transcriptomic type influences the timing of its activity in the intact brain. In other words, can transcriptomic cell type be extracted from the time series of a neurons activity? To address this question, we developed a new deep learning architecture that learns features of interevent intervals across multiple timescales (milliseconds to >30 min). We show that transcriptomic cell class information is robustly embedded in the timing of single neuron activity recorded in the intact brain of behaving animals (calcium imaging and extracellular electrophysiology), as well as in a bio-realistic model of visual cortex. In contrast, we were unable to reliably extract cell identity from summary measures of rate, variance, and interevent interval statistics. We applied our analyses to the question of whether transcriptomic subtypes of excitatory neurons represent functionally distinct classes. In the calcium imaging dataset, which contains a diverse set of excitatory Cre lines, we found that a subset of excitatory cell types are computationally distinguishable based upon their Cre lines, and that excitatory types can be classified with higher accuracy when considering their cortical layer and projection class. Here we address the fundamental question of whether a neuron, within a complex cortical network, embeds a fingerprint of its transcriptomic identity into its activity. Our results reveal robust computational fingerprints for transcriptomic types and classes across diverse contexts, defined over multiple timescales.

neuroscience↗