Biology subjects

Shavit, N.

Publications and source records attributed to Shavit, N..

3 recordsLinked to original sources

bioRxiv2024

A connectomics-driven analysis reveals novel characterization of border regions in mouse visual cortex

Leveraging retinotopic maps to parcellate the visual cortex into its respective sub-regions has long been a canonical approach to characterizing the functional organization of visual areas in the mouse brain. However, with the advent of extensive connectomics datasets like MICrONS, we can now perform more granular analyses to better characterize the structure and function of the visual cortex. In this work, we propose a statistical framework for analyzing the MICrONS dataset, particularly the V1, RL, and AL visual areas. In addition to identifying several structural and functional differences between these regions, we focus on the borders between these regions. By comparing the V1-RL and RL-AL border regions, we show that different boundaries between visual regions are distinct in their structure and function. Additionally, we find that the V1-RL border region has greater synaptic connectivity and more synchronous neural activity than the V1 and RL regions individually. We further analyze structure and function in tandem by measuring information flow along synapses, observing that the V1-RL border appears to act as a bridge between the V1 and RL visual areas. Overall, we identify numerous measures that distinguish the V1-RL border from the larger V1-RL network, potentially motivating its characterization as a distinct region in the mouse visual cortex.

neuroscience↗

bioRxiv2023

SmartEM: machine-learning guided electron microscopy

Connectomics provides nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole brain or even whole circuit reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step in automated connectomics. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a single-beam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM saves time by allocating the proper imaging time for each region of interest - first scanning all pixels rapidly, then rescanning more slowly only the small subareas where a higher quality signal is required. We demonstrate that SmartEM achieves up to a ~7-fold acceleration of image acquisition time for connectomic samples using a commercial single-beam SEM in samples from nematodes, mice and human brain. We apply this fast imaging method to reconstruct a portion of mouse cerebral cortex with an accuracy comparable to traditional electron microscopy.

neuroscience↗

bioRxiv2020

Connectomes across development reveal principles of brain maturation in C. elegans

From birth to adulthood, an animals nervous system changes as its body grows and its behaviours mature. The form and extent of circuit remodelling across the connectome is unknown. We used serial-section electron microscopy to reconstruct the full brain of eight isogenic C. elegans individuals across postnatal stages to learn how it changes with age. The overall geometry of the brain is preserved from birth to adulthood. Upon this constant scaffold, substantial changes in chemical synaptic connectivity emerge. Comparing connectomes among individuals reveals substantial connectivity differences that make each brain partly unique. Comparing connectomes across maturation reveals consistent wiring changes between different neurons. These changes alter the strength of existing connections and create new connections. Collective changes in the network alter information processing. Over development, the central decision-making circuitry is maintained whereas sensory and motor pathways substantially remodel. With age, the brain progressively becomes more feedforward and discernibly modular. Developmental connectomics reveals principles that underlie brain maturation.

neuroscience↗