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Abstract Genetically encoded optical indicators and actuators of neural activity allow for all-optical investigations of signaling in the nervous system. But commonly used indicators, actuators, and expression strategies are poorly suited for systematic measurements of signal propagation at brain scale and cellular resolution. Large-scale measurements of the brain require indicators and actuators with compatible excitation spectra to avoid optical crosstalk. They must be highly expressed in every neuron but at the same time avoid lethality and permit the animal to reach adulthood. Their expression must also be compatible with additional fluorescent labels to locate and identify neurons, such as those in the NeuroPAL cell identification system. We present TWISP, a transgenic worm for interrogating signal propagation, that addresses these needs and enables optical measurements of evoked calcium activity at brain scale and cellular resolution in the nervous system of the nematode Caenorhabditis elegans. In every neuron we express a nonconventional optical actuator, the gustatory receptor homolog GUR-3 + PRDX-2, under the control of a drug-inducible system QF + hGR, and a calcium indicator GCAMP6s, in a background with additional fluorophores from the NeuroPAL cell ID system. We show that this combination, but not others tested, avoids optical crosstalk, creates strong expression in the adult, and generates stable transgenic lines for systematic measurements of signal propagation in the worm brain. 

Abstract Establishing how neural function emerges from network properties is a fundamental problem in neuroscience¹. Here, to better understand the relationship between the structure and the function of a nervous system, we systematically measure signal propagation in 23,433 pairs of neurons across the head of the nematodeCaenorhabditis elegansby direct optogenetic activation and simultaneous whole-brain calcium imaging. We measure the sign (excitatory or inhibitory), strength, temporal properties and causal direction of signal propagation between these neurons to create a functional atlas. We find that signal propagation differs from model predictions that are based on anatomy. Using mutants, we show that extrasynaptic signalling not visible from anatomy contributes to this difference. We identify many instances of dense-core-vesicle-dependent signalling, including on timescales of less than a second, that evoke acute calcium transients—often where no direct wired connection exists but where relevant neuropeptides and receptors are expressed. We propose that, in such cases, extrasynaptically released neuropeptides serve a similar function to that of classical neurotransmitters. Finally, our measured signal propagation atlas better predicts the neural dynamics of spontaneous activity than do models based on anatomy. We conclude that both synaptic and extrasynaptic signalling drive neural dynamics on short timescales, and that measurements of evoked signal propagation are crucial for interpreting neural function. 

Understanding the intricacies of the brain often requires spotting and tracking specific neurons over time and across different individuals. For instance, scientists may need to precisely monitor the activity of one neuron even as the brain moves and deforms; or they may want to find universal patterns by comparing signals from the same neuron across different individuals. Both tasks require matching which neuron is which in different images and amongst a constellation of cells. This is theoretically possible in certain ‘model’ animals where every single neuron is known and carefully mapped out. Still, it remains challenging: neurons move relative to one another as the animal changes posture, and the position of a cell is also slightly different between individuals. Sophisticated computer algorithms are increasingly used to tackle this problem, but they are far too slow to track neural signals as real-time experiments unfold. To address this issue, Yu et al. designed a new algorithm based on the Transformer, an artificial neural network originally used to spot relationships between words in sentences. To learn relationships between neurons, the algorithm was fed hundreds of thousands of ‘semi-synthetic’ examples of constellations of neurons. Instead of painfully collated actual experimental data, these datasets were created by a simulator based on a few simple measurements. Testing the new algorithm on the tiny worm Caenorhabditis elegans revealed that it was faster and more accurate, finding corresponding neurons in about 10ms. The work by Yu et al. demonstrates the power of using simulations rather than experimental data to train artificial networks. The resulting algorithm can be used immediately to help study how the brain of C. elegans makes decisions or controls movements. Ultimately, this research could allow brain-machine interfaces to be developed. 

We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during movement. We report that population activity more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two largely distinct subpopulations are informative for decoding velocity and curvature, and different neurons’ activities contribute features relevant for different aspects of a behavior or different instances of a behavioral motif. To validate our measurements, we labeled neurons AVAL and AVAR and found that their activity exhibited expected transients during backward locomotion. Finally, we compared population activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons positively correlated with AVA during movement become negatively correlated during immobilization and vice versa. This work provides needed experimental measurements that inform and constrain ongoing efforts to understand population dynamics underlying locomotion in C. elegans .