Abstract
Nervous systems extract and process information from the environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a framework for using graph-theoretic features of neural network activity to predict ecologically relevant stimulus properties, in particular stimulus identity. We used the transparent nematode, Caenorhabditis elegans, with its small nervous system to define neural network features associated with various chemosensory stimuli. We first immobilized animals using a microfluidic device and exposed their noses to chemical stimuli while monitoring changes in neural activity of more than 50 neurons in the head region. We found that graph-theoretic features, which capture patterns of interactions between neurons, are modulated by stimulus identity. Further, we show that a simple machine learning classifier trained using graph-theoretic features alone, or in combination with neural activity features, can accurately predict salt stimulus. Moreover, by focusing on putative causal interactions between neurons, the graph-theoretic features were almost twice as predictive as the neural activity features. These results reveal that stimulus identity modulates the broad, network-level organization of the nervous system, and that graph theory can be used to characterize these changes.
Highlights
Animals have evolved mechanisms for encoding a multitude of chemical stimuli encountered in the environment
While most studies have focused on the neurons in the sensory periphery, recent advances allow us to probe how the rest of the nervous system responds to sensory stimulation
Each worm experienced three 21-minute long imaging sessions: one without stimulation (“Spontaneous”, where M9 buffer was present but not switched), one with buffer changes around the animal’s nose (“Buffer”, where we switched between two streams of M9 buffer), and one with chemical stimulation (“Stimulus”, where we switched between M9 buffer and an odorant or tastant that was diluted in M9 buffer)
Summary
Animals have evolved mechanisms for encoding a multitude of chemical stimuli encountered in the environment. Odor information is initially filtered by olfactory sensory neurons that are organized into specific expression zones within the vertebrate olfactory epithelium [1], or into specific invertebrate sensilla that are selective for pheromones [2], food odors [3], acids [4], oviposition cues [5], or toxic odors [6] In both cases, olfactory information is relayed to specific glomeruli and higher-order centers in the brain [7,8]. Taste information in both mice and flies is represented by spatial patterns of neural activity, likely using combinatorial coding [9,10,11] While these studies highlight how chemical information is encoded in the periphery and early cortical areas, its processing and representation within higher brain centers remain poorly understood. Neural features that encode these changes could be extracted
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