While isolated motor actions can be correlated with activities of neuronal networks, an unresolved problem is how the brain assembles these activities into organized behaviors like action sequences. Using brain-wide calcium imaging in Caenorhabditis elegans, we show that a large proportion of neurons across the brain share information by engaging in coordinated, dynamical network activity. This brain state evolves on a cycle, each segment of which recruits the activities of different neuronal sub-populations and can be explicitly mapped, on a single trial basis, to the animals' major motor commands. This organization defines the assembly of motor commands into a string of run-and-turn action sequence cycles, including decisions between alternative behaviors. These dynamics serve as a robust scaffold for action selection in response to sensory input. This study shows that the coordination of neuronal activity patterns into global brain dynamics underlies the high-level organization of behavior.

Using low-cost automated tracking microscopes, we have generated a behavioral database for 305 C. elegans strains, including 76 mutants with no previously described phenotype. The database consists of 9,203 short videos segmented to extract behavior and morphology features that are available online for further analysis. The database also includes summary statistics for 702 measures with statistical comparisons to wild-type controls so that phenotypes can be identified and understood by users.

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SummaryNervous systems are constructed from a deep repertoire of neuron types but the underlying gene expression programs that specify individual neuron identities are poorly understood. To address this deficit, we have produced an expression profile of all 302 neurons of the C. elegans nervous system that matches the single cell resolution of its anatomy and wiring diagram. Our results suggest that individual neuron classes can be solely identified by combinatorial expression of specific gene families. For example, each neuron class expresses unique codes of ∼23 neuropeptide-encoding genes and ∼36 neuropeptide receptors thus pointing to an expansive “wireless” signaling network. To demonstrate the utility of this uniquely comprehensive gene expression catalog, we used computational approaches to (1) identify cis-regulatory elements for neuron-specific gene expression across the nervous system and (2) reveal adhesion proteins with potential roles in synaptic specificity and process placement. These data are available at cengen.org and can be interrogated at the web application CengenApp. We expect that this neuron-specific directory of gene expression will spur investigations of underlying mechanisms that define anatomy, connectivity and function throughout the C. elegans nervous system.

Visible phenotypes based on locomotion and posture have played a critical role in understanding the molecular basis of behavior and development in Caenorhabditis elegans and other model organisms. However, it is not known whether these human-defined features capture the most important aspects of behavior for phenotypic comparison or whether they are sufficient to discover new behaviors. Here we show that four basic shapes, or eigenworms, previously described for wild-type worms, also capture mutant shapes, and that this representation can be used to build a dictionary of repetitive behavioral motifs in an unbiased way. By measuring the distance between each individual's behavior and the elements in the motif dictionary, we create a fingerprint that can be used to compare mutants to wild type and to each other. This analysis has revealed phenotypes not previously detected by real-time observation and has allowed clustering of mutants into related groups. Behavioral motifs provide a compact and intuitive representation of behavioral phenotypes.phenotyping | imaging | ethology | nematode T he study of unconstrained spontaneous behavior is the core of ethology, and it has also made significant contributions to behavioral genetics in model organisms. A powerful approach has been the careful expert observation of mutants to identify those with visible locomotor phenotypes, as demonstrated for many model organisms (1-6). However, as with most manually scored experiments, subjectivity can reduce reproducibility, whereas subtle quantitative changes or those that happen on very short or long time-scales are likely to be missed. Furthermore, manual observations are not scalable, and this has led to a widening gap between our ability to sequence and manipulate genomes and our ability to assess the effects of genetic variation and mutation on behavior.Several recent reports describe systems that begin to address this gap by automatically recording and quantifying spontaneous behavior in animals ranging from worms (7-15) to flies (16-19), fish (20, 21), and mice (22,23). The advantage of these approaches is that they provide a means to quantify movement parameters such as velocity precisely and in some cases to automatically detect predefined behaviors based on a manually annotated training data set. This automated analysis eliminates some of the problems of a purely manual approach, but it still relies on preselected behavioral parameters that may not be optimal for phenotypic comparisons and precludes the discovery of new behaviors that have not already been observed by eye. An alternative approach is to use unsupervised learning, which attempts to use the inherent structure of a data set to identify informative patterns; to do this, we first needed to extract worm postures from movie data and have as compact and complete a representation of worm behavior as possible.

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Connectomics has focused primarily on the mapping of synaptic links in the brain; yet it is well established that extrasynaptic volume transmission, especially via monoamines and neuropeptides, is also critical to brain function and occurs primarily outside the synaptic connectome. We have mapped the putative monoamine connections, as well as a subset of neuropeptide connections, in C. elegans based on new and published gene expression data. The monoamine and neuropeptide networks exhibit distinct topological properties, with the monoamine network displaying a highly disassortative star-like structure with a rich-club of interconnected broadcasting hubs, and the neuropeptide network showing a more recurrent, highly clustered topology. Despite the low degree of overlap between the extrasynaptic (or wireless) and synaptic (or wired) connectomes, we find highly significant multilink motifs of interaction, pinpointing locations in the network where aminergic and neuropeptide signalling modulate synaptic activity. Thus, the C. elegans connectome can be mapped as a multiplex network with synaptic, gap junction, and neuromodulator layers representing alternative modes of interaction between neurons. This provides a new topological plan for understanding how aminergic and peptidergic modulation of behaviour is achieved by specific motifs and loci of integration between hard-wired synaptic or junctional circuits and extrasynaptic signals wirelessly broadcast from a small number of modulatory neurons.