Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion

Dunn, Timothy W; Mu, Yu; Narayan, Sujatha; Randlett, Owen; Naumann, Eva A; Yang, Chao-Tsung; Schier, Alexander F.; Freeman, Jeremy; Engert, Florian; Ahrens, Misha B.

doi:10.7554/elife.12741

Cited by 261 publications

(299 citation statements)

References 70 publications

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“…As visual information is organized into channels in other brains (Gollisch and Meister, 2010; Huberman et al, 2009; Yonehara et al, 2009), we hypothesize that distinct channels may activate segregated motor nuclei to generate flexible behavior in other vertebrates. In particular, the nMLF or neighboring neurons may be functional homologs of the mesencephalic locomotor region in mammals (Dunn et al, 2016; Ryczko and Dubuc, 2013; Severi et al, 2014), whereas the vSPNs are functionally and anatomically similar to neurons in the mammalian reticular formation (Armstrong, 1988). Furthermore, the prevalence of caudal interhemispheric connections in the mammalian brain (Chédotal, 2014) is reminiscent of the zebrafish hindbrain, and these connections might also refine lateralized motor streams in higher-order vertebrates.…”

Section: Discussionmentioning

confidence: 99%

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response

Naumann

Fitzgerald

Dunn

et al. 2016

Cell

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232

261

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SUMMARY Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data.

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Section: Discussionmentioning

confidence: 99%

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response

Naumann

Fitzgerald

Dunn

et al. 2016

Cell

Self Cite

232

261

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“…Rapid progress is being made toward this goal, especially using animal models with relatively simple brains. Large quantities of neural activity data have been acquired simultaneously with behavioral data in larval zebrafish Danio rerio (Dunn et al 2016), the nematode Caenorhabditis elegans (Nguyen et al 2016, Venkatachalam et al 2016, and the fruit fly Drosophila melanogaster (Harris et al 2015, Lemon et al 2015, Seelig and Jayaraman 2015. These organisms are also the focus of past (White et al 1986) or ongoing (Takemura et al 2013) connectomic efforts to map the synapse-level connectivity between all neurons in the brain.…”

Section: Introductionmentioning

confidence: 99%

Systematic exploration of unsupervised methods for mapping behavior

Todd

Kain²,

Bivort

2017

Phys. Biol.

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To fully understand the mechanisms giving rise to behavior, we need to be able to precisely measure it. When coupled with large behavioral data sets, unsupervised clustering methods offer the potential of unbiased mapping of behavioral spaces. However, unsupervised techniques to map behavioral spaces are in their infancy, and there have been few systematic considerations of all the methodological options. We compared the performance of seven distinct mapping methods in clustering a wavelettransformed data set consisting of the x-and y-positions of the six legs of individual flies. Legs were automatically tracked by small pieces of fluorescent dye, while the fly was tethered and walking on an air-suspended ball. We find that there is considerable variation in the performance of these mapping methods, and that better performance is attained when clustering is done in higher dimensional spaces (which are otherwise less preferable because they are hard to visualize). High dimensionality means that some algorithms, including the non-parametric watershed cluster assignment algorithm, cannot be used. We developed an alternative watershed algorithm which can be used in high-dimensional spaces when a probability density estimate can be computed directly. With these tools in hand, we examined the behavioral space of fly leg postural dynamics and locomotion. We find a striking division of behavior into modes involving the fore legs and modes involving the hind legs, with few direct transitions between them. By computing behavioral clusters using the data from all flies simultaneously, we show that this division appears to be common to all flies. We also identify individual-to-individual differences in behavior and behavioral transitions. Lastly, we suggest a computational pipeline that can achieve satisfactory levels of performance without the taxing computational demands of a systematic combinatorial approach.

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“…Automated behavioral tools (though fully supervised) have also been used to track fish and study locomotion [76], prey capture [77], or social behaviors [78]. Most importantly, facilitated by its transparency, methods have been developed to rapidly image neural activity at cellular-resolution from the entire brain during fictive behavior [79].…”

Section: Dissecting Circuits For Sensorimotor Behaviorsmentioning

confidence: 99%

Quantifying behavior to solve sensorimotor transformations: advances from worms and flies

Calhoun

Murthy

2017

Current Opinion in Neurobiology

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The development of new computational tools has recently opened up the study of natural behaviors at a precision that was previously unachievable. These tools permit a highly quantitative analysis of behavioral dynamics at timescales that are well matched to the timescales of neural activity. Here we examine how combining these methods with established techniques for estimating an animal’s sensory experience presents exciting new opportunities for dissecting the sensorimotor transformations performed by the nervous system. We focus this review primarily on examples from Caenorhabditis elegans and Drosophila melanogaster – for these model systems, computational approaches to characterize behavior, in combination with unparalleled genetic tools for neural activation, silencing, and recording, have already proven instrumental for illuminating underlying neural mechanisms.

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Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion

Cited by 261 publications

References 70 publications

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response

Systematic exploration of unsupervised methods for mapping behavior

Quantifying behavior to solve sensorimotor transformations: advances from worms and flies

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