Article activity feed

  1. eLife Assessment:

    This manuscript uses a genetically-encoded calcium indicator to assess neural activity across a population of axons connecting the fly's brain to its ventral nerve cord while the tethered fly behaves on a floating ball. Changes in fluorescence signal correlate better with states such as walking, resting, and grooming than with particular limb movements or joint angles, suggesting that specific descending neurons represent the larger behavioral subdivisions. The preparation and large-scale analysis represent a significant step forward in determining how the brain compresses sensory and state information to convey commands to the ventral nervous system for behavior execution by motor circuits.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

  2. Reviewer #1 (Public Review):

    This manuscript argues that populations of descending neurons, connecting the fly's brain to its ventral nerve cord, encode high-level behaviors (resting, walking, or grooming) rather than specific limb movements or joint positions. This argument is supported by correlations between activity shown by functional imaging using a genetically-encoded calcium indicator expressed in many neurons passing through the neck connective and simultaneous measurements of the fly's behaviors in a tethered preparation.

  3. Reviewer #2 (Public Review):

    Aymanns et al. use a challenging preparation in which they were able to record from up to 100 descending neurons (DNs) simultaneously in tethered flies performing spontaneous and odor-evoked behaviors on a treadmill. They combine their recordings with motion capture approaches and automated behavioral classification, which allows them to correlate DN population activity with behavior on a large scale. This approach is valuable and adds a different perspective to previously published studies aimed at tying individual, often command-like DNs to different behaviors and characterizing their activity in detail. After correlating the activity of the DN population with aspects of walking and grooming, they outline an approach that allowed them to identify a single pair of DNs from the population data set. Overall, the study combines several cutting-edge methods and significantly adds to our understanding of descending motor control. For example, the authors demonstrate that a large number of DNs likely contributes to turning, whereas changes in walking speed are encoded, perhaps driven, by fewer, more distributed DNs.

    In their experiments, the authors did not perturb the activity of any of the DNs, either by activation or silencing. Moreover, the temporal resolution of the DN population recordings is relatively low compared to, for example, single cell patch-clamp recordings. This is fair given the scope of the study, but as a consequence it remains unclear whether a DN whose activity is correlated with a certain behavior is driving this particular behavior, or whether the DN is activated because the behavior is executed. The latter could for example be due to sensory feedback. This caveat makes it challenging to interpret the results presented, since a causal link between DN activity and behavior cannot be assumed. Overall, the authors are relatively careful when interpreting their data, but there are several instances where they overinterpret their findings. These instances need to be addressed and clarified.