Connectomic reconstruction of a female Drosophila ventral nerve cord.

A deep understanding of how the brain controls behaviour requires mapping neural circuits down to the muscles that they control. Here, we apply automated tools to segment neurons and identify synapses in an electron microscopy dataset of an adult female Drosophila melanogaster ventral nerve cord (VNC)1, which functions like the vertebrate spinal cord to sense and control the body. We find that the fly VNC contains roughly 45 million synapses and 14,600 neuronal cell bodies. To interpret the output of the connectome, we mapped the muscle targets of leg and wing motor neurons using genetic driver lines2 and X-ray holographic nanotomography3. With this motor neuron atlas, we identified neural circuits that coordinate leg and wing movements during take-off. We provide the reconstruction of VNC circuits, the motor neuron atlas and tools for programmatic and interactive access as resources to support experimental and theoretical studies of how the nervous system controls behaviour.

Synaptic architecture of leg and wing premotor control networks in Drosophila.

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles1. MN activity is coordinated by complex premotor networks that facilitate the contribution of individual muscles to many different behaviours2-6. Here we use connectomics7 to analyse the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. By contrast, wing premotor networks lack proportional synaptic connectivity, which may enable more flexible recruitment of wing steering muscles. Through comparison of the architecture of distinct motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

Synaptic architecture of leg and wing motor control networks in Drosophila.

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. Because individual muscles may be used in many different behaviors, MN activity must be flexibly coordinated by dedicated premotor circuitry, the organization of which remains largely unknown. Here, we use comprehensive reconstruction of neuron anatomy and synaptic connectivity from volumetric electron microscopy (i.e., connectomics) to analyze the wiring logic of motor circuits controlling the Drosophila leg and wing. We find that both leg and wing premotor networks are organized into modules that link MNs innervating muscles with related functions. However, the connectivity patterns within leg and wing motor modules are distinct. Leg premotor neurons exhibit proportional gradients of synaptic input onto MNs within each module, revealing a novel circuit basis for hierarchical MN recruitment. In comparison, wing premotor neurons lack proportional synaptic connectivity, which may allow muscles to be recruited in different combinations or with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

Disconnection of basolateral amygdala and insular cortex disrupts conditioned approach in Pavlovian lever autoshaping.

Previously established individual differences in appetitive approach and devaluation sensitivity observed in goal- and sign-trackers may be attributed to differences in the acquisition, modification, or use of associative information in basolateral amygdala (BLA) pathways. Here, we sought to determine the extent to which communication of associative information between BLA and anterior portions of insular cortex (IC) supports ongoing Pavlovian conditioned approach behaviors in sign- and goal-tracking rats, in the absence of manipulations to outcome value. We hypothesized that the BLA mediates goal-, but not sign- tracking approach through interactions with the IC, a brain region involved in supporting flexible behavior. We first trained rats in Pavlovian lever autoshaping to determine their sign- or goal-tracking tendency. During alternating test sessions, we gave unilateral intracranial injections of vehicle or a cocktail of gamma-aminobutyric acid (GABA) receptor agonists, baclofen and muscimol, unilaterally into the BLA and contralaterally or ipsilaterally into the IC prior to reinforced lever autoshaping sessions. Consistent with our hypothesis we found that contralateral inactivation of BLA and IC increased the latency to approach the food cup and decreased the number of food cup contacts in goal-trackers. While contralateral inactivation of BLA and IC did not affect the total number of lever contacts in sign-trackers, this manipulation increased the latency to approach the lever. Ipsilateral inactivation of BLA and IC did not impact approach behaviors in Pavlovian lever autoshaping. These findings, contrary to our hypothesis, suggest that communication between BLA and IC maintains a representation of initially learned appetitive associations that commonly support the initiation of Pavlovian conditioned approach behavior regardless of whether it is directed at the cue or the location of reward delivery.

The impact of a junk-food diet during development on 'wanting' and 'liking'.

The global increase in obesity rates has been tied to the rise in junk-food availability and consumption. Increasingly, children are exposed to a junk-food diet during gestation and early development. Excessive consumption of junk-food during this period may negatively impact the development of brain motivation and reward pathways. In this study we investigated the effects of a chronic junk-food diet throughout development on cue-motivated behavior ('wanting'), hedonic 'liking' for sweet tastes, as well as anxiety and weight gain in male and female Long-Evans (LE) and Sprague-Dawley (SD) rats. Here we found that chronic exposure to a junk-food diet resulted in large individual differences in weight gain (gainers and non-gainers) despite resulting in stunted growth as compared to chow-fed controls. Behaviorally, junk-food exposure attenuated conditioned approach (autoshaping) in females, particularly in non-gainers. In contrast, junk-food exposed rats that gained the most weight were willing to work harder for access to a food cue (conditioned reinforcement), and were more attracted to a junk-food context (conditioned place preference) than non-gainers. Hedonic 'liking' reactions (taste reactivity) were severely blunted in LE, but not SD rats, and 'liking' for sucrose negatively correlated with greater weight gain. Finally, junk-food exposure reduced anxiety-like behavior (elevated plus maze) in males but not females. These results suggest that junk-food exposure during development may give rise to dissociable differences in 'liking' and 'wanting' neural systems that do not depend on weight gain and may not be detected through Body Mass Index monitoring alone.

Spatial estimation: a non-Bayesian alternative.

A large collection of estimation phenomena (e.g. biases arising when adults or children estimate remembered locations of objects in bounded spaces; Huttenlocher, Newcombe & Sandberg, 1994) are commonly explained in terms of complex Bayesian models. We provide evidence that some of these phenomena may be modeled instead by a simpler non-Bayesian alternative. Undergraduates and 9- to 10-year-olds completed a speeded linear position estimation task. Bias in both groups' estimates could be explained in terms of a simple psychophysical model of proportion estimation. Moreover, some individual data were not compatible with the requirements of the more complex Bayesian model.