A Comprehensive Wiring Diagram of the Protocerebral Bridge for Visual Information Processing in the Drosophila Brain

Abstract

How the brain perceives sensory information and generates meaningful behavior depends critically on its underlying circuitry. The protocerebral bridge (PB) is a major part of the insect central complex (CX), a premotor center that may be analogous to the human basal ganglia. Here, by deconstructing hundreds of PB single neurons and reconstructing them into a common three-dimensional framework, we have constructed a comprehensive map of PB circuits with labeled polarity and predicted directions of information flow. Our analysis reveals a highly ordered information processing system that involves directed information flow among CX subunits through 194 distinct PB neuron types. Circuitry properties such as mirroring, convergence, divergence, tiling, reverberation, and parallel signal propagation were observed; their functional and evolutional significance is discussed. This layout of PB neuronal circuitry may provide guidelines for further investigations on transformation of sensory (e.g., visual) input into locomotor commands in fly brains.

What insects can tell us about the origins of consciousness

“…Outputs from this system could provide compass-like information to stabilize the direction of moving insects relative to the external celestial cue (78). This function for the CX has been described in locusts and butterflies (Danaus plexippus) but may be common across insect orders (79). Further, in S. gregaria (80), the cockroach Blaberus discoidalis (75), and Drosophila melanogaster (81,82), the CX encodes topographically organized visual information on moving objects and is capable of factoring out the confounding effects of visual motion generated by self-movement from moving objects in the environment (82).…”

A single-cell level and connectome-derived computational model of the Drosophila brain

“…Based on Lin et al 2013 (Lin et al 2013) and Wolff et al 2015(Wolff, Iyer, andRubin 2015), who reported the anatomy of the same circuits, we were able to derive the network connections of these neurons and use them as a reference to optimize our connection estimation protocol. Lin et al 2013 (Lin et al 2013) and Wolff et al 2015(Wolff, Iyer, andRubin 2015) reported the polarity and innervated subregions of each neuron. To construct the reference connectivity of these neurons, we assumed that a neuron that projects its axonal arbor to a glomerulus forms synapses with another neuron that has its dendritic arbor in the same glomerulus.…”

“…This issue was resolved by changing the parameter Th DP from 0.01 to 0.001. To validate the performance of the new classifier, we selected the 442 neurons that were reported in Lin et al 2013 (Lin et al 2013) as test neurons because their polarity has been reported in detail by two studies (Lin et al 2013;Wolff, Iyer, and Rubin 2015). We removed the EIP neuron class, which innervates the ellipsoid body, inferior dorsofrontal protocerebrum, and protocerebral bridge, because the reported polarity is inconsistent between the two studies.…”

“…The optimal values of D and R for the two criteria were determined by the following procedure: 1) we started from a small distance criterion (D = 1 ߤ m) and contact point criterion (R = 0.1%), 2) for every pair of neurons in the test neuron set, we calculated the number of contact points and determined whether the two neurons form synapses based on the criteria; 3) we compared the result with data from a previous research (Lin et al 2013) and calculated the true positive rate and false positive rate, and 4) we changed the distance and the contact point criteria and repeated steps 2-3. Finally, we used the receiver operating characteristic (ROC) analysis (Fawcett 2006;Lasko et al 2005) to determine the best criteria to be: distance = 13 ߤ m with contact points = 1% ( Figure 2D).…”

“…All procedures were performed with the 442 neurons reported in Lin et al 2013 (Lin et al 2013). Based on Lin et al 2013 (Lin et al 2013) and Wolff et al 2015(Wolff, Iyer, andRubin 2015), who reported the anatomy of the same circuits, we were able to derive the network connections of these neurons and use them as a reference to optimize our connection estimation protocol.…”