An international team of researchers headed by collaborators at Johns Hopkins University and at the University of Cambridge have produced a breathtakingly detailed diagram tracing every neural connection in the brain of the Drosophila fruit fly larva. The resulting, complete synaptic wiring diagram—or connectome—represents the first ever for a whole insect brain, and is larger and more complex than previously reported connectomes. The work represents a valuable resource for future experimental and theoretical studies of neural circuits and brain function, and could bring scientists closer to understanding the mechanisms of thought. The work may also offer up insights relevant to future machine learning technology.

“If we want to understand who we are and how we think, part of that is understanding the mechanism of thought,” said research co-lead Joshua T. Vogelstein, PhD, a Johns Hopkins biomedical engineer who specializes in data-driven projects including connectomics, the study of nervous system connections. “And the key to that is knowing how neurons connect with each other.”

Vogelstein is a co-senior author of the researchers’ published paper in Science, which is titled, “The connectome of an insect brain,” and in which they concluded, “Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures.”

Brains involves networks of interconnected neurons, and so understanding that network architecture is essential for understanding brain function, the authors explained. “A synapse-resolution connectome is therefore an essential prerequisite for understanding the mechanisms of brain function.”

Research co-lead Marta Zlatic, PhD, at the University of Cambridge, and MRC Laboratory of Molecular Biology, Cambridge, further explained, “All brains are similar—they are all networks of interconnected neurons—and all brains of all species have to perform many complex behaviours: they all need to process sensory information, learn, select actions, navigate their environments, choose food, recognise their conspecifics, escape from predators etc. In the same way that genes are conserved across the animal kingdom, I think that the basic circuit motifs that implement these fundamental behaviors will also be conserved.”

The first attempt at mapping a brain—a 14-year study of the roundworm initiated in the 1970s, resulted in a partial map and a Nobel Prize. Since then, partial connectomes have been mapped in many systems, including flies, mice, and even humans, but these reconstructions typically only represent only a tiny fraction of the total brain. Comprehensive connectomes have only been generated for several small species with a few hundred to a few thousand neurons in their bodies–a roundworm, a larval sea squirt, and a larval marine annelid worm. “Reconstructing and proofreading circuits from larger brains has been extremely challenging,” the authors continued. “Synapse-resolution circuitry of larger brains has therefore been approached only considering select subregions.”

Zlatic added, “The way the brain circuit is structured influences the computations the brain can do. But, up until this point, we’ve not seen the structure of any brain except of the roundworm C. elegans, the tadpole of a low chordate, and the larva of a marine annelid, all of which have several hundred neurons. This means neuroscience has been mostly operating without circuit maps. Without knowing the structure of a brain, we’re guessing on the way computations are implemented. But now, we can start gaining a mechanistic understanding of how the brain works.”

This team’s newly reported connectome of the Drosophila melanogaster larva brain represents the most complete as well as the most expansive map of an entire insect brain ever completed. It includes 3,016 neurons and each of the 548,000 connections between them. “It’s been 50 years and this is the first brain connectome,” Vogelstein said. “It’s a flag in the sand that we can do this. Everything has been working up to this.”

The morphologies of all brain neurons, reconstructed from a synapse-resolution electron microscopy volume (left), and the associated synaptic connectivity map of the entire brain (right). [Michael Winding & Benjamin Pedigo]

Mapping whole brains is difficult and extremely time consuming, even with the best modern technology. Getting a complete cellular-level picture requires slicing the brain into hundreds or thousands of individual tissue samples, all of which have to be imaged with electron microscopes before taking on the painstaking process of reconstructing all those pieces, neuron by neuron, into a full, accurate portrait of a brain.

The team purposely chose the 1st instar larva of the Drosophila melanogaster fruit fly because this insect species still shares much of its fundamental biology with humans, including a comparable genetic foundation. It also has rich learning and decision-making behaviors, making it a useful model organism in neuroscience. “Its brain structures are homologous to those of adult Drosophila and larger insects of other species,” the authors further pointed out. “The 1st instar larva already has as rich a repertoire of adaptive behaviors as the 3rd instar, including short- and long-term memory, value computation, and action selection.” And for practical purposes, the larva’s relatively compact brain can be imaged and its circuits reconstructed within a reasonable time frame.

Even so, the work took the University of Cambridge and Johns Hopkins 12 years, with the imaging alone taking about a day per neuron. The Cambridge researchers first created the high-resolution images of the brain and manually studied them to find individual neurons, rigorously tracing each one and linking their synaptic connections. The Cambridge team then handed off the data to Johns Hopkins, where the team spent more than three years using original code they created to analyze the larval brain connectivity.

“The most challenging aspect of this work was understanding and interpreting what we saw. We were faced with a complex neural circuit with lots of structure,” Zlatic noted. “In collaboration with Professor Priebe and Professor Vogestein’s groups at Johns Hopkins University, we developed computational tools to extract and predict from the structure the relevant circuit motives. By comparing this biological system, we can potentially also inspire better artificial networks.”

The Johns Hopkins researchers developed techniques to find groups of neurons based on shared connectivity patterns, and then analyzed how information could propagate through the brain. They were able to chart every neuron and every connection, and categorize each neuron by the role it plays in the brain. “We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions,” the scientists explained.

They found that the brain’s busiest circuits were those that led to and away from neurons of the learning center. “We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs … Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning.”

The methods Johns Hopkins developed are applicable to any brain connection project. “The approach and computational tools generated in this study will facilitate the analysis of future connectomes,” the team commented. Their code is available to whoever attempts to map an even larger animal brain, Vogelstein said, adding that despite the challenges, scientists are expected to take on the mouse, possibly within the next decade, although the mouse brain is estimated to be about a million times larger than that of the fruit fly larva.

Other teams are already working on a map of the adult fruit fly brain. Co-first author Benjamin Pedigo, a Johns Hopkins doctoral candidate in biomedical engineering, expects the team’s code could help to reveal important comparisons between connections in the adult and larval brain. As connectomes are generated for more larva and from other related species, Pedigo expects their analysis techniques could lead to better understanding of variations in brain wiring. “As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms,” the scientists stated.

A diagram depicting the connectivity, where neurons are represented as points, and neurons with more similar connectivity are plotted closer together. Lines depict connections between neurons. The border of the figure shows example neuron morphologies. [Benjamin Pedigo]
The fruit fly larva connectome showed circuit features that were strikingly reminiscent of prominent and powerful machine learning architectures. “Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations,” they wrote. The team expects continued study will reveal even more computational principles and potentially inspire new artificial intelligence systems. “What we learned about code for fruit flies will have implications for the code for humans,” Vogelstein said. “That’s what we want to understand—how to write a program that leads to a human brain network.”


Ongoing studies will aim to delve deeper to understand, for example, the architecture required for specific behavioral functions, such as learning and decision making, and look at activity in the whole connectome while the insect is doing things.