In Depth: A Map of 16,000 Fruit Fly Neurons

Hopefully as you see in a bit more depth how the paper unfolds, it will become more apparent that Chiang and friends make few real findings or even predictions. However, aside from the transparency and respect for freedom of knowledge that I mentioned in the Meat and Potatoes, the strongest analytical value of the paper is the predictive power of the “FlyCircuit” database they are introducing.

As I mentioned earlier, the FlyCircuit project took 3D images of single neurons and placed them as accurately as possible into the space of an average brain (they ran parallel male and female projects, with many fewer male than female neurons, so most of the paper deals with the female model). The average brains they constructed took into account the anatomically segregated neuropils of the Drosophila brain. The technique the authors used for labelling small groups of non-overlapping neurons or individual ones was predictably MARCM, which you can (eventually) read more about in the glossary. Then, through a method beyond my understanding and apparently beyond the scope of the paper, individual neurons were transformed into the space of the appropriate template brain, using a global coordinate system with its origin at the centre of the ellipsoid body. They did this for ~16,000 neurons, covering more than a tenth of the female drosophila brain’s neurons.

To check the accuracy of the registration of the neurons inside the template brain the group compared the size and a alignment of neuropil regions between their model brain and individual sample brains, finding 93% accuracy. However, when checking the registration of individual neurons – the parts that actually matter – accuracy was lower. In the one example they give of this, looking at intrahemispheric projection of specific neurons located in the antennal lobe, the accuracy was 69%. They could increas this to 95% accuracy, but only when they changed from their global registration algorithm to a local one that only considered the brain region containing that neuron’s cell body. They aren’t really clear on this, but I think that to construct a model of the entire brain they need to use the global algorithm, meaning that the overall accuracy of neuronal registration in their model might be as low as 69% or lower. Chiang and friends could have presented better summary data by giving the neuronal registration accuracy for the entire brain, or even better, for each neuropil pair, but they don’t try to make any firm conclusions, so they save themselves from major criticism. In the future though, if they want to make stronger assertions about how the brain is organized, registration accuracy will need to be documented more appropriately.

After describing their system, Chiang and friends define a “Local Processing Unit(LPU),” which seems to be what FlyCircuit is most appropriate for detecting. According to them, an LPU is “equal or smaller than the boundary of an anatomically demarcated neuropil” and has a segregated set of local neurons whose processes are confined to the the LPU. Later though, after applying a search algorithm to identify LPUs throughout the brain and offering the obscure tid bit that the ventrolateral protocerebrum may actually contain 2 LPUs, they offer 3 criteria for defining an LPU: 1.) An LPU has its own set of interneurons whose cell bodies are restricted to the LPU; 2.) nerve bundles projecting out of an LPU are segregated; and 3.) each LPU has its own long range tracts connecting it to and receiving input from distant partners. Then, directly following this list, they claim that, therefore, an LPU has its own ensemble of interneurons whose processes (and not only cell bodies as they mentioned before) are restricted to that area and each LPU receives input from at least one neural tract. This evolution of the definition of an LPU in the confines of a single paragraph is not explained, nor is it cited. Essentially they start off by defining an LPU with their search algorithm, but then it looks like once that algorithm pulls out a bunch of putative LPUs, they make a new definition. I am left wondering whether their LPU search algorithm creates the definition, or whether it is the traits of the discovered LPUs that came to define their concept of the LPU. So the major question I would like to ask the the group is, what the hell is an LPU? It seems to me that if they have an LPU search algorithm they must have a concrete set of criteria, or at least a set of very convoluted if-then terms, and a single definition shouldn’t be so hard to come up with.

Regardless of what the criteria are and what the algorithms look like, they applied them to the +16,000 female neurons they had squeezed into their model brain. They found 58 anatomically segregated brain regions, composed of 19 paired LPUs and 3 central, unpaired LPUs. The VLP was the only region that contained two separate LPUs, so maybe keep an eye out for a follow-up study looking into this. Four pairs of neuropils that couldn’t be defined as LPUs seem to be interregional tracts or communication hubs. I got a little hung up on this last part. That Chiang and friends’ definition of an LPU neglects hubs might suggest that there is no processing done at a communication hub, but I think a travelling signal could be modified drastically at a hub where information is being transmitted 1 to 1, converged or diverged – potentially very powerful forms of computation in their own right. Thus an LPU shouldn’t be seen as the be all and end all of info processing.

After sorting all their neurons into groups (LPUs or not) the group used graph theory (which I will neither attempt to explain or understand on this pass) to assess the extent of interconnectivity between the LPUs. From this analysis they determine that there are 4 segregated sets of densely connected LPUs, each of which tends to use different neurotransmitters. The specific implication of that tendency is lost on me, though. However, one interesting role for this wiring diagram is its ability to predict informaiton flow in the brain. Chiang and friends give a single example of a potential higher-level processing center based on this idea, but take it no further than that. This however may be the most important role for FlyCircuit to play in the future; it’s odd to think about, but we really know much more about the function of human brain areas than about flies’. Lesion studies, and now fMRI studies in humans have allowed us to peg a lot of our convoluted mess of a cortex with at least one, and more likely too many, functions. On the other hand, flies, which have taught us so much about genetics, molecular biology and development don’t yet have a functional brain map. FlyCircuit has hopefully opened the door for that.

Finally the group assessed the extent to which their model covers the volume of the brain, taking this to be a measure of completeness of their model. They find that 93% of voxels have at least 3 neurons intersecting them, while only 0.79% of voxels are without neurons.  I could not, however, find any mention of the volume of their voxels, which is an important piece of info: the larger the voxel, the higher the measurement of completeness is going to be. Regardless, they estimate that “the basic pattern of the brain’s connectivity is revealed with fewer that 5,000 neurons,” and they note that their 16,000 probably does a pretty good job. Despite this they offer that “further mapping of the remaining 90% of the brain is continuing,” so presumably only more realistic pictures are to come.

So, in the end Chiang and friends have amassed images of more than 16,000 individual neurons, fit them into a set of brain regions that they defined as LPUs or otherwise, and assessed the connectivity of those regions. The paper is a testament to a long and arduous journey. In fact, the NY Times quoted Chiang as saying “Painfully, we had to throw all the old data away,” when they developed a better way to process the raw data partway through the project. With that in mind you can’t help but be impressed with the work. However, all that work has yet to produce any concrete results. Chiang and friends do find that the VLP is the only anatomical brain region that contains 2 distinct LPUs, but the implications of this are currently unknown. They also show that there are four main groups of LPUs, and although this is potentially very interesting, it is also left untouched. Most importantly, the authors give a representative example of the ability of their database and algorithms to predict the direction of information flow in the fly brain. As Chiang and friends say themselves, the great strength of the system lies in the predictions it can make. So the paper’s worth shouldn’t be gauged by it’s tantalizing glimpses of real conclusions, but in its heralding of a powerful tool. Aside from their transparency and making the as of yet unexploited fruit of their labor available to drosophilists everywhere, the strongest feature of this project is its future.

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5 Responses to In Depth: A Map of 16,000 Fruit Fly Neurons

  1. Ed Ruthazer says:

    Nice report.

    This study raises two key questions in my mind:
    1. For a relatively “hard-wired” organism like Drosophila how much of the variability observed is noise and how much is true experience-dependent reorganization? (I would predict that experience plays very little role).
    2. For a more plastic brain area, like the mammalian neocortex, would a similar study, were it possible, (or even a much more ambitious connectomics study like the ones in the current issue of Nature) provide much useful information beyond that already know from bulk anatomical labeling. (I would predict, yes, but not anywhere in proportion to the budget allocated for such connectomics projects).

    Also, the most useful information, in my view, will come from comparing the connectivity of mutant/transgenic strains with wildtype animals. In Drosophila MARCM makes this plasusible. Is a comparable approach possible (or even sensible) in the mammalian brain?

  2. cksalmo says:

    1: Yeah I was wondering the same thing. I was under the impression that the Drosophila nervous system was relatively hardwired too, but after thinking about that for a while I realized that the evidence I’ve seen for that is all from the periphery. Chiang and friends state that the Drosophila central neurons are “sometimes even variable in their post synaptic targets,” citing this study (http://bit.ly/f5dEZg). That study reveals that “finer-scale connectivity of individual LNs [in the antennal lobe] varied considerably across brains,” and the authors conclude that their results “reveal an unexpected degree of complexity and individual variation in an invertebrate neural circuit.” Whether this variability is experience dependent, or just random imprecision of finer scale connectivity is going to be hard to tease apart in the fruit fly I think. I don’t know of any studies experience dependent connectivity in Drosophila.

    2: As for the mammalian cortex, as far as I can tell the general consensus is that large scale connectomics will identify general rules of connectivity, and for the most, in places like the visual and somatosensory cortices, that will probably mean verifying and refining prior physiological data. My hope is that when connectomics goes larger scale and becomes more of a feasible pursuit, it will allow for reconstruction of more obscure brain regions, like the PFC, where electrophysiological studies are harder to do because the precise stimuli feeding into it and the responses being output are harder to define. Then again I haven’t done much reading on the finer points of cellular bio and e-phys in these areas, so feel free to inform me.

    As for whether comparing connectomic data of WT and mutant strains of mammalian models will be of any use, I think it is hard to say. Perhaps the differences in wiring that lead to overt phenotypes are very nuanced and will be hard to pull out of potentially extremely variable wiring diagrams. But on the other hand, perhaps the general rules of connectivity are altered between WT and mutants. For instance, perhaps the changes in dendritic spine number associated with mental retardation (outlined here by Fiala and friends http://1.usa.gov/dOTICy) result not from a general increase in spine number, but an alteration in general rules of connectivity. Hopefully connectomics can get at these questions once it is a more feasible field of study.

  3. Denise says:

    I think the value of these large-scale descriptive analyses of the brain (connectomics, Blue Brain Project) will be the technological and intellectual advances that are made along the way. These advances will hopefully change the speed and way we approach the other big questions in neuroscience. So, although right now the cost-benefit ratio of doing this type of project might seem exorbitant (as it was for the Human Genome Project), we have no way to predict how far technological advances will take us in the future (mapping of whole human brains in 1 day?). The problem of individual variability in neuronal connectivity and random effects will probably be the biggest hurdle to overcome, which to this day remains one of the biggest challenges in whole genome sequencing.

  4. Keith Murai says:

    Quite a heroic study about keeping us in the ‘know’ about brain info flow. I agree with you in that the technical achievements in this study will have a more immediate impact on the field. In the longer term, I think this investigation will help fine-tune hypotheses and pave the way for more directed circuit mapping (with synapses involved!). Pairing fly behavioral phenotypes with altered connectivity between inter-communicating brain centers may give some really interesting insight into some brain diseases.

    • cksalmo says:

      I fully agree that looking at similar wiring diagrams for mutant flies will give insight into how certain genes regulate connectivity. In the future, once we have a definitive wild type fly brain model, you could even look at individual neuropils in mutants, and study hows its connections are altered, rather than mapping the entire brain for every mutant you want to study – but the connectivity would need to be nailed down first, which is what Chiang and friends are doing here.

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