It was my understanding that all this connectome-based research was largely a deadend, because it doesnt capture dynamics, nor a vast array of interactions. if you've ever seen neurones being grown (go search YT), you'll see it's a massive gelatinous structure which is highly plastic and highly dynamic. Even in the simplest brains (eg., of elgans), you get 10^x exponential growth in number of neurones and their connections as it grows.
Connectome-adjacent neuroscientist here. Definitely not a dead end! But also definitely not the whole picture.
One of the main open questions in neuroscience right now is how network structure, dynamics, and function are related in the brain. Connectomes provide tremendous insight into structure, but as mentioned this does not generically solve either the dynamics or function problem. For example, for many of these neurons we don't have a good understanding of their input-output relationship, and the nature of this relationship can strongly affect the dynamics that emerge in a highly connected network. Individual variability across connectomes, and how connectomes change over development are also a significant issue, but at least for the fly it's thought that many of the basic structures are pretty conserved across adult animals, even if many of the details could differ.
Modulo these caveats, knowing the physical network structure of the brain does still impose huge constraints on what kinds of models we should be using for gaining insight into dynamics and function. For example, there are well known areas (the "mushroom bodies") with specific feed-forward connectivity patterns that are very different from a random recurrent network. Further, there are at least some areas in the fly brain where we think there are indeed quite clean structure-function relationships, e.g. in the central complex of the fly brain, which contains a physical ring of neurons and is thought to support a "bump" of activity that acts as a sort of compass that helps flies navigate via a ring-attractor-like dynamical system. Thus, even though it has many missing pieces, a wiring diagram like this can be tremendously useful for generating hypotheses to guide more targeted experiments and theoretical studies.
How's Open Worm coming along? The connectome of C. Elegans has been known for years, and Open Worm tries to simulate it. [1] Not with enormous success.
You know you would have thought all the years and years of "donations" to "cancer research" there would be constant news stories about how we accidentally cured a bunch of ancillary medical problems, and wow its all free to everyone because it was from donations!
Even without that I'm not sure why you think that's a good point — it's very easy to find serendipitous examples in medicine in general, e.g. viragra which was initially a heart treatment, or even thalidomide whose anti-cancer uses were suggested by the very birth defects that made it infamous.
Specifically cancer research finding other things by accident:
Was there any "productionization" of the "cure" for baldness & gray hair, after it was discovered 7 years ago? I reckon, there's a huge market for that cure.
I mean I get this is a joke, but it's still homophobic at its core, imo. Why riff on the same on argument against homosexuality when it would've been much more humorous to "turn it around" and joke about finding a cure for heterosexuality and research indicating that homosexuality should be the de facto state.
Tho tbf when I've joked about that with str8 friends they get really upsetti spaghetti, yet somehow still can't link their spaghettiness to why I was offended when they said "just don't act gay" when I said I would love to see Egypt but didn't want to travel there.
Very Nice. --from a Connectome-Centric neuroimager :)
One technique that I am pursuing right now is information decomposition of timeseries to separate the mutual information of two timeseries into redundant and synergistic informational atoms (synnergystic here means the degree to which knowing both timeseries gives you more information than the individual parts give (more than sum of parts). The big limitation of the method is the geometric explosion in complexity of the decomposition as the number of time series grow, with most analyses being limited to two or three times series at a time. However, the scale of the data on which it is applied is not requisite, meaning the approach can equally be used on the mutual information between two regions of interest in rsfMRI , or two spiking timeseries from individual neurons.
https://en.wikipedia.org/wiki/Partial_information_decomposit...
Thanks for your insight! How repeatable are these structures between individual animals? Are they very similar or is it more like “here’s a feed forward kinda bit, here’s a toroidal bit, and over here it’s just a mess”?
preprint coming out soon about this specifically :)
in the meantime, here's a simple tool paper we wrote explaining how you can treat this like a cool graph database challenge [1] and a preprint showing how you could approach that question when your number of samples per animal is close to N=1 [2]. basically..... it's hard! but also.... it's cool!
This is done agaist an adult so all the neurons have already grown.
connectome isn't a dead end but it doesn't solve all known problems. It's like making a static map which you can then use to inspect all those cars driving around (the dynamics) and crashing (the interactions).
Giving scientists access to the connectome snapshot alone is very exciting. The first step to understanding why something is and how it came to be is seeing what it is.
There are systems at play that form the brain into what it is and we don’t know much about them. The individual neurons — we have a better understanding of, but not the emergent systems. Now that many more scientists will know what the target for these systems is — what is the brain they shape, we can start to understand the control and feedback loops that result in this snapshot state of the brain.
And that’s why it’s not a dead end. Just because it doesn’t immediately give some sort of a consumer product, doesn’t mean it’s not a step forward.
You don't get the dynamics from connectomes, but you absolutely need them. So it isn't that they are a dead end, it is that the dynamics by themselves are also insufficient and the connectome is insufficient, you need both. Further, if you want to actually be able to have anything to attach the dynamics to, you need the cellular anatomy, so connectomes are absolutely necessary. The fact that connectomes are insufficient does not mean that such research is a dead end, but rather that the prerequisites for understanding the nervous system are vastly more complex and demanding than some might have hoped.
It is like getting a static map of the country's roads with no cars on it.
You can not make it come alive with cars (activity), but you can infer where people need to drive but you don't know when and why they drive or what they are doing, but it is a major clue.
> It is like getting a static map of the country's roads with no cars on it.
I was thinking it was more like giving somebody iPhone schematics and die shots of all the chips and then asking them to figure out how Portrait Mode works in the Camera app.
The difference is that in the brain there's no real separation between hardware and software, so I'm your analogy, we also have the equivalent of the source code, but just maybe not the environment configuration needed to get it to run (nor would we at this stage have sufficient compute to fully run it).
Any man made hardware is rather too organized to be good analogy here. But we have better alternatives than came along recently - LLMs or any kind of AI models as a matter of fact. Personally I would use analogy of "try running a prompt locally and then explain what really happened inside in terms of CPU operations" :)
Sort of, but mostly not. The critical distinction is that, given better data (the instruction set, the source code or binary of the OS and camera app), the schematics and die shots aren't necessary or even useful.
It's unlikely that brains have an abstraction layer like that, so work like this is a necessary precondition to understanding the rest of how it works. That actual understanding may be elusive for quite some time to come, but without a connectome, forget it, no change.
It would be really inefficient and neurons inherently provide a great deal of flexibility. Larger animals might use this kind of thing, but insects don’t have that many neurons to work with.
Luckily this is science so we can actually find out.
> So, to run the same [fMRI, NIRS,] stimulus response activation observation/burn-in again weeks or months later with the same subjects is likely necessary given Representational drift
That's a bit like saying that sequencing the genome was a dead end, because it doesn't capture the molecular biology of the encoded proteins. Assuming the connectome is accurate, it's a major advance in our knowledge of neuroanatomy.
