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.
[1] https://openworm.org/assets/OpenWormPoster_Celegans_Glasgow_...
No need to treat research like a business.
Never heard a single story like this
"A Turning Point in Cancer Research: Sequencing the Human Genome" - https://www.science.org/doi/10.1126/science.3945817
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:
"Cancer researchers accidentally discover ‘cure’ for baldness, gray hair" - https://technology.inquirer.net/62453/cancer-researchers-acc...
"Cancer Researchers Accidentally Discover New Nylon Process" - https://www.popularmechanics.com/science/health/a8135/cancer...
[1] https://theonion.com/scientists-don-t-get-mad-but-we-acciden...
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.
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!
[1]: https://www.nature.com/articles/s41598-021-91025-5 [2]: https://www.biorxiv.org/content/10.1101/2023.10.16.562590v1....
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).
[edit: I forgot to mention that neuron growth in adults (across many species) is still a controversial topic; see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7554932/ for some commentary on the challenge in fly; https://en.wikipedia.org/wiki/Adult_neurogenesis for commentary on the larger problem ]
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.
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.
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.
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.
And maybe there’s some data or concept that will one day be discovered that will be the key to unlocking how brains work.
For my analogy, I was thinking more of how the connectome is, like schematics, static and the dynamic part is probably more interesting.
Luckily this is science so we can actually find out.
But it's the flow of information as signals pass through nodes where everything actually happens.
> 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
And isn't there n-ary entanglement?
There's obviously something to it or implementing what we map in software wouldn't give results as accurately as they do.