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Partha Mitra on brain connectome and neuroanatomy

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How do you map the wiring of an entire brain when neuroscience is simultaneously drowning in data and starving for the right kind? Partha Mitra explains why he left theoretical physics to build a whole-brain mesoscale connectome , and what it reveals about the gap between data richness and genuine understanding. Subscribe for more from the Convergent Science Network podcast series. In this episode, Partha Mitra describes the paradox at the heart of modern neuroscience: half a million abstracts published on PubMed each year, yet no comprehensive wiring diagram for any mammalian brain beyond C. elegans. Trained as a theoretical physicist, Mitra recounts how his growing humility toward the complexity of the brain drove him from abstract modeling to the lab bench, where he now leads an industrial-scale neuroanatomy project at Cold Spring Harbor Laboratory. His goal is to systematically map the mesoscale connectivity of the mouse brain , the level at which developmental programs lay down the architecture that sits between single synapses and whole-brain function. Mitra draws a compelling analogy to Google Earth: just as geographic data remained fragmented until a unifying spatial framework existed, neuroscience data lacks a scaffold on which to hang its heterogeneous findings. His project aims to provide that scaffold by injecting tracers across the entire mouse brain and building probabilistic maps of where axons from any given region project. He argues that this mesoscale is uniquely important because it is genomically patterned , shaped by developmental genes rather than purely by experience , making it the natural bridge between molecular biology and systems neuroscience. The conversation also tackles deep methodological questions. How much individual variability exists between brains of the same species, and can meaningful regularities still be extracted? Mitra hypothesizes that brains occupy a low-dimensional manifold of variation , constrained enough to reveal common design templates, yet variable enough to be scientifically interesting. He envisions comparative studies across species that could uncover conserved architectural principles shaped by convergent evolution, not just shared ancestry. Perhaps most striking is Mitra’s philosophical evolution. He advocates what he calls “ontological monism and epistemological pluralism” , one physical reality, but multiple legitimate theoretical frameworks for understanding it. He cautions against the assumption that all theories must reduce to one another, and urges neuroscientists to take engineering perspectives more seriously as a source of insight into how evolved systems solve functional problems.

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Both the triumphs of humanity and its most evil deeds have resulted from collaboration. In a time where humanity is required to aspire to the former and minimize the latter, the question arises of how collaboration arises and why it fails. Surprisingly, this phenomenon, so central to who we are, is not well understood. Hence, a collaborative effort is required to understand collaboration in its full biological, psychological, sociological, cultural, and economic complexity and to translate this understanding into operational impact. This series of podcasts is one step toward achieving these complementary goals. The Collaboration Podcast presents interviews with people who are central orchestrators of collaboration in various domains including business, government, science, art, health, sustainability, and the military. The discussions were conducted by Prof. Dr. Paul F.M.J. Verschure and members of the Program Advisory Committee of the Ernst Strungmann Forum on Collaboration (https://www.esforum.de/forums/ESF32_Collaboration.html) during 2021 and had the goal to sketch a map of opportunities, challenges, and obstacles in human collaboration. The forum took place in May 2022, and now we would like to share this series of interviews with a broader audience. The full report of the Forum will be published in 2023 by MIT Press. The podcast was produced by the Convergent Science Network (https://www.convergentsciencenetwork.org/). Context: The stability of social systems depends critically on realizing sustainable methods of “collaboration,” yet how and by which means collaboration is achieved is not clearly understood; neither are the conditions or processes that lead to its breakdown or failure. Collaboration can be understood as cooperation between agents toward mutually constructed goals. Part of the reason for our lack of understanding is that the phenomenon of collaboration is, by nature, a highly multidisciplinary problem, and effective research into its complexities has been difficult to achieve across the broad range of scientific and technical disciplines involved. The need for a fundamental understanding of collaboration, however, has become increasingly important. Not only does humankind demand answers as it attempts to address critical challenges at multiple scales (e.g., climate change, migration, enhanced automation, social and economic inequality), but ever-increasing technological and economic means of interconnecting people and societies are disrupting long-established, familiar patterns of how we interact. Radical technological changes that are ongoing have the potential to reshape collaboration in ways that are currently hard to predict or influence (e.g., by altering configurations in interaction, information creation, and modes of communication). On one hand, such changes could disrupt hitherto stable forms of collaboration by affecting critical communication channels and traditional roles, as can be observed in the rapidly changing patterns in governance, commerce, and social interaction. Conversely, technology could lead to the emergence of novel, successful forms of collaboration that deviate from traditional “hierarchical” architectures. Evidence of this can be seen in areas as diverse as highly automated manufacturing plants, the open science movement, collaborative software repositories, user-centered services, and the sharing of economy-based modes of organization. Without a fundamental understanding of the mechanisms, processes, and boundary conditions of collaboration, it is not possible to evaluate or predict which of these possible scenarios are sustainable or even plausible. The Forum “How Collaboration Arises and Why it Fails” (May 8–13, 2022, Location: Frankfurt am Main, Germany) Chairs: Andreas Roepstorff and Paul Verschure Program Advisory Committee: Jenna Bednar, Julia R. Lupp, Bhavani R. Rao , Andreas Roepstorff, Ferdinand von Siemens, and Paul Verschure

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  • fast_forward00:00:00 - So Paul Voucher and Tony Prescott. And Partha was describing to us an amazing
  • fast_forward00:00:04 - project where you have this sort of massive automated data collection on a very
  • fast_forward00:00:13 - detailed anatomical level that can lay a foundation for new insights in the brain.
  • fast_forward00:00:19 - So Partha, why don't you try to give us a short summary how you ended up doing this?
  • fast_forward00:00:25 - I was really shocked to find out how little we know about how the brain is wired.
  • fast_forward00:00:34 - That's what got me started.
  • fast_forward00:00:35 - I trained as a theoretical physicist, and when I came into biology,
  • fast_forward00:00:40 - I'm slowly coming to grips with the knowledge that we are very much limited by lack of information.
  • fast_forward00:00:49 - And we have this paradox that we are data rich and data poor at the same time.
  • fast_forward00:00:53 - We have lots of data, but sometimes critical pieces are missing.
  • fast_forward00:01:00 - And once I realized that even in the most thoroughly studied model organism, which is the rat,
  • fast_forward00:01:09 - the wiring diagram of the brain at a scale that neuroanatomists have studied
  • fast_forward00:01:14 - has large empirical gaps in it, I thought I would set out to try to rectify that situation.
  • fast_forward00:01:23 - But in some sense, it's very counterintuitive because often in neuroscience,
  • fast_forward00:01:28 - also when you talk with other neuroscientists, it's this feeling like,
  • fast_forward00:01:31 - well, we're drowning in data.
  • fast_forward00:01:33 - We have so much data, different scales, different species, physiology, anatomy, etc.
  • fast_forward00:01:39 - So how do you position that with respect to this data richness?
  • fast_forward00:01:43 - So how do you now deal with this paradox?
  • fast_forward00:01:46 - I think the way I think about it is that we are data rich.
  • fast_forward00:01:51 - You're quite right. there's more than half a million articles being
  • fast_forward00:01:55 - published on abstracts being published on PubMed every year so there's a tremendous
  • fast_forward00:02:00 - amount of information out there I think this information is very heterogeneous
  • fast_forward00:02:05 - and not really integrated there
  • fast_forward00:02:07 - is no super brain that really has all the information and so does you,
  • fast_forward00:02:14 - so yes we are data rich in this very heterogeneous sense lots of bits of data.
  • fast_forward00:02:21 - We're data poor in the sense in neuroscience compared with, let us say,
  • fast_forward00:02:27 - in genomics, where we have comprehensive information about a certain scale of organization.
  • fast_forward00:02:33 - Let's say the genome. We've got the full genome of multiple species,
  • fast_forward00:02:37 - but we can't say the same at any scale really of any organism for the nervous
  • fast_forward00:02:43 - system, that we have the full set of information with the exception of C.
  • fast_forward00:02:48 - Elegans, which has been kind of the you know example that people have put out um so for me the,
  • fast_forward00:02:56 - Reconciliation is that on the one hand, we do have a lot of heterogeneous,
  • fast_forward00:03:01 - unintegrated information, sometimes partial, because people have focused,
  • fast_forward00:03:06 - over-specialized on certain areas of the system.
  • fast_forward00:03:09 - A lot of studies have been attempted, a lot of studies of the visual system.
  • fast_forward00:03:15 - So if one takes an integrated perspective and does a project where you map out,
  • fast_forward00:03:24 - as in our in our case, the whole brain circuit at some level of resolution.
  • fast_forward00:03:31 - Yes, that's generating a significant data set.
  • fast_forward00:03:37 - But at the same time, it's not even at the scale of this large heterogeneous data that's out there.
  • fast_forward00:03:44 - But given how you summarize this argument, you're facing two challenges in some sense, no?
  • fast_forward00:03:50 - Because on the one hand, isn't one implication that you say,
  • fast_forward00:03:53 - well, yes, we have all this data, But you might as well have not collected it
  • fast_forward00:03:57 - because it's not organized in the right way.
  • fast_forward00:04:00 - Is that really the consequence of what you're saying? Would you agree?
  • fast_forward00:04:02 - Yeah, I mean, I think that that's one way of putting it. I think a good analogy
  • fast_forward00:04:07 - that is certainly not new at this point is thinking about Google Earth.
  • fast_forward00:04:11 - We probably have a lot of information about geography, about,
  • fast_forward00:04:15 - you know, restaurants in a particular city and so on and so forth.
  • fast_forward00:04:18 - But until there was a framework on which to hang all these pieces of information,
  • fast_forward00:04:23 - they remained disparate. it.
  • fast_forward00:04:25 - So perhaps this project that we've initiated, one thing that it will do is to
  • fast_forward00:04:31 - provide a framework in which to collect this heterogeneous information.
  • fast_forward00:04:35 - If that succeeds, then, you know, we will resolve the paradox to some extent.
  • fast_forward00:04:39 - Underlying your argument, though, is another assumption, which is really interesting,
  • fast_forward00:04:43 - because sometimes you're saying, well, the human genome project was actually
  • fast_forward00:04:46 - a great success, and I want to follow this paradigm now from neuroscience.
  • fast_forward00:04:51 - But is that really a reasonable assumption? I wouldn't quite put it in those terms.
  • fast_forward00:04:56 - I would say that, yes, it was a success, but success as defined in certain ways.
  • fast_forward00:05:04 - I think one way in which it succeeded is providing this integrated framework
  • fast_forward00:05:09 - for pulling information together.
  • fast_forward00:05:11 - Another way in which it succeeded was it helped us gain understanding of the evolution of genomes.
  • fast_forward00:05:20 - We've also gained information about human history.
  • fast_forward00:05:24 - So, we haven't solved certain problems which were promised and maybe we will
  • fast_forward00:05:30 - still solve those problems in the future, but in certain ways it has been successful.
  • fast_forward00:05:34 - And I think in those ways is where I would want to borrow from the success or hope for the success.
  • fast_forward00:05:43 - Looking ahead to when you've built this new huge data set, what's your idea
  • fast_forward00:05:50 - about how people are going to use it?
  • fast_forward00:05:52 - I mean, I can see sort of neuroanatomists looking at the raw data and say,
  • fast_forward00:05:55 - yeah, this fills the gap.
  • fast_forward00:05:57 - I don't any longer need to do that experiment because I can just look at your database.
  • fast_forward00:06:02 - But for those people that want to draw something out of it to condense it down
  • fast_forward00:06:06 - into some more compact understanding of the brain, what kind of tools do you
  • fast_forward00:06:10 - think they will be able to use on this database?
  • fast_forward00:06:13 - Well, we will try to provide integrated versions of the data at lower resolutions.
  • fast_forward00:06:20 - So in the ideal world, what we should be able to provide is a probabilistic
  • fast_forward00:06:25 - math saying that if you start from point A in a reference brain and you were
  • fast_forward00:06:33 - a neuron with a cell body near point A,
  • fast_forward00:06:35 - what's the probability that you had a projection or an arbor at point B?
  • fast_forward00:06:44 - So, that sort of information integrated across the injections,
  • fast_forward00:06:48 - we will be able to provide.
  • fast_forward00:06:53 - That will then presumably help people who are trying to gain an understanding
  • fast_forward00:06:58 - of the architecture of the whole brain circuit.
  • fast_forward00:07:03 - I mean, I know that this is a
  • fast_forward00:07:04 - nascent field in the sense that not a lot of people are trying to do that.
  • fast_forward00:07:07 - There's more interested in understanding the microcircuits in a particular region of the brain.
  • fast_forward00:07:14 - But we do hope to provide some integrated pieces of information apart from guiding,
  • fast_forward00:07:21 - thinking about how the system is organized and how it works.
  • fast_forward00:07:24 - My hope is that we will also get multiple species, so we will be able to address
  • fast_forward00:07:30 - evolutionary questions.
  • fast_forward00:07:32 - Previously, brain evolution has been studied largely in the context of shapes and sizes.
  • fast_forward00:07:37 - So cortex has grown in size, the olfactory bulb has shrunk its size, and so on.
  • fast_forward00:07:43 - But we haven't really studied it at the level of what circuits are and how they
  • fast_forward00:07:49 - have changed, with exceptions.
  • fast_forward00:07:51 - But it has been very much driven by non-circuit considerations,
  • fast_forward00:07:58 - the evolution. I don't know if you agree with,
  • fast_forward00:08:00 - Well, what I was wondering in that in that respect is also the incredible individual
  • fast_forward00:08:05 - variability you might try to get.
  • fast_forward00:08:06 - So how are you going to handle that? Right. So you might take,
  • fast_forward00:08:11 - let's say, a mouse or a rat or or a monkey from which you get your anatomical data.
  • fast_forward00:08:17 - But now you know that over individuals, there'll be an incredible dispersion
  • fast_forward00:08:22 - of these kinds of projections.
  • fast_forward00:08:24 - So how do you see yourself extracting the rules from that kind of variability?
  • fast_forward00:08:30 - I do have a premise that the variability will not be so large that we cannot
  • fast_forward00:08:37 - extract these regularities.
  • fast_forward00:08:41 - So going back to a comment you made during my presentation, is that I do have
  • fast_forward00:08:45 - a hypothesis, in some sense a weak hypothesis,
  • fast_forward00:08:48 - that there is something to be learned from these experiments because there is
  • fast_forward00:08:53 - some underlying organization that is relatively concerned from individual to
  • fast_forward00:08:58 - individual in this mouse strain,
  • fast_forward00:09:00 - we will do multiple injections and multiple repeats of the same injections in the same brain region.
  • fast_forward00:09:08 - So empirically, we will hope to answer this question. I don't think it's a theoretical question.
  • fast_forward00:09:13 - So I do have a theoretical hypothesis that the variation will be controlled
  • fast_forward00:09:17 - enough that there is a meaningful mesocircuit, but we will try to prove it empirically.
  • fast_forward00:09:23 - And how many different...
  • fast_forward00:09:26 - Since the brain is continuously changing also ontogenetically,
  • fast_forward00:09:29 - so how many measurement points do you think you would need in a single species?
  • fast_forward00:09:34 - We are doing it in the mouse at a single age.
  • fast_forward00:09:38 - We have not yet even started thinking about a developmental version of this.
  • fast_forward00:09:42 - One thing that I'm hoping is that yes,
  • fast_forward00:09:46 - we are running this particular experimental project in my lab but we are hoping
  • fast_forward00:09:52 - to demonstrate that this kind of project can be done quite economically in multiple laboratories.
  • fast_forward00:09:58 - So once that comes about, I think people should be able to fill in multiple ontogenetic steps.
  • fast_forward00:10:05 - What we are hoping to do is to take one age and then look at different mouse
  • fast_forward00:10:09 - strains, so different genetic constructs, and see how the circuit changed at
  • fast_forward00:10:14 - a given age. Right. So how,
  • fast_forward00:10:18 - There's something interesting about the trajectory that you went through.
  • fast_forward00:10:20 - As you said earlier, you started as a theoretical physicist and then sort of
  • fast_forward00:10:25 - discovering the brain, if you want, and in some sense now becoming much more
  • fast_forward00:10:31 - an experimentalist in some sense.
  • fast_forward00:10:32 - But then, let's say, a large-scale anatomist or industrial-scale anatomist.
  • fast_forward00:10:38 - And if I look at the way you present the argument, it seems also interesting
  • fast_forward00:10:44 - that along the way, you've been shedding more and more of your theoretical skin,
  • fast_forward00:10:48 - right? I do it at night privately.
  • fast_forward00:10:53 - Is that fair to say? No, my bedtime reading recently has been,
  • fast_forward00:10:57 - you know, formal logic and models of real computation.
  • fast_forward00:11:01 - I mean, I haven't shed my theoretical interests. It's just that I have become
  • fast_forward00:11:07 - much more conservative than when I started in making theoretical models because
  • fast_forward00:11:11 - I've come to realize how difficult the problems really are that we are trying to describe.
  • fast_forward00:11:17 - What happened to me when I was a theoretical physicist is I developed a great admiration of theory.
  • fast_forward00:11:23 - So I don't want to take the name lightly, so to speak.
  • fast_forward00:11:27 - It's very interesting. So in some sense, what you're saying is that in this
  • fast_forward00:11:32 - move from, let's say, the naive but maybe very ambitious theoretical person
  • fast_forward00:11:36 - into now, let's say, the anatomist at the bench,
  • fast_forward00:11:39 - you have acquired more humility towards the problem. Absolutely. Absolutely.
  • fast_forward00:11:43 - That's the point. Absolutely, yeah. I think that these are very hard problems,
  • fast_forward00:11:46 - and I still hope to make some theoretical contributions, but I would like them
  • fast_forward00:11:52 - to be solid contributions.
  • fast_forward00:11:55 - So where do you think we are in terms of theories of the brain?
  • fast_forward00:11:59 - What aspects of the brain do we understand well, and where do we need to pay attention to?
  • fast_forward00:12:05 - I think single-neuron biophysics is a clear success story,
  • fast_forward00:12:09 - to the extent that there's a well-developed theoretical framework,
  • fast_forward00:12:15 - and that it's got an attached experimental framework, and there's a close feedback
  • fast_forward00:12:18 - loop between the theory and the experiment.
  • fast_forward00:12:22 - Some other parts, I think it's piecemeal. I think certain systems in the brain
  • fast_forward00:12:26 - are well-developed theoretically.
  • fast_forward00:12:29 - I'm not saying that they are fully developed, but the sensory systems are relatively
  • fast_forward00:12:34 - well-developed in that way.
  • fast_forward00:12:35 - We have a good, at least, approach to the visual system or the auditory system.
  • fast_forward00:12:40 - Also, to a lesser extent, but also to motor systems.
  • fast_forward00:12:46 - At the peripheral levels, I think we've got good models. models,
  • fast_forward00:12:51 - what I feel is lacking is a more systems-level understanding of the whole system.
  • fast_forward00:12:59 - I think that the theoretical work that has been done there is non-mathematical,
  • fast_forward00:13:06 - but still legitimate theorizing,
  • fast_forward00:13:08 - and people who have done that are perhaps not thought of as theorists because
  • fast_forward00:13:14 - they've really thought about the system, they've articulated their thoughts.
  • fast_forward00:13:17 - I would still call that theory. I mean, Darwin didn't have a single equation, right?
  • fast_forward00:13:22 - So what is the status?
  • fast_forward00:13:27 - I think that there is a lot of work to be done.
  • fast_forward00:13:32 - Progress seems to me to be pragmatically occurring in fields which are tied into robotics.
  • fast_forward00:13:37 - Insect locomotion is one area that I've followed to some extent,
  • fast_forward00:13:41 - and it seems to me to be working quite well.
  • fast_forward00:13:44 - Of course, that's limited in the sense of understanding the brain.
  • fast_forward00:13:50 - One lesson that I've drawn from there is really to think about engineering theories
  • fast_forward00:13:55 - and to think about what the system does,
  • fast_forward00:13:58 - what's the logic that one would employ in constructing systems like that,
  • fast_forward00:14:02 - and then trying to understand whether the evolutionary processes are compatible with such thinking?
  • fast_forward00:14:10 - Is there a role for convergent evolution?
  • fast_forward00:14:13 - So if I were to say in a single sentence where I see the future of theory,
  • fast_forward00:14:17 - I would say take engineering much more seriously.
  • fast_forward00:14:20 - I think that it's certainly been a theme that has been explored since the times of cybernetics.
  • fast_forward00:14:26 - It has perhaps been sidelined in some areas of neuroscientific theorizing,
  • fast_forward00:14:31 - but I think it needs to be brought more to the fore and more training is necessary
  • fast_forward00:14:37 - in engineering disciplines of the,
  • fast_forward00:14:40 - some of the neuroscientific theorists.
  • fast_forward00:14:41 - Are you saying that we should give up this more physics-based dream of,
  • fast_forward00:14:47 - let's say, closed-form solutions of the phenomena that we're trying to study and go more towards,
  • fast_forward00:14:53 - let's say, open solutions or descriptions of these systems? Is that a key step there?
  • fast_forward00:14:59 - Yeah, I think that there has been a certain limitation in the theory community,
  • fast_forward00:15:04 - at least those of us who came from a physics background to take a point of view
  • fast_forward00:15:11 - that resembled too closely the successes of theoretical physics and simply hoping
  • fast_forward00:15:16 - that the same trick will work again.
  • fast_forward00:15:19 - I think that that's not working, is really the evidence.
  • fast_forward00:15:23 - There are some isolated examples like the Hauss-Gnostic equation,
  • fast_forward00:15:27 - a partial differential equation describing how the action potential works,
  • fast_forward00:15:31 - might lead us to believe that in general, ordinary and partial differential
  • fast_forward00:15:35 - equations are what we are looking for in terms of theoretical constructs.
  • fast_forward00:15:43 - Again, looking in a forward modeling perspective, observing some physical phenomena
  • fast_forward00:15:48 - in the brain and then modeling with ordinary or partial differential equations,
  • fast_forward00:15:52 - seems to me that that approach in spite of being very successful in one domain has not generalized.
  • fast_forward00:16:00 - It seems to me one needs a multiplicity of theoretical framework.
  • fast_forward00:16:04 - So I have coined this motto, I say ontological monism and epistemological pluralism.
  • fast_forward00:16:10 - So there is only one reality out there.
  • fast_forward00:16:12 - We all agree. You always win at Scrabble, I understand.
  • fast_forward00:16:17 - I don't think there's a disembodied soul that is driving my brain.
  • fast_forward00:16:23 - But at the same time, I think there are multiple legitimate descriptions,
  • fast_forward00:16:30 - epistemologies, ways of theorizing,
  • fast_forward00:16:33 - modes of theorizing. They may use different mathematical tools.
  • fast_forward00:16:36 - And it is not true that one reduces to the other. I think this is an assumption people have made.
  • fast_forward00:16:40 - So when people talk about reductionism, the discussion is a bit confused because
  • fast_forward00:16:44 - it's not whether they're talking about physical phenomena reducing to each other.
  • fast_forward00:16:49 - That doesn't make any sense to me because there's only one phenomenon.
  • fast_forward00:16:52 - The question is whether theories reduce to each other.
  • fast_forward00:16:55 - And sometimes they do, sometimes they don't. And I think one has to be a little
  • fast_forward00:16:59 - more Catholic. So you take a very Kuhnian approach here.
  • fast_forward00:17:03 - I would like to, yeah, take a more Catholic approach to theorizing.
  • fast_forward00:17:07 - And that is definitely new for me.
  • fast_forward00:17:09 - I mean, I did not come from a tradition where that is done, although having
  • fast_forward00:17:13 - trained as a condensed matter theorist, I was perhaps more open to it than if
  • fast_forward00:17:17 - I were, let's say, trained in particle physics.
  • fast_forward00:17:20 - Why do you think the mesoscopic scale is such an important scale to understand
  • fast_forward00:17:26 - the brain at, as opposed to other scales?
  • fast_forward00:17:28 - Or is it just that we haven't done enough work on that level?
  • fast_forward00:17:31 - Yeah, I think that it is a scale at which the brain needs to be understood and
  • fast_forward00:17:35 - a scale in which understanding is comparatively lacking.
  • fast_forward00:17:40 - I'm also inspired to study this scale because it seems to be developmentally
  • fast_forward00:17:43 - patterned. So it is in the genetic code, so to speak.
  • fast_forward00:17:49 - Given that there is a juggernaut out there, which is kind of genome biology,
  • fast_forward00:17:54 - and we have learned, certainly human knowledge has progressed in that domain.
  • fast_forward00:17:59 - It would be great if we could link up neuroscience with that progress.
  • fast_forward00:18:07 - One scale where I see that link occurring quite distinctly is at this mesoscale,
  • fast_forward00:18:12 - because the mesoscale is developmentally patterned out of the genome,
  • fast_forward00:18:15 - or at least that would be the quote-unquote hypothesis.
  • fast_forward00:18:18 - So the heterogeneity at the mesoscopic scale,
  • fast_forward00:18:22 - within elements at that scale, there might be Bohr-Humann homogeneity and the
  • fast_forward00:18:28 - potential for using learning algorithms or developmental algorithms to wire up systems.
  • fast_forward00:18:37 - Right, so the hypothesis would be that, let's say the genome patterns this mesoscale
  • fast_forward00:18:42 - with environmental input, but let's say the genome, that's understood in modern
  • fast_forward00:18:47 - day discussions, right?
  • fast_forward00:18:48 - But without making those caveats, let us just say the genome patterns the mesoscale.
  • fast_forward00:18:52 - Then variations in the mesoscale are, related to genomic variations,
  • fast_forward00:18:58 - then one should be able to relate those two things, if one looks across species
  • fast_forward00:19:01 - or if one looks across individuals.
  • fast_forward00:19:04 - There could be also environmental perturbations that cause fluctuations at the
  • fast_forward00:19:08 - mesoscale, but the idea is that it's genomically sculpted.
  • fast_forward00:19:12 - And then variations at a more micro scale, certainly some of that patterning is also due to rules.
  • fast_forward00:19:19 - Let's say one neuron has to connect to another neuron. So kind of self-organization,
  • fast_forward00:19:24 - would you hope that that would give us leverage at the smaller scale?
  • fast_forward00:19:27 - Right. One would expect that self-organization is playing a more important role at the smaller scale.
  • fast_forward00:19:32 - So this would be a way of separating where the developmental program is more
  • fast_forward00:19:36 - important from where the learning self-organization rules are more important.
  • fast_forward00:19:41 - But what's the metric of this mesoscopic scale exactly?
  • fast_forward00:19:44 - I don't think there is a fixed length scale that I would associate because if
  • fast_forward00:19:50 - you look in different parts of the brain, there are different sizes.
  • fast_forward00:19:54 - But I would simply point to classical neuroanatomical atlases,
  • fast_forward00:19:58 - where the sizes of regions of nuclei vary if you look across the brain.
  • fast_forward00:20:08 - They could get down to, depending on what brain you're looking at,
  • fast_forward00:20:12 - in the mouse brain, they could get down to hundreds of microns,
  • fast_forward00:20:15 - but they could also be larger, you know, millimeters.
  • fast_forward00:20:18 - Meters so we have to we have to refine or improve this definition certainly
  • fast_forward00:20:26 - if we want to do a comparative anatomy yeah i think it has to be data-driven i don't think there is a.
  • fast_forward00:20:34 - Definition i can give today in advance of having
  • fast_forward00:20:37 - gathered the data set what i hope is after we have data sets like this and we
  • fast_forward00:20:41 - already have some existence proof from the gene expression data set from the
  • fast_forward00:20:45 - allen institute that one can maybe empirically extract these correlation lengths
  • fast_forward00:20:52 - or scales directly by looking at the data.
  • fast_forward00:20:54 - What we can hypothesize based on neuroanatomical literature is that such scales exist.
  • fast_forward00:21:02 - And, you know, good guesses as to what those scales are in specific brain regions.
  • fast_forward00:21:06 - One thing you said earlier is that you felt that, let's say,
  • fast_forward00:21:12 - the genome would be controlling development at just one of these scales.
  • fast_forward00:21:17 - Well, let's say these lower levels would be more self-organizing.
  • fast_forward00:21:22 - Why are you saying that?
  • fast_forward00:21:25 - Well, it's a guess. Yes, I cannot articulate all my previous knowledge that
  • fast_forward00:21:31 - leads to the guess, but one reason is you look at different mice,
  • fast_forward00:21:37 - you get the same atlas.
  • fast_forward00:21:40 - This is just a statement about how the community is organized in this research.
  • fast_forward00:21:45 - If it was the case that there was tremendous environmental influence on the
  • fast_forward00:21:49 - scale at which classical neuroanatomical atlases are organized,
  • fast_forward00:21:53 - we would probably not be able to use atlases, So the very fact that we can use these atlases, to me,
  • fast_forward00:22:00 - is some indication that these are not specifically environmentally sculpted.
  • fast_forward00:22:10 - But this could also be a bias of how we treat these phenomena.
  • fast_forward00:22:13 - Like in psychology, it's a typical problem that, okay, we move into statistics
  • fast_forward00:22:17 - because we cannot understand humans from an individual level.
  • fast_forward00:22:20 - On the other hand, now in psychology, people are bumping into this limitation
  • fast_forward00:22:25 - statistical approach because actually there is not this normative person.
  • fast_forward00:22:30 - So maybe for your emphasis, you're facing a similar kind of problem.
  • fast_forward00:22:34 - I would certainly agree that there is not a normative mouse brain, the platonic brain.
  • fast_forward00:22:40 - But what I do believe is that there's a low-dimensional manifold of brains,
  • fast_forward00:22:44 - that it's not as high-dimensional a space as, let's say, the number of neurons
  • fast_forward00:22:50 - in the brain would lead us to believe.
  • fast_forward00:22:55 - So even though there is variability, and that variability could well be developmental
  • fast_forward00:23:01 - plasticity that has environmental impact on it, in the same way that we would
  • fast_forward00:23:05 - expect it in psychology.
  • fast_forward00:23:10 - But I at least have a strong hypothesis that this pace of variation.
  • fast_forward00:23:21 - Is quite constrained. It's quite small. And I would also say it differs from
  • fast_forward00:23:27 - region to region in the brain, and we will probably find that out.
  • fast_forward00:23:30 - This is an empirical question. We will be doing these studies in multiple mice,
  • fast_forward00:23:35 - so we will be able to look across individuals and really ask the question empirically.
  • fast_forward00:23:41 - I think that's the way to ask it.
  • fast_forward00:23:42 - What's your feeling about this low-dimensional manifold? And certainly,
  • fast_forward00:23:45 - if you look also across species or even including species that do not exist anymore.
  • fast_forward00:23:50 - I mean, what's this common template there? What's your feeling about this?
  • fast_forward00:23:54 - Are you expecting to converge to, say, a common design template for all vertebrates, for instance?
  • fast_forward00:24:02 - It's a very intriguing thought, right?
  • fast_forward00:24:06 - That there are, whether due to common ancestry or due to convergent evolution,
  • fast_forward00:24:13 - solution, in some sense,
  • fast_forward00:24:15 - selecting for the same circuit because the same function has to be subserved.
  • fast_forward00:24:21 - It's a very intriguing hypothesis, which I hope is right, that there are these
  • fast_forward00:24:27 - common design rules or templates that we will find.
  • fast_forward00:24:32 - It may not turn out to be true, but that would be the interesting hypothesis in my mind.
  • fast_forward00:24:38 - And it's an empirical hypothesis. this is, I would say that I hope it is true
  • fast_forward00:24:42 - because there is certainly some theoretical biases I have that are pushing me
  • fast_forward00:24:47 - in that direction. I think the system wouldn't work otherwise.
  • fast_forward00:24:49 - Well, it gives them the finite set of genes building the brain.
  • fast_forward00:24:54 - There you go. I mean, there is certainly some developmental rules, and I think that.
  • fast_forward00:25:02 - We have other examples like our musculoskeletal system that are also patterned
  • fast_forward00:25:08 - about which we don't ask these questions.
  • fast_forward00:25:10 - So is the brain really that different from all the other organs in the body
  • fast_forward00:25:14 - at the level, let's say, of this mesocircuit?
  • fast_forward00:25:20 - So your idea is to do a lot of the work in
  • fast_forward00:25:23 - your own laboratory at Cold Harbor but you're
  • fast_forward00:25:26 - trying to get other people on board this project because
  • fast_forward00:25:29 - by the sounds of it it expands to include many other species also to include
  • fast_forward00:25:35 - animals at different ages there's a huge work program here why do you think
  • fast_forward00:25:41 - people should prioritize this as opposed to all the other things that we might
  • fast_forward00:25:45 - be doing in the neuroscience well I guess you you know, it's a free world.
  • fast_forward00:25:50 - I have to choose from that side. No, no, no.
  • fast_forward00:25:56 - But more seriously, I think that, I don't know, talking about humility,
  • fast_forward00:26:02 - I've just come to realize that the problems are extremely hard.
  • fast_forward00:26:05 - So I would like to work on areas where clearly progress can be made.
  • fast_forward00:26:10 - Now that can be very dangerous.
  • fast_forward00:26:11 - One can get into instrumental fallacy, right? Right.
  • fast_forward00:26:15 - But I have tried to articulate some of the arguments in the position paper that
  • fast_forward00:26:22 - we put together that I would draw people's attention to,
  • fast_forward00:26:24 - plus computational biology that came out last year with the first author,
  • fast_forward00:26:32 - Jay Boland, and the last author being myself.
  • fast_forward00:26:34 - Self um but just to
  • fast_forward00:26:37 - you know try to lay out some motivations
  • fast_forward00:26:41 - uh one great
  • fast_forward00:26:44 - motivation is that we are at the technological cost where
  • fast_forward00:26:47 - this has just become doable within the funding constraints of individual laboratory
  • fast_forward00:26:52 - i think that's for me a great motivation and that was not true a few years ago
  • fast_forward00:26:58 - simply due to memory costs today it is doable yeah but this is the because we
  • fast_forward00:27:02 - can't argument so Because we can't argue with that.
  • fast_forward00:27:04 - I think it's an important argument
  • fast_forward00:27:06 - because there are many… You can win elections with that, you know.
  • fast_forward00:27:11 - That's it. No, I mean, I think it's an important argument.
  • fast_forward00:27:17 - But the second argument, I think, is that….
  • fast_forward00:27:24 - The scope of discovery, I think,
  • fast_forward00:27:26 - is large. We simply don't have these circuits at a very simple level.
  • fast_forward00:27:37 - How are we going to understand how the brain works? The brain is a circuit.
  • fast_forward00:27:41 - We don't have the circuit. People could argue we know certain bits and pieces
  • fast_forward00:27:45 - of it, but that's a very simple argument.
  • fast_forward00:27:48 - One can try to refine the argument, but that's one argument.
  • fast_forward00:27:51 - And then there's a third argument, which is this information integration that
  • fast_forward00:27:55 - we were talking about, that we are battling this huge deluge of data and information.
  • fast_forward00:28:00 - How to tie it together? Well, here's a way we could.
  • fast_forward00:28:05 - And it's clearly a project that is much bigger than something that I could handle in my laboratory.
  • fast_forward00:28:12 - So I'm definitely hoping that other people will want to join in.
  • fast_forward00:28:14 - And we are very open, by the way, with our data set, even before publication,
  • fast_forward00:28:19 - when the data will officially be made public, if people would like to collaborate
  • fast_forward00:28:24 - with us at the level of data analysis, we're happy to do that.
  • fast_forward00:28:28 - If people would like to learn how to do these experiments, or if they're interested
  • fast_forward00:28:31 - in setting up their own pipelines, we're also very happy to actually provide
  • fast_forward00:28:36 - that input and expertise.
  • fast_forward00:28:38 - Because I think that the reality is that we'll publish a two,
  • fast_forward00:28:41 - three-page paper, let us say, but that won't summarize what's going on.
  • fast_forward00:28:45 - Things are changing in that way. However, one thing I was wondering about now
  • fast_forward00:28:49 - is whether you're not stepping a bit too easily over this challenge that also Tony put in front of you.
  • fast_forward00:28:55 - Imagine now we have this whole federation of laboratories contributing to this
  • fast_forward00:29:01 - pipeline, and they all have their own species to work on or whatever.
  • fast_forward00:29:06 - You have a huge problem integrating all this data because at some point people
  • fast_forward00:29:09 - would like to bring in other kinds of data, physiological data, behavioral data.
  • fast_forward00:29:13 - Do you really have a taxonomy in place to bring all that information together in an integrated form?
  • fast_forward00:29:20 - Or do you think that's just going to self-organize? It's a work in progress.
  • fast_forward00:29:25 - Some people are working on it.
  • fast_forward00:29:29 - I do think it has to be, to some extent, data-driven.
  • fast_forward00:29:32 - And talking about theory, here is one place I think theorists really need to pitch in.
  • fast_forward00:29:39 - Because taxonomy is really a theory about the system.
  • fast_forward00:29:43 - Once you start dividing what the parts are and naming them, you've kind of made
  • fast_forward00:29:47 - your mind up about how to divide the system and how to name them.
  • fast_forward00:29:51 - But isn't it a scary thought to actually now open the floodgates,
  • fast_forward00:29:54 - but you don't have the buckets ready yet to catch the water?
  • fast_forward00:29:57 - I think that there are some starting efforts.
  • fast_forward00:30:05 - So it's not as bad as it could be in other fields. Because in anatomy,
  • fast_forward00:30:11 - there has been a scholarly tradition.
  • fast_forward00:30:13 - And so there is certainly a good head start, I would say.
  • fast_forward00:30:19 - But you're right. I mean, it is a challenge.
  • fast_forward00:30:24 - I think that we will be helped simply by having unified datasets.
  • fast_forward00:30:31 - This taxonomy problem, what we want to call a region will be less important
  • fast_forward00:30:38 - than having a coordinate where you can really say that, well, I call it A, you call it B,
  • fast_forward00:30:44 - but we are all referring to the same objective,
  • fast_forward00:30:47 - artifact and that's what will help that taxonomy problem
  • fast_forward00:30:50 - so what was amazing in the human genome project is
  • fast_forward00:30:53 - that there was this expectation that would take quite some
  • fast_forward00:30:57 - time to sequence human genome and then actually some people really industrialize
  • fast_forward00:31:02 - this whole process and it turned out could be done just in a few years which
  • fast_forward00:31:05 - was astonishing even though it's still debatable how informative all this data
  • fast_forward00:31:09 - is now how many many years is it going to take you to to get the data for,
  • fast_forward00:31:15 - let's say, a single mouse species?
  • fast_forward00:31:18 - How many men years are we talking about?
  • fast_forward00:31:21 - Well, we are hoping to release a draft next fall.
  • fast_forward00:31:24 - And there's about... Now, what I mean by... But I meant complete, right? Everything.
  • fast_forward00:31:31 - Everything will take infinitely long, by definition. The whole brain.
  • fast_forward00:31:37 - But I think on the scale of two to three years,
  • fast_forward00:31:43 - we will have given existing projects we will have a quote unquote mesocircuit even next year we will,
  • fast_forward00:31:54 - you know in every laboratory order 10 people maybe so you have 30 man years
  • fast_forward00:32:01 - you said 30 man years to get one mesocircuit of one species done.
  • fast_forward00:32:05 - I think that's reasonable but we have defined mesocircuit in a particular way
  • fast_forward00:32:11 - People are going to challenge that and have arguments about it.
  • fast_forward00:32:15 - Yeah, I think it's a doable project on a short time scale.
  • fast_forward00:32:19 - And for yourself, I mean, is there going to come a point where you say,
  • fast_forward00:32:22 - okay, I've got enough data of a sufficient quality.
  • fast_forward00:32:26 - I'll go back and be a theorist about this data.
  • fast_forward00:32:31 - It's happening already. Okay. You know, in the sense that even when trying to
  • fast_forward00:32:37 - interpret our very first data sets or in trying to analyze them,
  • fast_forward00:32:41 - the problems that come up are definitely of a conceptual theoretical nature.
  • fast_forward00:32:44 - I'll give you an example.
  • fast_forward00:32:47 - This question came up about quantifying connection strength.
  • fast_forward00:32:52 - You often see isolated neurons in different parts of the brain showing up.
  • fast_forward00:33:00 - If you have hundreds of thousands or tens of thousands of neurons in a particular,
  • fast_forward00:33:05 - or let's say region of the brain that you have injected, and if one neuron shows
  • fast_forward00:33:09 - up in some other part of the brain,
  • fast_forward00:33:12 - in some sense, the circuit is, or doesn't have this clean organization that
  • fast_forward00:33:18 - we theoretically started out.
  • fast_forward00:33:20 - That it has got these tails.
  • fast_forward00:33:24 - How do you even think theoretically about a circuit like that?
  • fast_forward00:33:27 - What are those individual neurons doing?
  • fast_forward00:33:29 - Are they artifacts of the fact that biology is not a clean engineer?
  • fast_forward00:33:34 - Or was it a reason? Is it a feature or a bug?
  • fast_forward00:33:38 - But now in this endeavor, which also your project got supported to go after
  • fast_forward00:33:44 - this connecto of the brain,
  • fast_forward00:33:46 - there are also other initiatives underway where people would look more at,
  • fast_forward00:33:50 - Let's say what we call the effective connectivity of the brain based on data
  • fast_forward00:33:54 - sets that are of a very different nature, using different kinds of imaging techniques and so on.
  • fast_forward00:33:59 - Do you expect a convergence among these different approaches,
  • fast_forward00:34:03 - or do you think that's just too far in the future?
  • fast_forward00:34:06 - I have a theoretical disagreement with, and I'll be very frank about this,
  • fast_forward00:34:10 - about the term functional connectivity being used to describe what are temporal correlations.
  • fast_forward00:34:17 - So, simply because two time series are correlated, and this we have known for
  • fast_forward00:34:24 - a long time, does not mean that there is a, let's say, anatomical connection
  • fast_forward00:34:29 - between those two points.
  • fast_forward00:34:33 - So, I don't see any reason why there should be a quote-unquote convergence.
  • fast_forward00:34:37 - It's certainly true that the anatomical connectivity is going to drive correlations,
  • fast_forward00:34:43 - but I don't see the reverse rub.
  • fast_forward00:34:47 - So the correlations are a necessary condition of the causal structure, right?
  • fast_forward00:34:50 - Of the anatomical structure. And I strongly advocate calling correlations correlations.
  • fast_forward00:34:56 - I think it is misleading and perhaps even distracting to call it connectivity.
  • fast_forward00:35:04 - Your database will be monosynaptic.
  • fast_forward00:35:08 - Our database is really of neuronal morphology. We don't really have synapse.
  • fast_forward00:35:12 - Oh, okay. But you'll be just looking along a single fiber.
  • fast_forward00:35:17 - So it'll still be a problem for me if I want to know if there's a connection
  • fast_forward00:35:22 - between A and B that might go via C.
  • fast_forward00:35:26 - I can tell there's a projection that goes into C, a projection from C into B,
  • fast_forward00:35:31 - but I can't know from your database that those two things meet up. Correct.
  • fast_forward00:35:35 - And there are other limitations to this project. Namely, we are not being cell-type specific.
  • fast_forward00:35:40 - There are other projects which are coming about which will try to be more cell-type
  • fast_forward00:35:44 - specific and yet get neuro-anthropomical connectivity in place.
  • fast_forward00:35:49 - I regard this particular mesocircuit that we are mapping out as operationally defined.
  • fast_forward00:35:56 - Operationally because it's defined in terms of retrograde and anterograde injections
  • fast_forward00:36:00 - and whole-brain imaging object. object, but there will be missing pieces of
  • fast_forward00:36:06 - information that will then have to be layered onto this thing, more projects.
  • fast_forward00:36:12 - Now, also in your presentation, also in your work, you have been spending quite
  • fast_forward00:36:16 - some time to look at gene expression patterns in the brain. Yeah.
  • fast_forward00:36:19 - And do you see that as giving you leverage in understanding these anatomical
  • fast_forward00:36:25 - pathways that you're trying to reveal right now, or do you see this really as
  • fast_forward00:36:30 - separate databases as well?
  • fast_forward00:36:33 - No, I see the connection that they are both indicating a meso-structure, so to speak.
  • fast_forward00:36:38 - You know, they are getting us objectively at this intermediate length scale
  • fast_forward00:36:41 - that is there in neuroanatomy.
  • fast_forward00:36:43 - The gene expression data sets are probably giving us indirectly information about cell types.
  • fast_forward00:36:48 - I don't think they are directly telling us about connections.
  • fast_forward00:36:52 - But yes, they are definitely connected. So within this gene expression data
  • fast_forward00:36:57 - you showed, there was something very puzzling because anatomically,
  • fast_forward00:37:00 - at least if I listen to an anatomist, I'm not an anatomist myself,
  • fast_forward00:37:03 - they would always tell you, look, cortex is relatively uniform,
  • fast_forward00:37:07 - well-structured, modular, etc.
  • fast_forward00:37:09 - And if you go to subcortical areas, things get more messy. It's more variable,
  • fast_forward00:37:15 - heterogeneous, and so on.
  • fast_forward00:37:17 - And if I look at your gene expression data, you made the point.
  • fast_forward00:37:21 - Actually, you have less genes expressed at the subcortical levels than at the cortical levels.
  • fast_forward00:37:25 - So what do you make of this apparent contradiction?
  • fast_forward00:37:30 - I actually think it's the other way around. I think the subcortical structures
  • fast_forward00:37:33 - are more homogeneously structured and more carefully sculpted.
  • fast_forward00:37:39 - You have much more interspersion of cell populations, also the kind of transmitter
  • fast_forward00:37:44 - systems. Correct. So they are more... Yeah, I see what you're saying.
  • fast_forward00:37:48 - It's more messy in the sense that the modules are not well separated,
  • fast_forward00:37:52 - but it may be less variable.
  • fast_forward00:37:58 - So, yes, it is messy. It will be messy for us to disentangle,
  • fast_forward00:38:03 - but it will be less variable, I think, from animal to animal.
  • fast_forward00:38:07 - So sometimes what you're saying is maybe cortex is so neatly organized because
  • fast_forward00:38:11 - there are just more genes being expressed there to keep that clean structure in place.
  • fast_forward00:38:16 - While you would have less genes expressed subcortically, leading to more of
  • fast_forward00:38:20 - a dispersion of how these cells actually anchor themselves in the substrate and so on.
  • fast_forward00:38:24 - Is that how I interpret what you're saying? That's an interesting hypothesis.
  • fast_forward00:38:29 - I guess the cortex is not translationally homogeneous, as we know from Brodmann.
  • fast_forward00:38:35 - Areas in the gene expression pattern certainly show that there are different...
  • fast_forward00:38:41 - Vertical structures. Cool. So look, I have two questions to finish up.
  • fast_forward00:38:48 - If we had to,
  • fast_forward00:38:51 - define the Partha law of science, our attempts on the brain,
  • fast_forward00:38:58 - what would be this Partha-Mitra law of investigation and understanding?
  • fast_forward00:39:03 - What's the law we should adhere to?
  • fast_forward00:39:05 - Law? Well, I made my aphorism, right?
  • fast_forward00:39:08 - There's one reality about multiple theoretical approaches, I think we have to
  • fast_forward00:39:14 - be open-minded about disciplines.
  • fast_forward00:39:19 - And this sounds like a truism, and it sounds really something that people say as a slogan,
  • fast_forward00:39:27 - but I really think that we need to educate ourselves in different disciplines
  • fast_forward00:39:32 - like engineering disciplines or neuroscientists need to learn more about population
  • fast_forward00:39:36 - biology and evolution and so on and so forth.
  • fast_forward00:39:38 - Perfect and then so so if i
  • fast_forward00:39:42 - go visit you again in cold spring harbor five
  • fast_forward00:39:45 - years from now i'm gonna say okay there's this
  • fast_forward00:39:48 - one prediction you gave to me today and today i'm going
  • fast_forward00:39:50 - to check whether it's true or not what's this one
  • fast_forward00:39:53 - prediction you're willing to stick your neck out today well that's
  • fast_forward00:39:57 - a very tough one come on you're a tough guy um i
  • fast_forward00:40:03 - guess this notion that the meso circuit is genetically wired and that genetic
  • fast_forward00:40:10 - polymorphisms will cause alterations in the mesocircuitry that may be correlated
  • fast_forward00:40:16 - with neuropsychiatric disorders,
  • fast_forward00:40:18 - which also have genetic predispositions.
  • fast_forward00:40:21 - That's for me, especially the affective systems that are related to emotional behaviors.
  • fast_forward00:40:27 - That for me is sort of the, I don't know if it's a prediction,
  • fast_forward00:40:33 - but that's a story that I hope in five years to really understand and gain insight into our behaviors.
  • fast_forward00:40:41 - Wonderful. Parthamitra, thank you very much. Thank you.

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