Update on the Great BRAINI Debates

rasterThe NSF, DARPA and the NIH sponsored a meeting last week that brought together scientists to brainstorm ideas for the new BRAIN Initiative (a proposal I’ve explored many times, starting here, and most recently, here). Organizers seem to have begun to reassure critics that they are developing more inclusive planning procedures and that the funding mechanisms will not siphon off resources from other projects. They still can’t seem to figure out how to get Science to publish their white papers outside the paywall, and there has also been criticism that they are not doing enough to include women scientists in the process. As I’ve mentioned before, I still have my qualms about selling the project to the public based on promises to alleviate clinical disorders that are least likely to be addressed by the proposed methods (as do others).

Still, the silliest critique of the goals of the BRAIN Initiative is that we (meaning systems neuroscientists) wouldn’t know what to do with the data from thousands (or millions) of neurons if we had it. I can assure you that we would, but before I explore that, let’s look at the different facets of this argument. One strain of critique contends that because systems neuroscientists don’t agree on the goal, then none exists. This is like saying there is no coherent point to sequencing the genome because cell biologists, evolutionary biologists, translational researchers and clinicians can’t agree on a set of specific aims. I’m willing to bet that the scientists making this argument would be faced with the same heterogeneity in their own disciplines if they were brainstorming a similarly transformative infrastructure project.

Another strain of this argument is that neuroscientists don’t know enough about the basic components of their field to study the emergent properties of large interacting populations. The argument often has the form “How can you study Y when you don’t even know how X works?” where Y is some presumed higher order function (like color perception) and X is some supposed component subsystem (like retinal neurons). In some ways this is a really just an element of personal scientific disposition. Some people like to study systems, some like reductionist approaches, some like top-down, some like bottom-up, PO-TAY-TO, PO-TAH-TO. Atomists argue that you can’t possibly understand systems without exhaustive explication of components, while systems people think reductionists fail to see the forests for the trees. My suspicion is that people who make the reductionist argument about the BRAIN Initiative really just don’t believe in systems neuroscience as a productive discipline at all. I’m certainly not going to convince those people in a blog entry. Nonetheless, we shouldn’t forget that all science involves judgments about the right level of analysis, the right amount of abstraction, the right topic, the right experimental model, the right modeling equations or the right techniques. We like to argue that these decisions are empirically founded, but mostly we don’t have enough information to make those claims, so we often default to personal preference. Am I arguing that we should throw up our hands and give scientists money to do whatever the hell they want? No. The proof is still in the pudding. Does the approach/model  produce concrete predictions and observable  tests of those predictions? That is not a questions we can answer simply by saying “but you don’t even know…” Returning to the genome example, we did manage to wring some useful insights from sequencing despite the fact that we still don’t have a general solution to how genes relate to protein form/function.

A related argument contends that neuroscience is too atheoretical to formulate relevant questions on which to spend the kind of money that BRAINI proposes.  Again, this argument rests on somewhat idiosyncratic ideas about what a good theory is (as I’m sure philosophers of science can attest). What one scientist sees as a foundational framework, another sees as fuzzy-headed hand waving.  Judging the appropriateness of a particular theory is even more wrought than picking an experimental model. Good theories provide a unifying framework to understand disparate data, but just how unifying can we expect neuroscience theories to be? What these critics seem to be asking for is some grand unified theory of human cognition, consciousness and intelligence. That’s a rather high bar. In fact, there are many fruitful neuroscience theories out there in particular animals, systems and circuits– theories of locomotion, vision, navigation, memory, olfaction, learning, rhythm generation, homeostatic regulation, etc. Different neural systems evolved under different constraints and selection pressures, so we would expect a certain level of heterogeneity in the details. Critics again seem to be conflating the lack of a single theory with the lack of any theory.

One critic of the BRAIN Initiative who seems to find systems neuroscientists particularly lacking in creativity or insight is DrugMonkey (and @drugmonkeyblog), who argues that BRAINI proponents are simply trying to recreate some previous era of generous funding for “neuron recording neuroscience.” S/He suggests that the proposals amount to nothing more than an effort to “record some more neurons.”  If s/he truly finds our entire field as intellectually so sterile, I’m certainly not going to change his/her mind. But I would like to argue that there is a transformative, qualitative difference in the jump from recording tens of cells to recording thousands of cells. This is because you begin to encompass functionally important networks with nearly complete sampling.

For example, what would I do with recordings from thousands of neurons? My dissertation research involved understanding how groups of cells fire together to create the rhythm that drives normal breathing in mice (and presumably, other mammals), so let’s take that work as an example . The cell group that I studied (called the pre-Bötzinger complex) is part of a broader network of circuits that coordinate muscles involved in different phases of breathing under different conditions.  These cell groups, located in the brainstem, are relatively hard for experimenters to access, so much of the basic science has been done in brain slice experiments, which necessarily disconnect networks from each other (and from the behavioral context of the living animal). Other researchers have used multicellular optical methods or multielectrode recordings in anesthetized animals, but for the most part, the interactions of different cell groups has been pieced together from separate recordings of single neurons. For our thought experiment let’s suppose that I had access to the proposed molecular ticker tape technology talked about for BRAINI. What kinds of questions could I answer?

The fact that respiratory neuroscience has not been able generate much data on intact, awake behaving animals means that the new technology would immediately provide physiologically relevant tests of theories from more ‘reduced’ experimental models. Where are the neurons that underlie breathing in the adult animal? How do the neurons fire in relation to breathing in or out? How do they fire in relation to different respiratory behaviors, like gasping or sighing or vocalization? How do the different underlying networks interact? Do some drive exhalation and others inhalation? Do different networks come online during exercise or asphyxia? How does the feedback from low blood oxygen or high carbon dioxide drive respiration? How are interactions between respiration and heart rate mediated?

The first month of experiments using BRAINI technology could address these questions in a depth that would replicate 100 years of research in respiratory neurophysiology. What would we do with the second month?  Development. Disease models. Pharmacology. It’s just the beginning.

And that’s just what I can think of in ten minutes. My systems neuroscience colleagues could easily come up with similar lists of questions in their particular subfields, and  the comparative rate of progress would be just as dramatic. Of course, I can’t guarantee that BRAINI technology would actually work, but I can assure you that systems neuroscientists are not at a loss to know what to do with the data if it does.


Update (minutes after posting). I originally assumed DrugMonkey was a ‘he’ purely from discursive style. I actually don’t know one way or another, so I changed the pronoun references.

Image: Visualization of multicellular activity from a simulation of the respiratory network.


~ by nucamb on May 12, 2013.

7 Responses to “Update on the Great BRAINI Debates”

  1. […] and the matter of making data publicly available. The NIH, for its part, is soliciting feedback. nucleus amiguous does a good job of summarizing the major concerns about the project, as well as debunking the […]

  2. […] and the matter of making data publicly available. The NIH, for its part, is soliciting feedback. nucleus amiguous does a good job of summarizing the major concerns about the project, as well as debunking the […]

  3. Dear Nucleus,

    I thank you for an insightful (if not verbose) piece, where unfortunately you mix the issues of funding with scientific endeavors. I wish these could be separated a bit, but the the fact of the matter is that we all need money to work and work to make money, in a vicious cycle.

    To your points,

    First of all, I have worked under DARPA, and they try to spend their money wisely. NASA, on the other hand is usually able to embark on projects with unknown potential, or shall we say, venturing “into space”, although funding has been curtailed as of late.

    It is a good thing, I think, that Science refuses to publish white papers. DARPA can do this by itself, but Science has its ethics. As to the clinical implications, I would keep on that, as that is for humanitarian good if not for profit.

    Now one of your most interesting points is that you have too much data to work with. That is certainly a challenge, but not one we are under-equipped to tackle. In my opinion, the bigger the mine, the bigger the gems, (so long as you know how to find them).

    Look, we have the tools, but we don’t have the knowledge as to how to approach vast resources of data. This does not mean we should leave it in the closet.

    In my opinion, if you want to analyze neural recording data of such scale you should use your best hunch, and create a neural network system than can learn to mimic the data. Thereby, you can later understand through analysis what the neural network learned (should it have succeeded), and understand the data better yourself.

    To put this in better context: Let’s say you have a small set of data on either real or artificial neurons. You use the “recorded” data combined with the known outcomes as the teacher, and you teach a neural network to mimic it. If this succeeds, then not only do you have a model of the subject, but also a means of analyzing its function.

    I hope you get my drift, here.

    Please let me know if this resonates with you.

  4. […] this blog before, but I really liked the last two posts over at Nucleus Ambiguous. The first is an update/commentary on the BRAIN Inititative. I think Nucamb has provided some of the best coverage and commentary available on the BRAIN […]

  5. […] most recent review on the BRAIN […]

  6. […] same thing as saying that brains are only computers (a confusion found often in criticisms of the BRAIN Initiative). Still, this computational view of brains makes some people very uncomfortable. For obvious […]

  7. I find BRAINI lacking *unique* creativity and insight. The question you have not answered is why your subfield is any more special and deserving of protected funds than is any other field. This is the issue at hand. Many other subfields of neuroscience alone can come up with 10 min introspections too. Multiply this across all of the ICs and where are we?

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