Behold, the behaving brain!

In my opinion, THE most important shift in neuroscience over the past few years has been the focus on how behavior changes neural function across the whole brain. Even the sensory systems – supposedly passive passers-on of perfectly produced pictures of the world – will be shifted in unique ways by behavior. An animal walking will have different responses to visual stimuli than an animal that is just sitting around. Almost certainly, other behaviors will have other effects on the animal.

A pair of papers this week have made that point rather elegantly. First, Carsen Stringer and Marius Pachitariu from the Carandini/Harris labs have gobs of data from when they were recording ~10,000 neurons simultaneously. Marius Pachitariu has an excellent twitter thread explaining the work. I just want to take one particular point from this paper which is that you can explain a surprising amount of variance in the primary visual cortex – and all across the brain – simply by looking at the movement of the animal’s face.

In the figures below, they have taken movies of an animal’s face, extracted the motion energy (roughly, how much movement there is at that location in the video), and then used PCA to find the common ways that you can describe that movement. Using this kind of common motion, they then tried to predict the activity of individual neurons – while ignoring the traditional sensory or task information that you would normally be looking at.

The other paper is from Simon Musall and Matt Kaufman in Anne Churchland’s lab. He also has a nice twitter description of their work. Here, they used a technique that is able to image the whole brain simultaneously (though I am not sure to what depth), though at the cost of resolution (individual neurons are not identifiable but are averaged together). The animals are doing a task where they need to tell the difference between two tones, or two flashes of light. You can look for the brain areas involved in choice, or the areas involved in responding to vision or audio, and they are there (choice, kind of?).  But if you look at where movement is being represented it is everywhere.

The things that you would normally look for – the amount of brain activity you can explain by an animal’s decisions or its sensory responses – explain very little unique information.
This latter point is really important. If you had looked at the data and ignored the movement, you would have certainly found neurons that were correlated with decision-making. But once you take into account movement, that correlation drops away – the decisions are too correlated with general movement variables. People need to start thinking about how much of their neural data is responding to the task the animal is doing and how much is due to movement variables that are aligned to the task. This is really important! Simple averaging will not wash away this movement.

There is a lot more to both of these papers and both will be more than worth your time to dig into.

I’m not sure if you would have noticed this effect in either case if they weren’t recording from massive numbers of neurons simultaneously. This is a brave new world of neuroscience. How do we deal with this massively complex behavioral data at the same time that we deal with massive neural populations?

In my mind, the gold standard for how to analyze this data comes from Eva Naumann and James Fitzgerald in a paper out of the Engert lab. They are analyzing data from the whole brain of the zebrafish as it fictively swims around and responds to some moving background. Rather than throwing up their hands at the complexity of this data and the number of moving pieces what they did was very precisely quantify one particular aspect of the behavior. Then they followed the circuit step by step and tried to understand how the quantified behavior was transformed in the circuit. How did the visual stimuli guide the fish’s orientation in the water? What were the different ways the retina represented that visual information? How was this transformed by the relays into the brain? How was this information further transformed in the next step? How did the motor centers generate the different types of behavior that were quantified?

The brain evolved to produce behavior. In my opinion there is no way to understand the brain – any of it – if you don’t understand the behavior that the animal is producing.

MRI now for dopamine?

The Jasanoff lab has been working on improving MRI for a while, using such cool terms as ‘molecular fMRI’. They are really attempting to push the technology by designing molecular agents to help with the imaging. For instance, they have sensors that can respond to kinase activity or to amines like dopamine.

MRI works by sending powerful magnetic fields at a tissue such as the brain, and measuring the time it takes for molecules in this tissue to ‘relax’ to its previous state. In order to detect molecules such as dopamine, they modified magnetized proteins to bind specifically to those molecules. The relaxation occurs in a specific ‘communication channel’ called T1, as opposed to the T2 ‘channel’ that is used to detect changes in blood flow for fMRI. Since the proteins have different relaxation times depending on whether they are bound or unbound, MRI can be used to measure when there is more or less dopamine in the tissue.

Although I’ve been hearing about these sensors for a few years (the dopamine one came out in a paper four years ago, the kinase one six), I hadn’t seen a paper that really used them until now. The Jasanoff lab has now shown that if you stimulate the nerves that release dopamine, their sensors can indeed detect it. Problem is: they have to inject the sensor directly into the brain. This means, first of all, that they probably aren’t able to measure dopamine activity across the whole brain using this technique. I’m not sure, but I imagine they can image the level of the sensor that is at any given point? But that level is going to affect the signal that they get. Further, someone suggested that because the sensor is large and polar, it’s not going to cross the blood-brain barrier so it’s not a plausible way to image dopamine release in humans. The field will just have to stick with PET imaging for now.

Finally, a personal complaint: they kept claiming they were measuring ‘phasic’ activity of the dopamine (ie, transient). Although they were stimulating the dopamine neurons phasically, I didn’t see any control to measure the tonic level of dopamine! I’m not sure I would have allowed them to get away with that if I were a reviewer. Still, it’s a cool technique that has a lot of potential in the years ahead. It should be exciting to see how it gets developed.

Unrelated, but the Jasanoff lab page claims they are doing MRI in flies. In flies! But I can’t find any papers that do this; anyone know about that?

The brain! The whole brain!

When you receive a text at 6am regarding a New York Times article on a new neuroscience initiative, you know there is excitement in the air.  Indeed: my facebook feed is littered with fellow neuroscientists all posting the article with a comment invoking some variant of “huh?” (also: yay, more money!).  You see, the nyt article – which is all we have to go on at this point – is full of meaningless gibberish that makes it all sound like the reporter doesn’t know anything about neuroscience and is just forwarding bits of whatever press release they received.  Hell, it includes the quote, “the advent of new technology that allows scientists to identify neurons firing in the brain has led to numerous brain research projects around the world.  Yet the brain remains one of the greatest scientific mysteries.”  I shit you not, the article actually says that.  And it’s a bit of a funny scoop, because one of the NIH directors sounds a bit surprised by it all; you see, there’s already a human brain map-building project.

So what’s going on?  It’s not quite clear, but a recent article by the scientists that are linked to the project may shed some light on the whole matter.  Although the reporting makes it seem like we’re getting ten years worth of funding to understand the active behavior of the human brain, the review article ends on this note:

For midterm goals (10 years), one could image the entire Drosophila brain (135,000 neurons), the CNS of the zebra fish (1 million neurons), or an entire mouse retina or hippocampus, all under a million neurons. One could also reconstruct the activity of a cortical area in a wild-type mouse or in mouse disease models. Finally, it would also be interesting to consider mapping the cortex of the Etruscan shrew, the smallest known mammal, with only a million neurons. For a long-term goal (15 years), we would expect that technological developments will enable the reconstruction of the neuronal activity of the entire neocortex of an awake mouse, and proceed toward primates.

And remember, these are (somewhat optimistic) goals.  I wouldn’t at all be surprised if they were accomplished in the given time frames, but nor would I be at all surprised if we totally failed to reach them.  Remember: make a goal, and estimate how much time it should take.  Now double it, and you’ve got a better estimate.

Now, remember we have not yet imaged the whole C. elegans nervous system (302 neurons); they hope that in ten years we might be able to image the whole fly brain, or maybe a mouse eye.  I’d love it if that was the target of the initiative but I don’t really think that politicians share my love of invertebrate nervous systems.  What about humans?

We do not exclude the extension of the BAM Project to humans, and if this project is to be applicable to clinical research or practice, its special challenges are worth addressing early. Potential options for a human BAM Project include wireless electronics, safely and transiently introducing engineered cells to make tight (transient) junctions with neurons for recording and possibly programmable stimulation, or a combination of these approaches.

Which sounds like a lot of great technology development, but I’m not sure how we go from there (and through clinical trials) to gathering gobs of data.

Although more money in neuroscience is great – yay money – it introduces some pretty serious worries.  Foremost among these is: where does the money come from, and how much will it crowd out other, legitimate projects?  And that is a serious, serious worry; but I’m more worried about ten years down the road.  Let’s say this happens, money floods the field and we’re all very happy researchers.  Now it’s ten years later, and what do we have to show for it?  What happens if we don’t get any working data from humans?  Does neuroscience become the next target of a politicized governmental waste campaign?

And will we wake up from the money hangover to find that grant acceptance rates really can get that much lower?


Alivisatos, A., Chun, M., Church, G., Greenspan, R., Roukes, M., & Yuste, R. (2012). The Brain Activity Map Project and the Challenge of Functional Connectomics Neuron, 74 (6), 970-974 DOI: 10.1016/j.neuron.2012.06.006