Posts Tagged ‘ connectomics ’

Podcast 1: Our Interview With Joshua Vogelstein

Here it is – the first Connectome podcast!

Click here to subscribe in iTunes.

Join us as we talk with Joshua Vogelstein, a leading connectomics researcher, about the Open Connectome Project, an international venture to make data on neural connectivity available to everyone, all over the world. It’s like Google Maps for your brain.

Here’s a direct link to download the mp3.

We’ve learned a lot while working on this first episode, and future ones will be much cleaner and higher-fi.

Anyway, enjoy!

Taking Vision Apart

For the first time, scientists have created neuron-by-neuron maps of brain regions corresponding to specific kinds of visual information, and specific parts of the visual field, says a new study.

At age 11, Cajal landed in prison for blowing up his town's gate with a homemade cannon. Seriously. Google it.

If other labs can confirm these results, this will mean we’re very close to being able to predict exactly which neurons will fire when an animal looks at a specific object.

Our understanding of neural networks has come a very long way in a very short time. It was just a little more than 100 years ago that Santiago Ramón y Cajal first proposed the theory that individual cellsneurons – comprised the basic processing units of the central nervous system (CNS). Cajal lived until 1934, so he got to glimpse the edge – but not much more – of the strange new frontier he’d discovered. As scientists like Alan Lloyd Hodgkin and Andrew Huxley – namesakes of today’s Hodgkins-Huxley neuron simulator – started studying neurons’ behavior, they began realizing that the brain’s way of processing information was much weirder and more complex than anyone had expected.

See, computers and neuroscience evolved hand-in-hand – in many ways, they still do – and throughout the twentieth century, most scientists described the brain as a sort of computer. But by the early 1970s, they were realizing that a computer and a brain are different in a very fundamental way: computers process information in bits – tiny electronic switches that say “on” or “off” – but a brain processes information in connections and gradientsdegrees to which one piece of neural architecture influences others. In short, our brains aren’t digital – they’re analog. And as we all know, there’s just something warmer about analog.

So where does this leave us now? Well, instead of trying to chase down bits in brains, many of today’s cutting-edge neuroscientists are working to figure out what connects to what, and how those connections form and change as a brain absorbs new information. In a way, the process isn’t all that different from trying to identify all the cords tangled up under your desk – it’s just that in this case, there are trillions of plugs, and a lot of them are molecular in size. That’s why neuroscientists need supercomputers that fill whole rooms to crunch the numbers – though I’m sure you’ll laugh if you reread that sentence in 2020.

But the better we understand brains, the better we get at understanding them – and that’s why a team led by the Salk Institute’s James Marshel and Marina Garrett set out to map the exact neural pathways that correspond to specific aspects of visual data, the journal Neuron reports. (By the way, if you guys are reading this, I live in L.A. and would love to visit your lab.)

The team injected mouse brains with a special dye that’s chemically formulated to glow fluorescent when a neuron fires. This allowed them to track exactly which neurons in a mouse’s brain were active – and to what degree they were – when the mice were shown various shapes. And the researchers confirmed something wonderfully weird about the way a brain works:

Each area [of the visual cortex] contains a distinct visuotopic representation and encodes a unique combination of spatiotemporal features.

In other words, a brain doesn’t really have sets of neurons that encode specific shapes – instead, it has layers of neurons, and each layer encodes an aspect of a shape – its roundness, its largeness, its color, and so on. As signals pass through each layer, they’re influenced by the neurons they’ve connected with before. Each layer is like a section of a choir, adding its own voice to the song with perfect timing.

Now, other teams have already developed technologies that can record memories and dreams right out of the human brain – so what’s so amazing about this particular study? The level of detail:

Areas LM, AL, RL, and AM prefer up to three times faster temporal frequencies and significantly lower spatial frequencies than V1, while V1 and PM prefer high spatial and low temporal frequencies. LI prefers both high spatial and temporal frequencies. All extrastriate areas except LI increase orientation selectivity compared to V1, and three areas are significantly more direction selective (AL, RL, and AM). Specific combinations of spatiotemporal representations further distinguish areas.

Are you seeing this? We’re talking about tuning in to specific communication channels within the visual cortex, down at the level of individual neuronal networks.

The gap between mind and machine is getting narrower every day. How does that make you feel?

Saving Faces

A brain area that’s specialized to recognize faces has a unique structure in each of our brains – and mapping that area’s connectivity patterns can tell us how each of our brains use it, says a new study.

The fusiform gyrus - home of the Brain Face Database.

The fusiform gyrus in the temporal lobe plays a part in our recognition of words, numbers, faces, colors, and other visual specifics – but it’s becoming increasingly clear that no two people’s fusiform gyrus structure is identical. By studying this region in a larger connectomic framework, though, researchers can now predict which parts of a certain person’s fusiform gyrus are specialized for face recognition.

Since the early days of neurophysiology – way back into the 1800s – scientists have been working to pinpoint certain types of brain activity to certain structures within the brain. Simple experiments and lesion studies – many of them pretty crude by today’s standards – demonstrated that, for instance, the cerebellum is necessary for coordinating bodily movement; and that the inferior frontal gyrus (IFG) is involved in speech production.

Things get trickier, though, when we try to study more abstract mental tasks. For example, debates over the possible existence of “grandmother cells” – groups of neurons whose activity might represent complex concepts like “my grandmother” – have raged for decades, with no clear resolution in sight. The story’s similar for “mirror neurons” – networks of cells that some scientists think are responsible for our ability to understand and predict the intent of another person’s action.

All these debates reflect a far more fundamental gap in our understanding – one that many scientists seem reluctant to acknowledge: To this day, no one’s been able to demonstrate exactly what a “concept” is in neurological terms – or even if it’s a single type of “thing” at all.

This is why you’ll sometimes hear theoretical psychologists talk about “engrams” – hypothetical means by which neural networks might store memories – a bit like computer files in the brain. But the fact is, no one’s sure if the brain organizes information in a way that’s at all analogous to the way a computer does. In fact, a growing body of research points toward the idea that our memories are highly interdependent and dynamic – more like ripples in a pond than files in a computer.

This is where connectomics comes in. As researchers become increasingly aware that no two brains are quite alike, they’ve begun to focus on mapping the neural networks that connect various processing hubs to one another. As an analogy, you might say they’ve begun to study traffic patterns by mapping a country’s entire highway system, rather than just focusing on the stoplights in individual cities.

And now, as the journal Nature Neuroscience reports, a team led by MIT’s David Osher has mapped a variety of connectivity patterns linking the fusiform gyrus to other brain areas.

They accomplished this through a technique called diffusion imaging, which is based on a brain-scanning technology known as diffusion MRI (dMRI). Diffusion imaging applies a magnetic field to the brain, causing water to flow along axons – the long “tails” of neurons that connect them to other areas – allowing the MRI scan to detect which areas are sending out lots of signals to others during certain mental activities. As you can imagine, this technique has been revealing all sorts of surprising new facts about the brain’s functionality.

In this particular study, the researchers found that during face-recognition tasks, certain parts of the fusiform gyrus lit up with active connections to areas like the superior and inferior temporal cortices, which are also known to be involved in face recognition. Intriguingly, they also detected connectivity with parts of the cerebellum – an ancient brain structure involved in bodily balance and movement, which no one expected to be part of any visual recognition pathway. Sounds like a Science Mystery to me!

The team even discovered that they could use the connectivity patterns they found to predict which faces a person would recognize:

By using only structural connectivity, as measured through diffusion-weighted imaging, we were able to predict functional activation to faces in the fusiform gyrus … The structure-function relationship discovered from the initial participants was highly robust in predicting activation in a second group of participants, despite differences in acquisition parameters and stimuli.

In short, they’ve discovered patterns of functional connectivity that directly correspond to our ability to recognize a particular face.

It’s still a far cry from an engram – after all, we still don’t know exactly what information these connections encode, or how the brain encodes that data, or what other conditions might need to be met for an “a-ha!” recognition to take place – but still, network mapping appears to be a very promising starting point for investigating questions like these.

The researchers plan to use this approach to study connectivity patterns in the brains of children with severe autism, and other patients who have trouble recognizing faces. They hope it’ll also be useful for understanding how we recognize scenes and other objects – eventually, a network-oriented approach may even offer clues about how we recognize familiar ideas.

In other words, for the first time in history, we’re using our brains’ natural love of connections to understand just how our brains form those connections in the first place. For those of us who love a good mystery, it’s an exciting time to be studying our own minds!

“M” Marks the Spot

A completely new method for mapping brain anatomy will give us a much clearer idea of where some areas end and others begin.

A brain gets all snazzy with myelin fMRI maps.

The new technique compares two different kinds of fMRI data to show where there’s myelin – the sheath that only surrounds long-range neuron branches (axons) – at a speed and level of detail never possible before. This breakthrough will help scientists look for the differences between the brain’s “surface streets” and its “highways” while that brain is actually working.

See, scientists have known since the late 1800s that the it’s pretty easy to find the borders of brain structures, by looking for the switch from gray matter (tissue containing cell bodies) to white matter (axons surrounded by myelin)1. Problem was, the only way to look for white matter was to cut open dead brains and chemically stain them in a certain way.

Earlier this year, a technique called SAXS-CT finally allowed scientists to take 3-D myelin-sensitive x-rays – but still, these were only static images, and they didn’t include the rainbow-colored detail of fMRI scans.

Now, though, we can look forward to lots of fun, safe, noninvasive myelin movies, an article in the Journal of Neuroscience reports:

We use the ratio of T1w/T2w [fMRI] image intensities to eliminate the MR-related image intensity bias and enhance the contrast to noise ratio for myelin. Data from each subject were mapped to the cortical surface and aligned across individuals using surface-based registration. The spatial gradient of the group average myelin map provides an observer-independent measure of sharp transitions in myelin content across the surface—i.e., putative cortical areal borders.

In other words, the technique compares data from T1-weighted fMRI and T2-weighted fMRI, which are two different ways an fMRI scanner can image tissue. Since myelin – conveniently – looks different on each type of scan, the method uses those differences to help it spit out fMRI-like images showing where the myelin is. And it’s convenient, too: all it takes to run the data is a ten minute fMRI scan.

What’s really impressive (to me) is that this technology was developed by a graduate student, Matthew Glasser of Washington University. He completed his research with help from the Human Connectome Project – a group of researchers on an epic quest to map every connection in the whole human brain. This is certainly a step in that direction.

The next steps are also going to be pretty cool:

The technique will make it possible for the Connectome Project to rapidly map myelination in many different research participants. Data on many subjects, acquired through many different analytical techniques including myelination mapping, will help the resulting maps cover the range of anatomic variation present in humans.

In short, it may not be too long before this technique (and others) start to give us a clearer picture of just how each of our brains is anatomically and functionally unique – and in what ways they’re very similar.

I mean, aren’t you just a little curious about that?

__________

1. So the next time someone tells you to “use that gray matter,” you can be a smartass and say, “I’ll need to use my white matter too!”

Mind-Mapping

The connectome of the humble roundworm, Caenorhabditis elegans, is revealing intriguing clues about how neural networks analyze and act on information.

C. elegans, apparently dancing at a rave.

The C. elegans connectome was officially mapped back in 1986. It contains only 302 neurons and about 8,000 synapses – compared to one hundred billion neurons and some seven hundred trillion synaptic connections in a human connectome. Even so, it’s only recently that a team led by Dr. Cornelia I. Bargmann at the Howard Hughes Medical Institute have made serious progress in understanding how this worm connectome (click that link; it’s awesome) represents data, passes it around, analyzes it, and converts its conclusions into action.

This research should provide a simplified framework for understanding this worm’s neural activity – and ultimately, the human connectome.

Though brains throughout the animal kingdom display a wide variety of anatomical organization, they’re all organized on the basis of nodal functional networks, in which certain areas act as processing hubs, which in turn feed their output to more central hubs. Here’s one example:

It turns out that [C. elegans] social behavior in the worm is controlled by a pair of neurons called RMG. The two RMG neurons receive input from various sensory neurons that detect the several environmental cues that make worms aggregate. RMG integrates this information and sends signals to the worm’s muscles. The usual role of the RMG neurons is to promote social behavior, but when the npr-1 gene is active, the RMG neurons cannot receive input from their sensory neurons, and the worms switch to solitary behavior.

This RMG circuit is obviously nowhere near as complex as the inner debate that goes on in human minds throughout an average day. Still, it’s fascinating to see how close we are to understanding some of the basic neural mechanisms that motivate action selection.

Even so, all these neuronal connections are just the beginning of the complexity Dr. Bargmann’s team is discovering. In addition to the connectome’s synaptic wiring – which uses chemical signals to mediate neurotransmission – the worm’s nervous system also incorporates gap junctions, which allow chemicals to pass directly between neurons through tiny pores. And the system only gets more complex from there:

Not only does the worm’s connectome … have two separate wiring diagrams superimposed on each other, but there is a third system that keeps rewiring the wiring diagrams. This is based on neuropeptides, hormonelike chemicals that are released by neurons to affect other neurons. The neuropeptides probably help control the brain’s general status, or mood.

The worm’s behavior cannot be computed from the wiring diagram [alone]: the pattern of connections is changing all the time under the influence of the worm’s 250 neuropeptides. The connectome shows the electrical connections, and hence the quickest paths for information to move through the worm’s brain. “But if only a subset of neurons are available at any time, the connectome is ambiguous,” [Bargmann] says.

So between synaptic connections, gap-junction connections, and overall neuronal availability mediated by neuropeptides, the functionality of the worm connectome looks to be as complex as we might expect from such a dense organic machine…or perhaps even more than anyone could’ve predicted.

The field of connectomics is undergoing explosive growth right now, but connectomic neuroscientists are still coming to grips with the true scale of the tasks before them. Like the first sailing voyages across the Atlantic, this journey promises to be as vast as it is rewarding.

Questions Answered

In the interest of transparency and open dialogue, I’ll be structuring this post in the popular “Q&A” format, which will be familiar to fans of talk shows, WoW conventions, and the Gestapo. So without further ado, here are a bunch of questions and answers I made up.

The human brain atlas.

Q. Did something completely awesome happen in the field of neuroscience on April 12, 2011?
A. Yes: the Allen Institute for Brain Science announced that they’ve completed the Allen Human Brain Atlas, “the world’s first anatomically and genomically comprehensive human brain map.” This digital map will allow researchers to examine the precise anatomy, biochemistry, and gene expression within any region of the brain through a point-and-click interface. Anyone can access it online for free.

Q. Will you write a post that explains this mapping technology in layman’s terms, with a sprinkling of humorous anecdotes?
A. Perhaps, if you ask nicely.

Q. Does this mean the human connectome is now mapped?
A. Nope; Seung’s team still have plenty of work cut out for them. The idea behind the Human Connectome Project is to map every structural and functional connection in the brain, so researchers can study digital models of phenomena like synapses and wave patterns at every level of detail.

Q. Will connectomic models enable scientists to build a strong A.I.?
A. It’ll certainly be a step in that direction. Connectomics integrates ideas from several fields that are closely related to A.I. research, like computational neuroscience, theory of mind, and cognitive psychology. The truth is, strong A.I. seems to be a little bit like obscenity – it’s hard to define, but we’ll know it when we see it.

Q. OK, so WTF was up with that last post, anyway?
A. I was trying to explain that visual hallucinations are just one reflection of disruptions that can affect all sensory perceptions - including one’s senses of space, time, and self. It all started when I stumbled on my blog’s doppelganger; the next thing I knew, I was knee deep in research papers, and I’d written three paragraphs on functional networks.

This should perhaps be considered lab equipment.

Q. Was whisky involved?
A. Let’s put it this way: I’m not going to say whisky wasn’t involved.

Q. What exactly is the relationship between neuroscience and alcohol?
A. Repeated ethanol exposure enhances synaptic plasticity in the ventral tegmental area, which initiates dopaminergic reward activity. In short, alcohol promotes the formation of positive memories related to alcohol consumption. This seems to apply most directly to memories of alcohol consumption, but it likely extends to experiences connected with said consumption as well. Thus, not only does the combination of The Macallan and J. Neurosci. make learning fun – when you’re drunkenly learning about your own drunken learning process, you’ve reached the coveted meta-cognitive level, which means you can wear tweed armor (thesis defense +10).

Q. What’s a common Science Misconception that bugs you?
A. That organic chemistry is equivalent to biological chemistry.

Q. So what’s the difference between “biological” and “organic”?
A.
Biological” is a term describing biota, objects that possess signaling and self-sustaining properties. “Organic” is a term used by Trader Joe’s to denote expensive food items. Or, you know, molecules that contain carbon and hydrogen.

Q. Conan! What is best in life?!
A. Tenure.

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