Posts Tagged ‘ structural networks ’

Connection Clusters

As our brains learn something, our neurons form new connections in clustered groups, says a new study.

Some clusters are juicier than others.

In other words, synapses – connections between neurons – are much more likely to form near other brand-new synapses than they are to emerge near older ones.

As our neuroscience friends like to say: “Cells that fire together wire together” – and that process of rewiring never stops. From before you were born right up until this moment, the synaptic pathways in your brain have been transforming, hooking up new electrochemical connections and trimming away the ones that aren’t needed. Even when you’re sound asleep, your brain’s still burning the midnight oil, looking for ever-sleeker ways to do its many jobs.

I like to imagine that this happens to the sound of a really pumped-up drumbeat, as my brain says things like, “We can rebuild this pathway – we have the technology! We can make it better! Faster! Stronger!”

What’s even more amazing is how delicate these adjustments can be. We’re not just talking about growing dendrites here – we’re talking about dendritic spines, the tiny knobs that branch off from dendrites and bloom into postsynaptic densities – molecular interfaces that allow one neuron to receive information from its neighbors.

Back in 2005, a team led by Yi Zuo at the University of California Santa Cruz found that as a mouse learns a new task, thousands of fresh dendritic spines blossom from the dendrites of neurons in the motor cortex (an area of the brain that helps control movement). In short, they actually observed neurons learning to communicate better.

And now Zuo’s back with another hit, the journal Nature reports. This time, Zuo and her team have shown that those new dendritic spines aren’t just popping up at random – they grow in bunches:

A third of new dendritic spines (postsynaptic structures of most excitatory synapses) formed during the acquisition phase of learning emerge in clusters, and that most such clusters are neighbouring spine pairs.

The team discovered this by studying fluorescent mouse neurons under a microscope (Oh, did you know there are mice with glowing neurons? Because there are mice with glowing neurons.). As in Zuo’s earlier study, they focused on neurons in the motor cortex:

We followed apical dendrites of layer 5 pyramidal neurons in the motor cortex while mice practised novel forelimb skills.

But as it turned out, their discovery about clustered spines was just the tip of the iceberg – the researchers also found that when a second dendritic spine formed close to one that was already there, the first spine grew larger, strengthening the connection even more. And they learned that clustered spines were much more likely to persist than non-clustered ones were, which just goes to show the importance of a solid support network. And finally, they found that the new spines don’t form when just any signal passes through – new connections only blossom when a brain is learning through repetition.

Can you imagine how many new dendritic spines were bursting to life in the researchers‘ brains as they learned all this? And what about in your brain, right now?

It’s kinda strange to think about this stuff, I know – even stranger is the realization that your brain isn’t so much an object as it is a process – a constantly evolving system of interconnections. You could say that instead of human beings, we’re really human becomings – and thanks to your adaptable neurons, each moment is a new opportunity to decide who – or what – you’d like to become.

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!

Surprising Synchrony

Our corpus callosum is a bundle of fibers that allows our brains’ left and right hemispheres to communicate – but even in people born without these connections, the hemispheres are still somehow able to synchronize their activity, reports a new study.

Activity in a brain with a corpus callosum (left) and one without (right). See, they're twins - just like Schwarzenegger and DeVito!

The brains of people born with a condition called agenesis of the corpus callosum (AgCC) – basically, absence of a corpus callosum – show activation patterns that are essentially the same as those of people with an intact corpus callosum. It’s a Neuroscience Mystery!

For decades, the corpus callosum’s purpose seemed straightforward enough: though certain areas of our left and right hemispheres specialize in certain tasks – the left hemisphere is more adept at retrieving specific facts, for instance; while the right is better at estimation – the two hemispheres work together to achieve most goals, and show a great deal of symmetry in their activation patterns. Since the corpus callosum is composed mainly of white matterconnective neural tissue – and severing it seems to prevent epileptic activity from spreading, it seemed obvious that this structure was crucial for communication between the two hemispheres.

But now, as the Journal of Neuroscience reports, a team led by Caltech’s J. Michael Tyszka has used fMRI scans to detect communication between the hemispheres in the brains of eight adults who lack a corpus callosum:

“This was a real surprise,” says Tyszka. “We expected to see a lot less coupling between the left and right brain in this group — after all, they are missing about 200 million connections that would normally be there. How do they manage to have normal communication between the left and right sides of the brain without the corpus callosum?”

How, indeed? Well, Tyszka’s team has a few bright ideas. The most promising one hinges on functional networkspatterns of brain connectivity that rely somewhat on anatomical (structural) networks, but are much more flexible. You might think of them as mental “software” as opposed to “hardware.”

This new research suggests that functional networks may be more flexible and adaptive than many scientists suspected:

We tested two distinct possibilities: (1) functional networks arise largely from structural connectivity constraints, and generally require direct interactions between functionally coupled regions mediated by white-matter tracts; and (2) functional networks emerge flexibly with the development of normal cognition and behavior and can be realized in multiple structural architectures.

In short, the two possibilities are that 1) functional networks mostly depend on structural ones, or 2) they arise in response to brain function, and aren’t particularly dependent on anatomical structure. The surprising functional symmetry in brains that lack a corpus callosum seems to strongly imply that #2 is closer to the truth.

Like many answers in neuroscience, this one raises a lot of new, intriguing questions – the first and most obvious among them being, How the hell are these hemispheres communicating? We don’t know yet. Just how flexible are functional networks? We don’t know yet. Might there be a link to the unusual white-matter connectivity in children with autism? Probably, but we don’t yet know what it is.

And what does this suggest about the nature of consciousness? Well, let’s see – you got a comfy chair and about three hours to spare?

Transforming Tracts

Our brains don’t stop developing in our teenage years – they keep changing well into our 20s, a new study shows.

White matter, switchin' it up (that's the actual scientific term).

By imaging the “wiring” of different brain areas, researchers have determined that white matterconnective brain material consisting mainly of the axons (branches) of neurons – continues to grow and change throughout our 20s. This means the connections between different areas of your brain may be transforming even as you read this article.

The idea that our brains’ functional connectivity patterns change as we age isn’t new – in fact, I’ve written about it here before. But this new data shows that even structural connectivity doesn’t stop changing when we grow out of adolescence.

As the Journal of Neuroscience reports, a team led by the University of Alberta’s Christian Beaulieu and UCLA’s Catherine Lebel figured this out by performing diffusion MRI scans on the brains of 103 volunteers between the ages of five and 32. They then compared this set of scans with a second set taken from the same volunteers a while later, and found that many of the subjects’ white matter was continuing to change its structure:

All tracts showed significant nonlinear development trajectories for FA and MD [two scanning parameters]. Significant within-subject changes occurred in the vast majority of children and early adolescents, and these changes were mostly complete by late adolescence for projection and commissural tracts. However, association tracts demonstrated postadolescent within-subject maturation of both FA and MD.

In other words, white matter tracts that carry information between separate brain areas, and out of the brain into muscles, or into the brain from sense receptors (projection tracts) and ones that carry information between the left and right hemispheres (commissural tracts) both generally stop growing by late adolescence – but tracts that carry information between different lobes of the same hemisphere (association tracts) continue to develop into our late 20s. Growth was particularly focused in the frontal lobe – an area involved in complex thought and decision-making.

It’s interesting to note, though, that some volunteers’ white matter tracts showed degradation over time. Because many psychiatric disorders first manifest themselves in adolescence, the researchers suspect these problems may be linked to connections between brain regions that degrade prematurely:

What’s interesting is a lot of psychiatric illness and other disorders emerge during adolescence, so some of the thought might be if certain tracts start to degenerate too soon, it may not be responsible for these disorders, but it may be one of the factors that makes someone more susceptible to developing these disorders.

If that turns out to be true, scans like these could soon be used to predict psychiatric diseases, or even chart their progress. By comparing the diffusion MRI scans of psychologically healthy patients against those of patients suffering from, say, depression or anorexia, doctors might be able to get a clearer sense of what’s going on in these patients’ heads – on both the physical and psychological levels.

So next time you’re reflecting on how much you’ve changed over the past few years, stop for a second and you literally have a different brain than you did then. And then maybe you could run into the street screaming, “Who am I?!” Try to remember to put pants on first.

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