Posts Tagged ‘ neuroanatomy ’

Neuroscience Friends!

I’ve just returned from a thrilling weekend at the BIL Conference in Long Beach, California (yes, the pun on “TED” is very intentional) where I met all kinds of smart, fun people – including lots of folks who share my love for braaaiiins!

The conference was held in... The Future!

So I thought I’d introduce you guys to some of the friends I made. I think you’ll be as surprised – and as excited – as I am.

Backyard Brains
Their motto is “neuroscience for everyone” – how cool is that? They sell affordable kits that let you experiment at home with the nervous systems of insects and other creatures. They gave a super-fun presentation where I got to help dissect a cockroach and send electrical signals through its nerves.

Interaxon
They build all kinds of cutting-edge tools that let home users study their brain activity, and even control machines and art projects with it. Their founder, Ariel Garten, has a great TED talk here – I’ve rarely met anyone else who was so excited to have weird new neuroscience adventures.

Deltaself and Dangerously Hardcore
Two blogs by the very smart Naomi Most – the first is about how scientific data is changing the way we all understand our minds and bodies; the second is about hacking your own behavior to stay healthier and live better.

Halcyon Molecular
Their aim is to put the power to sequence and modify genomes in everyone’s hands within the next few decades. They’re getting some huge funding lately, and lots of attention in major science journals.

Bonus – XCOR Aerospace
They’re building a privately-funded suborbital spacecraft for independent science missions. If there’s anybody who can help us all join the search for alien life in the near future, I bet it’s these guys.

So check those links out and let me know what you think. I’d love to get these folks involved in future videos, especially if you’re interested in any of them.

Forget Me Not

Having trouble remembering where you left your keys? You can improve with a little practice, says a new study.

"I've forgotten more than you'll ever...wait, what was I saying?"

It’s an idea that had never occurred to me before, but one that seems weirdly obvious once you think about it: people who train their brains to recall the locations of objects for a few minutes each day show greatly improved ability to remember where they’ve left things.

No matter what age you are, you’ve probably had your share of “Alzheimer’s moments,” when you’ve walked into a room only to forget why you’re there, or set something down and immediately forgotten where you put it. Attention is a limited resource, and when you’re multitasking, there’s not always enough of it to go around.

For people with real Alzheimer’s disease, though, these little moments of forgetfulness can add up to a frustrating inability to complete even simple tasks from start to finish. This is known as mild cognitive impairment (MCI), and its symptoms can range from amnesia to problems with counting and logical reasoning.

That’s because all these tasks depend on memory – even if it’s just the working memory that holds our sense of the present moment together – and most of our memories are dependent on a brain structure called the hippocampus, which is one of the major areas attacked by Alzheimer’s.

What exactly the hippocampus does is still a hotly debated question, but it seems to help sync up neural activity when new memories are “written down” in the brain, as well as when they’re recalled (a process that rewrites the memory anew each time). So it makes sense that the more we associate a particular memory with other memories – and with strong emotions - the more easily even a damaged hippocampus will be able to help retrieve it.

But now, a team led by Benjamin Hampstead at the Emory University School of Medicine has made a significant breakthrough in rehabilitating people with impaired memories, the journal Hippocampus reports: the researchers have demonstrated that Alzheimer’s patients suffering from MCI can learn to remember better with practice.

The team took a group of volunteers with MCI and taught them a three-step memory-training strategy: 1) the subjects focused their attention on a visual feature of the room that was near the object they wanted to remember, 2) they memorized a short explanation for why the object was there, and 3) they imagined a mental picture that contained all that information.

Not only did the patients’ memory measurably improve after a few training sessions – fMRI scans showed that the training physically changed their brains:

Before training, MCI patients showed reduced hippocampal activity during both encoding and retrieval, relative to HEC. Following training, the MCI MS group demonstrated increased activity during both encoding and retrieval. There were significant differences between the MCI MS and MCI XP groups during retrieval, especially within the right hippocampus.

In other words, the hippocampus in these patients became much more active during memory storage and retrieval than it had been before the training.

Now, it’s important to point out that that finding doesn’t necessarily imply improvement – studies have shown that decreased neural activity is often more strongly correlated with mastery of a task than increased activity is – but it does show that these people’s brains were learning to work differently as their memories improved.

So next time you experience a memory slipup, think of it as an opportunity to learn something new. You’d be surprised what you can train your brain to do with a bit of practice.

That is, as long as you remember to practice.

Learning Expectations

Researchers have isolated a specific pathway our brains use when learning new beliefs about others’ motivations, a new study says.

"M'lord! 'Tis improper to influence the lady's anterior cingulate!"

Though this type of learning, like many others, depends heavily on the neurotransmitter chemical dopamine‘s influence in a set of ancient brain structures called the basal ganglia, it’s also influenced by the rostral anterior cingulate cortex (ACC) – a structure that helps us weigh certain emotional reactions against others – indicating that emotions like empathy also play crucial roles.

As we play competitively against other people, our brains get to work constructing mental models that aim to predict our opponents’ future actions. This means we’re not only learning from the consequences of our own actions, but figuring out the reasons behind others‘ actions as well. This ability is known as theory of mind, and it’s thought to be one of the major mental skills that separates the minds of humans – and of our closest primate cousins – from those of other animals.

Though plenty of studies have examined the neural correlates of straightforward cause-and-effect learning, the process by which we learn from the actions of other people still remains somewhat unclear – largely because complex emotions like empathy and regret seem to involve many areas of the brain, including parts of the temporal, parietal and prefrontal cortices, as well as more ancient structures like the basal ganglia and cingulate cortex.

That’s why a team led by the University of Illinois’ Kyle Mathewson set out to track exactly what happens in our brains as we learn new ideas about other’s motivations, the journal Proceedings of the National Academy of Sciences reports.

The team used functional magnetic resonance imaging (fMRI) to study activity deep within volunteers’ brains as they played a competitive betting game against one another – focusing especially on moments when players learned whether they’d won or lost a round, and how much their opponents had wagered.

The researchers then used a computational model to match up patterns of brain activity with patterns of play – and found that the volunteers’ brains learned others’ behaviors and motivations through a complex interplay of several regions:

We found that the reinforcement learning (RL) prediction error was correlated with activity in the ventral striatum.

In other words, the ventral striatum – an area of the basal ganglia – was crucial for learning by reinforcement, much as the researchers expected…

In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning.

…while the anterior cingulate, on the other hand, seemed to dictate how attentively players watched their opponents’ patterns of play, and how much thought they put into predicting those patterns.

Thus, it appears that theory of mind is built atop an ancient “substructure” of simple reinforcement learning, which supports layers of more emotionally complex attitudes and beliefs about others’ thoughts, feelings and motivations – many of which are influenced by our perceptions of our own internal feelings.

And that points back to an important aspect of subjective experience in general: Many of our perceptions of the external world are extrapolated from our perceptions of our internal states. When we say, “It’s hot,” we really mean, “I feel hot;” when we say, “It’s loud in here,” we really mean, “It sounds loud to me.” In fact, the great philosopher Bertrand Russell has gone so far as to suggest that instead of saying, “I think,” it’d be more accurate to say “It thinks in me,” the same way we say “It’s raining.”

Anyway, no matter how you choose to phrase it, the point is that thinking isn’t a single process, but a relationship of many processes to one another. Which means that no matter how much we think we know, there’s always plenty left to learn.

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?

The Memory Master

A gene that may underlie the molecular mechanisms of memory has been identified, says a new study.

Some of us feel that "yellow" and "red" are open to interpretation...

The gene’s called neuronal PAS domain protein 4 (Npas4 to its friends). When a brain has a new experience, Npas4 leaps into action, activating a whole series of other genes that modify the strength of synapses – the connections that allow neurons to pass electrochemical signals around.

You can think of synapses as being a bit like traffic lights: a very strong synapse is like a green light, allowing lots of traffic (i.e., signals) to pass down a particular neural path when the neuron fires. A weaker synapse is like a yellow light – some signals might slip through now and then, but most won’t make it. Some synapses can inhibit others, acting like red lights – stopping any signals from getting through. And if a particular synapse goes untraveled for long enough, the road starts to crumble away – until finally, there’s no synapse left.

There’s a saying in neuroscience: “Cells that fire together wire together.” (And vice versa.) In other words, synaptic plasticity – the ability of neurons to modify their connectivity patterns – is what allows neural networks to physically change as they take in new information.  It’s what gives our brains the ability to learn.

In fact, millions of neurons are delicately tinkering with their connectivity patterns right now, inside your head, as you learn this stuff. Pretty cool, huh?

Anyway, synaptic plasticity’s not exactly breaking news – scientists have been studying it in animals like squid and sea slugs since the 1970s. Neurons in those animals are pretty easy to study with electrodes and a microscope, because a) the animals are anatomically simple compared to humans, and b) some of their neurons are so huge they can be seen with the naked eye.

Studying synapses in humans isn’t quite so simple, though. For one thing, most people wouldn’t like it if you cut open their brain and started poking around while they were alive and conscious – and besides, a lot of the really interesting stuff happens down at the molecular level.

That brings up an important point: though you normally hear about genes in connection with traits – say, a “gene for baldness” and so on – these complex molecular strands actually play all sorts of roles in the body, from building cells to adjusting chemical levels to telling other genes what to do.

That’s why MIT’s Yingxi Lin and her team set out to study the functions of certain genes found in the hippocampus – a brain structure central to memory formation – the journal Science reports. The researchers taught a group of mice to avoid a little room in which they received a mild electric shock, then used a precise chemical tracking technique to isolate which genes in the mouse hippocampus were activated right when the mice learned which room to avoid.

In particular, they focused on a hippocampal region with the sci-fi-sounding name of Cornu Ammonis 3or CA3 for short:

We found that the activity-dependent transcription factor Npas4 regulates a transcriptional program in CA3 that is required for contextual memory formation. Npas4 was specifically expressed in CA3 after contextual learning.

By “transcriptional program,” the paper’s authors mean a series of genetic “switches” – genes that Npas4 activates – which in turn make chemical adjustments that strengthen or weaken synaptic connections. In short, Npas4 appears to be part of the master “traffic conductor program” for many of the brain’s synapses.

Though they were pretty excited by this discovery (who wouldn’t be?) the researchers took a deep breath, calmed down, and double-checked their results, by testing memory formation in mice whose brains were unable to produce Npas4:

Global knockout or selective deletion of Npas4 in CA3 both resulted in impaired contextual memory, and restoration of Npas4 in CA3 was sufficient to reverse the deficit in global knockout mice.

In short, they make a pretty convincing argument that Npas4 is a necessary ingredient in a mouse’s ability – and probably our ability – to form certain types of new memories.

Exactly how that program relates to our experience of memory remains unclear, but it’s a promising starting point for fine-tuning future memory research. I don’t know about you, but I’d be thrilled to green-light such a project.

Guiding Neuron Growth

Our neurons’ growth can be shaped by tiny cues from spinning microparticles in the fluids that surround them, a new study reports.

An axon gets all friendly with a spinnin' microparticle.

The branching and growth of neurons is based on several kinds of guides, including their chemical environment, their location within the brain, and the dense network of glial cells that support and protect them. But as it turns out, they’re also surprisingly responsive to fluid dynamics, turning in response to the rotation of nearby microparticles – a bit like the way a vine can climb a fence-post.

Since the early days of neuroscience, researchers have dreamed of growing and shaping neurons for specific purposes – to patch gaps in damaged neural networks, for example; or just to test their workings under controlled lab conditions.

But it’s only in the past few years that technologies like microfluidic chambers and pluripotent stem cells have enabled researchers to grow healthy, living neurons according to precise specifications, and study those cells’ responses to all kinds of stimuli. In fact, it looks like it won’t be much longer ’til doctors can implant networks of artificially grown neurons directly into living adult brains.

But as the journal Nature Photonics reports, the big breakthrough this time comes from Samarendra Mohanty at The University of Texas at Arlington, who found that neuron growth can respond to physical cues – spinning particles in fluid, for instance – as well as to chemical ones.

Mohanty’s team discovered this by using a tiny laser to direct the spin of a microparticle positioned next to the axon of a growing neuron. The spinning particle generated a miniature counterclockwise vortex in the fluid – and wouldn’t ya know it; the axon started wrapping around the spinning particle as the neuron grew:

Circularly polarized light with angular momentum causes the trapped bead to spin. This creates a localized microfluidic flow … against the growth cone that turns in response to the shear.

In short, this is the first time a scientific team has used a mechanical device – a “micro-motor,” as they call it – to directly control and precisely adjust the growth of a single axon:

The direction of axonal growth can be precisely manipulated by changing the rotation direction and position of this optically driven micromotor.

So far, the micromotor only works 42 percent of the time – but the team is optimistic that future tests will lead to greater reliability and more precise control. In the near future, micromotors like this one could be used to turn the growth of an axon back and forth – or even to funnel growth through “gauntlets” of spinning particles.

Most conveniently of all, the particles could be injected, re-positioned, and removed as needed – providing a much simpler, more modifiable architecture than any other neuron-shaping technology in use today.

And for the slightly more distant future, Mohanty’s lab is hard at work on a system for providing long-range, long-term guidance to entire neural networks through completely non-invasive optical methods.

Until then, though, isn’t it amazing to stop and think about all the neurons that are growing and reshaping themselves – all the delicate intertwining lattices relaying millions of mysterious coded messages, right now, within the lightless interior of your own head?

Call me self-centered, but I think it’s just about the coolest thing on planet Earth.

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