Parietal decision sequences – and more of mice and monkeys

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From Figure 2 of Harvey et al

Back in the 1990’s, one of the most intense battlegrounds in systems neuroscience was in monkey posterior parietal cortex. Labs competed to claim what a little strip of cortex called lateral intraparietal area (LIP) really does – decision, movement planning, attention, reward, or all of the above – mostly using single cell recording in behaving monkey. The experiments were (and still are) tough: standard operating procedure requires a well-trained monkey who will perform hundreds if not thousands of trials a day and then isolating neurons one at a time to find ones that respond during some interesting part of the trial. And then lots and lots of repetition so that you can average over many neurons. All things considered, it’s remarkable how much the field has been able to learn with this toolbox.

Fast forward to present day, there’s a new kid on the block. As I discussed a few weeks ago, rodent behavior and physiology is booming. People are taking on questions previously studied mainly in primates and are taking full advantage of the recent storm of new techniques. This is typified by today’s paper by Chris Harvey, Philip Coen, and David Tank, which goes back to the question – what does posterior parietal cortex do during a decision task?  They imaged populations of neurons while mice used visual cues to navigate a virtual maze. Just like in primates, individual neurons were selective for different choices that the mouse made. But unlike in primate parietal cortex, where neurons tend to have sustained responses leading up to the time of decision, individual neurons responded transiently in different portions of the trial. So as a population, different choices were represented by distinct sequences of neuronal activity. This kind of sequential firing has been seen in other parts of the rodent brain such as hippocampus, but not in posterior parietal cortex.  Continue reading →

The Fine Architecture of Learning and Joint Publication

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(image courtesy of Svoboda lab, https://openwiki.janelia.org/wiki/display/SvobodaLab/Research)

You warily walk into a dark compartment, wondering if there is food inside. Suddenly there is a loud tone and you feel an uncomfortable surge of electricity through your feet. This goes without saying, but it won’t take long before you will learn to be afraid of that tone. However, over time, you hear the tone without the shock, and slowly (foolishly??) accept that the previous connection may no longer hold.

Or perhaps you are extremely motivated to work for food, given that in your home area, nutrition has been sparse and hard to come by. You see millet seeds seemingly just within the reach of your fore-limb. Though not a typical movement for you, you reach for it. In another instance, you find a different type of food that is difficult to handle. However, it is nourishment nonetheless, so you will learn the required motor skills.

SPOILER ALERT: In each of the above cases, you were a mouse the whole time (I know!) But this is a neuroscience blog, not M.Night Shyamalan’s IMDB page, so perhaps we should focus on what was taking place in the brain as each scenario played out. In both of the cases above, learning was occurring, with new information stored away within the appropriate neural connections of particular brain areas. These situations are on display in a pair of new(ish) papers out in Nature, exploring the structural substrates of such learning and identifying patterns underlying the observed structural changes as learning occurred. Continue reading →

Layer magic and monkey business

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Layers of human cortex drawn by Ramon y Cajal. Image from Wikimedia Commons

We’ve known for over a century that sensory cortex is arranged in distinct layers, each containing a different make up of neuronal types and projection patterns, but we don’t actually know that much about the actual computations performed in each layer.  Today a paper from Massimo Scanziani’s lab takes a big step towards cracking the function of the bottom layer (layer 6) in mice. Layer 6 neurons project both to upper cortical layers and to the lateral geniculate nucleus in the thalamus, which itself is the primary input to cortex, and so are primed to play a large modulatory role. Using a monumental combination of optogenetics, intracellular recording, and behavioral testing, the paper convincingly makes the case that layer 6 controls the gain of visual responses of upper layer neurons (i.e. changes the size of their responses without altering their selectivity). Gain control is a fundamental computation in cortex, and has been invoked as a mechanism for attention, perception, spatial processing, and more. The cellular mechanism here is worked out in primary visual cortex, but it could potentially operate throughout layered cortex.

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Lost in Translation — Chasing the Roots of Conditioned Fear Research

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I’m currently attending the Winter Conference on Neural Plasticity in lovely St. Kitts & Nevis and I’ll be tweeting when I can from #wcnp12 when the Internet access in the room decides to cooperate.

Today’s opening session at the meeting was a historical perspective on selected topics in neural plasticity. I thought I’d share an interesting piece of history about one topic that has exploded in terms of research output over the last 20 years: conditioned fear. Michael Fanselow gave the lecture on the history of fear research and focused on the era prior to the exponential growth of the literature, sticking to 1920-1980. Here’s a graph from a very recent review simply noting the number of “fear extinction” papers in the literature (one small sub-field in this topic,) just to give you a sense of how rapidly this field has grown:

Found on Google Images, not sure why it's in front of the paywall!!

I’ll do may best to channel Dr. Fanselow with the next few paragraphs:

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Telepathy? I think not

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From Supp Fig 10 of Kay et al.

There is just something about neural decoding that captures the imagination. Scientists “reading out brain activity” to infer what someone was seeing or doing sounds like the stuff of science fiction. But in practice, with the right dataset and right computer algorithm, it can be done – providing the question you are trying to query the brain is simple enough. But no matter how simple the question, with every paper comes an orgy of stories in the mainstream press about how scientists can eavesdrop on your thoughts or even engage in electronic telepathy. Thereby infuriating scientists and science journalists in droves, sometimes detracting from some very cool work.

Today I’m going back a few years to a paper that typifies this effect, a study from Jack Gallant‘s lab about a model for decoding natural images from fMRI activity in early visual cortex.

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Awakening dormant genes with cancer drugs

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From Figure 1 of Huang et al.

Here’s one that first appeared online at the end of last year by Benjamin Philpot, Bryan Roth and Mark Zylka about a finding that could lead to a therapy for Angelman Syndrome. Angelman syndrome is a rare neurodevelopmental disorder affecting 1 in 15,000 live births and is characterized by developmental delay, lack of speech, seizures, and motor difficulties. There are no therapies available for core symptoms and individuals generally require care throughout life. Autism is often diagnosed in Angelman Syndrome individuals, and the same genomic region has been fingered as a culprit in both disorders. Angelman Syndrome is most commonly caused by deletion of a region on the maternal copy of chromosome 15 containing the gene UBE3A, conversely, some forms of autism may also be caused by duplication of this region.

Although we all possess two copies of UBE3A, only the one inherited from the mother is active. Normally, the paternal copy is epigenetically silenced. This means that in Angelman Syndrome there is no functioning copy at all, which has consequences for multiple signaling pathways and brain circuits. The authors of this paper set out to find a workaround: something that could activate the intact, but dormant, paternal copy of UBE3A.  They made a reporter assay from neurons of mice expressing fluorescent paternal UBE3A protein, and performed a large-scale drug screen, testing over 2000 compounds for ones that would activate paternal Ube3A. None of the most likely suspects worked, but an unlikely class of drugs, topoisomerase inhibitors, did so reliably. Even better,  one of the best (topotecan) is already an FDA-approved cancer drug.

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Patients help bring the study of Alzheimer’s to the dish

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Israel et al. Supp Fig1: Experimental design.

Alzheimer’s disease (AD) is a devastating neurodegenerative disease that could become an even more massive public health problem than it already is, if current projections hold. Some predict that by 2050, 1 in 85 individuals will be affected by the disease. Currently, there is no cure, but there are neurotransmitter-enhancement-based strategies to slow down the cognitive deficits [the loss of cholinergic neurons is implicated in some of the memory problems associated with AD so therefore, pharmacological enhancement of brain acetylcholine concentration can partially alleviate some memory-based symptoms.] However, as with many neurodegenerative diseases, these stop-gap treatments only work for so long, until the cells responding to neurotransmitter supplementation treatments die off completely. Therefore, diverse strategies designed to cure or at least slow down AD are imperative.

While a number of AD transgenic mouse models have been created, based on the various mutations identified in patients, the trouble is that these models still utilize the cross-species approach of studying “diseased” mouse neurons expressing mutated human genes. And perhaps an even bigger problem with many mouse models, genetically-inherited forms of AD represent only ~0.1% of cases, with the remainder being “sporadic” (although there are genetic risk factors influencing the emergence of sporadic AD.)

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Timely inhibition

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Image: Tamily Weissman

This week’s paper is by Abigail Person and Indira Raman and is about information transmission between two cell populations in the cerebellum – purkinje cells in the cortex and their targets in the deep nuclei. Purkinje cells are justifiably famous for their spectacular anatomy  which enables integration of thousands of inputs. This paper, however, is about their output and how these exclusively GABAergic cells control the activity of downstream neurons. Conventional wisdom holds that there should be a straightforward inverse relationship between the firing rate of the two populations, but this has not always been observed. Person and Raman present a new solution based on spike timing – when purkinje cells spike asynchronously, their targets are inhibited (as expected), but when they spike synchronously, nuclear neurons can spike during the gaps in inhibition and end up time locking their activity to their inputs.

This is an intriguing proposal for how information is transmitted in the cerebellum that could have implications for how this brain structure controls movement, but it’s just the first step. The proposal is built from in vitro experiments, deduction, and some supporting in vivo data, but several crucial unknowns have to be resolved before we’ll know whether it’s relevant to actual behavior. There was plenty of spirited discussion during the review process about the strength of some of the authors’ assumptions. There were deeply divided views on whether the authors had made sufficiently strong a case for how the cerebellum IS operating, as opposed to just proposing how it COULD be. We had to decide whether to publish a paper that everyone agreed was interesting, but one that contained some pieces of indirect evidence and some good (but by no means universally agreed-upon) assumptions.

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A year of neuroscience in Nature

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Something light for the weekend: deconstruction of a year of neuroscience in Nature. This text cloud was created from the titles and abstracts of 83 neuroscience papers published in Nature in 2011. Click on the image to see a larger version. Frequency is represented by font size and common words such as “the” and “and” are excluded. Not too surprisingly, “neurons” came out on top (149 occurrences).

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Light dissection of reward

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Image courtesy of Jeremiah Cohen

Out online in Nature today: a paper from Naoshige Uchida and colleagues about cell-type specific reward and punishment signals in the ventral tegmental area (VTA) of mice. The VTA is a midbrain region heavily implicated in reward and addiction, and its outputs are thought to provide reward-related signals to other brain areas. One subpopulation of cells with the VTA, the dopaminergic neurons, have been the topic of intense study for their potential computational role in reward learning. Over a decade ago Wolfram Schultz and colleagues found that in monkeys, dopaminergic neurons fired for unexpected rewards, but were also suppressed if expected rewards were not received. Schultz and colleagues proposed that the neurons were representing the difference between expected and actual outcome, and also noted that such reward prediction error has been theoretically posited to drive reinforcement learning. Although reward prediction is by no means the only proposed role for dopamine, the idea that dopaminergic neurons carry reward signals has figured prominently into theories of VTA function and what goes wrong in disease.

But only around half of VTA neurons are dopaminergic; GABAergic neurons, which make inhibitory projections onto dopaminergic neurons, make up a big chunk of the remainder.  In the current paper, Uchida and colleagues asked how the two populations encode learned rewards and punishments. They recorded from VTA neurons in mice learning to associate odors with rewards and punishments and sorted the neurons post-hoc by their firing properties. Some neurons had brief phasic responses to rewards and reward-predicting cues. Others had sustained increases in firing during the delay between cues and rewards, and yet others sustained decreases. The authors then used optogenetic stimulation to establish dopaminergic or GABAergic identity in a subset of the cells. Dopaminergic neurons all belonged to the first class of cells with phasic reward and reward-predicting responses, and GABAergic neurons the second class with tonic increases. Most but not all dopaminergic neurons were inhibited by aversive stimuli,  most GABAergic neurons were excited. Continue reading →