neurodudes

If the Fish Liver Can’t Kill, Is It Really a Delicacy? [NYT, login]

Amazing. It looks like TTX (tetrodotoxin, a potent voltage-gated sodium channel blocker well-known to electrophysiologists) is not made by the pufferfish (which I had always assumed), rather it is from the bacteria/food consumed by the fish.

Decades earlier, another Japanese scientist had identified fugu’s poison as tetrodotoxin, a neurotoxin that leaves victims mentally aware while they suffer paralysis and, in the worst cases, die of heart failure or suffocation. There is no known antidote.

Researchers surmised that fugu probably got the toxin by eating other animals that carried tetrodotoxin-laden bacteria, developing immunity over time — though scientists then did not rule out the possibility that fugu produced the toxin on its own.

By this year, Mr. Noguchi had tested more than 7,000 fugu in seven prefectures in Japan that had been given only feed free of the tetrodotoxin-laden bacteria. Not one was poisonous.

“When it wasn’t known where fugu’s poison came from, the mystery made for better conversation,” Mr. Noguchi said. “So, in effect, we took the romance out of fugu.”

Aside from the interesting science, it appears there is also a small Japanese “industry” (de-ttx? detox?) seriously affected by TTX-free fugu. More after the jump (more…)

Neurons come in many shapes and sizes. Frequently, the shape of a neuron is characteristic to its type. Several theoretical papers have demonstrated that the shape of a neuron can crucially determine its pattern of activity, independently of other factors (Mainen & Sejnowski, 1996, for example). Several resources on the web such as neuromorpho.org and the Cell Centered Database are dedicated to maintaining repositories of different neuronal shapes (also known as morphologies).

Any computer scientist worth their salt, noticing this trend, is tempted to say: if neuronal shape is so important, maybe we ought to have good data standards to describe it. That’s just what a paper last year did. It surveyed the popular data standards for modeling, primarily in the NEURON and Genesis simulation packages. The result is a data standard called MorphML, which is part of a larger effort called NeuroML.

Neuronal shape is a weird data type for the computer science world, but I think an incredibly important and fundamental one for deeply coping with the complexity of real brain tissue. It seems to me that many areas of neuroscience research could benefit from the construction of more explicit models of the circuits they study.

This new technique from Cori Bargmann’s lab is one of the neatest that I’ve seen in a while. The authors split GFP into two pieces, expressing one piece presynaptically and the other postsynaptically. This creates functional (ie. fluorescing) GFP only at sites of synaptic contact where the protein can reconstitute. They call the technique GFP Reconstitution Across Synaptic Partners (GRASP). Check out an example labeling here:

The neurons are expressing mCherry in the cytoplasm but GFP is expressed only at the site of synaptic contacts where the split GFP peptides can be reconstituted into a complete GFP fluorophore.

Video-Rate Far-Field Optical Nanoscopy Dissects Synaptic Vesicle Movement

Just the optical engineering alone here deserves mention: 28 frames per second at 62nm resolution (well below the diffraction limit of 260nm for light of the wavelength used)! STED (or stimulated emission depletion, developed in Stefan Hell’s group) is ideal for visualizing synaptic vesicles, whose small size (~50nm) has typically confined them to the domain of electron microscopists. The ability to get high-speed STED allowed the researchers to track individual vesicles and their path dynamics. They conclude that vesicle movement has both motor-driven and diffusive components (ie. a biased random walk). I’m sure with more time and more analysis there will be a lot of interesting applications for this kind of real-time vesicle tracking. Perhaps in the near future we will have single vesicle “minis” monitored at multiple sites through microscopy instead of just one or two sites electrophysiologically…

Here’s the resolution difference between STED and confocal for a single vesicle:

And, for those of you with ~$1.25M lying around, you can now purchase a STED setup directly from Leica!

Haim Sompolinsky has written an excellent book chapter on the scientific view of free will and choice, pulling in good ideas from physics and neuroscience along with contemporary philosophical commentary.

I think this chapter might be helpful for neuroscientists outside of the lab. Often a dinner table discussion has moved to the idea of “quantum consciousness” or “quantum free will”. Often, someone will mention Roger Penrose, who has become something of a poster boy for this idea that quantum indeterminacy (eg. Heisenberg’s uncertainty principle) is one possible way that free will is really free. And then, people look around and say, “Well, you’re a neuroscientist. Do we have free will?” (And that’s when I take another big drink or bite while I figure out something semi-coherent to say.)

Sompolinsky does a nice job of evaluating such claims (in the end, he says we cannot rule out the possibility that the brain is an indeterministic system but it seems unlikely) and provides nice scientific insight. In his view, it is far more likely that the brain’s apparent randomness (eg. individual cell spike rasters vary across repeated presentations of the same stimulus) is more simply explained by thermal noise (think of varying channel gating properties) and chaotic brain dynamics. (Recall, a chaotic system is still deterministic; it simply exhibits aperiodic behavior due to exquisite sensitivity to initial conditions. It is difficult to predict the long-term behavior of chaotic systems. The more we know the initial conditions in detail, the better our prediction.) On the other hand, he argues that the relevant length and time scales for neurons (micrometers and milliseconds) are far larger by many orders of magnitude than those of quantum noise. Chaos might amplify such quantum events, but this is far from being the simplest, most parsimonious explanation. Given the current level of neuroscience understanding, this is almost idle speculation. Regardless of the (in)determinacy of the world, Sompolinsky effectively argues against any non-physical, purely mental (ie. dualistic) agent of causation.

Thus, in sum, the world and our brains might not be determined but, even given that, there’s no reason to believe we have any causative ability to change things in the sense of traditional free will. These observations seem right on the mark to me. I hope they bring some insight for others. Or at least a way to fend off the dinner-table-free-will-conversation barrage of questions.

The relatively recently discovered cannabinoid receptors has me wondering how many other neuroreceptors may be left to discover. One way to estimate the number of these is to screen the genome and look for sequences that look like receptors. This paper says that people have done that for the special case of G protein-coupled receptors (GPCRs), and that the result is that, excluding receptors involved in “chemosensory responses such as taste and olfaction”, there are “367 receptors (1), of which some 200 have been shown to bind known transmitters (3). This leaves about 160 orphan GPCRs that are not activated by any known transmitters and thus are genes with unknown function.”

Happy Labor Day (US)! Topping the NYT most popular articles list right now is an interesting article about a new schizophrenia treatment that targets certain glutamate receptors unlike previous dopaminergic drugs. The drug, which is being developed by Eli Lilly, is partially due to this interesting observation:

For decades, psychiatrists have known that users of PCP, a street drug sometimes called angel dust, have symptoms nearly identical to those of people with schizophrenia. By the 1980s, scientists had discovered that PCP blocked brain receptors that are triggered by an amino acid called glutamate. This led some companies and scientists to study ways to stimulate glutamate receptors as a treatment for schizophrenia.

But the brain has many different kinds of glutamate receptors, and figuring out how to stimulate or block them in medically beneficial ways has proved complicated. Instead of focusing on the receptors blocked by PCP, Dr. Schoepp concentrated on modulating the action of glutamate receptors in the brain’s prefrontal cortex, an area responsible for personality and learning.

(more…)

“It sounds like a science-fiction version of stupid pet tricks: by toggling a light switch, neuroscientists can set fruit flies a-leaping and mice a-twirling and stop worms in their squiggling tracks. But such feats, unveiled in the past two years, are proof that a new generation of genetic and optical technology can give researchers unprecedented power to turn on and off targeted sets of cells in the brain, and to do so by remote control…”

Reviews the use of photosensitive proteins in neuroscience and even gives a shout-out to Ed Boyden, of Stanford and MIT fame…

– Davie (who had the same advisor as Ed for about a day and is therefore 0.01% more famous by association)

This article (from last December) has identified what it thinks are 244 new human microRNAs from human fetal brain tissue, of which 20 seem to be species-specific (see figure 1d). They also mention 2 previously-known human-specific microRNAs.

Eugene Berezikov, Fritz Thuemmler, Linda W van Laake1, Ivanela Kondova, Ronald Bontrop, Edwin Cuppen and Ronald H A Plasterk Diversity of microRNAs in human and chimpanzee brain. Nature Genetics - 38, 1375 - 1377 (2006)

Comment on “Ongoing Adaptive Evolution of ASPM, a Brain Size Determinant in Homo sapiens” — Yu et al. 316 (5823): 370b — Science

Some new evidence contradicting previous claims that a particular haplotype of the ASPM gene was selected. We posted about some related work in 2005.

An excerpt:

We also assessed evidence for selection at ASPM by carrying out the long-range haplotype (LRH) test (9). This test assesses whether a haplotype is too young to have risen to its frequency without selection. The LRH test is not affected by uncertainty in recombination rate estimates. We compared LRH results for the A44871G polymorphism to SNPs of matched frequency in HapMap CEU (3, 10) (Fig. 1C). We observed at least as strong a signal for selection at 90% of the regions examined (3, 11). Several genome-wide surveys using similar methods also failed to find evidence for selection at ASPM in European-derived populations (4, 12, 13). The one survey that did find a signal near ASPM did so only in individuals of Chinese ancestry (13), failing to support the contention of (1) of recent selection in European history. Based on linkage disequilibrium (LD) breaking down within ~100 kb on either side (Fig. 1B), we estimate that the G allele arose in European history at least tens of thousands of years ago and possibly more than 100,000 years ago (14) (table S3 and SOM Text). These dates are difficult to reconcile with selection ~6000 years ago, as suggested in (1).