neurodudes

In this nice open-access (ie. free!) essay in October’s PLoS Biology, David Kleinfeld and Oliver Griesbeck describe the revolution in neural recording that is taking electrophysiology from the realm of dark-arts (lots of training) to simpler genetically-encoded, imaging-based techniques. A lot of ground is covered in the article, including the creation of many new colors of fluorescent proteins (XFPs) that can be genetically targeted and the tagging of the XFPs with Ca, voltage, and pH sensors. A nice summary table is included comparing the techniques too:

As you have likely noticed, Bayle and I post heavily about these new recording techniques because of our strong belief that a lot of neuroscience will be enabled by improving our ability to stimulate and record from entire networks of neurons with high resolution. Yesterday, I was listening to one of the many recent neuroscience talks here at MIT in which philosopher Pat Churchland suggested, as many others also have, that the problem of consciousness might be more of an artifact of primitive science than an actual scientific problem. She made a very nice analogy with a problem from centuries ago when scientists were unsure about the existence of life forces and what precisely made an animal alive. Of course, with modern cell biology, we now have a cellular theory of life, disease, and death. (To be fair, Churchland went on to say that people like Christof, Crick et al. are misguided in attempting to study neural correlates of consciousness. I completely disagree with that; at the very least, those scientists are helping to extend our understanding of the visual system and the difference between perception that we are aware of [conscious] and perception that has a neural correlate but that we are not aware of [unconscious]. Honestly, who cares if they say they’re studying consciousness or not — make a judgement based on the science.)

A web page tracking work on the optical control of neural activity, “focusing on the applications of channelrhodopsin-2.”

Maintained by Edward Boyden (the first author on the September optical stimulation paper in Nature that we previously talked about).

This Is Your Brain Under Hypnosis - New York Times

Very interesting stuff. Subjects were hypnotized and told that days later they would see “gibberish” symbols printed in particular colors. They needed to report back the color that the word appeared in. (For those unfamiliar, the Stroop test presents color words, like “red”, in a different color, such as the word “red” written with green ink. People have difficulty reporting the color of the word because we have a strong need to “read” the written word.)

The highly hypnotizable subjects (grouped according to a predetermined measure) essentially showed no Stroop effect (ie. no reaction time difference with conflicting word and color). And, with fMRI, they saw that normally activated visual-reading areas were not activated in these subjects.

His Holiness has spoken. He wants neuro-drugs to take and electrodes stuck in his brain so that he doesn’t have to spend hours meditating each day. (Enlightenment now!) If you want to do hot stuff, study physics or brain science. His interest in neuroscience stems from a long-standing interest in body hair. Yes, body hair. Americans need to figure their own way through this whole intelligent design business. Not all antidepressants are alike; for instance, the Dalai Lama is against tranquilizers. Definitely against tranquilizers. And, perhaps most surprisingly, His Holiness, approves of animal research — when it’s done right and with respect.

Minute-by-minute liveblog follows after the jump.
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New genetically encoded fluorescent voltage-sensitive dye.

Sensitivity: up to 34% change (delta F/F) per 100 mV.
Time constant: .5 ms.
Phototoxicity: You can expose the cells to light for up to 100 seconds without much effect; over 200 seconds is noticably damaging.
Location: specific to cell membranes.
Not ratiometric (if you don’t know what that is, don’t worry about it).

Excerpt from figure caption: “(c) Confocal section of HEK293 cells expressing eGFP-F shows very little fluorescence from internal membranes. Scale bar, 20 mum. (d) Fluorescence response (shown as colored increments) of hVOS on voltage pulses from -120 to +120 mV in steps of 20 mV from a holding potential of 0 mV in patch-clamped HEK293 cells.”

Baron Chanda, Rikard Blunck, Leonardo C Faria, Felix E Schweizer, Istvan Mody and Francisco Bezanilla. A hybrid approach to measuring electrical activity in genetically specified neurons. Nature Neuroscience 8, 1619 - 1626 (2005)

Haven’t tried it but this free, open-source software called Neuroscholar seems interesting. The idea seems to be to provide an easy tool for collecting and comparing facts and interpretations across different papers. It uses a SQL database backend and some graph-like data representation for the front-end.

I’d be curious to hear about anyone’s experiences using it or any similar tools. I still have yet to find a good way to organize my PDFs…

From the website:

NeuroScholar is an open-source and is free for download from http://www.neuroscholar.org/ (click on Software > NeuroScholar ). We also have several demonstration movies available from the movies section to show the functionality of the system.

Can Brain Scans See Depression? - New York Times

At first glance, this doesn’t seem like anything new (imaging-wise) to neuroscientists, but there are some interesting opinions in the article.

Interesting fact:

In a range of studies, researchers have found that people with schizophrenia suffer a progressive loss of their brain cells: a 20-year-old who develops the disorder, for example, might lose 5 percent to 10 percent of overall brain volume over the next decade, studies suggest.

And I like the way this guy thinks:

In an interview, Dr. Amen said that it was unconscionable that the profession of psychiatry was not making more use of brain scans. “Here we are, giving five or six different medications to children without even looking at the organ we’re changing,” he said.

But is this true?

“The thing for people to understand is that right now, the only thing imaging can tell you is whether you have a brain tumor,” or some other neurological damage, said Paul Root Wolpe, a professor of psychiatry and sociology at the University of Pennsylvania’s Center for Bioethics.

Does anyone know of any good work applying machine learning to doing discrimination of neural disease (like ADD, general depression, and anything that’s basically not a giant lesion/tumor) in imaging scans?

In the August PLoS Biology, there is an article showing the production of pure neural stem cells from human embryonic stem cells.

The procedure is quite simple: Add growth factors FGF-2 and EGF to the ES cells and you get pure NS cells, which overcomes several of the limitations of previous neurosphere-based assays [Nature Methods].

Bacterial speech bubbles : Nature

Bacteria secrete signals to other bacteria of the same species through vesicle packets.

Mashburn and Whiteley describe the unexpected convergence of two seemingly unrelated areas of microbiological research: how bacteria talk to their friends, and how they attack their enemies. The authors studied the bacterial pathogen Pseudomonas aeruginosa, which releases a hydrophobic molecule called the ‘pseudomonas quinolone signal’ (PQS) to send messages to other bacteria of the same species. The surprise is that, rather than being secreted as single molecules, PQS is released in bubble-like ‘vesicles’ that also contain antibacterial agents and probably toxins aimed at host tissue cells as well.

I wonder if this is evolutionarily connected to synaptic vesicles or if this is a case of something like convergent evolution…

Jimbo, Tateno, and Robinson did a network plasticity experiment using cultured networks and a multi-electrode array.

They determine the effect of a tetanus at one electrode in a network on the network. Specifically, they look at how the tetanus potentiates or depresses the ability of a test pulse at another electrode to evoke spike trains at various neurons across the network.

They grew cultures on a MEA for a month. They stimulated each electrode in succession with a test pulse. They recorded the response at all electrodes after each test pulse. They used spike sorting to identify the reponses of individual neurons out of the electrode traces. They found that the network’s response to a given test pulse was reproducable for about 50ms after the test pulse.

Then they applied a strong stimulus (a tetanus) to a single electrode (to make it learn ). After that they re-characterized the network’s responses to test pulses at every site.

They found that some electrode sites became more potent (”potentiated response”) after the tetanus was applied. This means that, when a test pulse was applied to this electrode site, neurons in all areas of the network responded either the same, or more strongly than they had before the tetanus.

Other sites became less potent (”depressed response”) after the tetanus was applied.

Surprisingly, it was very rare for any given electrode site to become better at stimulating some neurons and worse at stimulating others as a result of the tetanus.

What determined which electrode sites became potentiated and which ones became depressed? The tetanus potentiated electrodes which evoked spike trains that tended to contain spikes which were within 40ms of the spike trains evoked by the tetanus electrode, and depressed others. That is, it potentiated sites which evoked patterns similar to the patterns evoked by the tetanus site.

However, the spike trains evoked by both potentiated and depressed neurons became more synchronized with the tetanus electrode after applying the tetanus.

See page 5 of “Distributed processing in cultured neuronal networks” for another review of this work.

See this NeuroWiki page for more details (the strange {{}} over there are because we will soon have footnotes).

Jimbo, Y., Tateno, T., and Robinson, H. P. C.,
Simultaneous Induction of Pathway-Specific Potentiation and Depression in Networks of Cortical Neurons. Biophysical Journal, 1999. 76: p. 670-678.