Way too much about bitter taste perception

Last time I repined that there aren't enough "organic" reviews out there, so today I'll give it a go myself.

Theories of bitterness


When you eat food, you are able to identify it via its smell, texture in your moth, and how it activates taste cells on your tongue. The classic "taste modalities" are sweet, sour, salty, bitter, and umami.  For sweet, sour, salty, and umami, there is a single taste receptor; the only information you get from those senses is degree of activation.   In contrast, there are over twenty five bitter receptors, called T2Rs.


So is bitter taste similar to the other modalities, a single labeled line, or is it more complex? Some people (the Zuker lab chief among them) propose that, like sweet and sour, we can only detect the extent to which something is bitter. That is, there is a single "labeled  line" for all bitter tastes. This would reduce our sense of taste to five labeled lines.  The ability to discriminate between similar tastes - e.g. between two citrus fruits - would be due to extra information from olfaction.


There is the alternative possibility that bitter is not a single labeled line, but more than one line. Individual bitter taste cells could express a subset of T2Rs, have individualized receptive fields, and discriminate between different bitter compounds. Today I'm going to review the evidence for both theories at the level of the tongue, brain, and behaviour.


Bitter on the tongue


The bitter receptors were discovered circa 2000, and reported in a series of  papers.  In one of those papers (Adler, et al 2000), the authors performed in situ hybridization against multiple T2Rs on the tongue. The found similar numbers of cells were labeled whether they used probes for 1, 2, 5, or 10 T2Rs (see below, left). Notably, they state that labeling 2+ receptors labeled 20% of taste cells, while labeling with only 1 receptor labeled 15% of taste cells. Here, the difference could be due to simple labeling inefficiency. They alternately verified this by performing double-labeled fluorescent in situs, and found "most" cells coexpressed multiple receptors. From this, they concluded that individual bitter taste cells in the tongue express most T2Rs, and are sensors for bitter, generally.

Individual taste cells express multiple bitter taste receptors. c. In situ label using 10 probes for T2Rs. The number of cells labeled here is similar to single label probes. d. Fluorescence in situ double-labeling for T2R3 (green) and T2R7 (red). Most cells express both T2Rs.
From Adler et al 2000.

The next year, the Roper lab reported potentially contradictory results. Caicedo and Roper performed confocal calcium imaging on isolated tongues from rats while applying five bitter tastants. Of the taste cells they imaged, 18% (69/374) responded to one of the bitters, but most cells responded to only one or two of the tastants (see below).

Individual taste cells respond to only a subset of bitter tastants. A. Three example taste cells each respond to different tastants (denatonium, quinine, cycloheximide, phenylthiocarbimide, and sucrose octaacetate). B. Response for all responsive cells to bitters. Cell ID on left, tastants on top. From top to bottom, cells respond to more tastants.
From Caicedo and Roper, 2001.

It is hard to reconcile these two results. I am not an expert on in situs, but it is possible that the labeling specificity is not 100% specific (as is often the case for antibody staining). Yet, I think you have to trust that the researchers were competent. I would only emphasize that "most" receptors is not "all" receptors, and so these results are not completely mutually exclusive.


In 2005, Zuker fired back. Bitter signaling uses a G-protein coupled cascade that signals through PLCβ2; PLCβ2 KO mice lose all bitter taste. Mueller et al took PLCβ2 -/- mice, and then expressed PLCβ2 under a T2R promoter, like mT2R5 (m for mouse). When they did that they were able to fully rescue bitter taste.

Expression of PLCβ2 under the expression of a single bitter receptor rescues all bitter taste. The "relative response" measures the amount of licking mice did of bitter compounds (inverse). Control mice do not lick bitters. PLC -/- mice cannot taste bitter, and so lick bitters. PLC expressed behind the promoters for mT2R5, mT2R32, and mT2R19 is able to rescue bitter perception.
From Mueller et al 2005.

However, again, I feel this is not quite conclusive. If bitter taste receptor expression overlaps randomly, while an individual taste cell may not express all T2Rs, the whole population of mT2R5 taste cells could express all the other T2Rs. And hence allow full recovery of bitter sensitivity.

Bitter in the brain

There's a lot more interesting stuff about bitter taste on the tongue - for example, the Meyerhof lab has identified the ligands for many human taste receptors - but let's move to the brainstem (and beyond!).

Few labs have recorded from the taste areas of the brainstem. Chief among them are David V Smith and the Travers of Ohio State. In 2006, Geran and Travers recorded from NST of rats while applying the classic tastants + a set of bitters. And they found that some cells in NST responded differentially to cycloheximide and denatonium. And if the brain can discriminate between different bitters, surely the tongue must as well...

Individual NST neurons can discriminate denatonium (DEN) from cycloheximide (CHX). Pardon the figure, it's excised from a MUCH larger one. y-axis is response rate, x-axis is neuron ID. On the right are the bitter sensitive neurons (B-best). The first three neurons respond to denatonium, while the rest do not. There also may be quinine neurons.
From Geran and Travers, 2006.

As far as I know, no one has presented animals with multiple bitters while electrically recording from gustatory cortex. However, two weeks ago I covered a recent paper on Ca²⁺ imaging in gustatory cortex. While the main focus of the paper was taste hotspots in gustatory cortex, they also presented the following figure in the supplementary data. They applied multiple bitters while imaging cortex, and found that not all cells responded to all the bitters presented. In general, there was some unreliability in there results, - many "responsive" cells only responded in a subset of trials - but this does raise the possibility that cortical cells can discriminate between different bitters.

Gustatory cortical neurons may be able to discriminate between different bitters. left. Map of responsive cells to three bitter: denatonium, cycloheximide, and quinine. middle. Overlaid map of cells to left, color coded for cells that respond to all 3 bitters (red), 2 bitters (yellow), and 1 bitter (white).  right. Bar chart of # of cells that respond to bitters.
From Chen et al, 2011.

Bitter in the "mind"

So what about, you know, bitter perception itself? This has been rather ill studied. Many people have shown that typical "bitter" stimulants are aversive. Only a couple have tested whether mice can discriminate between them.
The best paper I've found tested whether mice could discriminate between quinine and denatonium (I wonder if scientists choose these chemicals so often because they're much easier to pronounce and remember than sucrose octaacetate). To ensure that the mice were not discriminating between different intensities of bitterness, they measured the aversiveness of each chemical, and used iso-yucky concentrations.

Thirsty mice were allowed to lick a water bottle for five seconds. The lick rate over the last 3 seconds of the trial determined the "stimulus licks" and were normalized to water licks. Dashed rectangles denote equivalently aversive concentrations.
From Spector and Kopka, 2002.

Once they had determined the equivalent concentrations to use, they employed a two-alternative forced choice task to measure discrimination. They first validated their system by testing whether animals could discriminate between quinine and KCl (below, left). Then they switched quinine for denatonium to see if the mice noticed, and found that the mice continued to discriminate between denatonium and KCl, as if quinine and denatonium were the same. As a positive control, they swapped NaCl for denatonium, and found that the mice needed a few testing sessions to relearn the new task (middle). Finally, they tested whether mice could discriminate quinine and denatonium, and found that discrimination was at chance level (right).

Mice are unable to discriminate between quinine and denatonium. See above for details.
From Spector and Kopka, 2002.

Similar experiments were performed in flies, using the proboscis extension reflex as a measure of palatability (Masek and Scott, 2010). I'm sure someone has tested this in humans, but I have not read the study yet. My main issue with these experiments is that they are using such high, aversive concentrations of the bitter stimuli that they may be beyond a discriminatory range. For example, bitter taste may have two functions: discrimination, and aversion. At low concentrations, certain chemicals may be useful for discrimination, and non-toxic; at high concentrations, however, they signal toxicitiy. It would be interesting to see how discrimination worked at lower concentrations.


After all this data, what can you conclude? The evidence for a single bitter labeled line come from the taste input and output, the receptors and the behaviour. In the brain, however, it seems that individual cells can discriminate between bitter tastants. It's certainly possible that taste neurons can discriminate between bitters before discarding the information as useless.  I think the main issue here is the old scientific problem of, "just because you can't detect it doesn't mean it's not there." I know reading these papers has suggested a few experiments to my mind.


References

Adler, E., Hoon, M a, Mueller, K. L., Chandrashekar, J., Ryba, N. J. P., & Zuker, C. S. (2000). A novel family of mammalian taste receptors. Cell, 100(6), 693-702.

Caicedo, A., & Roper, S. D. (2001). Taste Receptor Cells That Discriminate Between Bitter Stimuli. Science, 291(5508), 1557-1560. doi:10.1126/science.1056670

Chen, X., Gabitto, M., Peng, Y., Ryba, N. J. P., & Zuker, C. S. (2011). A Gustotopic Map - Supplement. Science, 333(6047), 1262-1266. doi:10.1126/science.1204076

Geran, L. C., & Travers, S. P. (2006). Single neurons in the nucleus of the solitary tract respond selectively to bitter taste stimuli. Journal of neurophysiology, 96(5), 2513. Am Physiological Soc. doi:10.1152/jn.00607.2006.

Mueller, K. L., Hoon, Mark a, Erlenbach, I., Chandrashekar, J., Zuker, C. S., & Ryba, N. J. P. (2005). The receptors and coding logic for bitter taste. Nature, 434(March), 225-230. doi:10.1038/nature03366.1.

Spector, A. C., & Kopka, S. L. (2002). Rats fail to discriminate quinine from denatonium: implications for the neural coding of bitter-tasting compounds. The Journal of neuroscience : the official journal of the Society for Neuroscience, 22(5), 1937-41.

Organic reviews

Long ago, on the nascent form of this blog, I wrote a little diatribe on the shortcomings of the peer-reviewed journal system. My basic gripe is that the system slows the dissemination of information for marginal benefit. For example, people claim peer review makes science more reliable, but it has been found that, "at least 50% of published studies from academic laboratories cannot be repeated in an industrial setting." And that's for the most reproducible natural science, chemistry.

Review papers are frustrating for different reasons. Certainly, most review papers are not delayed by peer reviewers. Instead, reviews are hobbled by their very format: review articles come out every few months, and cover a field of research as a whole. Their infrequency means they become outdated as soon as another important paper comes out. And their scope means they are forced to rehash the same basic information (there's only so many ways to say AMPA receptors are important for LTP). I often find reading reviews tedious, trying to segregate what's new from what I already know.

I wish we had a form of organic review. A format wherein you could write a complete overview of a field, and then update it piecemeal as new findings emerge; wherein you didn't have to rewrite the entire review; wherein you could stay current to within a month, or even a week.  In effect, I wish we had review wikis.

There are a few non-traditional review sources out there, but they are all lacking in different ways. Most obviously there's Wikipedia. Like a lazy undergrad, I often turn to Wikipedia first when I read an unfamiliar term (what's a diencephalon, again?). The articles on popular things, like AMPA receptors, are fairly thorough, while the articles on more obscure things like T1R3 contain enough information for me to look elsewhere. Yet Wikipedia is true to its nature as an encyclopedia, and is rarely up-to-date, or technical enough to be useful to scientists.

Some people have tried to improve Wikipedia. A couple years ago the Society for Neuroscience tried to ameliorate the situation by launching the "Neuroscience Wikipedia initiative." Unfortunately, it appears to have netted less than 100 edits.

I myself have dabbled in editing Wikipedia. When I train students, I try to get them to read papers, and synthesize them into a whole. Instead of getting them to write a staid essay, I have them edit the Wikipedia page on whatever they're studying. For example, I worked with one student to develop a PI3K FRET sensor, so he added to the section on PI3K in long term memory (his username is Wc18, mine is Amphipathic).

Besides Wikipedia, there are a few other web resources that almost act like organic review. There is wikigenes, which has useful lists of citations, but lacks any bird's-eye perspective on research. Some labs have wikis, but they are often quite focused (the Hayashi lab's is quite good). And some adventurous souls have set up regular ol' web pages dedicated to their field of interest, but static webpages by their nature cannot organically evolve. In general, I'd say these alternative forms of review fail because they are too superficial, lack Weltanschauung, or are too focused.

The review wiki is such an obvious idea that it must come to fruition. The biggest obstacles are probably authorship (people want credit), reliability (no one trusts a random web page), and quality control. The easiest solution to these problems would be for a known organization to sponsor a wiki. For example, I bet a neuroscience department could gain reputation by starting an awesome, up-to-date wiki. Over time, as the wiki grew, it could serve as an alternative sort of textbook (in fact, if you search for science wikis, you'll see many hits from teachers looking for textbook alternatives). They could brand the wiki with the department or university. Then when undergrads inevitably discover the wiki, they'll assume the authors are important. The first mover advantage here would be huge.

(Why don't I take action and start a wiki? I don't have the stature to get people to use it, nor get buy-in from others to expand it.)

Until then, I will continue to read traditional reviews, and supplement them as best I can. The precious few neuroscience bloggers out there do a decent job reviewing recent papers, and in doing so comment on the state of the field. I hope some of my blog posts can do the same.

Walk Along the Paper Trail: Taste Hotsprings

I haven't done many walkalongs about new papers, so let's review a new paper from Charles Zuker's lab.


Trail Prep


First, two pieces of background. There are two diametrically opposed theories of taste coding. The "labeled line" theory states that each taste quality (sweet, salty, bitter, etc.) is encoded by a single cell type, and individual cells respond to single taste qualities. In contrast, the combinatorial, or "across fibre," theory states that taste is encoded in the population response of neurons, and individual neurons can respond to multiple tastants.


In general, taste coding shifts from a labeled line representation to a combinatorial representation as information flows from the tongue to the CNS. The taste receptors for most taste qualities have been identified, and recordings of the nerve fibres that project from the tongue show each fibre encodes one taste quality. In contrast, recordings from the brainstem, thalamus, and gustatory cortex have found that individual neurons can respond to multiple tastants. The number of taste responsive cells can change depending on your criteria: only ~10% of gustatory cortex (GC) neurons respond tonically (longer than 1s) to taste application, while 30-40% respond if you consider phasic responses, inhibition, or ensemble coding.


The second piece of background you need to know about are the idea of cortical maps. In visual, auditory, and somatosensory cortex, there is a clear organization of physical space in the cortex. For example, in visual cortex, a picture of the world is mapped onto a corresponding 2D map of visual cortex, called retinotopy. Similarly, the body is mapped from head to toe in the somatosensory cortex (with the tongue area abutting taste cortex). In contrast to those well-organized cortices, odors do not seem to be chemotopically organized in olfactory cortex.


The Carleton lab has attempted to image taste maps in gustatory cortex using intrinsic imaging. They found that individual taste qualities were represented in segregated areas, but that there was significant overlap between areas. Furthermore, the pleasant and unpleasant tastes seemed to somewhat separated.

Taste qualities are vaguely mapped in the gustatory cortex of rat, with pleasant qualities anterior, and unpleasant qualities anterior. From Carleton et al, 2010.

On the Trail


With that background, we can look at the Chen paper. They performed calcium imaging in taste (insular) cortex, which allows one to image the activity of dozens of cells at the same time. Taste cortex is on the lateral side of the brain, so to access this area you have to remove the cheek-muscle, and turn the animal on its side. The authors noted in the supplemental methods that only one-in-four animals were usable after these manipulations.


(Extra methods for those interested: They opened an area about 1mm², and were able to image areas of 350um x 350um using a 40x objective. To better identify taste cortex, the injected a virus containing GFP into the taste thalamus, which they could see while imaging. To image calcium, they bath applied Oregon-Green BAPTA. Images were acquired at 2Hz. Animals were anesthetized with urethane and isoflurane.)


While they were imaging, they applied tastants for sweet, salty, bitter, etc. and found that most of the purported taste cortex was unresponsive. However, they identified a portion of posterior insular cortex that responded well to bitter tastants, which they dubbed a bitter hotpot.

There is a bitter hotspot in posterior insular cortex. A/B. Individual neurons in the bitter hotspot respond to bitter tastants (red/white dots). E. Approximately 30% of neurons in the bitter hotspot respond to bitters, while few respond to other tastants. G. Calcium response of individual cells show they are selectively tuned to bitter tastes.
From Chen et al, 2011.

In the bitter hotspot, approximately 30% of the neurons responded to bitter tastant, while less than 10% responded to other tastants like sweet or sour (panel E/G). The hotspot was approximately the size of their imaging area. They performed a neat experiment to verify that this was a bitter hotspot. There are ~36 bitter receptors in mice, each of which responds to different bitter compounds. One receptor, T2R5 is the only receptor for cycloheximide. So they imaged T2R5 knockout mice, and found that the bitter hotspot no longer responded to cycloheximide, but still responded to other bitters like quinine.


From here, we diverge into the supplemental figures. First, I want to mention the reliability of the responses. They performed 4-7 trials for each tastant, and considered a cell responsive if it responded in two of  four trials. In a supplemental figure, they applied a bitter tastant three times, and noted that some cells responded during all three trials, while others responded only once or twice (panel a, below). I realize that any response is going to be noisy, but it is somewhat troubling that there is a 30-60% "failure" rate of bitter responses in the bitter hotspot.

Responses of neurons in the bitter hotspot during multiple trials, and to multiple bitters. a. In the bitter hotspot, many cells respond to only 1 or 2 trials (the % of cells was not given). b. Many neurons respond to three bitters tastants, while others respond only to a subset.
From Chen et al, 2011.

Given the diversity of bitter receptors, they also looked at how different bitter compounds are represented in the bitter hotspot. They applied three bitters, and found that many cells responded to all three, while other cells responded to only one or two of the bitters (panel b, above). This may have implications for how you interpret whether these cells indicate a labeled line model. If the labeled line is "bitter," then each cell should respond to all bitter compounds. However, there could be individual labeled lines for bitter subcomponents. Yet, here there are cells that respond to multiple bitters, which would be a combination of labeled lines.


In another supplemental figure, they used a tungsten electrode to record from neurons inside and outside the hotspot. 13 of the 31 neurons recorded in the hotspot responded to bitters, and nothing else, while only 1 of 39 neurons outside the hotspot responded to anything.

Electrical recordings of neurons near the bitter hotspot. a. Map of recording sites.  Unresponsive neurons outside the hotspot shown in black, bitter neurons in red, and unresponsive neurons inside the hotspot in white. c. (left) PSTH of neurons inside the bitter hotspot, and (right) firing rate change of these neurons. b. Average firing rate changes for responsive neurons inside the hotspot and all neurons outside the hotspot.
From Chen et al. 2011.

I have to say, this is an odd figure. The characterization of the firing rate change in terms of "delta spikes / 5s," seems weird; if you divide by the 5s, you get a rate change of 2 Hz, which is neither impressive nor terrible. The responses shown in panel c above are not that convincing, especially considering they're probably the best responses they have. Furthermore, they didn't note the depth they're recording from. Remember this when considering what a "responsive" neuron is.


After focusing on the bitter hotspot, they also found hotspots for three other taste modalities: sweet, salty, and umami. In each of the areas, ~30% of the neurons responded to the relevant stimuli. In the left panel, below, you can see that these generally pleasant areas are all rostral (anterior) to the bitter hotspot. They could not find a sour hotspot.

Non-bitter hotspots. E. Sweet, salty, and umami hotspots are all anterior to bitter. F. Cells in the salty hotspot respond to NaCl, but not other salts (KCl, or MgCl). This response is blocked by the sodium channel blocker amiloride.
From Chen et al, 2011.

Reminiscences on a long walk


Phew, that was a long one. It's sad that this was a Science paper, where so many interesting figures were shoved into the Supplement where no one will see them.


Earlier, I mentioned that the taste representation generally shifts from a labeled line code to a combinatorial code. In the discussion, the authors state:

Notably, existing models of taste coding in the insula included proposals of broadly tuned neurons across taste qualities ... with no region dedicated to the processing of only one taste quality. Although we cannot rule out the existence of sparse numbers of broadly tuned cells distributed throughout the taste cortex (i.e., nonclustered), our results reveal that the individual basic tastes are represented in the insula by finely tuned cells organized in a precise and spatially ordered gustotopic map, where each taste quality is encoded in its own (segregated) stereotypical cortical field.

They have convincingly shown that there are hotspots in taste cortex, with pleasant tastes represented rostrally. This is in agreement with the previously shown intrinsic imaging, as well as with genetic tracing studies.

This is a pretty cool study, so I had to include it. They made mice expressing wheat germ agglutinin (WGA) in T1R3 and T2R5 taste bud cells. WGA can cross synapses, so it's able to go to all the cells that are downstream of the taste receptor. They found that the sweet (triangle) and bitter (circles) cells were generally segregated in the brainstem and thalamus, but started to overlap in the cortex. Still, the sweet was generally anterior to the bitter.
From Sugita and Shiba, 2005.

However, I must disagree that they have shown GC neurons are "finely tuned." Repeated recordings from multiple labs throughout the brainstem, thalamus, and cortex have shown that taste neurons respond to multiple taste qualities. However, these responses are complex, including short responses, inhibition, and coordinated firing. If you restrict your definition of "taste responsive cells" to those that have a tonic firing rate increase, only 10% of GC neurons are responsive, and these responses are generally to few taste qualities (left panel, below). If you expand your definition to include phasic firing, inhibition, and other types of coding, neuronal tuning becomes more broad (right panel, below).

"Taste responsiveness" depends on the time window. Left: 13 neurons response to tastants, averaged over 2.5s. Here you can see the neurons respond to few tastants. Right: Two neurons' responses to tastants in 500ms bins. Here, the neurons respond to different tastants at different times.
From Katz et al, 2001. For more, see here.

Calcium imaging is a great tool for asking very specific questions, like looking at spatial maps, or sampling many cells simultaneously. It is poor at characterizing fast temporal responses. They are imaging at 2Hz, and quantifying responses over ten seconds, which will miss those dynamics. Furthermore, Ca2+ imaging acts as a filter, only reporting the strongest activity. Of course they found few responsive neurons responsive, and those responses were sparse. Those were the only neurons they can find.


Later in the discussion, they write:

What does the rest of the insula do? the inter–hotspot regions might be involved in other aspects of taste coding , such as the representation of taste mixes, and thus may help to code the perception of “flavor ” [e.g., responding to several tastes simultaneously]. In addition, the insular cortex responds to more than just taste, and it is often thought of as a site for multisensory integration. Thus, these areas may participate in the integration of taste with the other senses.

Here, I can agree.


If I may reinterpret their results: GC contains neurons that respond to a startling diversity of stimuli: somatosensory, olfactory, hedonic, motor, and taste. Previous recordings have shown that GC responses are quite phasic, and decay within one lick. However, a select subset of GC neurons have long-lasting taste responses, which have been sampled here. These specifically gustatory neurons seems to cluster together, with "unpleasant neurons" in the back, and pleasant to the front.


They also speculate that, in contrast to e.g. vision, taste maps are more developmental than functional, which would be in accord with Sugita's findings.


My paper-muscles are exhausted. I am glad to see taste get a high profile paper. Hopefully it makes it easier for the rest of us.

Reference


Chen X, Gabitto M, Peng Y, Ryba NJ, & Zuker CS (2011). A gustotopic map of taste qualities in the mammalian brain. Science (New York, N.Y.), 333 (6047), 1262-6 PMID: 21885776

Annals of Underwhelming Papers: The Pink Panthers*

As I explore the background literature on taste perception, I try to read deeply rather than broadly. Most recently, I read a batch of papers about taste coding in the brainstem. Taste information goes from taste bud cells on the tongue to the nucleus of the solitary tract (NST) in the brainstem, then to the parabrachial nucleus (PBN), and then the thalamus and onward (in primates, taste info does not travel via the PBN). A few labs have recorded from these areas in primates and rats, most notably David V Smith. Today I'm going to briefly describe one of his papers, and why it left me unsatisfied.

Stim and Response

A couple posts ago, I described how the arcuate nucleus of the hypothalamus regulates feeding. The arcuate nucleus does not directly project to any taste areas, but does project to the lateral hypothalamus, which then projects to NST and PBN. In today's paper, Cho et al recorded from NST while stimulating the lateral hypothalamus.

They recorded 99 taste responsive neurons in NST via glass micropipette (they do not say how many total neurons they recorded form). To stimulate LH, they simply stuck an electrode in there, and stimulated with square pulses at 0.33 Hz. Approximately half  (49/99) of the taste-sensitive NST neurons responded orthodromically to LH stimulation. The taste receptive fields and LH sensitivity were distributed across all taste modalities.

Taste responses of LH-responsive and non-responsive neurons. All taste modalities are represented in both groups.
From Cho et al, 2002.

Of the hypothalamic stimulation responses, a majority were excitatory (43/49), while the rest were inhibitory. Only two neurons responded antidromically to stimulation. To rule out fibres of passage, they applied a glutamate agonist in the LH, and saw it could still affect the NST. Finally, they stimulated the LH while applying tastes, and found that the combination caused more firing than a tastant alone. This is not surprising given a majority of LH input was excitatory.

Le Pink Panther

This is still a state-of-the-art experiment for recordings from NST, so why is it disappointing? In the age of molecular mouse models, electrical stimulation is antiquated. There are multiple cell types in the LH, and stimulating all of them is just too dirty. This seven year old paper is already outmoded.

It reminds me of old movie comedies, like The Pink Panther.  My dad loves those movies, and as a kid I liked them too. But if you watch them now, they are predictable, obvious.

Most papers inevitably turn pink, as we stand on giants' shoulders. New methods outdate the old, and people are just more thorough now. More famous examples of Pink Panthers would be the early papers in LTP by all the big bears like Malenka, Malinow, Kauer, etc., where they applied a few antagonists and called it a Science paper.

Other old papers still hold up in a timeless way, like Fat and Katz, or Hubel and Wiesel. While they're simple, and have been surpassed technically, the core result is still clean. We still describe visual cortical neurons in terms of orientation selectivity. These are the Some Like it Hots and Airplane!s of neuroscience.

The Pink Panthers deserve a large, hospitable wing of the Annals of Underwhelming papers, where they can live out their senescence. I am going to read all of David V Smith's papers, and take from them what I can. And hopefully outdo them.

* As always, my disappointment in a paper should not reflect on the scientists who performed the experiments. Nor do these opinions reflect those of the lab.

Compendium of Analyses, Part II, Ensembles

A couple weeks ago, I listed a variety of standard analyses which can be used to characterize single neurons. Today, I'm going to cover analyses that describe how populations of neurons encode information. These analyses are more complex than the single-cell analyses, and I do not know the details of how they are all implemented. Unlike the single-cell analyses, which are all from a similar Weltanschauung, each population analysis requires a slightly different perspective.


Cross-correlation


The simplest population analysis requires looking at the smallest population: two neurons. One way to do this is via cross-correlation, which answers the question, "how often do two neurons fire near each other in time?"

To do this, you start with the spike trains of two neurons.  For each spike neuron A fires, you identify the spikes that neuron B fires around the same time, and note the time difference. As you repeat this, you will build a histogram of these time differences, centered on t=0 lag. If two neurons' spikes are uncorrelated, the histogram will be flat, as the neurons fire at random times; if the neurons' firing is correlated, you will see peaks in the histogram.

Cross-correlation between two gustatory cortex neurons. The two neurons' firing is normally uncorrelated (thin traces), but when two tastants are applied, they become correlated (purple and blue lines).
From Katz et al, 2002

Given the simplicity of this analysis, you would think it's trivial to implement, but it's not.  I was playing around with some olfactory data, and looked into using MATLAB's xcorr() function, given there's a neuroscience blog named after it. And xcorr works great, for analog data. However, action potentials are digital. Someone else in the lab had implemented an autocorrelation using pdist(), but that doesn't work for pairs of neurons. So I had to root around the internet for a quick and dirty implementation of cross-correlation for spikes. You would think this would be standard by now.

LFP


When you think about action potentials, it's natural to take a cell-centric view, and think about how ions flow in and out of cells. From an expanded perspective, though, large groups of neurons can significantly effect the electrical milieu around them.  This is called the local field potential (LFP), which you can measure during extracellular recording. The LFP typically oscillates; in the olfactory bulb, the most prominent oscillations are at the gamma frequency.

While the LFP does not reflect population coding in the traditional sense, it does reflect population firing, and the modulatory state.  Modulatory centers can change the LFP's amplitude or frequency, both changing how individual neurons fire, and the environment all neurons fire in.

(Update from July 2012: Looking back on this, it's  embarrassing that I didn't mention the actual analyses you can use to look at LFPs. In any case, for the curious, the basic ones are: power spectrum analyses of epochs (using FFTs), spectrograms (via wavelet decomposition or FFTs), coherence/correlation, and spike-triggered LFPs (and vice-versa)).

Population response vectors and PCA


For single-cell analyses, you typically describe neurons in terms of what stimuli they respond to. On the population level, you need to reverse this, and ask, how is a stimuli encoded by the population?

The easy way to do this is to create a population response vector for different stimuli. To do this, you calculate the firing rate of each neuron you recorded from following a stimulus. Then you take all the firing rates, and put them into a vector, which gives you the population spike response.  Then you repeat this for different stimuli, or time points.  To find out how similar or different the representation of two stimuli are, you just subtract the population vector for one stimuli from the other to get the population spike distance.

Schematic of population spike response. Each cell responds to a stimuli (top row).  You convert these responses into a single number, the firing rate, then put these responses into a vector, where each row is a cell. You can then look at how the population representation changes over time, or with different stimuli, by subtracting one population vector from another.
From Bathellier et. al., 2008

Population spike vectors can get unwieldy for large number of neurons, so people often reduce the dimensionality via principal component analysis (PCA). I have covered PCA before, but the basic idea is that the responses of different neurons in the population vector are correlated, and you can create artificial variables called "principle components" that include this correlation.  If you are lucky, the first few components will explain a majority of the response.

Once you have the population vectors (or principle components) for a a set of responses, you can really have fun. For example, you can see whether the population spike distance between odors is correlated with their perceived similarity. Or you can see how the principal components of an odor response change over time, forming dynamic cycles (below). Principle components are a great way to make data easier to visualize and manipulate.

This shows the PCA response over time to different odor mixtures in the zebrafish. Trajectories start at the arrowheads. The trajectories for the different stimuli generally follow two trajectories.
From Niessing and Friedrich, 2010

Hidden Markov Models

Another way to think about the population response is that neuronal firing represents a "brain state." For taste, this state could be, "I am tasting something sweet." This idea of neuronal firing reflecting internal states can be represented by a Hidden Markov Model (HMM). You assume that the animal has an internal state that you do not know (it is "hidden"), and try to infer the state by the firing patterns of neurons. The math of how this is done is complex, but it involves making assumptions about what states are hidden, and then testing different states to see if they fit the data better.

The beauty of HMMs is that rather than worrying about receptive fields, and firing rates, you simply try to measure the "state" of a set of neurons. This further frees you from trying to guess when states start and end, and lets you find state transitions as they naturally occur. The downside is that it is more difficult to interpret what these states mean in human terms.

A. Spike trains from 10 neurons in GC in response to sucrose. Different states identified by HMM are numbered 1-4. B. Four more example responses. Note that the state change occurs at different times, something that would be missed by typical PSTH analyses. C. Firing rates of the neurons in each state.
From Jones et al, 2007.

Hidden Markov Models have been uncommonly used in neuroscience. I tried implementing them in MATLAB for some of our data. MATLAB has an HMM function (hmmdecode), but like xcorr(), it is optimized for single observations of analog data. All of our olfactory bulb recordings are multiple observations of digital data. At some point I'm going to have to write some code myself, or ask the Katz lab for it, to see if it yields anything interesting.*

Conclusion


That's all for today. To be over-reductionist, I would say most analyses are some variation on receptive fields (single neurons) and population response vectors. There are obviously many more analyses out there, like stimulus (odor) prediction, or spectral power, which I will save for when I better understand them. Hopefully this is useful as an inventory of all the simple, standard things you can ask of data.

* It amazes me how much time people spend re-implementing simple techniques. For example, implementing a HMM for multiple observations is a moderately tricky thing, but should be general enough to be useful for anyone analyzing multiple spike trains. Yet, there's no code on the internet.


There are some reasons for this. No one wants to put their code out and find there's a typo. And each lab's experiments are different enough to not make them completely generalizable. Shoot, I don't have any code out there myself. But reusable code for simple things like HMM, or cross-correlation would save man-years of time. Which is why I really hope the NIH develops better software for neuroscientists.