Archive for the ‘Trends in Neuroscience’ Category

Micah Manary has written a good summary of the recent Heijtz et al paper linking these two. The authors compared germ-free mice, which are raised in a completely sterile environment, and specific pathogen free mice, which are free of mice pathogens but otherwise have a normal gut microbiota, on a number of measures. First, the germ-free mice run more in an open field test, which is considered a sign of increased anxiety:

SPF = specific pathogen free = has bacteria in gut but no known mice pathogens; GF = germ free micel; top = distance traveled in the box per zone and in total over the 60 mins; bottom = representative tracings of movements in each group

Note the trend in the representative tracings, as the specific pathogen free mice tend to move less as time progresses, which is a sign that they are becoming “used to” the novel environment more quickly than the all germ free mice. Next, the authors conduct a number of gene and protein expression assays to show that there are statistically significant differences in various regions of the brain. For example, the germ-free mice have significantly reduced expression of the “early responder” gene nerve growth factor-inducible clone A:

open bars = normal gut microbiota but pathogen free mice; closed bars = germ free mice; OFC = orbital frontal cortex; AO = anterior olfactory region; left = representative autoradiograms

The authors used four mice in each condition, and some of the brain regions seemed to have significant expression trends for certain factors but not others. So we should take these results with the standard amount of caution. But the interaction between bacteria and the brain, especially during development, and especially related to anxiety, is certainly a trend to watch for in the coming years.


Heijtz RD, Wang S, Anuar F, Qian Y, Björkholm B, Samuelsson A, Hibberd ML, Forssberg H, & Pettersson S (2011). Normal gut microbiota modulates brain development and behavior. Proceedings of the National Academy of Sciences of the United States of America, 108 (7), 3047-52 PMID: 21282636


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Akul et al discuss this in a recent editorial, and the main challenges they touch on are: 1) how can we link tiers of analysis such as macro- and micro-connectomes?; 2) can we uncover how distinct gene mutations lead to a similar clinical phenotype by integrating info?; and 3) most generally, what are the best tools and methods to combine our disparate data types: genetic, cellular, anatomical, electrophysiological, behavioral, evolutionary, and computational?

The Neuroscience Informatics Framework is the place to go to find links to sources of raw data. For example, there are currently 145 public microarray data sets available, most of which compare expression between different conditions or different brain regions. The search is OK, but, as the authors urge, it could become better if up-loaders recognize that the most important thing they can do is make their data machine-readable. Although not explicitly mentioned, what I noticed most in the article was a tremendous amount of excitement.


Huda Akil, et al. Challenges and Opportunities in Mining Neuroscience Data.  Science 331, 708 (2011); DOI: 10.1126/science.1199305

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Two independent studies just published (here and here, summarized here) have taken optogenetics to in vivo, free-moving C. elegans. One of the key technical advances is real-time registration of where the nematode is moving, which allows the microscopic stage’s motors to move, keeping the worm centered.

Liefer et al’s system has speed of ~50 frames per second and spatial resolution of ~30 μm, while Stirman et al’s system has a speed of ~25 frames per second and a spatial resolution of ~ 14 μm. Both papers demonstrate the precise control this offers researchers.  For example, using different light sources to stimulate different types of neurons, or targeting spatially distinct regions of the body to target different cells.

When will we have a working model of the C. elegans nervous system? Not today, not tomorrow, but sometime sooner with this advance.


Brown and Schafer, 2011 Unrestrained worms bridled by the light, doi:10.1038/nmeth0211-129.

Leifer AM et al, 2011, Optogenetic manipulation of neural activity in freely moving Caenorhabditis elegans, doi:10.1038/nmeth.1554

Stirman JN et al, 2011, Real-time multimodal optical control of neurons and muscles in freely behaving Caenorhabditis elegans, doi:10.1038/nmeth.1555

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Are synaptomes impossible?

Javier DeFelipe writes eloquently about the progress from our basic understanding of two neurons to efforts to map all neural connections in a recent commentary. Check it out. At one point, he writes that “circuit diagrams of the nervous system can be considered at different levels, although they are surely impossible to complete at the synaptic level.” However, he doesn’t offer a time scale for this prediction. I agree with him that it would be impossible given our current technology, but at some point in the future, who can say? One thing we know is this: past bets against advancements in science have not fared well.

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One of the assumptions of biology is that structure should predict function. Classifying neural cells is no exception: we classify cells that look alike (Purkinje cells, spiny neurons, etc.) on the assumption that they will function alike as well. Otherwise the classification would serve no purpose.

But reversing the causality, and attempting to classify neurons on the basis of just their structure (as opposed to, e.g., simpler staining methods), has proven to be a difficult endeavor. There is little consensus to the taxonomy. One problem is that there is no established set of geometrical measurements which should be used.

Zawadzki et al mined data from NeuroMorph, an online database with quantitative structural data from 5000+ neurons. First, here is a diagram of the structural data that NeuroMorph provides for each neuron:

arXiv:1003.3036v1; height, width, and depth determined post alignment via PCA; bifurcation = when a branch splits into two (new) branches

When authors upload their neurons to NeuroMorph’s database, they often give the cell class that they have assigned to each neuron. The most common of these neuron classes are: 1) pyramidal cells from the hippocampus (Pyr-Hip), 2) medium spiny cells from the basal forebrain (Spi-Bas), 3) ganglion cells from retina (Gan-Ret), 4) uniglomerular projection neurons from olfactory bulb (Uni-Olf). Zawadzki et al then compared these classifications with the results of a naive statistical algorithm that categorizes cells solely based on the similarity and differences of their structures. The “temperature” result that their algorithm spits out seems to be basically the result of a principal component analysis.

As you can see, on the basis of this temperature, there is plenty of overlap between the classes:

arXiv:1003.3036v1; think of it as temperature = relative place in parameter space, and susceptibility = relative likelihood of neuron class being in that portion of parameter space

The pyramidal neurons are scattered across the parameter space, suggesting that their morphological features overlap with the other categories. In contrast, the medium spiny neurons have the least overlap and thus have the most morphological homogeneity, indicating that it medium spiny neurons are the easiest class to segregate based on morphology. Overall, it seems fairly difficult to disambiguate neural classes based solely on the structure given some of the most popular current classifications.

One solution to this problem is presented by Bota and Swanson, who suggest an ontological approach to classifying neurons. Here is their hierarchical classification schema:

doi = 10.1016/j.brainresrev.2007.05.005

They also argue that lower level neuron classifications should be regarded as a hypothesis that certain cell taxa fulfill distinct functional roles. This hypothesis can be tested and potentially discarded. Given the infancy of the field of neuron classification, such low barriers to change appear expedient.


Zawadzki K, et al. 2010 Title: Investigating the Morphological Categories in the NeuroMorpho Database by Using Superparamagnetic Clustering. arXiv:1003.3036v1

Bota M, Swanson L. 2007 The neuron classification problem doi: 10.1016/j.brainresrev.2007.05.005. Available in PMC here.

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An old study by Cohen et al looked at the structure and protein composition of postsynaptic densities (PSDs) in neurons of the cerebral cortex. They isolated the PSDs by breaking apart the cells (homogenizing them) and then centrifuging to separate other organelles from the nerve terminals. Here is a electron micrograph of part of the synaptosome they isolated, with arrows shown on the postsynaptic densities:

doi: 10.1083/jcb.74.1.181

The authors estimate that ~ 2% of the proteins in the postsynaptic density fraction are due to membrane contamination. They speculate that the majority of this contamination occurred during homogenization. So, the native morphological appearance of PSDs is largely maintained. This bodes well for other extraction and preservation techniques.


RS Cohen,  F Blomberg, K Berzins, and P Siekevitz. The structure of postsynaptic densities isolated from dog cerebral cortex: I. overall morphology and protein composition. J Cell Biol 1977 74:181-203. Published July 1, 1977, doi:10.1083/jcb.74.1.181

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According to Kubota et al (here), three of the main problems are:

1) Thickness: Accurately determining the thickness of serial sections. Messing this up will introduce error into 3d reconstructions. To get around this people use the minimal folds method. This looks for protrusions in the plane where small folds of tissue self-adhere. Section thickness is assumed to be one-half the width of these protrusions. However, in reality this method can give very variable results. Kubota et al suggest an “optical method” that uses a 3D laser confocal microscope instead, which gives much more reliable results, at least under resin-based conditions:

2) Synapse identification: Often synaptic junctions parallel to the section plane (or at a low angle to it) are not counted as synapses. This occurs ~ 25% of the time! So the authors use a sequence of 3d section analysis that accurately predicts the presence of a synapse, which you can see in the picture below: “(1) many synapse vesicles (A / B below), (2) presynaptic grids (C), (3) synaptic cleft structures, (4) postsynaptic densities (D), and (5) cytoplasm of postsynaptic dendrite or spine (E)”:

This method can be used to ID a lot of the synapses that older methods would have missed.

3) Shrinkage: They note that in their previous studies, after fixation, dehydration, and embedding, their GABAergic nonpyramidal cells often shrunk to up to 90% of their original size. They offer no solutions for this problem.

Elsewhere, Cordona et al note that “reconstruction of microcircuits requires serial section electron microscopy, due to the small size of terminal neuronal processes and their synaptic contacts. Because of the amount of labor that traditionally comes with this approach, very little is known about microcircuitry in brains across the animal kingdom. Many of the problems of serial electron microscopy reconstruction are now solvable with digital image recording and specialized software for both image acquisition and postprocessing.”


Kubota Y, Hatada S and Kawaguchi Y (2009) Important factors for the three-dimensional reconstruction of neuronal structures from serial ultrathin sections. Front. Neural Circuits 3:4. doi:10.3389/neuro.04.004.2009

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