On this front, in Nov ’11 Chung et al published a paper describing their research into visualizing mouse brain-wide data generated by a knife-edge scanning microscope. The 29 s, soundless video below shows one of their data sets sweeping through each of the imaging planes (sagittal, coronal, and horizontal).

You can view the data set in a web browser here. It is still in “beta” mode and on my browser it is pretty slow, but worth the wait.

At high zoom, the data is fairly precise. In my screenshot below, you can make out individual somata.

Specimen: C57BL/6J Mouse; Stain: Golgi; Dimension: 600 x 375 x 10 (pixels); Current Layers: 2951 - 2960

The Golgi is called a “sparse” stain because it marks only a subset of the neurons, typically ~1%. On the plus side, it is considered to stain neurons randomly, so any conclusions drawn from the connectivity differences between brain regions in this data set should not be systematically biased.

Of course, we’d first have to convert the image stacks to structure calls, which is far from a settled problem.

Reference

Chung JR, Sung C, Mayerich D, Kwon J, Miller DE, Huffman T, Keyser J, Abbott LC and Choe Y (2011) Multiscale exploration of mouse brain microstructures using the knife-edge scanning microscope brain atlas. Front. Neuroinform. 5:29. doi: 10.3389/fninf.2011.00029

The authors go on to discuss how this approach has also been applied, with less fanfare, to differential interaction network analysis. In this paradigm, if an interaction between nodes (e.g., protein concentrations) in the network is present above noise in one condition, but not another, then they would call that a differential interaction.

"Static genetic interaction maps are measured in each of two conditions (left)... Condition 1 is subtracted from condition 2 to create a differential interaction map (right)... In the differential map, weak but dynamic interactions (dotted edges) are magnified and persistent ‘housekeeping’ interactions are removed (bottom right)." ; doi:10.1038/msb.2011.99

Very similar ideas can be applied to the study of neuronal network function. If we can say that an interaction between neuronal “nodes” (which could be, depending upon the scale, neurons, cortical columns, or brain regions) is differentially present between healthy and disordered states, then it suggests that that interaction is somehow involved with the disorder.

This is not a perfect paradigm, in part because the network “connections” can be less representative of the physical reality than we’d like, but I anticipate that we have much to mine from it about the operations of the nervous system.

Reference

Ideker T, Krogan NJ. 2012 Differential network biology. doi:10.1038/msb.2011.99

Nara et al. recently set out to answer this question. They put dissociated mice olfactory epithelium cells on glass coverslips, loaded them with a calcium indicator, and monitored them for changes in calcium signaling following the application of 13 different odorant mixtures.

OSN = olfactory sensory neuron, the actual number of neurons in each group is shown above each bar (out of a total of 217 tested); doi: 10.1523/ JNEUROSCI.1282-11.2011

As you can see above, most neurons responded (i.e., demonstrated an increased calcium concentration) to just one mixture, but some neurons did express receptors which allowed them to respond to many mixtures.

If a neuron responded to a given mixture, it was then tested for a response to each of the individual odorants in that mixture. Here is the response curve for one of their neurons, which responded to many different mixtures:

a sharp line indicates a change in fluorescence intensity, which is a proxy for neural activity; the bars indicate when the odors were added; the final response to KCl indicates neuron viability; Fi = change in fluorescence; doi: 10.1523/ JNEUROSCI.1282-11.2011

This neuron was considered an example of a “broad tuning,” since it responded to different odors with high structural variability.

Although all of the responses marked in red were classified as a “recognition,” some responses (such as 4-2) seem to be stronger than others (such as 6-10).

It might be interesting to analyze multiple replicates of responses from the same odor on the same neuron, to allow the authors to parse signal from noise and see whether these gradations in response are significant.

Cracking the full odorant code will require an even more high-throughput set of experiments, and probably would have to have at least one data point from each type of odorant receptor. This study is a clear proof of principle that such an extension would be possible and valuable.

Reference

Nara K, et al. 2011 A Large-Scale Analysis of Odor Coding in the Olfactory Epithelium. doi: 10.1523/​JNEUROSCI.1282-11.2011

neurons recorded = the number of neurons that can be recorded from simultaneously; the neuron and computer scales are exponential fits to data; doi:10.1371/journal.pcbi.1002291

Given the above DNA sequencing trends, it’s no surprise that groups in many different fields are developing strategies to turn the problem they are trying to study into a sequencing problem.

See, for example, Jonathan Weissman’s talk on ribosome profiling, which is an elegant way to use DNA sequencing of mRNA molecules tethered to the ribosome as a way to study translation.

In his article, Kording touches on a couple of intriguing sequencing technologies that might help make the “data-out” step of a given neuroscience experiment more high-throughput.

The method for connectomics he describes is particularly fascinating. The idea is to assign neurons a unique DNA barcode that is spread to each of its synaptic partners via a transsynaptic virus, and then sequence the set of barcodes from a given group of cells.

One aspect that I think Kording might have underemphasized is that these technologies would improve greatly if we improved our ability to sequence the DNA of individual neurons.

For example, typical protocols for probing the expression of intermediate early genes rely on harvesting cells from mass culture or coarse brain regions before sequencing. This is powerful, but it would be much more so if we could analyze the distribution of gene expression between cells rather than across them.

Single-cell genomics is advancing, but it is not yet at the point of routine laboratory use for a typical sequencing experiment. And in order to really take advantage of DNA sequencing technology in understanding how networks of neurons work together, it will presumably need to reach that point.

References

Kording KP (2011) Of Toasters and Molecular Ticker Tapes. PLoS Comput Biol 7(12): e1002291. doi:10.1371/journal.pcbi.1002291

Link to Jonathan Weissman’s 11/16/11 talk.

Oyibo H, et al. 2011 Probing the connectivity of neural circuits at single-neuron resolution using high-throughput DNA sequencing. Presentation at Computational and Systems Neuroscience Meeeting, pdf.

Saha RN, et al. 2011 Rapid activity-induced transcription of arc and other IEGs relies on poised RNA polymerase II. doi: 10.1038/nn.2839.

Kalisky T, et al. 2011 Single-cell genomics. doi:10.1038/nmeth0411-311

We are able to classify these sparse moving points as biological motion and can often even make inferences about the characteristics of the moving agent. See for yourself in this 31 s video:

Previous studies have indicated that toddlers with autism have deficits in perceiving biological motion. This is not surprising, because social information is embedded within the stimuli.

Kaiser et al took this further by using this point light display paradigm and fMRI on 1) children with ASD, 2) siblings of children with ASD, and 3) control children.

They looked for regions differentially activated between biological light displays and scrambled light displays. They then compared the degree of differential neural activity between groups.

Brain regions were classified as having 1) less differential activation in ASD children in biological conditions as compared to siblings and controls (orange below), 2) less differential activation in ASD children and siblings as compared to controls (yellow), 3) enhanced differential activation in siblings (green), or 4) no statistically significant difference in differential activation between groups (uncolored).

top = sagittal slice; middle = coronal; bottom = axial; doi: 10.1073/pnas.1010412107

Their approach helps tease out the neural circuits underlying why some individuals with genetic risk factors don’t develop ASD. The two main brain regions they implicated were the vmPFC (of emotional decision making fame) and the right posterior STS. Could we imagine some study attempting to stimulate these regions in a model of ASD to mimic the development of compensatory mechanisms?

Their study compared young 3 month and geriatric 29 month old mice, which, as the authors note, correspond to roughly 20 and 80 years in humans, respectively. However, it’s always important to keep in mind that mice differ from humans in many important ways.

The researchers cut out muscle tissue, sectioned it in 20 um segments, and double stained with antibodies for both synaptophysin (to detect pre-synaptic nerve terminals) and α-bungarotoxin (to detect postsynaptic muscle endplates).

They then classified neuromuscular junctions that stained positive for both synaptophysin and α-bungarotoxin as innervated, and classified junctions positive for α-bungarotoxin only as denervated. Below is an example of a confocal image of a double stained tissue slice.

EDL = extensor digitorum longus; synaptophysin = red; α-bungarotoxin = green; overlay = yellow; white circle = example of endplate positive for only α-bungarotoxin; scale bars = 75 um; doi:10.1371/journal.pone.0028090.g002 part d-f

Across all samples analyzed, ~7 +/- 2% of neuromuscular junctions were fully denervated in 3 month old mice and ~20 +/- 3% of neuromuscular junctions were fully denervated in 29 month old mice. Such denervation could help account for any age-related decrease in muscle function.

Interestingly and importantly, the researchers did not find a similar trend in the soleus. The lack of concordance underscores some of the variability across tissues of the same type in aging.

Reference

Chai RJ, Vukovic J, Dunlop S, Grounds MD, Shavlakadze T (2011) Striking Denervation of Neuromuscular Junctions without Lumbar Motoneuron Loss in Geriatric Mouse Muscle. PLoS ONE 6(12): e28090. doi:10.1371/journal.pone.0028090

Initially, these fasciculated neurites should be in tensile equilibrium. However, during development and migration the cell with the stronger tensile force will tend to pull the other neurite (and thus its associated neuron) closer.

Over time, this tendency would cause neurons to cluster together. An interesting new study from Sun et al demonstrates this clustering effect nicely.

When cells are plated at a relatively low density, they tend to form multiple clusters. For example, see the schematic below showing the migration of hippocampal neurons plated on a circular substrate over 24 hours:

"the blue arrow begins from the original position of the cells at 0 hr, and ends at the final location at 24 hr"; transparent pink shapes = clusters at the end; scale bar = 200 µm; doi:10.1371/journal.pone.0028156.g003 part c

When the cells are relatively more dense, they will typically form one big mega-cluster.  As an example, see this set of time-lapse images of hippocampal neurons grown over 12 days in vitro (DIV):

red arrows = clusters; scale bar = 200 µm; doi:10.1371/journal.pone.0028156.g003 part a

Their model predicts that a genetic or biochemical intervention which inhibits neurite fasciculation would reduce the clustering of neurons in this sort of system.

Reference

Sun Y, Huang Z, Yang K, Liu W, Xie Y, et al. (2011) Self-Organizing Circuit Assembly through Spatiotemporally Coordinated Neuronal Migration within Geometric Constraints. PLoS ONE 6(11): e28156. doi:10.1371/journal.pone.0028156

…[C]onsider the example … regarding the significant resources and time being put into deciphering the structural connectome of the brain. This massive amount of accumulating data is qualitative, and although everyone agrees it is important and necessary to have it in order to ultimately understand the dynamics of the brain that emerges from the structural substrate represented by the connectome, it is not at all clear at present how to achieve this. Although there have been some initial attempts at using this data in quantitative analyses they are essentially mostly descriptive and offer little insights into how the brain actually works. A reductionist’s approach to studying the brain, no matter how much we learn and how much we know about the parts that make it up at any scale, will by itself never provide an understanding of the dynamics of brain function, which necessarily requires a quantitative, i.e., mathematical and physical, context.

That’s Gabriel Silva, more here, interesting throughout.

Reference

Silva GA (2011) The need for the emergence of mathematical neuroscience: beyond computation and simulation. Front. Comput. Neurosci. 5:51. doi: 10.3389/fncom.2011.00051

"Boring figure", via Wikipedia user Bryan Derksen

An important finding related to these types of illusions is that we don’t perceive both possibilities at once, but rather switch spontaneously between them.

Buesing et al.’s recent study formalized a network model of spiking neurons, equivalent to sampling from a probability distribution, and used it on a quantifiable model of such visual ambiguity, binocular rivalry.

This allowed them to show how spontaneous switches between perceptual states can be caused by a sampling process which produces successively correlated samples.

In particular, they constructed a computational model with 217 neurons, and assigned each neuron a tuning curve with a preferred orientation such that the full set of orientations covered the entire 180° interval.

They then ran a simulation of these neurons according to their rules for spiking and refraction, computed the joint probability distribution, projected it in 2-d, and drew the endpoints of the projections as dots, shown below. They took samples every millisecond for 20 seconds of biological time.

the "prior distribution"; each colored dot is a sampled network state; the relative orientation of each dot corresponds to the primary orientation of the perception at that time point; a dot's distance from the origin encodes the perception's "strength"; doi:10.1371/journal.pcbi.1002211.g004 part d

Note that there is a fairly homogenous distribution across the whole orientation spectrum, indicating a lack of preference for one direction. You might think of the above as the resting state activity, as there was nothing to mimic external input to the system.

In order to add this input, the authors did another simulation in which they specified the states of a few of the neurons, “clamping” them to one value. In particular, they clamped two neurons with orientation preference ~45° to 1 (“firing”), two neurons with preference ~135° to 1, and four cells with preference ~90° to 0 (“not firing”).

Since the neurons set to firing are at opposite sides of the semicircle, this set-up mimics an ambiguous visual state. They then ran a simulation with the remaining 209 neurons as above, with the results shown below.

the "posterior distribution"; the black line shows the evolution of the network states z for 500 ms during a switch in perceptual state; doi:10.1371/journal.pcbi.1002211.g004 part e

As you can see, in this case the network samples preferentially from states that correspond to the clamped positions at either ~45° or ~135°. The black trace indicates that the network tends to remain in one high probability state for awhile and then shift rapidly to the other.

As compared to the above “prior” distribution, this “posterior” distribution has greatly reduced variance.

Although the ability of their network to explain perceptual bistability is fascinating, it is perhaps most interesting due to its broader implications for how cortical regions might be able to switch between cognitive states via sampling.

Reference

Buesing L, Bill J, Nessler B, Maass W (2011) Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons. PLoS Comput Biol 7(11): e1002211. doi:10.1371/journal.pcbi.1002211

This illustrative figure shows some of the major cellular players involved and lists some example proteins involved in four important pathways:

"numerous cell types (microglia (green), astrocytes (orange), oligodendrocytes (blue), and neurons (violet)) and subcellular components (mitochondria (brown), endoplasmic reticulum (green), cytoskeleton (orange/red), and synaptic machinery) are affected by brain aging"; doi: 10.3389/fnagi.2011.00008

As you can see, many proteins have been implicated, although the degree of up-/down-regulation of these proteins is not fully elucidated.

The authors mention the value of standardizing efforts to profile the proteome in important brain regions across the lifespan of rodent models. This step would make these results more robustly quantitative and help iterate towards a consensus.

Reference

VanGuilder H. D. and Freeman W. M (2011) The hippocampal neuroproteome with aging and cognitive decline: past progress and future directions. Front. Ag. Neurosci. 3:8. doi: 10.3389/fnagi.2011.00008