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Archive for the ‘Connectomics’ Category

Trends in neurodevelopment are, at least to me, a bit counterintuitive. It is surprising that there would be the most synaptic connections in humans at ~ 8 months after birth rather than, say, 18 years. But following the logic of synaptic pruning, this is the world we live in.

Using light and electron microscopy, a new study sheds some light on these processes. The authors provide quantitative measurements of the trade-off to large numbers of synapses in newborn mice, which is that each individual axon and synapse is smaller.

They study the motor axons of neuromuscular junctions, but presumably the same patterns of redistribution generalize to elsewhere in the nervous system. Some of their findings:

  • At birth, the main branch of the motor axons entering muscles had an average diameter of 1.48 ±0.03 μm, compared to 4.08 ±0.07 μm at 2 weeks old
  • In the cleidomastoid, at birth each motor axon innervated an average of 221 ±6.1 different muscle fibers, compared to 18.8 ±3.0 at 2 weeks old
  • At embryonic day 18, each terminal axon branch covered an average of 14.2 ±11.4% of the muscle’s acetylcholine receptors, compared to ~100% by single axons in adults

These results and others in the paper show that although there are fewer total synapses in later stages of development, each axon/synapse is bigger and more specific.

Reference

Tapia JC, et al. Pervasive synaptic branch removal in the Mammalian neuromuscular system at birth. 2012 Neuron, PMID: 22681687.

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In order to make serial section electron microscopy neurite reconstruction truly high-throughput, it will be essential to find a way to automate the image recognition component. Unfortunately, as I’ve written before, it’s quite difficult to segment and recognize patterns in electron microscopy images.

Inspired by other citizen science approaches, Sebastian Seung & co have come up with the possibly ingenious idea of enlisting the help of the everyman in this task. Their website is called Eyewire. It challenges users to reconstruct ganglion cells from electron microscopic images in the retina.

The images are stained in their cell membranes via a dye to create contrast. In theory, this contrast allows machines and humans to distinguish precisely where the neurite travels. In practice, the dye can invade to organelles, creating noise, or it can stain the cell membrane incompletely, creating artifacts.

Or, the machine learning algorithm might just miss it, because of some sort of bias, like missing boundaries that are outside of its field of view. This is where you come in. Your task is to move from slide to slide and pick out the regions that the algorithm misses.

I just opened up the game and in the first section I was assigned, I came upon this error. Here’s the first slide, which, as you can see, is completely filled in within its stained cell membrane boundaries:

And here’s the next image stack up:

As you can see, but for whatever reason the ML algorithm cannot, there is a hole in the second image which should be filled in. Eyewire allows you to do this yourself,

by filling the hole in with the light teal.

Sometimes the missing holes are more consequential. Filling in some holes means that whole undiscovered branches of a neurite can be found.

In a very nice feature, the algorithm automatically propagates your changes to the rest of the image stacks, so that you don’t have to do so manually.

When you have enough people doing this, the results can be pretty interesting. For example, here is the current reconstructed version of cell #6:

How would you go about quantifying the branching neurites of this neuron and what can you learn from its structure about how it works? These are the kinds of questions that we’ll be able to address as we collect more of these.

Sebastian Seung calls the game “meditative.” In the hours I’ve played so far (my account name is porejide), I have found it quite fun when it’s working fast and I can zoom through the stacks.

On the other hand, at times the internet connection at my house couldn’t really keep up, leading to some lag, which caused me to experience a sensation that I would not call meditative. But perhaps that’s just the fault of my internet connection.

One angle that I especially appreciate is the friendly competition between users. After you fill in a set of image stacks, the game rewards you with a number of points that is meant to be proportional to what you accomplished.

I have no small amount of pride in reporting that yesterday I played well enough (and for long enough) to reach #2 in points for the day, with 981 points, although xo3k was way ahead of me with 3450. As I was playing I could see user vienna717 was gaining ground on me quickly, which gave me the competitive juices I needed to go faster.

This is a great infrastructure, and has the potential to get even more fun if they gamify it further. For example, perhaps users could join teams with other people and play for a glory greater than the self.

This all sounds dandy, but what if you don’t care about retinal ganglion cells? Frankly, I don’t care that much myself. To the best of my understanding, the main thrust of the game is not to build the 3d maps of these ganglion cells, although that will be informative.

Rather, the idea is to provide a huge training set for machine learning algorithms, so that they can learn to better incorporate the insights of humans. This will scale much better than having humans do it, and will in theory allow us to reconstruct neural connections on much larger scales.

This, in turn, will allow us to rigorously test some of the most fundamental questions in neuroscience.

There is no guarantee that Seung & co’s approach will actually get us there, and even if it does, it will take a lot of time and effort. In the meantime, I’ll see you on the leaderboard!

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The ability to use variability in structural connectivity to explain variability in clinical outcomes would be a critical validation of connectomics, and would help push the field forward.

Towards that end, Tymofiyeva et al used DTI to map the structural connectivity of a clinical cohort of six-month old infants with perinatal hypoxic ischemic encephalopathy.

Since there is no well-established atlas for the rapidly changing brain at such an early stage of development, the authors relied on two unbiased parcellation techniques, illustrated here:

colors arbitrarily refer to distinct brain regions; a = partitioned spherically, b = partitioned along linear dimensions; doi:10.1371/journal.pone.0031029

They then used these parcellation schemes to compute adjacency matrices for each baby. Here are representative matrices for each of the above parcellation schemes:

binary adjacency matrices where a connection (denoted in white) were called if there was a fiber tract with end points located in both regions; c = partitioned spherically, d = partitioned along linear dimensions ; doi:10.1371/journal.pone.0031029

The authors then attempted to correlate a neuromotor score of the infants with the brain network’s degree of clustering. For this, they chose to test 1) the average shortest path length between any two nodes, and 2) the average clustering coefficient.

By the authors’ own admission, a larger sample size and a longitudinal design would be ideal to make inferences from this sort of study. In the scatterplots they presented, they found one positive correlation below the arbitrary threshold of p=0.05, but the effect seems to be mediated in no small part by one outlier.

Still, this is an innovative approach and has great potential. One thing that might be interesting would be to take a more unbiased approach to the data analysis. That is, instead of choosing the network summary statistics to test a priori, use some sort of ensemble learning method to let the data tell you how best to predict the neuromotor scores on the basis of the adjacency matrices.

Reference

Tymofiyeva O, Hess CP, Ziv E, Tian N, Bonifacio SL, et al. (2012) Towards the “Baby Connectome”: Mapping the Structural Connectivity of the Newborn Brain. PLoS ONE 7(2): e31029. doi:10.1371/journal.pone.0031029

Casanova R, et al. 2012 Combining Graph and Machine Learning Methods to Analyze Differences in Functional Connectivity Across Sex. doi:  10.2174/1874440001206010001.

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Visualizing nervous systems, both the raw images and their reconstructions, is a hot field for a good reason. Once we have these sort of maps we’ll be able to make much more precisely quantitative statements about how the information flow in neuronal networks is constrained.

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

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What has been the growth rate of computing power, multi-neuron recording, and DNA sequencing over the past decade? Konrad Kording provides an illuminating chart pertaining to this question:

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

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Humans seem to have developed dedicated systems for detecting the prototypical gait of moving animals. One paradigm for operationalizing this ability is a point light display, which simulates animals moving in the dark with just a few lights on their joints.

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?

Reference
Kaiser M, et al. 2010 Neural signatures of autism. PNAS doi:10.1073/pnas.1010412107.

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…[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

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