neuroscience

“Connectomic Analysis of Brain Networks: Novel Techniques and Future Directions. Authored by Cazemier et. al”

Happy Wednesday to all interested parties!

Today we will be taking our first official journey into an in-depth analysis of this riveting article. It was published in Oct 25, 2016, and authored by Cazemier et al. who is a neuroinformatics & neuroscientist at several european universities, namely Donders Institute, Radbound University & Autonoma University. What makes this 2016 article so appealing is that its main focus- obviously- takes accord in the insightful connetomics theorem. It projects new methods used in neural mapping, and a dozen estimations and predictions on where these methods will lead our community in the coming years. Cazemier et al. elucidate the rather “technically challenging aspect” of the connectomic project, but also offer high level technologies that are considered by neuroscientists to accomplish this goal. A list of these methods are listed in the article, such as: sensitive cell labeling, high resolution imaging, molecular labeling, and microscopy approaches.

Without further adieu, I think it most pertinent to first discuss the concept of the word “connectome”, or my connotation of it ” connectomics theorem”. The connectome as an accepted and understood word was first coined into neurobiology by Olaf Sporns in his 2005 article. “ The Human Connectome: A structural Description of the Human Brian“. Olaf related that the structure-to-function relationship,  which is widely regarded in neuroinformatics is key to the foundations to our practice. He also stated that neurons inherently have a very unique and intricate structure that unfortunately produces a false dichotomy, wherein a singular neuron should be studied on its own rather then a sum of the whole. This relates to the revered Aristotle, and his 4th century quote ” ..The whole is greater then the sum of its parts.”. Olaf stated that since neurons mostly move in a fluid forward moving neuronal network, it should be pertinent to understand how their structure in this fashion is a relation to its function as a whole.

This paradigm of forward momentum in neurons-first pioneered by Santiago Ramon Y Cajal- is a dynamic implementation of the Connectomics theorem & vastly important to keep in mind. Within Connectomics, the main goal is to essentially distinguish how a singular neuron in a neural network is innervated by its peers & also vice versa. Although as stated previously, this neural mapping is by a grander design, markedly difficult to measure with accuracy due to neural population density as well as Glial Cell interactions. That does not mean it cannot be done, in light of which I am proud to inform you- a connectome has already been completed through and through, on a small scale albeit. A tiny worm of the Phylumm Nematoda, Caenorhabditis elegans (C.Elegans), has had its complete connectome mapped beautifully by Shibata et al. in 2015. Its minute amount of 302 neurons consisting of 118 morphologicallymade this a relatively less daunting task in comparison to any mammalian nervous system. Nonetheless the 5,000 synaptic connections that comprise this nematodes brain has helped research study many vertices of neurological functions such as: chemotaxis, thermotaxis, mechanotransduction, learning, memory, and mating behaviours. The aforementioned functions that were studied has produced keen hopes in the community. it provides a foundation for the connectomic project in more complex mammalian nervous systems, as well as new methods/inlets to study neuronal structures & functions in quantitative clarity. 

Now that the connectomic theorem has been concisely defined above, we can now enter todays primary focus brought to us by Cazemier et al.. In this section I will be discussing firstly the most viable tracing methods involved in terms of new techniques and clearly equating the positive outcomes of these techniques, and secondly the limitations that may intervene in fluid accuracy of a neural mapping. This is the best method to decipher this article, as there is scarce accredited data elucidated from these techniques that have produced qualitative empirical information that is useful in both individual and network arrays. While holistically looking at both microscopic and macroscopic brain models, we will distinguish whether the studies conducted in this article regarding the connectomic theorem brings us closer or further from current brain model paradigms. While there are several methodologies discussed in the article we are analyzing, I will cast our literary gaze upon the two I find the most riveting. This will be in terms of: results produced by said methodologies and limitations that seem surmountable in the near future with either technological advancement or an advantageous biological paradigm shifts.

Firstly, Cazemier et al. discusses an in silico (computer simulation) study conducted with using anatomical and electrophysiological data sets to perforate a small connectome of a rat neocortex. Using in silico with the collected data set allowed the team to not only create an anatomically similar computer simulation to better view the connections between neurons in the neocortical region of interest, but allowed for intriguing postulates on synaptic relationship, I.e. dendritic-axonal connections. This modality of mapping I believe will prove to be the most versatile when connectome data is combined with acquired data sets from other modalities.  in silico models will produce hopefully large scale neuronal chains in cellular resolution, once they have been sufficiently collected via other methods, such as: transfection of viral vectors for fluorescent protein expression & intra/juxtacellular labeling with biocytin like molecules. A pitfall of in silico studies is are that it inherently does not produce actual synaptic connection models, it is a modality used to correlate and fixate the data acquired from labeling procedures. Nonetheless, it will likely be the winning contender for creating broad maps of neuronal chains, as opposed to light microscopy/electron microscopy (LM/EM) with cubic millimeter limitations for slide preparations. The limitations we could discuss involving  in silico studies would be a lack of information as to how we define connections.. is it determined by proximity of an axon to a dendrite, and vise versa, or is that a completely false assumption. Unfortunately without having synaptic labeling procedures for each axon/dendritic synapse, we will not be able to discern for certain, besides a statistical correlation that may or may not be correct.. Thus we could state that the most major limitation for in silico studies would be our current lack of understanding in creating a synthetic virtual, vastly diverse connectome, consisting of multiple cell types & supportive glial cells. Reliability of said data sets is also a major concern. When dealing with connectomic studies, as previously mentioned the accuracy of the data is limited to the perception of how we define a synapse that are cartographed by labeled/fluorescent synapses.

Moving on to the second modality, intra/juxtacellular labeling. In this discourse, we will not be looking at the modalities of how we trace them- I.e. confocal laser scanning microscopy, photon microscopy, compound fluorescence microscopy, light sheet ultramicroscopy, serial block face EM. I will be exclusively discussing the processes involved in the actual labeling of pre/post synaptic heads as well as minor components such as: labeling of molecules, vesicles, viral vectors, synaptic densities..etc. Genetically encoded synaptic marker for electron microscopy (GESEM), is the utmost intricate bioengineered labeling schemes neuroscientists have to deliberately target and illuminate specific cells. Essentially, it involves transgenic mice retaining a certain membrane protein that is specific to that genetic lineage of the engineered mice. Subsequently introducing a complimentary protein tethered to a viral targeting vector to bind with certain cell areas the transgenic mouse is predisposed for. It is more clearly outline and described in the article and I advise that my readers thoroughly investigate the process to gain a more concise conceptualization. GESEM is useful for elucidating the synaptic terminals of  a small cluster of neurons, and one of its hallmark features is providing a dark area in EM when certain markers are established. A unfortunate limitation of GESEM is the difficulty in producing any results on transregional neuronal chains as it would be unfeasible within the parameters of slide preparations.

In conclusion, It seems that the variety of methods and procedures being developed and intrigued upon will soon elucidate a plethora of empirical data on physiology of synapses. Unfortunately, we seem to be rather focused on distinguishing the best methods to map neuronal chains. Researchers are forgetting the reason behind mapping networks in the first place- to correlate singular neural cell physiology and what processes are involved in their interactions with other cells, especially neural glial- which seems to be cast aside as an unimportant component..

I hope this was informational in my interpretations and excerpts and gave a glimpse as to what the article discussed!

Cheers,

Blake Thomas Endres

 

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