“Hallucinogenic Compounds use as Target Models for Schizophrenia By Blake Endres”



Model psychosis is the use of psychoactive and hallucinatory compounds for empirically
understanding via experimentation the cellular mechanics and behavioral manifestations in relation to developed disorders of atypical perceptual/delusional/hallucinatory thoughts, such as is the case in Schizophrenia. In recent years, with the advantageous aspect of increasing technological methods, certain hypothesis are being questioned; chief among them, the dopamine hypothesis. In regard to a paradigm shift, the (5-HT 2A) serotonin hypothesis stands rather prominent. As stated by the Research Department of Psychiatric University Hospital Zürich, Switzerland, “Support for this hypothesis stems from observations that schizophrenia patients show alterations in cortical serotonin receptor binding; novel atypical antipsychotics have potent antagonistic action at 5-HT2A receptors; and classic indoleamine hallucinogens, which interfere with the serotonin system, can elicit schizophrenia-like symptoms in humans.”. The similarity present in the relationship between 5-HT2 agonist effects and natural effects seen in early stages of Schizophrenia are extremely interesting to the possible connection of the two conditions. Given the circumstance that there is a relation between the neuropharmacological aspect of psilocin and Schizophrenic psychosis, it could incite new areas of interest, such as:understanding how neuronal networks are instrumental for the specifics of induced individual psychosis. The discussion in this article contains the investigation of the possible ways to record the similarities between Schizophrenic psychosis and psilocybin induced psychosis, by the following methods:Functional Magnetic Resonance Imaging with functional connectivity between target areas {FMRI)-FC}, and dose-dependent antagonistic serotonin-2A ketanserin during psilocin agonism/Schizophrenic psychosis.


In many cases with people who are afflicted with pathological psychosis, the observation of psychoses are severely limited to direct behavioral observation and self report documentation from the patient. This is a very inefficient and ineffective way to document variety of psychosis that is occurring within the patient, and does not allow for further progression in understanding the pathology behind the psychosis. If discriminant variety of psychosis can be observed in a multitude of ways, then a biological connection could be found and traced between induced and disordered psychosis. Successively, if a consistent pattern of observed pathological psychosis is documented for a specific variety of psychosis, then it can be used as a model comparison for disordered psychosis. Model psychosis is a synthetic version of recreating Schizo-type behavior and hallucinatory occurrences with the use of psychoactive compounds such as MDMA, amphetamines, cannabinoids, and psilocin, which all are analogs or isotopes of endogenous neurotransmitters.

The goal of model psychosis, is to present an empirical object to directly study all of its effects in relation to a comparison to the controlled variable of schizophrenic variety psychosis. But first, the controlled variable of Schizophrenic psychosis must be explained in biological mechanisms and traced neuronal network in innervation sites, so that an accurate comparison may be issued with confidence, rather than speculation. With the many different hypothesis for Schizophrenia, it is extremely hard to determine a fundamental basis on which to compare biological/pathological mechanisms on model psychosis neural innervation to Schizophrenia neural innervation. Since the target compound of choice for this article, psilocin, is a 5-HT2a agonist,the fundamental basis for biological comparison will be with the serotonin system recently thought to be involved in Schizophrenia. Thus all the representative data found via the chosen methods in model psychosis studies pertaining to the serotonin system will be superimposed on data found in Schizophrenic psychosis using the same methods, for an ideal isolated comparison.

FMRI on target areas of interest

Topic preliminary questions to think about: 1. Will the idea of superimposed FMRI activity orthogonality result in any noticeable similarities when using two different people for the comparison, as in one patient with induced psychosis and one patient with Schizophrenic psychosis. Meaning, will each individual’s connectome provide enough microstructural similarity to accurately propose a valid means of comparison?

        In an extensive study conducted by Centre for Neuropsychopharmacology, Imperial College London, on the functional connectivity between target areas of interest (AoI) pre and post intravenous injection of psilocybin, the study was meant to measure the effects of psilocybin on resting-state network and thalamocortical functional connectivity (FC). With primary analysis focused on the changes caused by psilocybin on functional connectivity in the default-mode network (DMN) which supplements introspection, and the task-positive network (TPN) which support externally focused attention ( Carhart-Harris et al.2013) . While the DMN and TPN are polar opposites in functionality, they interestingly enough show orthogonality in fMRI time series in spontaneous activity (Carhart-Harris et al.,2013). Meaning that in fMRI time series studies, spontaneous activity in both DMN and TPN overlap in sensory mapping, even when both networks individually uphold different functions. The DMN consists of many regions that all retain relative functional connectivity to each other. The anatomical subsections that derive the DMN are as follows: posterior cingulate cortex; medial prefrontal cortex, mPFC; and lateral inferior parietal cortex (Carhart-Harris et al.,2013). The idea of measuring the functional connectivity between DMN and TPN in detail, was due to some previously learned data sets that show increased FC within the DMN and TPN is seen in psychotic, mediatory and sedative states, which is similar to the classical psychedelic state induced by hallucinogenic compounds, like psilocin.

        From the premises mentioned above, the research group at Imperial College London (Carhart-Harris et al.,2013), was able to conclude the following ideas:

 1.)           “ DMN-TPN FC is related to the separateness of internally and externally focused states.   We suggest that this orthogonality is compromised in early psychosis, explaining similarities between its phenomenology and that of the psychedelic state and supporting the utility of psilocybin as a model of early psychosis.

2.) “  If, however, activity in the DMN and TPN was to become less orthogonal, then this might cause a confusion of states and a disturbance of cognition such as is seen in early psychosis.”

An unfortunate fact that persists to surface in this field of research, is the lack of pre-existing baseline mechanic explanations for a disordered pathophysiology for use in comparison with the model data found. Thus, in this case, “early psychosis” remains as an undefined variable.

Psilocin effect on FC within the DMN and TPN

Topic hypothesis: “If, however, activity in the DMN and TPN was to become less orthogonal, then this might cause a confusion of states and a disturbance of cognition such as is seen in early psychosis.”

        The test subjects consisted of 13 males and 2 females (mean age = 32, standard deviation of 8.9) , all of which were inexperienced with hallucinogenic drugs. fMRI tests implemented for DMN- TPN FC consisted of a 12 minute closed eye time lapse BOLD fMRI scan on two occasions spaced at 7 day intervals, the first was placebo (10ml saline, 60-s intravenous injection), the second being psilocybin (2mg dissolved in 10ml saline). The 60-second intravenous infusion began at 6 minutes into the scan, so that a baseline could be established during the same time series fMRI (Carhart-Harris et al.,2013). The volumes of time series images were processed in an ICA (independent component analysis), which produced 11 “meaningful”  RSN (resting-state networks). Basically, the fMRI images were analyzed for prospect of images that can be compared to a chosen dependent variable ( aDMN; anterior loaded DMN), and yield support for the topic hypothesis . {I could not discern precisely word for word, why they chose the aDMN for the dependent variable, when it was concurrently a “meaningful” RSN. I speculate, it was due to the need for models of an after psilocybin DMN activity time series to compare with after psilocybin TPNs.}

        The 11 “meaningful” RSN, consisted of the following: anteriorly loaded DMN (aDMN), posteriorly loaded DMN (pDMN), right- and left-lateralized frontoparietal networks (rFPN & lFPN), an auditory network (AUD), salience network (SAL), visual network, (VsN), precuneus network (PcN), dorsal attention network (DAN), cerebellar network (CereN), and sensorimotor network (SmN), (Carhart-Harris et al.,2013). For clarification, essentially all of these RSN (resting-state networks) are TPN (task-positive networks), the term RSN is used due to the cognitive behavior of  “ resting ” while in the fMRI, thus not being “ task-positive” because the lack of locus of control or isolation of a task.

The results of the fMRI dependent-independent variable analysis were given as side by side comparison in the form of the time series volume images of the 11 RSNs collected via ICA involving placebo v.s psilocybin.

        In a short elaboration on the results of DMN-TPN FC that Carhart-Harris et al. found between aDMN and the various 11 RSN to support the hypothesis, “ decreased orthogonality between the DMN and TPNs would predict experiences of disturbed ego boundaries and cognition”.  Carhart-Harris et al. implemented linear regression to assess the FC strengths between the aDMN and 11 RSN. Listed are the statistically significant FC strengths that will be considered result of psilocybin agonistic action on 5-HT2a receptors, which caused the gradient of FC to increase, as seen when compared to the baseline time series of postinjection.

  1. aDMN-SAL (P = .0002) salience network showing the most dramatic increase in FC
  2. aDMN-rFPN (P = .0003) right frontoparietal network
  3. aDMN-AUD (P = .0006) auditory network
  4. aDMN-DAN (P = .0009) dorsal attention network

{P representing the product strength using pearson’s correlation coefficient analysis via linear regression}(Carhart-Harris et al.,2013).

Default mode network functional connectivity in schizophrenia in relation to default mode network functional connectivity in psilocybin induced psychosis

McKiernan et al. found interesting complementary data in Schizophrenic fMRI scans, “task-induced deactivation of the default mode network increased as task difficulty increased in a subsequently administered cognitive probe. They hypothesized that this was due to a reallocation of processing resources from the default mode network to areas used in task performance.”. Similarly, the Carhart-Harris et al. hypothesis “decreased orthogonality between the DMN and TPNs would predict experiences of disturbed ego boundaries and cognition”, adds a supporting point for the role of compromised DMN in Schizophrenic psychosis and drug induced psychosis. Conclusively, it could be stated that statistically significant increases in functional connectivity observed by linear regression between aDMN- (1-4) RSN caused by psilocin ( 5-HT2a), causes decreased orthogonality.In turn, this hints at the association between 5-HT2a receptors (serotonin system), and abnormal functional connectivity in aDMN region, that results in schizotypal behavior/cognition.


        The results from the Carhart-Harris research and the sub-conclusion insinuated by McKiernan et al., all dictate the direction in which the scientific community involved with studying model psychosis, should take; traceable biological comparisons for understanding pathology in disordered psychosis. As for a conclusion to the article, it is conclusively seen that the particular orthogonality that is disrupted between aDMN and TPNs via functional connectivity strengths is seen with psilocin agonistic action on the 11 meaningful RSNs. This increase of FC in some areas between the aDMN and the most drastically increased RSN that is hypothesized to result in psychotic states of Schizophrenia, is also the case within psychotic states resulting from Psilocin agonistic action. Therefore inciting a key component for this theory ; The serotonin system ( in regard to receptor concentration of the networks involved in the 11 meaningful RSNs) has an instrumental role in psychotic states, either due to reverberation effect of the serotonin analog on the orthogonality of the DMN and TPNs, or by other means which are not yet known.




      Reference list

  1. Carhart-Harris, R. L., R. G. Wise, A. Feilding, D. J. Sharp, J. Evans, J. M. Stone, T. M. Williams, D. Erritzoe, R. Leech, and D. J. Nutt. “Functional Connectivity Measures After Psilocybin Inform a Novel Hypothesis of Early Psychosis.” Schizophrenia Bulletin 39.6
  2. Garrity, A. G., Pearlson, G. D., McKiernan, K., Lloyd, D., & al, e. (2007). Aberrant “default mode” functional connectivity in schizophrenia. The American Journal of Psychiatry, 164(3), 450-7. Retrieved from 1343-1351. Print.
  3. Mason MF, Norton MI, Van Horn JD, Wegner DM, Grafton ST, Macrae CN.Wandering minds: the default network and stimulus-independent thought. Science.2007;315:393–395
  4. Nicasio Mdel P, Villarreal ML, Gillet F, Bensaddek L, Fliniaux MA. Variation in the accumulation levels of N,N-dimethyltryptamine in micropropagated trees and in in vitro cultures of Mimosa tenuiflora. Nat Prod Res. 2005; 19(1): 61–7.
  5. Simonienko, K. , Waszkiewicz, N. , Szulc, A. (2013). “Psychoactive Plant Species”.
  6. Winkelman MJ. (2007). “Therapeutic bases of psychedelic medicines: psychointegrative effects”.
  7.  Winkelman MJ, Roberts TB. Psychedelic Medicine: New Evidence for Hallucinogenic Substances as Treatments 1. Westport, Connecticut: Praeger. pp. 1–19.
  8. Vollenweider FX, Geyer MA. (2001). “A systems model of altered consciousness: integrating natural and drug-induced psychoses”. Brain Research Bulletin. 495–507

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“Finding the Unfair Advantage in Neuroscience through the Paradigm of Connectomics By Blake Endres”

I would like to begin this section of the paper with a brief introduction to my perspective
on the methodological error that psychology commits by assuming that everyone’s personality will fall under, a specific set of traits, or characteristics, based on questions that could have multiple interpretations, by each existentially “unique” individual. Every person will interpret all variables differently, this is enforced empirically, by the now intensively studied field of connectomics, which is tracing the four quadrillion synaptic connections that reside within each individual’s neuronal network, which also as a conceptual structure itself is being completely rethought and rediscovered. Rather than just assuming that each individual will be stimulated in the same functional neuronal unit, by each subjective question, and also create a unique output that is according to their personality. A specific segment of research that has intrigued me, and
also brought me to write this paragraph, happened to be discovered during a search for the target cells that were triggering epileptic seizures in a patient of a UCLA neurosurgeon named Itzhak Fried. as the patient was in a Fmri, Fried noticed that a particular neuron, was being stimulated, surprisingly every time the patient was looking at Jennifer aniston( the actor), even more startling, That same neuron was reacting to even distinct traits of jennifer aniston(chin, smile, eyes, nose).

Now in my mind, the question of “ to what informational potential, can individual neurons contain. And what is the variability that each functional neuronal network can differ amongst each person. And could these individual neurons be studied in vivo, to look at variables such as action potential strength, inhibition tolerance, excitability, or secretion of specific neurotransmitters, that will enable direct measurement for the importance of this jennifer aniston neuron, to this specific person, or even the effect on its corresponding synaptic connections, for example the content of G-coupled protein receptors for site-specific neurotransmitters, could
help us examine disorders like depression by localizing a specific neuron that is only responsive to depressing stimulus ”. With this new perspective in mind, the interpretation of these variations of site-specific or network sites, could prove extremely insightful towards, the formation of psychological/neurological disorders, personality, cortical injury, and possibly some cancers, if neuroglia can react in a similar fashion to neurons. This demonstrates that connectomics as emerging perspective, is producing a vast amount of empirical data on the immense proportions of variables that can come from synaptic connectionism, but also provide great empirical instruments for psychologist to reinvent the interpretation of personality, by finding an actual
meaning to the traits that each unique person may exhibit, and letting their own brain cells provide the information.

I. History and clarification
At first, I would like to clarify a few preliminary definitions of the topics in this essay to
be as concise as possible before the introduction of an eminently new paradigm, Connectomics, strongly advocated by the Massachusetts Institute of Technology’s professor of computational neuroscience, Sebastian Seung. Neuroscience, as a practice, is relatively new considering its predecessors; physiology and psychology, which have made their own distinct marks upon the texts of literature since as far back as classical era greece. But it was not till around 1865, until neuroscience as discipline began to take form, and completely separated itself from the falsified
and criticized grip of psychology. This was in part due to an intelligent medical graduate student at University of pavia, Italy, named Camillo Golgi. Golgi was interested in microscopy of neuronal tissue, but like most cellular components, they are even difficult to view in clarity and resolution with the aid of microscopes. So Golgi resorted to probing neural tissues with silver nitrate, a method that was deemed inadequate for neuronal tissues, by physiologist and biologists in the latter part of the 19th century. Golgi’s results were as he called a “Black reaction”, which allowed the microscopist a elaborate visualization of the structures and architecture that neuronal cells had established. Golgi’s discovery was very confounding, because he took a simple staining
method, albeit with a new goal, and created an astounding new concept and understanding of neurobiology. But to his contemporaries, who were well shocked by the use of silver nitrate to expose neuronal tracks, it would have been considered an “ unfair advantage” if he did not publicize his method. Now that neurons could be visualized and tracked through their large schematic of networks, it allowed for the availability of that structure to be studied and consequently a theory on how neurons interacted, where they interacted, and most importantly why they interacted in such a fashion. This important question posed an even more important
goal: determine the functionality of said network, and its individual parts. But it was not
necessarily Golgi who figured out key elements to the question, but rather a Spanish scientist named Santiago Ramon Y Cajal, who found this discovery more than captivating. Cajal experimented with the “Golgi method” of staining neurons with silver nitrate, and soon contributed his own thoughts to the newly developing discipline. Cajal had suggested ‘the neuron doctrine’, in which he described the fundamental function of a neuron, the capability of polarization in neurons cell bodies, which travel down axonal appendages, and propagate a new polarization in the following neuron. During this time Cajal had been recognizing certain characteristics that occurred in his experiments with neuronal synapses, he noticed that some neurons, specifically in the cerebellum and hippocampus, that neurons could abruptly disconnect synapses with others, which was contrary to the present belief that the nervous system was reticular, in that it somehow connected and fluid with each and every other neuron, virtually all were one, and one where all. But if neurons could detach synaptic connections, then that would
mean, as Cajal said “ Contiguity over continuity”, that all neurons could be sharing the same extracellular matrix, and be smushed together, but also not necessarily making all neurons, essentially 1 object.

II. Connectionism

What is connectionism? you may say, it is how things are connected, or the state of being
connected, or even the principle of connection. All of these are correct, but when applied to such a complex and abstract field of neuroscience, they are all wrong. In neuroscience, Connectionism is implicitly complex. For one simple reason, everyone is different, from genetics, learning experiences, relations, environment, etc. Everything makes everyone different. One could say that people learn the same way ( auditory, visual, etc) , everyone has relations, everyone is exposed to environment, but does that mean that everyones neuronal synaptic connections will be the same? absolutely not, the nervous system is comprised of roughly 100 billion neurons, with a ratio of neurons to neuro glia at 1:10, which astrocytes also play a role in synaptic connectivity, via control of neurotransmitters precursor material and reuptake.

This equates to around 10^24 synaptic connections within the central nervous system. Since all learning is unique to the individual, why would our neurons connect even remotely in the same chains? The answer is, they will not. But that uniqueness has qualities that could result in extremely efficient & accurate results, once we find ways to harness the individuality of each patient and their target neuronal chains, with methods and devices that the paradigm of Connectomics will produce. And with the current extensive research well underway at this very moment, that focus on studying the extremely formidable and challenging tasks, such as: Grandmother neuron, (ambien) non benzodiazepine hypnotics effect on stroke victims with partial/complete brocas aphasia, FTLD-TDP43, which are just a few dire projects that are giving rise to new concepts, and even more( for lack of a better word) revolutionary initiating, methods of instrumentation and technology. Without any doubts, it could always be said that instrumentation is the key to a revolution, for example pertaining to neuroscience, Golgi’s Silver nitrate stain. A simple chemical formula, that was known for at least four decades before Golgi thought of using it as a means to view a world that no one has ever seen before, and Cajal as well implemented a technique of using double impregnation with silver nitrate to view astrocytes, a type of neuroglia.

Other discoveries that lead to revolutions in the same manner, were called “ unfair advantages” by Sebastian Seung in his book Connectomics some examples are Antione van leeuwenhoek’s invention of a superior compound microscope, that allowed him to view cellular structures with much higher magnitude than any of his contemporaries, making him the proponent of the cell theory. Now Connectomics on the other hand, will require much more than simply increasing the magnitude of our Fmri, PET, and Voxel based neuronal imaging scans. If we hope to trace and map the entire connectome of the human central nervous system, it will require advanced technology that will allow for: 4-dimensional viewpoint, neuron startpoint/endpoint targeting for neuronal chain tracing, combined with the inherent properties of an electron scanning

III. Unfair Advantage

Thomas S Kuhn, the author of ‘ The Structure of Scientific Revolutions’, said that “paradigms can lead to new methods of instrumentation”, from this context, it implies the methods or production of certain devices, that later on help distinguish the paradigm by attributing a specific advantage that will add more information and specificity. Now, is this the case with all paradigms? most likely not, I believe there must be adequate information in the paradigm as a prerequisite for such inventions and devices to be conceived. But in the case with neuroscience, inventions were in a sense, borrowed from very similar fields such as chemistry, in example: radioisotope glutamate markers, Protein markers, receptor agonists/antagonists, neurotransmitter mimetics. now could concepts and instruments be taken from other fields and applied to Neuroscience as well, in hopes of finding the answer to our dilemma of how to go about mapping 10^24 synaptic connections in the nervous system? Well, for example, Magnetic Resonance Imaging was discovered by Herman Carr, a Physicist, but it was Raymond
Damadian, a medical scientist who realized its use for visualizing metastasized tumors in vivo. So my interpretation of this extremely profitable occurrence, leads me to believe that if any characteristics in a method of instrumentation/ device is useable in some fashion that aides in solving a problem in the discipline of an unrelated or related field alike is worth using to its fullest potential. Do these inventions and methods only originate from random occurrence, or could there be a sort of algorithm to its creation. Well, in retrospect, Golgi’s dilemma,was his goal was to find any method to view the functional units of the nervous system, what would dictate the limitations? for it is a goal that is unprecedented, with algorithms and trial-and-error being mostly useless because of the fact. Since Golgi was already aware and well practiced with tissue staining methods, he was experienced with its reagents, and the respected chemical
properties that they retained. So, Gogi’s Discovery could have been merely simple problem solving method of heuristics.

IV. Construction of a possibility

In a rough schematic of function, I have theorized an attempted model that most likely would not work for mapping, but in expectations it would be exclusively, only 1- millimeters superficially of the cerebral cortex, with the combined use of

● Focused High Intensity ultrasound
● polycationanion
● fluorodeoxyglucose
● PET Scan

This theory would only work on a patient who suffered considerable anoxia, resulting in
neuronal oxidative damage, and would have to be done within ~ 20 hours of the stroke. It would begin with intraarterial injection of fluorodeoxyglucose via carotid artery, somehow coupled with a polycationanion. Since the neuronal bodies in the penumbra(area of effect) would have been recently necrotized, the polycatioanions should be able to attach somewhere on the neuronal soma, via G-coupled protein receptor, given microglia are not already active. Then the patient will undergo a PET scan, that should only be able to couple to the necrosed neurons, due to connection of the polycationanion, it will inhibit metabolism by live neurons and astrocytes.
The patient would then be subjected to High intensity focused ultrasound, which would be guided in conjunction with the PET scan computer system. The goal of the HIF ultrasound is to ‘scan’ over affected area, and when focal point comes in contact with the chemical tags, the sounds waves are reverberated back. ( the sound waves still need a way to be converted into a HD image, then be able to be imaged via Electron scanning microscope, it would have to be along the lines of sound transduction into some form to be susceptible to the electron beam).


“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!


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A Journey Begins

Thanks for joining me!

Don’t forget to check with us every Mondays and Wednesdays for new postings!

“The elegant study… is consistent with the themes of modern cognitive neuroscience . Every aspect of thought and emotion is rooted in brain structure and function, including many psychological disorders and, presumably, genius. The study confirms that the brain is a modular system comprising multiple intelligences, mostly nonverbal.” -Steven Pinker