Summary and thoughts on the course

The course is now coming to an end, only the exam is left. Throughout autumn and now early winter, we have learned a lot on the matter of neuroscience, and answered quizzes based on our knowledge. As well as attended excursions to relevant places and written this blog on matters related to this course.

On the first week some basic terminology was showcased, such as what anterior and posterior mean, as well as explaining how the course is arranged. Some of the structure of the brain, what each area generally is responsible for, was shown in images. A few of the basic structural components that make up the brain, neurons and protons, were also addressed briefly.


This was further elaborated on the second week, what makes up a neuron was showcased, as well as connections from neuron to neuron, synapses. How proteins are utilized, for example in creation of action potentials which allows the neurons to perform a task, was also in the second week. The first assignment on brain anatomy was due on the second week, usually another assignment was due each week after.

Third and fourth week were about transmission of information in the brain between neurons or between synapses. Synapses transmit a signal, chemical or electrical, between neurons. They contain receptors and neurotransmitters which allows this to happen. Listed were the most common neurotransmitters (GABA) and receptors that receive proteins which trigger an event and the different events that each receptor is responsible for.

Fifth lecture was about different effects of chemicals, and their receptors were explored in detail. Such as dopamine, and its motivational behavior. Sixth explained the complex process of how the human eye and brain process sight. Which involves activation photopigment, which activates a G-protein along a disk membrane, in turn activating an enzyme which sends a message to the optic nerve to the occipital cortex of the brain.

Hearing and how it works trough hair cells being affected by certain frequencies that travel through air was addressed in lecture seven. And localization of sound is done the brain which receives sounds differently as they propagate in our ears depending on the altitude and latitude of the sound in comparison to our heads. Our first excursion was in Elekta, about their innovative brain imaging technology and their history as a company was done on the same week 44.

How each area of the brain’s motor center controls a specific area of the brain, and how the process of the signal getting from brain to the muscles works was in lecture 8. The spine is used separately in balancing, it handles the automatic process of walking and sitting and standing by itself, not in the brain. Another excursion involved effecting the brain trough sending electric impulses to have the desired positive effects. And finally lecture 9 involved the connections of the brain, how each area connects to the other, seen below.


Final excursion was specialized in measuring the brain activity of babies. The final exercise had us perform a measurement of brain activity to a willing subject.



What each of us got out of this course:

Mikko: This course was a great introduction to my new major.  If my interest in neuroscience was earlier specific to the needs of my grand plan of cyborgization, I now have a much wider appreciation for the field. The sheer complexity of the human brain fascinates me, and I’m eager to learn more about it. Getting a better understanding of the brain has also played an important role of my personal thoughts on philosophy. Epistemologically, it is interesting to see how many layers of interpretation and filtering all our perceptions go through before reaching our consciousness. Consciousness itself is also a huge philosophical question, and I think I have much clearer view towards it now after this course.

Markus: I liked the course, it introduced the subject of neuroscience, or brain structure in an interesting and not too obtuse manner. It cemented my choice of Major, and should step stone very well to future studies. It helped me understand much more deeply how consciousness and all other processes in our brains work. I thank the teachers and lecturers, and my group of fellow students and hope that one day I will be able to work in this field.

Meo: The course provided a lot of insight to the working principles of the human brain, which added nicely to my previous knowledge on human physiology. The topics were broad and left me with more questions than answers, which I think is a good thing. The discussion, blogs and the information provided by the book raised many thoughts and opinions on how we, perhaps, should go about studying neuroscience and especially developing treatment methods. It seems to me that we tend to be far too simplistic in this quest. I believe that the blogs provided a good medium for self-reflection, which I find is an important tool in studying the brain. I’m highly motivated to study more and strongly believe that studying the brain needs professionals from many fields. That’s why I’m grateful that this has been taken into account with the course staff, too!

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Functional imaging and connectivity

In the exercise this week, we conducted a little EEG experiment. So, let’s talk a bit about where the signal comes from.

Non-invasive functional brain imaging can’t detect the activity of a single neuron. The electromagnetic signals measured in EEG and MEG, and the blood flow changes detectable by fMRI are formed because of approximately simultaneous activation of many neurons close to each other.

So, why do mental activities so often activate a larger area in the brain, making them detectable by imaging? It’s a question of efficiency. The processes in brain are complicated and usually require activity of more than one neuron. And placing those neurons close to each other reduces the needed total axon length, saving time and energy spent on the action potentials traveling between them.

The selection of the efficient organization happens on two levels: evolution and individual development. Of course, the selection processes happening in the individual development have themselves developed through evolution. The first form of these processes is the placement of the different types of cells and the formation of the axons. This happens mainly before birth, and is based (among other things) chemical signals that different cells emit around them to guide each other, and gene activation/deactivation that makes the cell types different.

Later, both before and after birth, learning processes happen. These processes change the connections between neurons. The simplest model of these processes is Hebbian learning: “neurons that fire together wire together”. This kind of learning (in addition to being important for memory) amplifies the connection distributions that result from geometry. For example, one reason that ocular dominance columns are formed in the brain might be, that it just is more efficient to pass messages from one eye to one area and from the other eye to another area, and Hebbian learning then makes this contrast stronger.

So, together these many kinds of processes make brain structure be both very efficient and, luckily for us scientists, easier to measure and understand.

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Visualizing the Brain

As this week’s topic was brain connectivity, one may find it an interesting question how different types of connectivity information can and should be displayed to the user. This tries to be a brief overview of some methods that have been used in the last decade or so. As we already noticed in the very first exercises of this course, brain anatomy is difficult. It is as if the brain was more complicated than all the other body parts and organs summed up. When you see anatomical sections for the first time, it is (at least for many people) immensely difficult to visualize the information in 3D. If one manages to imagine the brain in 3D, it is far more difficult to imagine brain connections – countless of them – in 3D, based on some connectivity matrices or some format of 2D illustrations. Luckily, today’s technologies are here to rescue us from this problem. Despite being very useful overall, playdough does not help much with visualizing connections, after all!

Historically, it has been difficult to make sense out of the brain. As we know, the brain surface has countless folds – sulci and gyri. Sometimes, the brain is presented as though having a smooth surface. Still, drawing a smooth or smooth-ish brain surface gives a false impression about the proximity of two points on the surface. Furthermore, colors are sometimes used to display the folds. However, if other information needs to be displayed, it would be wiser to save colors for that purpose. Fortunately, we now have the tools to build quite accurate 3D models of the brain. These models can then easily be rotated and zoomed into. They can enable changing the opacity of some layers to see others, as well as opening up the folds of the brain to a wanted degree. So, who builds these models and how? How accurate are they?

One huge project is the Human Connectome Project, also mentioned on the lecture. In this project, analysis is performed e.g. on the time series data of brain function and functional connectivity maps are formed. On the other hand, The Allan Institute for Brain Science collects data in their huge, free database: The Allen Brain Atlas, in which they started a large-scale program to gather data about the human brain in May 2010. The Atlas maps gene expression across the human brain, combining genetic and anatomical information. They slice donated, frozen brains into thin micro-slices and, for example, mark RNA molecules with in situ hybridization techniques. Thus, they acquire information of which genes are expressed and to what degree. Combining this with anatomical information acquired with MRI and DTI, they are able to produce highly detailed 3D models which can be viewed with their Brain Explorer 3D software. With this tool, researchers can investigate clues to the role and function of genes of interest in disease. So, this was already great back in 2013, but what is done now, four years later?

Screenshot of the Allen Brain Atlas database

Today, we have several technologies which utilize the idea of gamification in mapping brain connectivity. One of these is Mozak – the brainbuilder, and another is Eyewire. Mozak attempts to trace and classify neurons from neuronal 3D images, whose structures are branched and thin and therefore difficult to correctly detect with computational methods. Humans can help in this by playing the game. Eyewire – like its name suggests – aims at discovering how neurons process visual information. At the same time, the user-generated reconstructions can be used to train their AI further.

The explosion in computational power and novel technologies are beginning to also enable real-time analysis of brain circuits. Hopefully, what is utilized in research, will soon be available for educational purposes, and we can enjoy the 3D maps on our lectures and exercises. The VR hype is widespread, so this is to be expected. One of the TEDx Talks presenters compared the development of brain visualization techniques to the birth of Google Maps – and not without a reason.

Some interesting TEDx Talks from 2013: [1] [2]

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Using magnetic stimulation to combat depression

During this week, we attended two excursions. A topic that came up in parts of the trips was how depression can be treated using electroconvulsive therapy, and more specifically transcranial magnetic stimulation. The idea of doing this is fascinating, as it would mean depression could be localized in the brain, and the effects that cause depression (or the effects caused by depression as well, presumably) can be reversed with relative ease. Depression seems to be such a complex issue that one wouldn’t think could be simply “turned on or off.” Perhaps this only effects depression that doesn’t have an active contributing cause for it, such as a death of a loved one? Although of course all thoughts are caused by some brain activity, I doubt the cure would in essence, overwrite thinking to prevent negative thoughts.

First, the area where effects of depressions are happening must be located, usually one side of the prefrontal cortex of the brain is less active than the other. After knowing which area to interact with, TMS works by first placing electromagnetic coils on a subject’s forehead. A pulse is created by a magnet, which stimulates the desired nerve cells. This pulse should cause no significant side effects apart from a slight tingling sensation, and consciousness is not interrupted. Some slight hand or feet movement might also be observed. In rare cases where the treatment is usually applied incorrectly to the wrong area of the brain, an epileptic episode can occur, but there have been no lethal consequences. This treatment needs to be repeated for up to 40 minutes a session, multiple times per week for months to have the desired, long lasting positive effects against depression. Once successful, depression should be lowered or removed for many months with little to no continuing treatment. For some people there is no change in their level of depression, although for more than half of the subjects tested there is. Perhaps this calls back to my earlier speculation on different forms of depression?


Some of the people who have been able to benefit from TMS include people who suffer from post-natal depression, when depression is accompanied by psychotic features, bipolar disorder, mania, schizophrenia and those who do not respond to antidepressant therapy. There are those who cannot receive TMS therapy, for example those with brain stimulators, metallic implants, shrapnel, tattoos with magnetic sensitive ink or aneurysm clips or coils.

There are other forms of ECT, implanting a device under the collarbone skin which periodically stimulates the brain is called VNS, but it is rarer and only recommended to long term reoccurring depressive patients. As the technology improves, hopefully more people will be able to benefit from this treatment, and the long lasting effects of it are made more accurate and permanent!

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Muscle control and the computational power of the subconsciousness

This week’s subject was muscle control in brain and spinal cord. The lecture and the book gave lots of information, but one simple anecdote greatly astonished me: details of walking are controlled by the spinal cord, not by the brain. I can consciously decide to walk, and also where to walk and how fast, but usually I make no decisions about and pay no attention to the actual movements of my feet. Thinking about this while actually walking on the break in the middle of the lecture made me feel a mixture of awe and horror (yes, sometimes I am a bit dramatic). In a sense, I felt I was lacking control over myself!

Many movements get more detailed instructions from the brain, but even there, conscious mind is mostly interested in the big picture and not the details. There are many brain regions that work on movement control. The motor programs of cerebellum seem to be pretty important for the details of movement.

The authors of the course book (Bear, Connors & Paradiso) really like using baseball as an example of motor control. I don’t know much about baseball, but I think there are players whose job is to catch a flying ball and players whose job is to hit it with a bat. So, the get changing visual data, from which they have to calculate the nearest point where the ball is going to pass them. Then they will calculate the suitable movements for each muscle in their bodies in order to catch or hit the ball. This is a very complicated mathematical process, and the players seem to achieve the solution in less than a second. How is this possible?

The comparison of brain to a computer is as old as computer science itself. Apparently, even Countess Ada Lovelace considered this metaphor while mentally simulating programs for Charles Babbage’s analytical engine in the early nineteenth century. This comparison has its limitations, but also benefits, and seems very suitable for the discussion at hand. (By the way, I recommend John von Neumann‘s posthumously published book The Computer & the Brain. It is unfinished and partially outdated, but has some interesting points and of course historical significance. It’s a short book, I read it completely yesterday.)

Why is doing complicated maths on Python interpreter or Java virtual machine much less efficient than doing them on a C program? Why is the C program still slower than an optimized Assembler program? The difference is in the abstraction level. The case of Java virtual machine is the most obvious one, since the answer is in the name: it is a computer simulated on another computer. Obviously, a computer can’t simulate (in real time) another computer faster than itself. In each case of a higher level system, the code has to be somehow interpreted on a lower level. To put it simply, this interpretation process takes some part of the efficiency.

The brain can be seen as a very powerful computational machine (but not one having the von Neumann architecture on which almost all artificial computers are based). The conscious mind, then, is a program or an operating system running on this machine. It is a complicated piece of software that has a very high abstraction level. Thus, it also has quite limited mathematical capacity. It is often said that human working memory has capacity for about seven variables at a time (but because of the high abstraction level, these variables can have quite different amounts of information).

So, it is quite understandable that our subconsciousness has a much greater computational power than the conscious mind. That’s why we can safely automate big part of our everyday life, such as walking.

I’m ending on a side note this topic reminds me of. A course mate recently said me something like “we can image brain function but we have never seen a thought”. Well, that is because of many reasons (none of them requiring any supernatural assumptions). In addition to the limitations of modern brain imaging technologies, our interpretational capabilities are limited. How could a conscious mind with its limited mathematical power and working memory even begin parsing a thought from the action potential frequencies of the millions of neurons producing it?

Computers, of course, can help. Using them to interpret all aspects of a human thought would be an enormous task, but they have already been used to reconstruct some human-understandable material from brain activity.

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Sound perception and tone deafness

This week’s topic was the auditory system in the brain, starting from the anatomy of the ear, ending with sound perception. Since valuable information of systems – especially in the brain – is often acquired by investigating disruptions and diseases in the system, it is quite natural to start thinking about disturbances in sound perception. Music has always been a big part of my life, and thus I find it interesting how it is possible that some people cannot distinguish notes from each other. Moreover, some people seem to be able to learn this skill, if they put in time and effort, while others seem to be incapable of this.

It is, indeed, the case that most people are able to learn to improve at pitch discrimination and singing in tune. In this case, there is no apparent impairment in the brain structure, but these kinds of skills, of course, require some training. Intuitively, the auditory system seems much simpler than the olfactory system, for example, because there are far less tones than there are smells. Yet for some people, learning pitch discrimination seems to require a huge amount of effort, while for others it is completely effortless.

The “real” tone-deafness is called amusia, which can be “acquired” as a result of brain damage or it can be innate, in which case it is called congenital amusia. About 4% of the population suffers from congenital amusia, where fine-grained pitch discrimination does not function properly. Different types of amusia may combine dissociations in of the elements of music processing: rhythm, melody and emotional associations. People with congenital amusia cannot recognize dissonant chords, whereas most newborns already possess this ability. In addition, they are more likely to also lack a sense of rhythm.

It seems that there are many ways by which amusia could result. In EEG experiments, it has been often observed that compared to “normal brains”, amusic brains lack auditory communication to higher brain areas. Structural neuroimaging experiments support this conclusion, as people with congenital amusia have weaker connections between frontal areas and posterior auditory areas. Therefore, the problem lies in perceiving the pitch, not in the brain detecting the notes. Based on “acquired amusia” brains, a few areas have been reported to have a connection to it. In addition to the primary and secondary auditory cortices, these include reduced white matter concentration in the right inferior frontal gyrus and the absence of the superior arcuate fasciculus.

Sound perception seems to be incredibly complex. If someone can be tone deaf, would it be possible that to some people, harmonious chords sound like they are not in harmony at all, but do not sound like noise? How can we know that our experiences of the chords are similar, in the first place?

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The Visual System and Intuition

The visual system. That is what this week’s lecture and exercise session attempted to teach us about. The visual system is interesting in the sense, that it is the way through which us animals can infer the most information of the “real world” surrounding us. Yet, it has plenty of limits and it thus “fails” us in many situations. This becomes especially evident in 2D-images, as our brain seems to be used to interpreting the 3D-world, where moving around can give more information of the “actual” situation.

I watched a neuroscience discussion video, where Dale Purves, who is Geller Professor of Neurobiology Emeritus in the Duke Institute for Brain Sciences, Jean-François Gariépy and some others discuss the function and evolution of the visual system. Dale Purves held an opinion that we should not, in fact, talk about our visual system “failing” us, because the term itself is misleading. It is true that we cannot see the outside world as it is “in reality” – whatever it may mean – but the mechanism we perceive it is always the same. If there are no errors in the mechanisms, then we should not call it a failure.

One of the discussion participants held a Bayesian view of what happens in the visual post-processing: in our brain, we have a model of the world, and every time we see something which differs from the model, we either change the model or become convinced that what we are seeing is somehow false information. To me, this sounds intuitively very convincing. Professor Purves, however, was skeptical towards this perspective, as there is “no evidence” for this actually happening on the level of neural circuits.

To me, all this discussion highlights an important point: it matters, how we talk about things, what kind of vocabulary we use. It matters, because the way we talk about things automatically shapes and limits our understanding of the concepts discussed. It may be that we miss the actual nature and mechanism of the phenomenon we are investigating, just because we intuitively feel that it should work in such and such way. Especially now, that the subject matter is our own brain, we are prone to get mislead by our intuition. Therefore, we should be careful with the language.



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Dopamine addiction and drug usage

This week during the lecture a subject which was touched on was dopamine paths and receptors in the brain. Since dopamine creates pleasurable feelings when released, it can be easy to be addicted to any activity which releases the substance. One of the ways to release it is to use drugs, however many drugs cause significant negative health effects and death to the user if taken in too high concentrations which is easy to do due to their addictive nature.

When the neurotransmitter dopamine is released in the nucleus accumbens, the human mind feels the sensation of pleasure, hence the region is often referred to as the brain’s pleasure center. The intensity of pleasure depends on the amount of dopamine released in a short time period. Hence not all substances cause the same amount of pleasure, nor are they as addictive. As the mind is rewarded with a big dose of dopamine, it will be programmed to crave that same release. The intention of pleasure after all, is to motivate our mind to perform some beneficial action to either ourselves or to keep our species going. Hence eating tasty food and having sex release dopamine and are addicting.

brain slices

Drugs bypass the dose which is naturally possible to release, the amount can be up to 10 times higher compared to the previously mentioned methods. As the “intended” amount is exceeded, there are negative consequences. The problem with drug use is that the brain adapts to the overwhelming amount of dopamine which is released by producing much less dopamine and eliminating dopamine receptors to try to lower the quantity to normal levels. This causes the situation where more drugs need to be taken to achieve the same intensity of pleasure, and as less and less dopamine is available, ultimately the original level of pleasure becomes impossible. Thus, the doses of drugs taken become higher and the effects lower, and the brain increasingly craves the pleasurable feeling which it has been trained to expect.

Recreational drugs are not deadly in small doses, but as explained before the users are compelled to increase the dosage. Not all drugs are as deadly, but some of the more dangerous ones are opioids. Heroin for example, which is an opioid, kills by respiratory failure. Another example is Cocaine which kills by heart attack, and many prescription drugs can be overdosed.

Drug overdose is the leading cause of death for Americans under 50.

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Serotonin and depression

Currently, the lectures of the course are very much concentrated on neurotransmitters. I will use this as an excuse to write about depression and it’s medication.

As everything in neuroscience, depression can be seen from many different perspectives, and combining these gives us a wider field of vision. The perspectives I can offer are the neuroscientific one (based mainly on combination of Bear, Connors & Paradiso and Wikipedia) and the one of personal experience. I was diagnosed with depression last winter. Other useful point of view would be for example that of mental health care professionals’.

In colloquial language, depression has become a synonym of sadness. It is, however, more than that: a serious mental illness. Sadness and low mood are definitely symptoms of depression, but additionally there is for example lack of energy and motivation, a feeling of simple (and even fun and desirable) tasks being impossible to face. Additionally, sadness as a symptom of depression is different than as a regular feeling: it is generally more intense and doesn’t require a direct reason, instead being more like a positive feedback loop of negative emotions. On the other hand, depressed people aren’t necessarily always sad, and one can’t just decide someone is not depressed by just looking at their face.

Psychologically, depression might be triggered by stress or traumatic events, but typically this happens to people who have some kind of (genetic) susceptibility for it. From neurochemical perspective, this seems to be related to the neurotransmitter serotonin. Among other functions, this amine regulates mood.

Selective serotonin reuptake inhibitors, or SSRIs  for short, are one class of antidepressants. There are also other kinds of drugs against depression, but I will discuss only SSRIs here, because they are perhaps the most common kind and I have personal experience on them.

As the name suggests, SSRIs inhibit reuptake of serotonin to cells, increasing its concentration in synaptic cleft. Thus, the first interpretation would be, that SSRIs help the travel of of serotonin-mediated signals that are associated with positive feelings. The truth, however, is not as simple as that.  The anti-depression effects of these drugs take several weeks to really start showing. At that point, the cells have adapted to their presence by reducing the amount of serotonin receptors. It is not clear why this reduces the symptoms. One effect demonstrated is increased neurogenesis in hippocampus.

In addition to the science really not being settled, there is the fact that each individual human is different. There are many different kinds off SSRI drugs. Some of them work for some people and not for others. Some people will experience side effects. Scientific research can usually give us only general ideas. For example, some studies suggest that at least some SSRIs work better for people with more severe depression. For an individual, the only way to see if some specific (clinically approved) drug is suitable is trying it for couple of weeks, seeing how it feels and then possibly adjusting the dosage before waiting again for the effects to take place.

For example, I was first prescribed escitalopram. I felt that it made me more socially initiative, but the symptoms of depression kept getting worse. (At that point I didn’t have the depression diagnosis yet. The initial prescription was for anxiety, for which the SSRI medication is also used.) After I was barely able to handle my studies and other aspects of life even with highest dosage of escittalopram generally in use, I was switched to sertralin. Initial dose for that was also too small, but after a couple of times of adjusting it, the effects became clear. I have energy to study and do things I love. I have a working sleep cycle. I have lots of creative ideas. I feel like I have got my own personality and self back.

The medication isn’t a simple magical cure for depression. SSRIs have their problems. The important thing is that they can give the strength needed for rising from the depression. I have a weekly session with my psychotherapist and do mindfulness exercises to get better understanding and control over my emotions.

From our course book I learned that serotonin is made by cells from tryptophan, which we can get from many kinds of foods, including chocolate. It’s not scientifically proven that more dietary (non-purified) tryptophan leads to more serotonin in the brain, but chocolate can improve the mood in many ways 😉

So, remember to eat chocolate every now and then and please be kind to people with mental illnesses!

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Modeling neurotransmitter receptors

The third lecture and Chapter 5 of the course book handled synaptic transmission – especially chemical synapses, which play a huge role in developing effective psychoactive drugs, which could help e.g. in the treatment of depression and anxiety disorders. Chemical synapses work much more slowly than electric synapses, as it takes time for the release and diffusion of the neurotransmitter before the next neuron will be activated. The mechanisms of recycling the neurotransmitters are quite complex: in addition to simple diffusion, there are reuptake mechanisms which result in enzymatic degradation or recycling of the neurotransmitter. Alternatively, the transmitter can be degraded in the synaptic cleft. Autoreceptors at the presynaptic terminal often ensure that the signal transmission stops at some point.

As we have seen, the mechanisms and molecules related to synapses can be quite complex. It leaves you to wonder, how you could model the situation somewhat realistically. It must be essential to be able to, say, determine the proper dosage of psychoactive drugs. It should be understood, what the desirable levels of, for example, the monoamine neurotransmitters serotonin, dopamine or norepinephrine are. Excess and deficit amounts of these transmitters have been associated with many disorders, such as depression, ADHD and even schizophrenia [1].

One challenge is the discovery and analysis of the receptor structures involved in chemical synapses. Once it is known that by inhibiting a receptor we can have a certain effect on the brain, we have new questions. What kind of a molecule can we use to inhibit this receptor? What is the obtained level of inhibition? Modeling can offer some insight to this question. A pharmacophore is such a combination of features that defines how ligands are able to bind to a receptor. It may, for example, define locations of cationic and anionic groups and hydrophobic and hydrophilic parts of the molecule. The development of pharmacophore models and visual screening (VS) based on them can thus suggest molecules that would inhibit the receptor [2]. Drug development is often centered around the concept of trying to create compounds that have very similar structures to the ones that are known to work. Visual screening, however, can provide good potential inhibitors that have very different structures.

After this stage, however, many questions remain. How effective is that molecule in reality? Is it selective? Does it have potential side effects? Is it able to cross through the blood-brain-barrier? The deeper you go into this topic, the better you understand that we barely understand anything at all!

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