The “spectrumization” of neurological disorders
Historically and socially, psychiatric diseases have been defined as these yes-or-no disorders of the brain: he has schizophrenia, he doesn’t. He has autism, he doesn’t. For the cases that were not crystal clear, the neurologist just had to take his or her best guess as to whether or not the patient suffered from a disease, or whether the patient was within the boundaries of normal. But with the ever-increasing understanding of how absurdly complex the brain is came a shift toward a “spectrumization” of psychiatric disorders. Researchers and physicians started noticing just how similar some of these psychiatric patients were in their biomarkers, even though they had different diagnoses. Patients were no longer simply a binary diagnosis, but became a single point along a spectrum of symptoms and disorders.
With this newer view on psychiatric disorders, there is currently much more flexibility in terms of the diagnosis of an individual. The most well-known of these spectrum disorders is the autism spectrum disorders (ASD) that include Asperger’s to full blown autism to Rett’s syndrome. Side note: all of these are thought to be developmental disorders, along with schizophrenia, demonstrating just how ridiculously important the proper development of the nervous system is. I’ll write more on this later since neurodevelopment is just so ridiculously interesting. Each of these autism spectrum disorders has some or many overlapping symptoms with one another (behavioral, electrophysiological, genetic etc.), with varying degrees of severity. However, each disorder still has its own unique symptom or combination of symptom(s) that make it its own disorder. This is true for mood disorders (depression, bipolar, etc.) and other psychiatric disorders too (different symptoms of schizoprenia and schizoid disorders for example). I am sure that our classification scheme for psychiatric disorders will change as the research into these fields becomes even more fruitful.
It isn’t wholly surprising that this trend has developed. We know the brain to be a very densely packed mess of highly connected cells that communicate with each other using a myriad of different chemical compounds. The effect of subtle but significant changes in the wiring and firing patterns of these neurons can be quite large, and similar changes between individuals at the level of neuronal connections can lead to similar changes at a higher level such as behavior. What complicates matters is that in many instances the same abnormalities at the circuitry level can be caused by different molecular and genetic underpinnings, and that very similar circuit level differences can in fact lead to vastly different behavioral outputs. Most psychiatric illness diagnoses are at the level of behavior currently, but there is a huge push in neuroscience research to find reliable biomarkers that are not as subjective as behavioral tests.
This of course stems from the belief that all thoughts, feelings, emotions, and consciousness are firmly rooted in the physical world ie our neurons within our brain. If you are a believer in dualism (a la Descartes) then this obviously does not apply, but I find it very difficult to believe in dualism with our current understanding of our nervous system, as limited as it may be. If the thoughts of an individual are irrational, delusional and/or fly in the face of reason and evidence, then it follows that something must be different in the substrate responsible for these thoughts when compared with a “normal” substrate. But, how does one define a brain as being normal? This is a tough question that I don’t have a good answer for, as it is a mix of both biological and social criteria that defines a “normal” brain. But, I imagine that there is quite a broad spectrum of what can be categorized as “normal”, just as there are broad spectrums for the various diseases as well.
Objectivity in Science (and prions)
One of the most difficult things to do as a scientist is to objectively view your own research/data without either underselling it or overselling it. The challenge lies in being keen enough to interpret the “true” meaning of the data you have obtained, without being sidelined by false hypotheses. To become more certain of your own singular hypothesis that could explain everything you see, you naturally do more experiments to rule out the other possibilities that you do not believe in. This of course is a never-ending cycle, as the deeper you explore the research area the more you realize you need more information.
See the problem here that can arise if you’re not careful? The motivation behind doing these experiments is to support your hypothesis and rule out others, but if you exclude the possibility that your initial hypothesis might be wrong then you are suffering from bias (the way I put this makes it sound like a disease, I guess it can be viewed as an intellectual disease of sorts). You have to let data guide your ideas, not the other way around.
I recently had a chance to talk with a very famous scientist who had previously given a talk that same day about a potentially fascinating mechanism related to learning and memory. He proposes that naturally-occurring prions are involved and critical for proper memory formation, which is a completely wild idea.
Some background on prions: The term “prion” comes from the words “protein infection” because, for all intents and purposes, the infectious agent in this case is literally a misfolded protein that can cause other proteins to misfold as well (probably through physical contact with another copy of the protein). This protein misfolding is like a snowball effect, where over time the copies of misfolded proteins become exponentially greater. These misfolded proteins form aggregates that accumulate in neurons and cause neurodegeneration to occur within the brain.
Here is a diagram of how the prion infection spreads at a protein level. The red is the prion form of the protein, while the blue is the normal form of the protein. You can see once the proteins in the native conformation interact with the proteins in the prion form, the native proteins turn into the prion form and are added onto the protein aggregates.
Normally we think of infectious agents as being viruses or bacteria, all of which are DNA/RNA based agents. Just imagine how small these things are in terms of physical size, and then think about how prions are literally at the scale of a single protein and can cause neurodegeneration/death. There still is no cure for the prion diseases in humans, and it seems like it will be rather difficult to discover one. The initial prion disease that was found was called Kuru, which is a great story to read, and the major one we think of today is Creutzfeldt-Jakob disease. Relevant to this new finding of involvement of prion-like mechanism in learning/memory is that there have been no documented cases of a prion-like mechanism being beneficial in any higher eukaryotes.
This famous scientist’s prion model for learning and memory is essentially that the reason we can have such long-lasting memories is due to the persistence of prions within our neurons. When new information is learned by the organism, it causes aggregation at synapses of the prion-like protein they have identified in the brain, thus causing a long-lasting change at that specific synapse. Wild idea, but to me this model just cannot explain all of the phenomena we have already observed related to learning and memory. When I asked him about how his prion model for learning/memory could explain the fact that specific memories move around from brain structure to brain structure, he couldn’t provide an answer. When I asked him how the neuron can regulate the prion aggregation and prevent a snowball effect, he couldn’t provide an answer. To me, these are a few of many fundamental questions that need to be addressed before I would believe such a model for learning and memory, but this scientist is pushing for his model very hard. I do not believe it is a case of scientific neglect, but simply a case of tunnel vision. He wants to believe this model so badly that he does not search hard for alternative explanations to his data.
This objectivity is something I need to keep in mind for my own career as well. Finding the perfect balance between skepticism and cynicism toward your own data can be very difficult in science.
Perception of graduate school and what it means to me
After telling friends and people that I am a third year graduate student, the inevitable question that follows is: “So how many years do you have left until you graduate?” And my response usually goes: “We’ll see. Could be 2, 3, 4, 5…..years.” They usually respond with a “Sounds tough” or “Good luck” or even just a “Damn”, and I just smile. These people are confused as to why anyone would subject themselves to the torture of years of more schooling.
The very real truth of the matter is that I absolutely love what I do and wouldn’t want to be doing anything else. No other kind of school (save maybe medical school) or lifestyle is as rewarding for me at someone of my level of experience. Biology (and neuroscience in particular) is just too fascinating and learning about these subjects and developing strategies to answer unknown questions in the field are things that really really excite me. One of the most intellectually rewarding things I have come across is just talking science with other people in your own field, or even outside of your own field. Yes, technically I am still in school, but it doesn’t feel like it. PhD graduate students in the sciences get paid a stipend and even though it is probably half of what people my age are making at a real job, we get PAID to LEARN. How did I ever end up in such a fortunate position in life to actually be paid to learn things? It still amazes me to this day. Plus, while undergraduate education involved mostly taking classes, graduate school is all about doing research in a lab which is a much more interesting way of learning about science.
Yes, you have probably heard the stereotype of the cynical/pessimistic/depressed graduate student who is 40 years old and still working on his/her dissertation, but I honestly can’t say that I’ve ever really expressed worry about not having enough data to graduate. I have greatly worried about my experiments not working and not producing data, but that is strictly because I wish to discover the answer to the scientific questions that I have sought to answer, not for any other reason. It is the questions that drive me to work hard and to get results, not the desire to graduate. For me, the degree is only viewed as a secondary bonus: the real prize is finding convincing evidence to support an interesting scientific hypothesis.
I know I may be in the minority in thinking this way, but to me it is the most logical way of thinking about things. I know that the job market for life science PhDs is very difficult, and most individuals will not be getting paid a large salary for the huge amounts of work they put into their career. I knew all of this before going to graduate school. What difference does it make to me whether or not I have a PhD? The nature of the work doesn’t change very much after you graduate—you still are at the bench, or are writing grants, or talking to other scientists. This is why it is the desire to find answers that drives me, not the desire to graduate. I know that this is most likely idealistic of me to think this way, since right now I don’t have to provide for a family or have any other strong monetary obligations, but since I know I really like the scientific life I will make it work in the future somehow.
I believe the crux of the problem is people’s perceptions of the value of education. Many students who attend undergraduate universities view it as a means to an end. Get a degree, then find a job. But to me, it has always been that education in of itself is the end. Learning in of itself is the reward. I wish more students and people would view their education this way, as it would make their lives much more enjoyable I believe.
This is a confocal image of a mouse hippocampus, specifically the dentate gyrus region of the hippocampus. The red staining is against a protein that is expressed in mature neurons, while the neurons marked with green are transgenically expressing green fluorescent protein (GFP). The reason why you only see a few green neurons as opposed to the red staining which marks so many more neurons is because this transgenic mouse strain’s neurons only express green in neurons that have undergone some form of electrical activity. This image was taken after a learning and memory task, so presumably these green neurons are the ones directly responsible for encoding the new memories that were learned, since they were the ones that have been activated as determined by the GFP expression being turned on in these cells.
Taken from Matsuo et al 2009 Frontiers Behav Neurosci
My brain visually stored this image as soon as I saw it because it was so striking to me. This is an image of a dissected, opened mouse retina that shows labeling of a specific subset of retinal ganglion cells (neurons within your eye that are responsible for the different qualities of vision). The transgenic labeling shows that these neurons have dendritic arborizations which point in a single direction (downward/ventral). Most likely due to this one-sidedness of their projections, this class of cells is responsible for recognizing only movement in the upward direction. The thin streaks that are also labeled (and where the arrow is pointing) are the axons of these cells, all converging near the center of your eye to form the optic nerve, which then sends visual information to be processed by your visual cortex.
Image taken from Sanes et al 2008 Nature
One of the difficulties in fluorescent imaging of neurons has been that light penetration of the excitation lasers needed to observe fluorophores does not penetrate deeply enough. The laser scatters the deeper you go due to the tissue itself scattering light, which renders clear imaging impossible at very deep levels in the cortex and other brain structures deeply embedded in the brain. However, if the brain were clear, then light would be able to penetrate much deeper and clearer images can be taken at much deeper parts of the brain.
Recently, a group at Riken has been able to get around this issue by developing a reagent that turns tissue clear and, thus, amenable to imaging. Left is a mouse embryo that has been soaked in saline (salt water), while right is a mouse embryo that has been soaked in their reagent known as Scale. It is pretty clear from this photo that they have made the tissue of the mouse embryo transparent! This is not usable in living tissue yet since the reagents used are pretty harsh to the tissue, but the group is developing something in hopes of applying it to living tissue. Think of the possibilities!
From Riken
The bombardier beetle in a defensive posture, spraying its acid spray in response to a potentially threatening stimulus (in this case a finger). The mechanism behind this defense system was discovered by entomologist and chemical ecologist Thomas Eisner. This beetle stores two inert compounds (hydroquinone and hydrogen peroxide) inside two separate ducts, but when threatened, the contents of the ducts are emptied into a single cavity that secretes enzymes (catalases and peroxidases). This produces an exothermic reaction that heats up the fluids to boiling point, and causes the pressure required for the explosive nature of the expulsion.
RIP Tom Eisner
Food for thought
“I have often wished that Jefferson had not used that phrase, “the pursuit of happiness”, as the third right—although I understand in the first draft was “life, liberty and the pursuit of property”… Still, I would rather he had written life, liberty and the pursuit of meaningfulness or integrity or truth.
I know that happiness has been the real, if covert, goal of your labors here. I know that it informs your choice of companions, the profession you will enter, but I urge you, please don’t settle for happiness. It’s not good enough. Of course, you deserve it. But if that is all you have in mind—happiness—I want to suggest to you that personal success devoid of meaningfulness, free of a steady commitment to social justice, that’s more than a barren life, it is a trivial one.”
Toni Morrison, Rutgers University Commencement 2011
This quote has been on my mind ever since I read it. I think it is incredibly true.
For those of you who don’t believe there is beauty in science. This is a dissociated hippocampal neuron with an antibody stain against MAP2, a common cell body and dendritic marker. Billions of these guys make up the human brain through trillions of synaptic connections. Neurons really are beautiful.
Image taken from our own lab.
Sleep, and why we need it
One of the most enigmatic behaviors we engage in occurs every night (or, for some of us, every morning or afternoon too). Sleep has long been observed and practiced evolutionarily speaking, but the exact functions of sleep are still unknown to us. Some claim that sleep is not necessary since there are people who do not sleep for decades but are still fine, while others say that sleep is critical for every single biological function ranging from physical recovery to memory processing. We do have some ideas as to why organisms sleep, but what is the main function or functions of sleep and how do we test this?
To test the possible functions of sleep, most of the research has been done on human subjects since many of the readouts from these tests have to deal with cognitive functions. One cannot very easily ask a mouse whether it feels less alert or whether it doesn’t remember things as well (although there are tests that indirectly address these cognitive functions). As you may know from personal experience, sleep deprivation has been shown to have some pretty negative effects on your brain’s functioning, including attention span, memory acquisition, memory consolidation, memory retention, etc. the list goes on… These brain functions have been implicated in sleep due to studies that test the effects of sleep-deprivation on humans or animals, and reporting back whether or not deficits are present in different behavioral tasks. Using a wide range of techniques from fMRI brain imaging to single neuron recordings, we are pretty confident in claiming that if you lack sleep you will not function optimally in many memory-associated behaviors.
One pretty cool model for sleep that has arisen from these sleep-deprivation studies is that sleep functions to “dampen” or weaken the synaptic connections. The “synaptic homeostasis model” is pretty straightforward: when we are awake our synaptic connections increase, and when we are asleep our brain downscales these connections back to a proper, functional level which would free up more “space” for subsequent newer connections. Thus, when sleep does not occur, neuronal connections and neuronal circuits would essentially saturate and hinder any new learning that might be attempted. Other bodies of research strongly implicate sleep in proper consolidation of long term memory. Both of these ideas have come about from sleep deprivation studies, but in order to convincingly show sleep’s normal role is for these functions, we have to be able to induce sleep. (Sidenote: this is a pretty important point in science, in that for a model or hypothesis to be correct it cannot truly be supported by loss of function experiments alone, but also must be supported by gain of function experiments.)
Recently, we have finally been able to control the sleeping and waking of a model organism—the fruit fly aka Drosophila melanogaster. Surprisingly, sleep or a sleep-like state has been observed in Drosophila, and even as low on the evolutionary ladder as the nematode C. elegans. Drosophila in particular have provided lots of insight into our understanding of sleep, from behavior to circuitry underlying sleep. Since these model organisms are particularly amenable to genetic manipulations, it is no surprise that Drosophila is the first organism where we are able to manipulate sleep through the use of tricky genetic tools.
On the left is an electron micrograph image of a whole fly, when you are looking head-on at the head. On the right is an image of the fly brain with all neurons in red, in the same orientation as the left EM. The green is labeling a subset of neurons that make up important olfactory memory structures, and you can clearly see the morphology of a single one of these neurons
This paper is the first paper to functionally manipulate a neuronal circuit and actually induce sleep in flies. This now provides us with a great tool to study the effects of sleep and not just sleep deprivation! They determined this by first expressing a special type of temperature-sensitive cation channel in a very specific structure of the fly brain (fan-shaped bodies). When placed in a hot environment, the channels would open and allow cations such as Na+ to flow in, and the net effect was that it induced sleep in the flies.
Next, they tested the synaptic homeostasis model for sleep mentioned above by housing the flies in a “socially enriched” environment. This environmental condition has previously been shown to increase the number of synaptic terminals, and when the researchers on this paper tested the flies’ long term memory after being raised in this environment, they found that memory was impaired when they tried to test the flies after training them. However, if they induced sleep right before training the animals, they found that the flies were now able to retain the memory! In effect, this strongly supports and corroborates the existing data regarding sleep’s effects on synaptic homeostasis
To test the effect of sleep on memory consolidation, they trained the flies using a behavioral protocol that shouldn’t induce long term memory formation. But, if they induced sleep right after training the flies, the flies then were able to form long term memories from their training! So now we have good, positive evidence for the functional role of sleep not only from sleep deprivation studies, but from sleep-induction studies. This is the first paper that has ever been able to really cleanly show an endogenous function for sleep, and it’s actually quite exciting to me.
From these results, you can make your own decision as to whether or not you should change your sleeping pattern, or whether you want to pull an all-nighter next time to study. Keep in mind that this has only been shown in Drosophila so far, but I am hopeful that data from other model organisms such as transgenic mice will provide even further support.
I realize that many of the topics I post about pertain to memory, but I do think it is one of the “holy grails” of neuroscience which is why so much effort and research is devoted to better understand it.
