Studying one chemical at a time in cells or rats will not be able to predict what the effects in women might be. As one basic scientist said to me the problem with studying women is that “they are so messy and we can’t control all the factors.” But it is this messiness, or complex web of factors that leads to breast cancer. And it is only by studying women in all their complexity that we will be able to figure out all the causes of breast cancer.

This kind of bluntness is much needed in the media, although it should also be noted that this complexity does not rule out the extant utility of animal studies.

*I respect Dr. Susan M. Love for not using her post as a promotion of her book, Dr. Susan Love’s Breast Book, or to overtly push her organization, The Dr. Susan Love Research Foundation.

Image via Wikipedia

You are never alone. Not even when you might want to be. Tucked away within the ~100m² of your bowels are ~10¹⁴ (there are ~10¹³ somatic and germinal cells in the human body) of your closest friends, collectively termed The Microbiota. They eat, spawn, conjugate, die, poop, fight, and secrete right there inside of you, unseen and mostly unthought of except when something is wrong. This system, the remarkably homeostatic mammalian gut, forms what is perhaps the densest and most complex microbial ecology on this planet.

These teeming microbes are not mere freeloaders living off of your excess at their own convenience, they are true symbionts. In exchange for a warm, wet home and nutritional supply, they break down starches for us, metabolize complex molecules, and synthesize some key compounds, such as Vitamin K. It has been found that gnotobiotic, or germ-free, animal models require ~30% more calories to develop normally without a microbiota to help them out. In humans that have been on a broad-spectrum antibiotics, hardier inhabitants (such as Clostridium difficile) can bloom when all of their more sensitive neighbors (such as Bacteroides spp. and Bifidobacterium spp.) are killed off, which causes very unpleasant colitis and diarrhea, that can then be cured by a transplant of fresh microbiota from a healthy individual (colloquially referred to as “poop soup”). Microbiome transplants can also transfer physiological characteristics from one individual to another. For example, the microbiomes of obese individuals have been found to have reduced numbers of Bacteroidales spp., and transfer of these microbiota via poop soup into germ-free mice resulted in obese mice, theoretically because these microbiota were more efficient at releasing calories from food.

Microbes exist, or can exist, in virtually every segment of the gastrointestinal tract from mouth to anus. In the mouth, a variety of Actinomyces spp. are associated with the formation of plaque. In the forbidding and harsh environment of the stomach, only Helicobacter pylori can thrive (it does so by hiding among the mucous lining the stomach and modulating the host immune response) and it has been found to directly cause stomach ulcers and has been further implicated in the formation of gastric cancers (it’s the only organism classified as a BSL 2+ carcinogen). The proximal portion of the small bowel is relatively sparsely colonized at ~10⁴-10⁵ microorganisms/ml lumenal contents, which contrasts sharply with the densely colonized colon (~10¹⁰-10¹² microbes/ml contents).

In the human and other mammals, diverse and distinct microbial ecologies also exist in the sinuses, ears, genitourinary tract (largely Lactobacillus spp. in the vagina; the bladder is generally only colonized in disease states [long-term catherization and/or pyelonephritis] by uropathogenic Escherichia coli, Proteus mirabalis, et al), and on the skin as a whole (mostly Staphylococcus spp.). These others will, however, be excluded from the present discussion.

However, what’s very puzzling about all of this is: how does the mammalian immune system manage to differentiate from the massive basal antigenic signals coming from the microbiome from pathogenic antigens? In other words, why isn’t the immune system raging against the huge number of microbial signals in the gut?

One of the exquisitely elegant features of normal gut physiology is that gut-associated lymphatic tissues (GALTs) mediate fine-tuned hyporesponsiveness to commensal microbiota while remaining responsive to pathogenic microbes. This flies directly in the face of most immunology, which holds that microbial antigens will always provoke a stimulatory response when ligated to TLRs, CLRs, or NODs (conserved receptors of the immune system that bind conserved molecular patterns associated with pathogens). In vitro data support this. Physiology doesn’t.

Physiologically, the germ-free mouse is weird. A germ-free animal is one that has been reared in an environment completely free of all microbes, fungi, and exogenous viruses and as such they have no native intestinal microbiota. Not only do they require more calories and vitamin supplementation, but they also tend to accumulate undigested fibrotic material in their ceca, which predisposes them to gut twists and bloat. Additionally, they feature underdeveloped Peyer’s patches (distinct GALT sites on the gastric mucosa), altered CD4+ T-cell and IgA-producing B-cell population profiles, and the follicles in the spleen and lymph nodes where T- and B-cells mature are poorly formed. All of these abnormalities can be rescued by adding back microbial signals such as LPS, even without the microbes themselves. Due to these alterations, it is becoming accepted that the microbiome plays a crucial role in the normal development of the immune system. But to reconcile this with the dogma of microbial signal + PRR —> inflammatory immune reaction is somewhat difficult, or at the very least complex.

Immune cells that reside in the lamina propria underneath the gastric epithelium generally show signs of recent activation and a particular subset of dendritic cells (CX3CR1+) has been found to extend dendritic processes up through the tight junctions binding gastric columnar epithelial cells together to directly sample the lumenal contents. M cells that cap the Peyer’s patches have been found to shuttle lumenal contents, and any antigens contained therein, to the dendritic cells and lymphocytes underneath. These pathways of antigen exposure are thought to be involved in the induction of immunological tolerance to microbiotal antigens, which could explain why the immune system does not attack the commensal microbiota. However, it does not explain how pathogen antigens processed by the same pathways are recognized as pathogenic and stimulate the immune system to attack.

Recent evidence strongly suggests that the intestinal epithelium itself is responsible for the differentiation of nonpathogenic microbiota from pathogens. Canonically, the intestinal epithelium is thought of as a simple barrier that is involved in the absorption and transcytosis of metabolites and nutrients. But it seems that it is much more involved that we had previously believed.

It turns out that intestinal epithelial cells (IECs) express TLRs and directly modulate the composition of the microbiome itself as well as the responsiveness of immune cells. This ranges from TLR expression on Paneth cells in the small intestine that secrete potent antimicrobial molecules (RegIIIg) when ligated [Dr. Lora Hooper, in seminar given 11/19/08] to actual expression of MHCII and direct antigen presentation. It was previously believed that MHCII expression was restricted to antigen presentation by dendritic cells.

When investigators deleted TLR4, NOD1, or MyD88 (an adapter protein involved in many TLR-mediated NF-kB inflammatory pathways) in murine IECs they found that the mice were more susceptible to bacterial infections, which implies that the TLR signalling on the IECs is essentially to the development of normal protective immunity. A second feature of this is that IEC TLRs and NODs are located intracellularly, instead of on the cell surface as in immune cells, which means that they’d only be ligated and activated when an invasive pathogenic microbe breaks into the IECs themselves (e.g., Salmonella typhimurium, Vibrio cholerae) as opposed to the more peaceful commensals. It may be that noninvasive gastrointestinal pathogens are recognized by the proteins that they shoot into IECs via Type IV secretions systems (e.g., Tir and Escherichia coli O157:H7) in the same manner.

The commensal microbiota is also at work on the IECs themselves, actively acting against IEC-mediated inflammation. Bacteroides thetaiotaomicron has been found to induce the PPARg anti-inflammatory (acts by increasing cytoplasmic shuttling of pro-inflammatory NF-kB away from the nucleus) mechanism in vitro. Commensal-derived metabolites such as butyrate (a short-chain fatty acid) have been found to inhibit expression of pro-inflammatory cytokines and increase expression of anti-inflammatory cytokines in IECs.

It is now thought that IECs regulate dendritic cell function through secretion of thymic stromal lymphopoietin (TSLP) and modulate T-cell activity through expression of MHCII in the abscence of costimulatory molecules. TLSP acts directly on dendritic cells and inhibits their production of pro-inflammatory cytokines (such as IL-12), which in turn promotes dendritic-cell-mediated activation of regulatory T-cells. TSLP is also implicated in skewing the immune response to a T_H2-type T-cell response, which is implicated in both response to metazoan parasites and pulmonary atopy. If naive T-cells are being exposed to MHCII on IECs without co-stimulatory molecules, then the T-cells will either kill themselves off (anergy) or mature into tolerogenic T-cells that limit the immune response to those given antigens. This, combined with widespread TGFb secretion by IECs, directly indicates an active role for IECs in promoting immune system hyporesponsiveness to the antigens present in the gastrointestinal system. Without this direct suppression of active, inflammatory immune responses, the immune system would be in a continual inflammation state due to not knowing what to do with a safe commensal antigen vs. a dangerous pathogenic antigen. Indeed, emerging research indicates that dysregulation of this process may underlie the pathophysiologies of inflammatory bowel disease and Crohn’s disease.

It’ll be interesting to see what’s found next.

Artis, D. (2008). Epithelial-cell recognition of commensal bacteria and maintenance of immune homeostasis in the gut Nature Reviews Immunology, 8 (6), 411-420 DOI: 10.1038/nri2316

[Note: this text is cross-posted from my own old science blog.]

1] I note from your publication history that you often try to better understand evolutionary paradoxes through mathematical modeling, particularly the emergence and persistence of metabolically costly phenotypes. Which of these paradoxical phenotypes most intrigue you?

Recently I am focused on the evolutionary maintenance of phenotypic traits related to cooperation under strong selective pressure in microorganisms. For me, one of the most exciting features of microbial cooperation and communication is that we can safely rule out any kind of “intentionality” from among the evolutionary mechanisms producing them – bacteria and yeasts do not have brains or nerves to mediate their behavior. Cooperation, communication and cheating – microbes obtain and evolve all these features through relatively simple genetical and biochemical mechanisms. This helps us understanding the driving forces of biological evolution in its purest form, without having to consider “cultural” aspects that could possibly complicate the issue. It just adds to the fun that sometimes we can easily translate the results into terms of animal or even human behavior.

2] What initially attracted you to the study of these systems through the lens of mathematical modeling?

I think that there is almost nothing in science that can be fully understood without modeling. Whether or not we are aware, when trying to solve a scientific problem we always think in terms of models, i.e., logical constructions deducing the consequences of a certain assembly of premises. This can be done in various ways, from using intuitively appealing metaphors and sometimes sloppy handwaving arguments to rigorous mathematical models and computer simulations. The latter are just the most reliable tools for arriving at the right conclusion from a given set of assumptions. Relatively simple evolutionary questions like the one we treated in this paper are usually approachable by mathematical or at least by computational methods, and provided that the methods are used appropriately, the results are unquestionable. What can always be questioned are the assumptions, of course…

3] Philosophically and/or aesthetically, what is more beautiful to you: the input, the output, or the mathematical model itself?

Each one, and each for a different reason. The input – the facts known about the biology of our microbes and relevant to our topic – are very interesting themselves, not just for their obvious social connotations, but also for the simple elegance of the biochemical machinery in which they are implemented. The methodology – devising and programming the simulation model – requires a very tight chain of logically structured steps, and those are usually a lot of fun to take too. But the output – the conclusions that could have never emerged without the model – is the ultimate source of professional and aesthetical satisfaction, especially if they are somewhat counter-intuitive, solve an unsettled problem, or go against the “conventional wisdom” of earlier theoretical work while staying in line with empirical observations.

4] In this paper, you used a cellular automaton in a spatial lattice approach to model cell behavior. Can you please explain what this technique is and what it means?

The name “cellular automaton” is somewhat misleading. It denotes a modeling framework fit for studying the collective behavior of any kind of locally interacting objects, whether they are living cells or something completely different. Solid state physicists are the real power users of this method, because they are mostly interested in the short-distance interactions of localized particles like atoms in a crystal. An example from the other end of the scale: sociologists use cellular automata to predict large-scale patterns of social interactions, considering whole settlements (villages or towns) as the interacting units.

The basic structure of a cellular automaton is ridiculously simple: take a square lattice of sites, and imagine a single object in each node of the lattice. These objects are called “cells”. In our model they happen to be cells – bacteria – indeed. Each cell is attributed one out of a few possible states. In our case the state of a cell was the genotype of the bacterium sitting in the given node (one of the possible 8 different genotypes). Now let the state of each cell change in every time unit, according to its own state and that of its immediate neighbors in the lattice. The instructions determining the next state of a cell are the “transition rules” of the cellular automaton. In our model the transition rules defined in what circumstances one bacterium may invade a neighbor’s node. That’s all – not very complicated, is it?

The power of the cellular automaton approach rests in the emergent collective properties of the whole lattice of cells it produces. Such emergent properties are the numbers of cells in the different genotypes after a certain number of time units elapsed, or the spatial distribution (pattern) of the genotypes, in our model. These are very difficult – in fact usually impossible – to predict from the transition rules themselves, and there is no mathematical (paper-and-pencil) method to determine the outcomes either. There are a few specific exceptions, but those are usually far too simple to be of interest for us. The sole tangible method to see the outcome of our model is computer simulation.

5] What advantage in modeling this scenario does the cellular automaton model offer over the cellular Potts model?

The cellular Potts model (CPM) is the generalized stochastic cellular automaton itself, in which the transition rules are not specified beyond universalities like the requirement for random updating. In that respect, our model is a CPM with a specific set of cell state definitions and transition rules.

6] In the paper you placed cooperative behavior in the context of metabolically costly common goods such as siderophore nutrient scavengers. In a complex ecology with a dynamic topology, such as the mammalian gut microbiota, could the same modeling principles be applied to bacteria-produced chemorepellants, such as indoles, or interspecies warfare bacteriocins?

Of course it could – in fact we (I mean, my co-author Rolf Hoekstra from Wageningen University, The Netherlands, and myself) already published a paper a few years back in PNAS on the spatial ecology of bacteriocin-producing bacteria, trying to explain the extraordinary diversity of bacterial strains in certain spatially constrained habitats like forest soils. There the idea was that bacteriocin production, as well as the production of resistance factors against the bacteriocin, are metabolically costly. Bacteriocin-producing Killer (K) strains pay the cost of both the toxin and the resistance factor, while resistants (R) pay only the latter. Sensitive (S) strains pay none of these costs, but are killed by the bacteriocin excreted by the (K) strain. This amounts to a circular pattern of competitive relations: K excludes S excludes R excludes K. This is like playing the Rock-Scissors-Paper game with all the neighbors in a lattice habitat. We show that this circular interaction pattern in the spatial setting is sufficient to maintain a huge diversity of different strains within the same habitat. I think the same principles can be transferred (mutatis mutandis) to the microbial ecology of the mammalian gut.

7] I found it particularly intriguing that the “Lame” phenotype of bacteria, which had a fully functional quorum sensing system but was unable to produce the common good, did not emerge in any significant fraction under any of the conditions tested. Is it possible that in a scenario with high spatial mixing and individual motility a “Lame” phenotype could indeed emerge to find a parasitize “Honest”, “Blunt”, and “Shy” individuals? Or would the concurrent presence of “Vain” phenotypes make “Lame” too metabolically risky?

I think there is a simple explanation for the (almost) complete lack of the “Lame” phenotype: the “Liar” is better in all respects and excludes it wherever they appear together. This is because the “Lame” pays the metabolic cost of both components (the signal molecules and the receptors with the signal transduction cascade) of the quorum sensing machinery, whereas the “Liar” pays just for signal production. This gives the “Liar” a metabolic advantage. The “Lame” operates the signal reception and transduction machinery, but gains nothing from it, because it does not have a working cooperation allele that the signal could switch on or off. In fact the price of signal detection is paid in vain by the “Lames”. If it comes to gain advantage from cheating, the “Liar” phenotype always does better. This could not be helped by any different spatial setting either. However, the intensity of spatial mixing does make a big difference regarding the overall outcome of the interaction: in a completely mixed habitat (like in a stirred batch culture) cooperation would never show up, therefore operating quorum sensing would be also a waste of energy. Thus the stirred habitat maintains only the “Ignorant” phenotype. Yet as we show through many simulations, at small to moderate mixing cooperation can be maintained, and quorum sensing is worth adopting either as a signaling system or as a “cheating device”.

8] With these results in hand, what do you hope to model next?

We are working on a new model that will hopefully shed some light on a mysterious phenomenon well known in fungi: the existence of many vegetative compatibility groups (VCG’s) within the same species. Fungi do crazy things sometimes that neither animals nor plants dare to do in general. Somatic fusion is one such thing: two different individuals (hyphae) may come together, fuse, and exchange cell nuclei. This is not a sexual process, since it involves neither meiosis nor any kind of chromosomal recombination: it is just the fusion of two vegetative bodies and exchange of nuclei. For some – largely unknown – reason this is worthwhile for many species of fungi to practice. Yet, they also restrict the occurrence of somatic fusion by developing different vegetative compatibility groups among which fusion is not possible, or if occurs then the fused hyphae die. Somatic fusion is allowed only between individuals belonging to the same VCG. Our main question is: why do fungi evolve VCG’s to constrain fusion, given the assumption that fusion is beneficial?

9] Lastly, just for fun, do you listen to music as you work? If so, what type or particular composer?

Yes, of course I do – while I wait for the simulations to complete, for example. Programming and writing the papers requires all my attention, then I need to be alone in silence. Otherwise I listen to Marillion, Pink Floyd, Jan Garbarek, Keith Jarrett, Mozart, Bach, among many others. My kids say I am a bit like an old dinosaur with my music, but I was happy to realize lately that they steal my CD-s sometimes .

You are never alone, even when you might want to be. 100 trillion of our closest friends, our intestinal microbiota, colonize and inhabit our intestines, helping us to digest our food, regulate our inflammatory immune system response, and even shape the functional morphology of our gut epithelia. Luckily, evolution has selected for the survival of bacteria that help us out, whether by synthesizing the Vitamin K we need, breaking down complex carbohydrates for us, or secreting the butyrate that helps gastrointestinal epithelia to mature correctly. Even so, the competition of bacteria to survive and inhabit that space is fierce and cutthroat and oddly enough, that competition has led to the evolution of both cooperative behaviors and subterfuge in bacterial populations.

Recent research in PLoS Computational Biology [Czárán T, Hoekstra RF (2009) Microbial Communication, Cooperation and Cheating: Quorum Sensing Drives the Evolution of Cooperation in Bacteria. PLoS ONE 4(8): e6655. doi:10.1371/journal.pone.0006655] has helped to reconcile the evolutionary contradiction of one organism helping another survive when it could use that energy to reproduce with observed cooperative behavior through computational modeling. Dr. Czárán et al used a cellular automaton model in which a point was assigned cell-like functions. In this case, Dr. Czárán assigned functions that mimicked a metabolic cooperation system in bacteria: an emitter for a common good product, a detector for other bacteria, and an emitter of an “I’m here!” signal. When some bacteria give off the “I’m here!” signal, other bacteria that produce the detector can tell whether or not potentially beneficial neighbors are around. When there are enough good neighbors, the bacteria know that they will get something in return if they secrete a common metabolic good product and they switch that machinery on. Dr. Czárán explored the conditions under which this kind of cooperative behavior emerges.

By placing these cellular models into a 3D lattice where each can interact with its neighbors in an automatic manner dependent upon whether or not they are able to make a product, see the signal to make it, or produce the signal, it was found that cooperation only emerges under conditions in which the dispersal of related cells in a liquid medium is limited, and this aligns well with evolutionary theory in that it helps one cell spread its own genes by helping closely related cells. If the medium in which cells are living is turbulent, then the probability that cells would be helping related cells is decreased and cooperative behavior doesn’t make much metabolic sense. It was also found that cooperation can exist without a molecular system to detect other bacteria, but the presence of cooperation in a population pushes for the selection of quorum sensing so that cooperating cells can guard against parasites. Parasites will still emerge even with quorum sensing systems in that some cells will realize the evolutionary benefit of secreting signals that get other cells to make communal metabolic products, but the quorum sensing helps cooperating cells discriminate whether or not to produce a common good. Overall, this modeling has revealed the evolutionary logic and mechanisms behind cooperative behavior in bacterial populations with diverse emergent behaviors.

This research sheds considerable light into the dynamics of large bacterial populations under a variety of conditions. This could help scientists decipher the drama being played out in our microbiomes that can acutely impact our health.

Image via Wikipedia

About 5 years ago I was sitting in an introductory biomedical engineering course when the professor* suddenly got very excited and started to spin around wildly, demonstrating what he called “The Electron Dance”. Professor Matthew O’Donnell’s frenzied dancing promptly shocked all of us out of our mid-day stupor as he yelled out what each part of the dance signified. Within moments, he had all of us out of our seats doing the dance with him. Prof. O’Donnell was trying to teach us how magnetic resonance imaging (MRI) works, and his hands-on lesson has certainly stuck with me.

As a class, the spin of our bodies are we swung in increasingly sloppy circles represented the spin of hydrogen atoms and the angle of our arms in relation to our body represented the orbit of our lone electron. Each time Professor O’Donnell would yell “BANG!” we were to drop our arms and slowly raise them back up as we continued to spin around, representing how the orbit of a hydrogen atom would be uniformly perturbed by the massive magnetic pulses inside of an MRI machine and then recover. The data, and hence the pictures, lie in how different densities of tissue provide different electromagnetic contexts for their hydrogen atoms. The different densities will show up differently on an MRI scan because their hydrogen atoms will respond to a strong magnetic pulse differently than other tissues around them.

When someone is loaded into an MRI machine, they are also being placed in the middle of a very strong, uniform magnetic field. As they enter the field, all the hydrogen atoms in their body start to spin in the same direction**, providing a smooth reference. When the imaging itself starts, another incredibly powerful magnet is fired in a brief burst at an angle to the reference field, and all the hydrogens’ electrons fall over. The rate at which the electrons get back up varies by the density of the surrounding tissue and because the electrons continue to orbit even as they’re climbing back up they give off a measurable oscillatory signal that we can detect. We can then compile all of those signals into one composite picture through the use of a Fourier transform algorithm that allows exquisite detail of the internal body to be revealed.

MRI, however, is static. Patients have to lie very still or else the images are ruined because the machine peers into the body a tiny slice at a time and even miniscule movement would jostle one slice out of alignment with another. To visualize real time changes going on inside the body, doctors and researchers can use fMRI instead. fMRI is just like regular MRI, but it adds a wrinkle to the process that allows functional data to be gathered, particularly from the brain. fMRI relies upon a special magnetic characteristic of blood: oxygenated blood is diamagnetic, which means that it pushes against an external magnetic field, while deoxygenated blood is paramagnetic, which means that it pulls towards an external magnetic field. This means that fMRI is able to map real-time changes in blood oxygenation by monitoring changes in push or pull against its magnetic field in blood vessels.

The human brain consumes an enormous volume of energy and nutrients relative to its size, and this means that its proper function is dependent upon large volumes of oxygenated blood. By measuring the places in the brain at which oxygenated blood becomes deoxygenated in an MRI machine, doctors and researchers can determine which parts of the brain are active in response to a given stimulus. This, in turn, allows for finely detailed, almost real-time(1-5s delay, usually), representations of functional brain activity. This has been applied to a wide range of cognitive behaviors, from reading processes in the study cited this Monday to the neural circuitry of lying vs. truth telling and has yielded powerful results and insights into the mechanics of our human consciousness. fMRI as a tool has allowed for unprecedented accuracy and detail to be obtained in neuroscience research, and the benefits of its application thereto will continue to evolve in the coming years.

*This was at the University of Michigan – Ann Arbor, before Professor O’Donnell became Dean of Engineering at the University of Washington.
**Luckily this process doesn’t hurt.

1)    What initially attracted you to the study of the neuroscience of reading?

I am primarily interested in unraveling whatever is unconscious, thus making it conscious. Reading offers a particular opportunity since it operates so fast (hundreds of words per minute), automatically and in such an irrepressible manner, that we are completely overlooking the numerous processes underlying it. By looking at brain activation underlying such subtle processes, we mirror people the remarkable quality of the brain, that is, its automatic and unconscious mechanism, like a computer. However, we are more than just a computer, since we also have the possibility of watching this animate computer. I think that neuroscience allows us to take distance and to watch what’s happening in our brain.

2)    Are there any concepts within the field of neuroscience that you find particularly captivating?

Yes, as said above, I think that neuroscience offers now many tools to help us understand how we operate in daily life, which is in other words, gaining consciousness or if you prefer, understanding of our body-mind. I am also attracted to brain-computer-interface investigations, which little by little offer a direct support and application to the advances in neuroscientific knowledge.

3)    Was there some odd wrinkle in the literature of this science that led you to formulate the hypothesis that you tested in this literature?

Not really. I was just curious about how our behavior (reading skill) can be predicted by our brain dynamics. So what we did was to take a group of participants and examine the major areas that they activated during reading. However, these were not just simple areas, but rather reflecting some of the most prominent reading processes (visual, orthographic and pre-lexical, phonological processes). We did so by testing brain activations induced by many possible linguistic and linguistic-like stimuli, from pseudo-letters (letter segments were visually rotated and manipulated so that they do not remind letters, but at the same time consist in the same visual complexity as letters do) to words and pseudo-words (illegal words, yet maintaining orthographic rules of real words, e.g. ‘scneer’ which I made from ‘screen’). The next step was that we checked information trafficking between these areas. We were intrigued to find out such a strong correlation between such trafficking and reading skill.

4)    One of the points that was stressed in this paper was that the functional processes involved in word processing were located in anatomically distinct areas.  Although we have known of specific functional areas, such as Broca’s and Wernicke’s Areas, for quite some time, popular culture continues to view much of the brain as an ephemeral computer.  Is the general field of quantitative neuroscience building up enough data to replace the popular conception of the brain with a more discretely compartmentalized view?

Of course. I actually thought that everyone thinks of the brain as very compartmentalized; I guess I am too long in the field … I actually recall that when I was teenager and I first came across the work of Antonio Damasio, and that I was so much fascinated by the accuracy between an isolated region and its emotional or cognitive function. I remember myself imagining how just by “altering” in some way (without cutting though!) one brain area, my whole daily life behavior would change, from a shy guy I would be heroic and brave…

In the field of cognitive neuroscience, we are often attempting to assign a function to a region. However, this is not that easy since there is a lot of variability between individual’s brain anatomy and also their functioning, but there is also variance within one individual, depending on the task to operate, on the characteristics of the stimulus, and even depending on the day or on the time of the day! So it is really not that simple, especially that in my field, laboratories all over the world also conduct experiments in different languages! This is why in my opinion we are often surprised to find different, or even reversed results, obtained by another group of researchers.

Nevertheless, there are some areas that area consistently appearing and reappearing, even if with variations of anatomical localization or response amplitude; such was the case also in our study where the areas which were the most prominent for the participants while they were reading, were very famous in their linguistic role (e.g. Broca’s area or the left occipito-temporal junction).

5)    It was noted in this paper that patients with acquired phonological dyslexia may have impairments in their left inferior-parietal and left infero-frontal regions, which handle grapheme to phoneme conversion.  If I understand correctly, grapheme to phoneme conversion is the process of translating the shapes of written letters into the sounds of words.  Why do we need to convert written words to auditory memories in order to comprehend text?

Actually we don’t. There are two ways of reading a word, by converting its letter forms to sounds and then extracting its meaning, or by directly extracting its meaning. This is broadly the principle behind the hypothesis of the dual-route cascade model. Acquired phonological dyslexics have trouble with reading stimuli that should be read while following grapheme to phoneme conversion (pseudo-words), while they have no problem in reading irregular words, which do not follow these conversion rules (e.g. yacht, sword, Lincoln); their neuronal pathway for non-lexical reading is impaired. Acquired surface dyslexics have the reversed pattern of impairment, namely, they easily read normal (regular) words but they are unable to read those irregular words. Their neuronal pathway for lexical reading is impaired. So to return to your question, we don’t need to read words in a ‘non-lexical’ way, which is more cumbersome and slow, unless unfortunately, our lexical pathway is damaged. In our paper we showed that the pathways between the prominent areas that we obtained could account for these two ways of reading, although we are sure that other different pathways are also involved.

6)    Similarly, do disparate languages with different transcription rules involve different requirements of grapheme to phoneme conversion in native speakers?  For example, the transcription of German is very literal and exacting whereas spoken French does not sound much like it is written.

Actually the conversion rules in French are quite straightforward… although I think that this statement may be difficult to be agreed by non-French speakers

Yes, different languages result in different linguistic awareness and abilities, and furthermore, it even involves different brain regions. In our work we discussed this point when we compared our results to those obtained in English which is very non-straightforward (opaque) in terms of grapheme to phoneme conversion rules.

7)    Is it possible that all the experimental subjects used in this study were native French speakers introduced a confounding variable into the results?

Yes of course, as said above, it involved variations in the anatomy of the functional results.

8)    Would you anticipate that data from a different patient sample (all university students in this study) might yield different conclusions?  For example, would an adult who became literate later in life process written words in comparably novel ways relative to individuals who have been literate since a very young age?

Certainly. We know that reading acquisition starts very early at school time, and as a result, neuronal specialization for reading is enhanced at that time. An important point to retain is that our brain is particularly malleable at early age. Later on, plasticity is always present, but probably to lesser a degree. Actually we addressed this point in our work since we also checked whether the age and reading practice of the participants could predict their reading skill, and if it could even predict variations in information routing within the brain. We found that age (nor gender) did not exert any influence, but that reading practice predicted reading skill. More interestingly in my opinion, we found that non of these factors could predict the dynamics within the neuronal network of reading. So this comes back to your question by suggesting that the neuronal dynamics are shaped early (early school), and that later practice (university) cannot change them, but it can change their reading skill. Interesting to know hah?

9)    With this data in hand, where do you hope to go next?  Are there any promising data or new tools coming in that you can disclose?

I am now using different tools, magneto-encephalography, which is more apt for measuring neuronal oscillations, which are a good predictor for consciousness studies, in which I am more involved now.

10)    I note in your credentials that you are formally affiliated with four different universities in three different countries.  How does one manage their time between these institutions and has the formation of the EU enabled this kind of collaborative spreading out?

I am affiliated to the INSERM UMR 825 in Toulouse (France) and to the Donders Institute for Brain, Cognition and Behaviour in Nijmegen (the Netherlands). At the same time I am doing my PhD degree (finished in a couple of months!) in a co-tutelle (joint) program under that academic umbrella of the Université de Toulouse (UPS) and Radboud University Nijmegen (RU). Yes, the EU helped a lot to this international exchange by creating such cooperative agreements, and my directors Dr. Jean-François Démonet and Prof. Pascal Fries were fantastic in accepting to coordinate me in this exchange.

11)    Lastly, just for fun, what music do you listen to most often when you are working on science?

I don’t listen to music while working, instead, I go often for walks in the park, it helps me to reflect…

Levy J, Pernet C, Treserras S, Boulanouar K, Aubry F, et al. (2009) Testing for the Dual-Route Cascade Reading Model in the Brain: An fMRI Effective Connectivity Account of an Efficient Reading Style. PLoS ONE 4(8): e6675. doi:10.1371/journal.pone.0006675

Image via Wikipedia

When our eyes flit over the words on this page and convey semantic understanding, when we engage in this profound yet imperfect distant communication, we take the ability to read largely for granted.  Yet Murphy’s Law could strike down anyone.  We could be blind, mute, or have hands deformed and unable to hold a pen or tap at a keyboard.  Or we could be dyslexic, and even the most common words could appear jumbled and bizarre to our mind’s eye.

Recent research published in PLoS ONE (Levy J, Pernet C, Teserras S, Boulanouar K, Aubry F, et al. (2009) Testing for the Dual-Route Cascade Reading Model in the Brain: An fMRI Effective Connectivity Account of an Efficient Reading Style. PLoS ONE 4(8): e6675. Doi:10.1371/journal.pone.0006675) built on past neurological research into the mechanisms underlying acquired dyslexia by utilizing fMRI brain scanning of normal individuals while they were reading.  Levy et al demonstrated that there are 2 distinct pathways by which the brain begins to read and through which it sorts regular words vs. irregular words.  Every day we encounter normal words that are spelled exactly like they sound, e.g. “red”, “talk”, or “done”, but also may encounter new words that aren’t quite right or are novel derivations of previously known words, e.g. “rofl”, “grimp”, or “sanged”.  The latter category can even include nonsense words, such as “grackle”, or words in a foreign language, such as “hors de oeuvres”.   This research has found that a dedicated channel from the middle occipital gyrus to the left parietal cortex handles normal words while weird words take a detour through the left occipitotemporal junction first.  This demonstrates that the different functional processes involved in reading take place in anatomically distinct areas of the brain.  It should be noted that this research dealt primarily with visual processing of letter combinations rather than delving into the semantic and contextual processing that underlies the comprehension of written words.

Interestingly, this research also found that better readers are more adept at switching between the two processing channels as their reading material requires while poor readers may improperly rely on one channel to the exclusion of the other.  This may be because uncommon or novel new words (blin!) break up the flow of normal words (ordon!) as we read, and these glottal strange words that bramble out of context implicitly yamp automatically activate xk’yen the left-occipitotemporal junction as it tries to match snails to words we’ve already seen before.  It is thought that perhaps this additional processing slows down the rate at which we understand words.  Improper routing of nonsense or novel words through the wrong channel could underlie both innate reading deficiencies and those acquired after brain injury.

This research sheds light upon the early events of reading a word, and it is hoped that it may be utilized to help formulate better literacy teaching methods as well as more effective therapies for dyslexic individuals.

[This research is open-source and as such is freely accessible to the public here at PLoS ONE.  Comments and user rating are enabled on the PLoS site and I encourage you to cross-reference any comments you may leave here to the original article as well.]

That is the difference between your immune system encountering a pathogen on its own and encountering it after you’ve been vaccinated against it.  Without a vaccine, your immune system is going to be slow and clumsy and could well cause more damage than can ever be repaired.  This was commonly the case in polio and whooping cough before vaccines against them became available.  Many children were permanently disabled and too many died.  With the vaccine, your immune system knows what to look for because it has seen similar things before and as such it would have to work much harder to cause the same degree of catastrophe.

Your immune system without vaccines (left) and after vaccination (right).

At a molecular level, vaccines and pathogens aren’t all that different, at least not in the eyes of the immune system[1].  The key difference for your health is that vaccines cannot replicate in your body and as such do not cause the disease that the whole pathogens do.  Most vaccines are chemical soups of disabled pathogens, containing dead bacteria, weakened viruses, or just chunks of them.  Vaccines often also include an adjuvant, which replaces the alarm signals that normally get the immune system to react to living pathogens[2].  Together, the pathogen soup and adjuvant cause the immune system to wake up and mount a response against the soup.

When the immune system begins to process the pathogen soup, that soup is chewed up by specialized cells, such as dendritic cells[3], into tiny protein bits called antigens.  Those specialized cells put the antigens into specific proteins[4] that the players of adaptive immunity, such as T-cells, can specifically see inside of.  The T-cells then wildly proliferate and send signals to the B-cells, which in turn also proliferate and begin to pump out antibody that is reactive specifically against the activating antigen and binds strongly to that antigen when it’s seen on the whole pathogen.  The bound antibody acts as a beacon for innate immune cells to home to.  Together, the T- and B-cells harness the destructive power of the innate immune system, which can chemically bomb and chew up invading pathogens and damaged surrounding tissues, to mount a precision attack on that pathogen.

The thing is, though, that vaccines don’t contain infectious pathogens, just pieces of them that the immune system processes.  When the immune system realizes that there isn’t anyone to actively fight against, it stops beefing itself up[5] and becomes quiescent again.  When it does so, some of the cells of the adaptive immune system differentiate themselves into memory cells that dwell in tissues like sentinel ninjas, silent until they see the enemy again, then they leap out and swiftly stop them in their tracks.

Vaccines skip the messiness and potential danger of an actual infection and teach the immune system how to guard against it, no lasting damage or death required.  This represents not only an incredible win for us as modern individuals, but also has inarguably made the world a better place through the elimination of smallpox, and the near-elimination of polio, whooping cough, lockjaw, scarlet fever, measles, and mumps[6].

I, for one, am happy to not have to risk destroying any of the priceless art that keeps me alive.

[1] So to speak.  The immune system has eyes in the sense that its cells have receptors that scan their environment for danger signals.  Dendritic cells do most of this.

[2] OK, so viruses aren’t technically alive to begin with.

[3] In the immunological canon, dendritic cells are the primary antigen processors, but under certain conditions other cells, including macrophages and basophils, can do the same.

[4] These proteins are called the MHC proteins, which stands for major histocompatibility proteins and act as molecular lenses for the volatile cells of the adaptive immune system (T- and B-cells) that can attack their own body if they get inappropriately activated.  That has been found to occur in conditions ranging from asthma to inflammatory bowel syndrome.

[5] The proliferation of adaptive immune system cells in an infection is responsible for the palpable swelling of the lymph nodes that we can feel under our chins or armpits when we have the flu.

[6] In the industrialized Western world, at least.

AH: Today is August 5th, 2009.  I’m sitting down with Dr. Coelho, who has recently published “Dynamic Modeling of Vaccinating Behavior as a Function of Individual Beliefs” in PLos Comp Bio.  He’s graciously agreed to answer some questions about epidemiology and his methods.  How are you this morning, Dr. Coelho?
Dr. FC: I’m fine.

AH: Allright, so shall we just get right to it?  Personally, what initially attracted you to the study of epidemiology?  Are there any particular concepts or parameters within the study of epidemiology that you find particularly captivating?

Dr. FC: My first interest in epidemiology, particularly epidemiology of infectious diseases, is to be able to understand the dynamics of spread of infectious disease in large human populations.  There is a number of interesting questions, some partially answered, some still unanswered, about those dynamics.  And in today’s globalized world where people travel quite a lot between countries and different areas of the planet, understanding how human behavior influences the spread of infectious diseases is a very fascinating and very important topic.  So that’s basically what attracted me to this field and as a mathematical modeler there is a lot that can be done by setting up mathematical models to represent those dynamics.

AH: One of the concepts that’s often given with vaccination and epidemiology is the concept of herd immunity, where enough people are vaccinated that those who are not vaccinated are protected by proxy effect.  So within epidemiology, does the percentage of population coverage necessary for effective herd immunity vary by pathogen or is it fairly constant?

Dr. FC: No, it does vary by pathogen because it depends on how transmissible is the disease, meaning how easy it is for the disease to go from one person to the other.  And then according to that and how fast that disease or how well it spreads you have to have a higher coverage in order to obtain herd immunity as compared to diseases that spread with less speed or efficiency.  So you cannot have one figure that will determine what is the best vaccination coverage for any disease.

AH: Allright, in the study of epidemiology, how can data from past epidemics inform us of the course of potential future epidemics when the world has changed so rapidly in terms of moving people and goods around?  In particular, what can the 1918 Spanish Influenza tell us about the potential course of the emerging swine flu?

Dr. FC: Well, the study of past epidemics is very important because, even though the dynamics of human population movements are not the same, to a certain extent on some certain smaller geographical scales there is a lot to learn about how a certain disease spreads in a reasonably homogeneous population.  For instance, in a large city like London or New York where you have certain population density and depending on how many and what fraction of the people there are susceptible to a disease, meaning they have not been exposed to it before, as you examine the dynamics of past epidemics you’re able to determine certain key parameters about the mechanisms of transmission and the speed and things like that about particular diseases.  Even though diseases and pathogens evolve through time, this evolution is not sufficiently fast that you cannot really extrapolate from past epidemics certain facts that help you understand present epidemics.  And in the case of influenza and the current swine flu, this is also true. As you know, flu is one of the fastest-evolving pathogens that we have as a common infectious disease and it’s a seasonal disease because it evolves so fast, that every year we have a slightly different strain that we’re not longer immune to or not completely immune to, but even so the patterns of the transmission of flu don’t change much.  Flu is still spreading pretty much the same way now that it was spreading during the Spanish Flu in the beginning of the 20th century.  So the patterns of transmission don’t change a lot.  Pathogens change, but their modes of transmission tend to be more conserved in general.  Of course it may change for certain diseases but it’s a rarer event that the mode of transmission is changed.

AH:  In particular, within this frame, in the Western media there was a lot of initial very urgent media coverage of the emergence of swine flu and it has kind of faded to footnotes even as the epidemic has grown.  Based upon the results of your research, do you regard this reduced media coverage as beneficial for the emerging vaccination campaign, if a vaccine is developed?

Dr. FC: I my paper I was mainly concerned with media coverage of side-effects of vaccination having a negative influence in voluntary vaccine uptake. I think in the particular case of the swine flu the media coverage  has been a positive thing, with no ill effects on the prospective vaccine image because initially we had no vaccine. So the media did a good job of creating awareness about the disease and the behavioral changes which can minimize transmission. Compared to other vaccines available on the market today, there’s not a lot of resistance towards influenza vaccines as compared to some other vaccines that are compulsive in some countries, like MMR,Measles, Mumps and Rubella.  Some of those find some resistance among people who think they impose more risks than protection.  But in the case of influenza there is not much resistance towards the idea of getting immunized, so I think that the fact that the media is giving coverage to the swine flu pandemic, it will help us have a good acceptance and good vaccination coverage when the vaccine is finally available.  On the flip side of the story, there’s another problem, which is that vaccine production is pretty expensive and resource consuming and that there is no way we can produce in the few months of vaccine production enough vaccine doses to cover everybody that is at risk in the world.  So a big rush to vaccination, probably a big irrational rush in people who are not really in threat zones and zones where there is higher risk—this big rush could mean that people who need it most will not get it, particularly in developing countries.  So that’s the flip side of the high attention that the disease is getting.  It’s inevitable, but if the media coverage is of high quality that people understand what their risks are and who really needs to take the vaccine, that may help lessen that side effect that I just mentioned.

AH: I hope so.

Dr. FC: Yeah.

AH: In your opinion, has the rise of instant international communication helped manage and mitigate epidemics or has the deluge of news diluted people’s trusts in their governments and thereby weakened the effectiveness of vaccination campaigns?
Dr. FC: Well, as I mention in my paper, the relationship of information availability and decision-making in epidemic scenarios it’s a very complex issue and it depends on many factors, for instance, it’s highly sensitive to what the general public’s relation of trust with their own government.  There is, and I cite in the paper the example of Brazil and the yellow fever scare of an epidemic that never was, that scare was derived pretty much because the population did not trust that the government would tell them the truth, that they would try to cover up a real epidemic in order to avoid the political impact and thus the population decided to protect themselves no matter what the official recommendations from the Ministry of Health were.  So you see that it depends on what kind of trust relationships that is built over the handling of different similar scenarios in how the population will react.  It’s kind of hard to tell, but I think that in all, in each country the public health officials should have a pretty good feeling of what level of trust they have, what kind of relationship they have with their particular audience.  They should take that into account when they plan their interventions for those epidemic or pandemic threats.

AH:  On the same note, in terms of media coverage, recently, in the past couple years in the West, there have been anti-vaccination activists citing a discredited study that claimed a link between the MMR vaccine and the development of autism. As one who is interested in vaccines and epidemiology, do you think there any basis to this argument?

Dr. FC: I have to say that there’s not hard evidence to back that up.  There are some cases that might be associated.  It came up, if I’m not mistaken, during some campaign in the UK and it gained public attention but there is not exactly hard evidence to back this fear up.  Now, fears of vaccination stem from many directions.  There are some people who don’t vaccinate for religious issues, so it’s really a weird thing, it’s like, some people don’t believe vaccination as they don’t believe evolution and it’s not really a rational or data- or evidence-based behavior.  So you cannot treat resistance to the concept of vaccination as a homogeneous thing.  What we can do about, you know, to preserve the public image of vaccines is mostly to explain to people who are willing to accept evidence presented by the media or the government as real and honest evidence.  But there will always be a large, or, I don’t know, probably not a large, but a fraction of the population that will not be moved by hard evidence.  They have other reasons to fear vaccines and they will use fake associations between vaccines and ill effects to create their own scare and their own small communities, so that’s inevitable.  But what I try to point in my paper was that even, you know, you trust vaccines as a general fact, it’s also a fact that some vaccines have a small risk of adverse effects and they do occur.  So that should be taken into account in the way people react to vaccines.

AH:  In a similar vein, one of the arguments that the anti-vaccination campaigns often invoke is that Western children receive far too many vaccines too soon after birth and that this overloads their immune systems due to alum adjuvants, chemical preservatives, or trace amounts of mercury in the vaccines.  Is there any basis whatsoever to that?

Dr. FC: Well, I think that, clearly, there’s a lot of exaggeration in those claims but no matter what you might be able to argue scientifically against the number of vaccinations given up to a certain age, I don’t personally know of any hard evidence saying that that is really something bad.  But the reason for that is some real threat of childhood diseases and the real epidemic threat that they present to the entire population because if you stop vaccination for something as simple as chicken pox, for instance, in children, in a country like the US, or like Brazil right now, it can be a major epidemiological problem because a lot of the older population is no longer immune because the vaccine effects wane and people have not been exposed enough to the virus.  And chicken pox at a later age can be a serious illness and it can even lead to death or neurological problems—the disease, not the vaccine.  So for certain diseases, you have to keep them out of the population as best you can.  And the regular point of entry of any disease in a population that has some level of immunity is through infants because infants are born without immunity to those diseases because they’ve never been exposed before.  So unfortunately the only way we can guarantee that diseases will not get in is to get them be vaccinated against those diseases early on before they have a chance to be exposed.  So I don’t think personally that it’s a lot of vaccines that we give to children.  And the good it has done to humanity is much larger than any ill effects and there is no evidence for that, actually, I don’t believe that there’s hard evidence for this being a bad thing.

AH: In some of the seminars I have attended recently, the guest speakers have been talking about things such as DNA vaccines or transdermal vaccines.  How would you hope the development of these effective vaccines, if they can be effective, would impact consumers’ choices about vaccination trust?

Dr. FC: Naturally, the emergence of, especially DNA vaccines, they will certainly offer the ability of providing protection from diseases that we have so far been unable to have immunization tools for.  But, as with any new technology or any new thing, it has to be, there’s bound to be some risks that are still not known and as with any vaccine development or drug development, careful clinical trials have to be performed to understand [what] the possible side effects of such vaccines are.  We’re at the dawn of the DNA vaccines, which have a mechanism that’s completely different from traditional vaccines, which in any case mimic our natural immune behavior, so DNA vaccines are not only not the same as traditional vaccines, but also not anything like we’ve ever had naturally.  So it’s very hard to predict what possible associations it may have with other aspects of our health.  So that has to be looked at carefully—it’s not to stop research or anything, but it has to be looked at.  Of course, there will be risks that will have to be addressed and I’m sure that the technique and the science of it will evolve considerably in the next few years.  Now as for transdermal vaccines, I’m really not very familiar with what is being proposed so far so I would rather not comment.

AH: What I understand is that it’s primarily a push for vaccines that don’t require refrigeration, have longer shelf life, and don’t require needles for administration, particularly for use in rural areas of the developing world.  But to move on to the next question then.

Dr. FC: OK

AH: So, within the paper, from what I understood, the model was based on log pooling of Bay inference of individual choices.  So how was this developed and are there any particular caveats of the model in fitting this behavior to real life historical events?

Dr. FC: The model used logarithmic pooling to combine beliefs, represented as probability distributions, and Bayesian inference to update the pooled beliefs based on available information. So the model tries to represent our decision about vaccination as derived from our beliefs in how safe it is to vaccinate.  Because every decision in a person’s day to day life is based on what we know.  So if I’m going out on any given day I’ll take my umbrella if I think I’m on a rainy season if has rained a lot and there’s clouds in the sky.  So past experiences drive how we take from the simplest decision to the most complex ones.  So if we want to model something as complex as vaccine uptake in a population, we cannot start as has classically been done that people will vaccinate just because someone told them, or that they will vaccinate just as a fixed fraction of the population in a given situation.  It’s not as simple as that.  Another thing is that, to be able, another key contribution of this paper, was to not only make the decision derived from what people believed about it but also to allow that belief to vary throughout the epidemic according to the unfolding of events of a regular epidemic.  So that’s the paper’s core contribution: to use beliefs to drive decisions, and allow beliefs to vary in time in a dependent way with the facts unfolding during an epidemic.

AH: With these conclusions from the paper, it seemed from what I read of it, I mean I read it but I may not be understanding everything, but from what I got out of it, it seems early coverage of adverse events significantly affects a population’s willingness to vaccinate regardless of the gravity of the disease scare whereas if there’s a disease scare and only later reports of adverse vaccine events, coverage becomes much greater more quickly.  So with those conclusions, where do you hope to go next with you research?

Dr. FC: I think that the natural extension to my conclusions and the methods developed so far in this paper, the next natural step would be to bring the model closer to reality. There are two different avenues I can follow right now.  One would be to better parameterize the model from some field measure of what people believe and how they behave subsequently, but I have no expectations as to when I’ll be able to do that because that would require the opportunity to do fairly large-scale field work and interviewing people and then following up whether they vaccinate for a given disease or not.   The other, more theoretical, follow-up to this would be to complexify a little bit the belief model that I used, not in order to simply make it more complete, but to make it more realistic in a sense, because in any situation we don’t take a single fact or a single issue as a source of our decision.  Normally we combine different things in order to make a decision.  For instance, in the umbrella example I gave, I’ll take the umbrella not only depending on my beliefs on whether it’s going to rain or not but also if I expect to be outside walking on the streets for a long time or just taking a car ride.  So it’s a multivariate problem, so that multivariate approach to belief modeling connected to the epidemiological modeling is very important.  So another thing is that part of this model that could be further extended is the media amplification.  I introduced in this model the concept of media amplification factor and that’s a very important one, which is that media tends to latch on to things that sell and things that sell normally are the bad things.  Like sometimes if someone took a vaccine and were safe, it’s not news material, but if someone took a vaccine and died or went to the hospital, that’s news.  And it’s a fact that the news, the media industry, is biased towards things that are a lot more radical.  It introduces a bias in what people are exposed to, the kind of information that people are exposed to.  So I’d like to explore the concept of media amplification, the sources of bias that it could introduce.  The model that I had in this paper was a very simple thing.  I just said that vaccination ill effects were some number of times more likely to appear in the news than positive effects, and that’s basically a very simplistic way of talking about media amplification.  But I think that deserves some more careful and complex understanding.

AH:  So, out of curiosity, when developing this model, did you start with the MMR vaccine scare and the yellow fever disease scare?  So did you start from past historical examples, or did you develop the model first and replicate the behavior in those examples.

Dr. FC: I started with, my first motivation was to get belief, behavior, and epidemiological dynamics together.  And then I went out to look for historical examples, past situations where that might have been a key issue, where that might have played the role in concrete situations.  And then I gradually pulled together the elements that composed my approach.

AH: Given that PLoS is Open Science, why did you choose to go with PLoS Computational Biology?

Dr. FC: That’s a good question.  Despite the fact that PLoS is open source, PLoS Computational Biology is also a very respected journal and I first of all thought that it was a good venue for the idea I was trying to convey.  But on the other hand I’m a big believer in open sources for scientific information because scientific information is funded by the public and the information should be available without delay to the public, so open source, open access publications are key to that.  Moreover, PLoS is introducing some very interesting social networking technology to make the way people interact with the published papers more rewarding to the authors.  If you go to my article page, you can rate my article, you can select pieces of text and add comments, and that’s unheard of in traditional pubs and I think that when people start to get accustomed to talking back to researchers and adding comments and using all those tools that are so widespread in the way that people interact throughout the Internet nowadays, it will be a big way of society interacting with science and it can only be a good thing.  And that’s the moral reason, if you will, why I chose PLoS journals.  And I intend to keep publishing in PLoS whenever I can because I think that they’re pretty much the only guys out there offering this.

AH:  OK, thank you very much for taking the time to sit down with me.

Electron micrograph of a Varicella (Chickenpox) Virus. Varicella or Chickenpox, is an infectious disease caused by the varicella-zoster virus (CDC/PHIL)

When a child comes down with a fever and itchy rash without having been playing in the poison ivy, parents know that they are dealing with chicken pox.  Thankfully, chicken pox is a mild disease in childhood in most cases, especially when compared to the children’s scourges of the past, such as mumps, whooping cough, and scarlet fever, that vaccines have now mostly eradicated in the Western world.

Currently, there is a vaccine against chicken pox available to protect against the rare cases in which chicken pox is very serious and even life-threatening.  However, this vaccine is not mandatory, so there are now children growing up who are not resistant to the virus that causes chicken pox because they were not vaccinated or the vaccine was not effective in their particular case.  Unfortunately, adult cases of chicken pox tend to be much more dangerous and damaging.  As such, a low rate of chicken pox vaccination among children will indeed decrease childhood incidence of chicken pox by deceasing those who can be infected, but this would also increase the proportion of adults many years down the road who are vulnerable.

At the heart of this issue is how physicians convince patients to vaccinate themselves and their families against the dangers of any given disease, particularly a newly emergent one.  Vaccination campaigns can be highly effective at quickly arresting the spread of an infectious disease before it becomes epidemic in a population, but at the same time they are easily derailed by the wrong mixture of social ingredients.  Recent research published in PLoS Computational Biology by Drs. Flávio Coelho and Claudia Codeço* [Dynamic Modeling of Vaccinating Behavior as a Function of Individual Beliefs, published 7/10/09] develops a descriptive mathematical model of how individuals in a population will behave with regard to vaccination in an emergent disease scare.  An individual will choose whether or not to vaccinate against a new disease by deciding between the risk of death from the disease itself and the very small but ever-present risk of an adverse reaction to the vaccine itself.  By and large, people rely upon the media for new and accurate information about the world, including new diseases.  As such, Drs. Coelho and Codeço found that the media’s coverage of a vaccine, or, conversely, its coverage of adverse vaccine reactions, can significantly influence the behavior its constituent population.  “Like sometimes if someone took a vaccine and were safe,” said Dr. Coelho, “ it’s not news material, but if someone took a vaccine and died or went to the hospital, that’s news.  And it’s a fact that the news, the media industry, is biased towards things that are a lot more radical.  It introduces a bias in what people are exposed to, the kind of information that people are exposed to.”

Drs. Coelho and Codeços’ primary finding was that widespread, early coverage and bad reactions to vaccines will greatly reduce the rate at which people go to get themselves immunized against the disease.  This creates a dangerous storm in which the disease has more time to work its way through a larger susceptible population, which can make it even more difficult to contain in the long run, as well as causing a lot of suffering in the meantime.

Dr. Coelho first started with the recent MMR scares in Britain and an urban yellow fever scare in 2007 in Brazil as real-life examples to draw behavior from in developing his mathematical model.  Though he found that negative press decreased the rate of vaccination in both cases, he also found that a population’s trust in their governmental health authorities had a large influence upon that population’s behavior.  In the case of an urban yellow fever epidemic in Brazil in 2007, Dr. Coelho noted, “that scare was derived pretty much because the population did not trust that the government would tell them the truth, that they would try to cover up a real epidemic in order to avoid the political impact and thus the population decided to protect themselves no matter what the official recommendations from the Ministry of Health were.  So you see that it depends on what kind of trust relationships that is built over the handling of different similar scenarios in how the population will react.”

Drs. Coelho and Codeços’ work represents a significant advancement in the study of how diseases spread in that it takes into account both news and the public’s opinion regarding vaccines varying over time as new findings and statistics become available.   As Dr. Coelho said, “If we want to model something as complex as vaccine uptake in a population, we cannot start as has classically been done that people will vaccinate just because someone told them, or that they will vaccinate just as a fixed fraction of the population in a given situation.  It’s not as simple as that.”  Traditionally, vaccine epidemiology has assumed that populations behave homogenously, but Drs. Coelho and Codeços’ work shows that this is inaccurate in that it does not allow for individual choices for whether or not to vaccinate.  By modeling the individual decision based on best available data, and allowing both to change over time, this research has allowed for a much more nuanced view of how individual behavior in a disease outbreak influences eventual vaccine coverage, giving public health officials new and useful tools in their fight to keep the public healthy.

The West has already almost eradicated mumps, measles, whooping cough, polio, and scarlet fever.  Compared to these deadly and damaging childhood terrors, chicken pox seems less significant, but the ongoing efforts to promote vaccination against it will indeed help to protect vulnerable adults against its potentially serious complications.  And just as vaccines themselves have been, that can only be good for society.

*Dr. Flávio Codeço Coelho is affiliated with the Theoretical Epidemiology Group, Instituto Gulbenkian de Ciência, in Oeiras, Portugal and Dr. Claudia Codeco is affiliated with the Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, in Rio de Janeiro, Brazil.

[The source research maintains this column's standards in that this article is freely available to the public under PLoS's open-access policies.  I encourage you to cross-reference any comments you may have with the article itself here to broaden the public feedback loop to the researchers themselves.  A transcript of my interview with Dr. Flávio Codeço will be published 8/19/09.]