Mapping the connectome is a great step toward understanding the brain. The problem is, what do we do with a connectome once it’s built? There’s a lot of important information about the brain’s connectivity packed into a connectome, but how do we extract it? Read on for an approach to broad-stroke, comparative brain region analysis based on frequency normalization. (Fairly technical and not recommended for a general audience.)

The challenge: turning connectomics data into knowledge.

There’s a worm called c. elegans that scientists study a lot because it’s so simple– its whole body has only 302 neurons. We’ve made a lot of progress on understanding it, but we’re still working on figuring out what each neuron does and mapping the logic of its neural circuits. With the human brain having a hundred billion neurons, it’s clear that we need some big-picture tools to make sense of all the wonderfully combinatorial complexity in our brains.

It’s not an easy task: what sort of analytical lens can we use to study the brain’s neural networks that elegantly illuminates system properties and differences between people, simplifying without being too simplistic? What sort of analysis ‘carves our brains at the proverbial joints’, to paraphrase Aristotle?

A thought: frequency is the internal language of the brain.

The brain is horrendously emergently complex— but all the really interesting signal encoding and processing in the brain is frequency based. Most likely, the texture of our cognition and emotion derive from the high-level frequency dynamics in our brains. Even more strongly and less vaguely, I’d argue that most other elements of our neuro ontology (neurotransmitters, action potentials, fMRI activity, etc) are functionally important only insofar as they influence, or are proxies for, the frequency dynamics of the brain.

It’s a strong statement! But I think it’s warranted. Following this, if we want to understand neuron activity on large scales we’ll eventually need to enable comparative frequency analysis. To normalize neural networks somehow to allow apples-to-apples frequency comparisons between neural circuits or brain regions or people.

An approach: can we construct a normalized framework for analyzing neural networks by flattening them into a one-dimensional bundle of interconnected pathways?

In the previous section I said we’ll need to enable comparative frequency analysis to really understand and predict neuron activity on a large scale. But this is a very hard problem. It’s hard because frequency analysis (e.g., performing a fourier transform) depends on a certain sort of structural consistency in the medium under measurement. There’s a difference in kind between sound waves bouncing around a room and electrical pulses bouncing through a neural network. They’re both based on frequency, but neural networks have complex topology and state*, whereas sound does not. If we want to analyze network frequencies on scales larger than individual neurons (which do not shed much light on overall frequency dynamics), and particularly if we want to apply non-trivial amounts of audio terminology (which seems like a no-brainer to me), we need to control for these differences.

Leaving aside the question of internal state of neurons for now, I have a suggestion on taming the topological issue such that we could attempt structure-normalized, apples-to-apples comparisons of high-level frequency dynamics between neural circuits or brain regions or people.

In short, we could try to computationally flatten out the pathways of a neural network (e.g., a brain region’s connectome) into a one-dimensional bundle of interconnected pathways. Essentially it’d involve trying to flatten the network into a semi-redundant, 1d, massively parallel, linear dataset, of which we could take cross-sections to measure frequency profiles, and which can be easily compared between neural networks.

There would be lots of technical hand-waving required… and lots of pruning too, given the combinatorial nature of the dataset. E.g., perhaps we’d need to filter pathways via minimum threshold of feedback loops. Fully appreciating the benefits, drawbacks, and process of this transform would take much more mathematics and expertise with complex topologies than I have. But I believe it could be an approach that drastically reduces the complexity of a neural network while still preserving many important network properties, and enables comparative frequency analysis on the network.

Ultimately I think the best way to understand large neural network will not be in terms of neuron connection statistics, but in terms of the frequencies and rhythms the network can generate and support. To go a bit further, I believe this structural transform also makes it easier to quantify resonance inside neural networks (and treat a brain region as an acoustic chamber, in which certain patterns resonate much more strongly than others), something I think is very important.

*A little more on the complex topology and state of neural networks: neural networks have ‘state’ in that each neuron is a unique device with its own internal variables. We can’t understand exactly how the network works, and what frequencies and rhythms it can support, unless we can decipher the internal settings of each neuron in the network (and this changes over time). Contrast this with sound, where the signal medium doesn’t have state.

Likewise, neural networks have ‘complex topology’. Sound waves travel in straight lines in three dimensions (“simple/euclidean topology”). However, signals in neural networks travel along the connections between neurons, which are definitely not arranged in neat, straight lines. It’s more like high-dimensional, non-euclidean space, an “Alice in Wonderland” style area where things don’t move in straight lines so much as between a tangled web of nodes. This is NOT to say that there is NO frequency to be analyzed, nor that other elements of audio theory (resonance, harmonics, constructive/destructive interference) aren’t present. There is and they are. It’s just that we need to find SOME way to simplify the topology before we can start to quantify the frequency data and apply concepts from audio theory. And if we ignore the problem and don’t bother with this sort of simplification/normalization, we’ll never get good frequency data, period.

At any rate, this is a possible approach, and it may or may not end up workable. But however we enable frequency analysis inside the brain, I hope more people start thinking seriously about the normalization problem soon. Frequency is of central importance in neural function, and having a method to normalize frequency within the brain will be central in understanding how it all works.

Image: Neurons from the limbic area of the Central Nervous System. Image copyright and courtesy of Bryan W. Jones.

[1] I’m reminded of this curmudgeonly letter regarding the ‘omics’ situation:

It is an old maxim that if you want to get on, invent a new word for your particular niche in an old area, and so become an instant expert. This process seems to have gone mad. A recent article in The Scientist that referred to “nutri genomics” [1] prompted me to see just how many -omics had now been coined. Well over 100 neologisms are listed at http://www.genomicglossaries.com/content/omes.asp. A few of the more ghastly examples are foldomics, functomics, GPCRomics, inomics, ionomics, interactomics, ligandomics, localizomics, pharmacomethylomics and separomics. None of these refers to areas of work that did not exist before the coining of the new word. Perhaps, as an electrophysiologist working on recombinant ion channels, I should dub myself an expert on ohmomics.

This habit of coining fancy words for old ideas might be thought harmless, merely a source of endless mirth for thinking scientists. I’m not so sure though. Apart from reinforcing the view of scientists as philistine illiterates (at least when it comes to etymology), actual harm is done to science as the public becomes aware that some among us seem to prefer long words to clarity of thought.

David Colquhoun

University College London

Modern neuroscience is increasingly suggesting that a great deal of a person’s personality, pathology, and cognitive approach is encoded into which of their brain regions are activity-coupled together. That is to say, which of someone’s brain regions are more vs. less wired together, compared to some baseline, determines much about that person.

Right now such coupling is largely invisible and unquantifiable. If we are to move toward a clearer understanding of individual differences, not to mention psychiatric conditions, it would be invaluable to have a test for this activity coupling. A combination TMS+fMRI alternated pulse device- as it could stimulate a specific brain region/network, and measure how it affected the activity in other regions- may very well provide an objective basis for psychiatric diagnosis and treatment recommendations, and perhaps even a firmer foundation for psychology as a whole.

The following is a somewhat technical writeup of the idea. Not into detailed neuroscience stuff? Click here.

Technology:

A Transcranial Magnetic Stimulation (TMS) device able to perform precisely targeted regional stimulation paired with a functional Magnetic Resonance Imaging (fMRI) device, calibrated such that one will be active while the other is dormant, and able to toggle swiftly between the two. Stimulate one brain region (TMS), then observe which other regions light up (fMRI).

TMS devices function by lowering neuron activation thresholds by rapidly oscillating a magnetic field through the target area. They are commonly found in research institutions and are used for both research into brain region function (e.g., disable the function of a specific brain region by overstimulating it via TMS, and observe which cognitive functions are and are not affected), and in treatment for depression.

fMRI devices function by using the proxies of bloodflow and oxygenation to measure regional brain activity. More active regions need more bloodflow to function, and this increased level can be measured via applying a magnetic pulse and measuring the changes in local magnetic permeability.

Existing approaches:

Brain scanning techniques: Most current types of brain scans are ill-suited to gathering regional activity data, as they either tend to focus on physical neuroanatomy (CAT, MRI) or involve poor spacial resolution (PET, EEG, MEG). fMRI is currently the tool of choice for looking at brain region activity, and researchers have used it to look into whether, how, and why brain regions may be linked, and how variations in these linkages may affect cognition. However, the passive nature of fMRI complicates drawing any firm conclusions about the causality involved: e.g., one might show subjects pictures of spiders, or naked women, or christmas presents, and record their fMRI response, but what is really being measured? Are the results clear, reproduceable, and useful, e.g., enough such that they could form the basis of a diagnosis or treatment recommendation? Very often not. On the other hand, TMS Sonar bypasses the ambiguity involved in trying to induce a standardized abstract processing state and instead tests for objective, content-neutral activity coupling between brain regions, something fMRI cannot. In short- TMS Sonar could tell us new and interesting things about regional dynamics that fMRI alone will never be able to.

Personality questionares: There are a plethora of personality tests: the MMPI, Myers-Briggs, Big 5, and so on. However, psychologists tend to be the first to list the limits of these tests. E.g., they rely on a subject to truthfully and accurately answer what are ultimately subjective and relative questions, and they focus more on what can be measured than deeper brain dynamics that, as Plato would say, “carve nature at its joints.” TMS Sonar could be objective and illuminating in ways that questionares will never be.

This approach in a nutshell:

Systematically ‘ping’ different brain regions with TMS and map which other regions light up via fMRI.

Types of data this could gather:

This device seems promising for exploring at least two different neural phenomena at the macro level: linkages and leakages.

Linkages:

An important part of how the brain works is the layout of which regions of tissue become activity-coupled. There seems to be significant natural variation in this, and it also seems to lie at the center of many sorts of brain disorders: i.e., a region should be strongly activity-coupled to a nearby region yet isn’t, or is activity-coupled to a region it shouldn’t be. These abnormal coupling patterns are for the most part currently invisible to us, but would likely show up under TMS Sonar.

Two particularly good examples of cases where TMS sonar could add to our existing analysis would be in PTSD and Depression, where the causal core may involve certain regions of the brain undergoing abnormal activity linkages or delinkages. Specifically, not only will many types of Depression likely center around activity coupling problems, but presumably different types of depression will have different and indicative activity coupling signatures, which can then inform more specific treatment recommendations.

The general hypothesis here is that activity linkages between brain regions vary considerably between people, and this will always have functional implications. Presumably these activity linkages will often correlate to a fair degree with interesting macro-level conditions, including some forms of psychopathology, and may be useful when diagnosing and designing individual treatments.

Leakages:

The other key use of this technology would be to identify and map microstructural variation as it pertains to information movement in the brain: i.e., how much information ‘leaks’ between different brain regions. Preliminary data indicates this varies across individuals and is implicated in certain types of pathology, and may inform larger, non-pathological differences in cognition.

A focal point for thinking about this issue is synesthesia. Examples of synesthesia include seeing numbers as inherently colored, or experiencing sounds in response to motion, in an automatic, involuntary way. It’s thought that this emerges from deficits in neural pruning between functional brain regions leading to cross-talk between them, and we know this condition manifests itself through sensory perception in roughly 1 out of every 23 people. However, it’s quite possible that this figure underestimates the prevalence and cognitive significance of synesthesia, since when synesthesia exists between two non-sensory regions of the brain there will likely be no consistent indicative symptoms (though it may still play a significant role in cognitive function). A pulse scanning method such as TMS sonar could likely map synesthesia across the entire brain.

The general hypothesis here is that everyone has a little bit of synesthesia, and a method to map individuals in terms of how much and which regions are affected could lead to great leaps in understanding certain forms of psychopathology and individual differences.

Hurdles:

– TMS is generally used to disrupt a brain region’s function by overloading it. The assumption that is made here is that either

1. a sufficiently low dose of TMS can be found to still effectively stimulate regional activity without disrupting regional function; or,

2. stimulation via TMS, though it may disrupt regional function, will still be an effective trigger for a representative sampling of activity coupling.

In other words, will TMS, as a general regional stimulation, evoke a coherent-enough activity response so as to be indicative of functional linkages?

3. fMRI doesn’t have great temporal resolution. I’m not certain this would matter for the purposes it’s being used, and the tech is constantly improving, but it’s a potential caveat.

– Though both of these devices are currently in use, there may be engineering challenges in creating equipment that can handle both TMS and fMRI in rapid succession, particularly since they both use magnetic pulses in the course of their operation. The solution to this may involve some temporal latency in the fMRI measurement.

Why stop at measurement? The case for using this to cure diseases:

If abnormal BRACs were found in someone suffering from a serious disorder, presumably we could use a TMS conditioning treatment to alter these to a known-healthy baseline by activation condition via TMS pulses (brain regions that fire together, wire together?). If pathology is caused by abnormal BRACs, presumably this could cure them (with a particular emphasis on very destructive disorders where therapy and drugs have failed). An option of last resort, perhaps, but an option nonetheless.

Discussion/Open Questions:

– This idea has a lot in common with Giulio Tononi’s approach for analyzing coma patients: stimulate their brains with TMS, and see how long it takes for the ‘ringing’ to die down. If it takes a while (like it does in normal people), they probably still have their higher cognitive functions intact. If it doesn’t, they’re probably vegetables.

– What constitutes a ‘brain region’? I have in mind something akin to a Brodmann area, though I’m not married to that specific organizational system.

– What would this process feel like?

– Could sonic stimulation work better than TMS in some cases?

– How close is this sort of evoked Brain Region Activity Coupling (BRAC) to how BRAC works ‘in the wild’? Relatedly, how contextual is BRAC: do which regions are coupled together change significantly when one is in different moods, or will there be significant-enough commonalities such that this usually won’t be a problem in diagnosis?

– What are the underlying causes of BRAC? Relatedly, how plastic is it?

– Could this type of challenge-response hybrid be able to measure such things as relative dominance between brain regions, or a particularly high or low tendency for a region to fall into a coherent pattern given stimulation? What would the likely functional/psychological implications of this data be?

– It’s really unknown how central BRAC is in neural function. There’s not a lot of data out there because we can’t currently measure it with any degree of rigor or precision. Straight-up differential regional activity appears very important right now– but it’s difficult to see how it wouldn’t appear important for brain dynamics, as we model things based on what we can measure, and that’s one of the few functional measurable attributes.

– Could we compare evoked BRAC with coupling topologies derived from connectomes and diffusion-weighted fMRI?

Neural resonance is currently an underdefined curiosity at the fringes of respectable neuroscience research. I believe that over the next 10 years it’ll grow into being a central part of the vocabulary of functional neuroscience. I could be wrong– but here’s the what and why.

Resonance, in a nutshell

To back up a bit and situate the concept of resonance, consider how we create music. Every single one of our non-electronic musical instruments operate via resonance– e.g., by changing fingering on a trumpet or flute, or moving a trombone slide to a different position, we change which frequencies resonate within the instrument. And when we blow into the mouthpiece we produce a messy range of frequencies, but of those, our instrument’s physical parameters amplify a very select set of frequencies and dampen the rest, and out comes a clear, musical tone. Singing works similarly: we change the physical shape of our voiceboxes, throats, and mouths in order to make certain frequencies resonate and others not.

Put simply, resonance involves the tendency of systems to emphasize certain frequencies or patterns at the expense of others, based on the system’s structural properties (what we call “acoustics”). It creates a rich, mathematically elegant sort of order, from a jumbled, chaotic starting point. We model and quantify resonance and acoustics in terms of waves, frequencies, harmonics, constructive and destructive interference, and the properties of systems which support or dampen certain frequencies.

So what is neural resonance?

Literally, ‘resonance which happens in the context of the brain and neurons’, or the phenomenon where the brain’s ‘acoustics’ prioritizes certain patterns, frequencies, and harmonics of neural firings over others.

Examples would include a catchy snippet of music or a striking image that gets stuck in a one’s head, with the neural firing patterns that represent these snippets echoing or ‘resonating’ inside the brain in some fashion for hours on end.[1] Similarly, though ideas enter the brain differently, they often get stuck, or “resonate,” as well– see, for instance, Dawkins on memes. In short, neural resonance is the tendency for some patterns in the brain (ideas) to persist more strongly than others, due to the mathematical interactions between the patterns of neural firings into which perceptions and ideas are encoded, and the ‘acoustic’ properties of the brain itself.

But if we want to take the concept of neural resonance as more than a surface curiosity– as I think we should– we can make a deeper analogy to the dynamics of resonant and acoustic systems by modeling information as actually resonating in the brain. That there are deep, rich, functionally significant, and semi-literal parallels between many aspects of brain dynamics and audio theory. Just like sound resonates in and is shaped by a musical instrument, ideas enter, resonate in, are shaped by, and ultimately leave their mark on our brains.

I thought the brain was a computer, not a collection of resonant chambers?

Yes; I’m essentially arguing that the brain computes via resonance and essentially acoustical mechanics.

In short– the future of medical diagnosis is to use a gene expression panel along with functional and correlative connections between gene expression and pathology to perform thousands of parallel tests for every single human illness we know of– no matter whether it’s acute, chronic, pathogenic, mental, or lifestyle. In short: one simple test that’ll uncover all health problems.

What do you mean? And how would it work?

Gene expression is a measure of which (and to what extent) of your 20,000+ genes are being made into proteins and RNA. A gene expression test is much like a traditional genetic test, but since it goes beyond merely listing which genes your body has, and shows how much your body is using each one, it’s a much better view of what’s actually going on inside your body. Genes may be a blueprint of physiological potential– but gene expression is a snapshot of actual physiological function.

The Vast majority of illnesses leave a significant imprint on a person’s gene expression. A failing kidney, an inflamed appendix, obesity, a manic episode– these will influence which genes are activated, and in very specific, fingerprint-like ways. In a real way, gene expression is what’s happening in your body. It’s possible, and I think fairly probable, that the imprints distinct physiological insults leave on gene expression will be largely unique, and so in theory we should be able to work backward from gene expression to things that we care about, like diseases, syndromes, and injuries. Your gene expression is a record of what’s happened to and happening in your body. The information’s almost certainly in there, encoded in the relative expression levels of 20,000+ genes, if only we can interpret it.

We can decipher some of this by tracing causation, but realistically the only way to figure out what any given gene expression means clinically (for now) is through correlation. But once we’ve gathered a Large collection of gene expression-known illness pairs (we could build this dataset by requiring e.g., hospitals to collect a gene expression sample when a diagnosis is made), we can start to train computers to identify what gene expression conditions are connected to each illness. Finding these sorts of connections is almost impossible for humans, but there exist computational approaches which in theory are fairly ideal.[1] We’ll know that people with X gene expression tend to have Y disease, and so forth. If we can dive deeply into gene expression and find the predictive elements for each condition, we’ll be able to say this with great confidence– rivaling or perhaps surpassing the current thresholds for diagnosis.

It’s not a trivial computation, but there’s really nothing standing in the way of gene expression tests which use broad-spectrum correlative analysis to screen for all known illnesses at once. Gene expression tests will likely be fairly non-invasive (perhaps involving drawing a bit of blood) and extremely cheap, making it practical to preventatively screen everyone for almost everything 1-2 times a year. Moreover, the nature of the test means it can test for many kinds of things we simply can’t test for now, and easily distinguish between conditions with very similar symptoms but different causes– it’s not just a replacement for nearly all of our current tests, it’s way, way better.

The large-scale data collection necessary for correlative analysis is, I think, the biggest hurdle, though finding solid correlations in a massive dataset against a background of variable application of diagnostic criteria is also non-trivial. But it’s coming. And being able to catch more problems, catch them earlier, and catch them more cheaply could save society billions of dollars in the long-run, not to mention avert an incredible amount of human suffering.

Discussion:

Challenges:
– Training computer programs (e.g., classification ANNs) requires a lot of good data, as does screening out false positives in a sample as wide as a full genome. Getting enough *good* samples where all the right diagnoses have been made will be challenging.
– Gene expression analysis is still having growing pains. E.g., “Protein sequencing gone awry: 1 sample, 27 labs, 20 results“.
– Crunching the numbers on which of 20,000+ different genes (and potentially some non-protein-coding, RNA-producing genes) are correlated with each illness is far from a trivial problem.

Unknowns:
– Will gene expression from multiple locations be needed to diagnose some illnesses?
– It seems fairly safe to say we’ll be able to diagnose e.g., kidney failure or malaria from gene expression data. But what about internal bleeding? And what about some of the more tricky or subjective mental illnesses? This technology will have its theoretical limits: I think the limits are pretty wide, but what are they?

[1] This task falls outside the scope of this writeup, but just going with what I know, I’d take a set of gene expression-known illnesses pairs, divide the gene data up into smaller, more tractable pieces (perhaps along specific gene-network faultlines, perhaps randomly?), and train a classifier neural network on the pieces, which will attempt to predict a specific illness based on what it finds to be the most significant data in the subset. Layer these subset-based classifier neural networks under a ‘master’ classifier neural network which gives the final yes/no prediction. Test this model on progressively larger out-of-sample data sets. Repeat for each illness. There are undoubtedly solutions orders of magnitude better than this– but it’s a baseline start.

ETA 15-20 years.

Edit, 1-22-10:Based on the available information, I think gene expression is a strongly representative abstraction level from which to draw. However, I see strong arguments for also including the metabolome and metagenome if it’s feasible to do so. This doesn’t materially change the situation.

How similar are the genomes of dogs and humans? How does this compare to cats and humans? What about mice and cats? How close, genetically, are mice and corn?

We have all of these genomes sequenced, but we don’t have particularly good and intuitive ways to answer these sorts of questions.

Whenever we can ask simple questions about empirical phenomena that don’t seem to have elegant answers, it’s often a sign there’s a niche for a new conceptual tool. This is a stab at a tool that I believe could deal with these questions more cogently and intelligently than current approaches.

Logarithmic Evolution Distance: an intuitive approach to quantifying difference between genomes.

How do we currently compare two genomes and put a figure on how close they are? The current fashionable metrics seem to be:

– Raw % similarity in genetic code— e.g., “Humans and dogs share 85% of their genetic sequence.” However, there are many ways to calculate this, depending on e.g., how one evaluates CNVs, segment additions and subtractions, and functional parity in sequences. One may get wildly different ‘percent raw similarity’ figures depending on whom they ask.

– Gene homologue analysis— e.g., “The dog genome has gene homologues for ~99.8% of the human genome.” However, this metric involves similar but more subjectivity than the first, with many ways to count homologues. Subjective assessments include trying to quantify homologue function, assessing what constitutes a similar-enough homologue, how to deal with additions and subtractions, and in dealing with CNVs– and this ‘roll up your sleeves and compare the functional nuts and bolts of two genomes’ approach is also extremely labor-intensive.

– Time since evolutionary divergence— e.g., “The latest common ancestor of dogs and cats lived 60 MYA, vs that of dogs and humans, which lived 95 MYA.” Time is often a relatively good proxy for estimating how far apart two genomes are, but there are many examples of false positives and false negatives for this heuristic. Selection strength and rate of genetic change can vary widely in different circumstances, and it breaks down very quickly for organisms with significant horizontal or inter-species gene transfer.

None of these approaches really give wrong answers to the questions I posed, but neither do they always, or often, give helpful and intuitive answers. They fail the ok, but what does it mean? test.

I think it’s important to note, first, that all of life is connected. And as evolution creates gaps, it could also bridge them. I say we use these facts to build an intuitive, computational metric for quantifying how far apart two genomes are.

To back up a bit, a (rough) way to explain what this idea is about is, let’s imagine we have some cats. We can breed our cats, and every generation we can take the most genetically doglike cats, and breed them together. Eventually (although it’ll take a while!) you’ll get a dog. What this tool would do, essentially, is computationally estimate how many generations worth of mutations it would take to go from genome A (a cat) to genome B (a dog). The number of generations is this ‘evolution distance’ between the genomes. You could apply it to any two genomes under the sun and get an intuitive, fairly consistent answer.

A long time ago (2008) I got really curious about dark energy. A common intuition pump is to treat matter’s gravitational effect on spacetime as compression: e.g. spacetime is like a trampoline and big chunks of matter are like balls compressing the trampoline and creating a gradient. Gravity can be thought of as the natural movement of matter down this gradient. But we see a “dark energy” factor which is making the universe expand, and we don’t know where it’s from. What if we extended the trampoline metaphor and treat spacetime as a membrane over something like an incompressible liquid? Local compression somewhere means it will ‘bulge out’ elsewhere. Similar to if you poke your fingers into a water balloon — you push in somewhere, everywhere else bulges a little. Maybe “dark energy” is this nonlocal bulge.

In a perfectly homogenous universe, this would mean the force of gravity would be zero — it would not exist as a force, since it would everywhere be opposed by an exactly equal force. Like trying to squeeze a water balloon evenly everywhere at once — you end up with no change. However, as we shift away from a homogenous state and structure formation occurs, it would be like many small fingers are pushing into spacetime, leading to more bulging elsewhere. Gravity and dark energy would cease canceling each other out.

The core prediction: if this is true we should expect the amount of dark energy to exactly track structure formation: an immediate enormous jump as quantum effects clump matter up, a later jump from nucleosynthesis & EM-driven clumping, and a gradual acceleration during multi-scale star/galaxy/cluster/supercluster formation. If such structure formation ever stopped, dark energy should cease increasing. The timeline (and ultimate trajectory) of dark energy is afaik an open mystery, and this would offer some tightly constrained predictions which are consistent with the (coarse) data we have.

Basically I want to say we could think of gravity as a nonlocal gauge theory, and a result of spontaneous symmetry breaking, but via the gauge theory relationship some underlying symmetry is preserved. Dark energy would be a manifestation of what is preserved under the gauge theory.

I’m not precisely clear whether the relevant magnitudes work out, or how to mathematically characterize total gravitational inhomogeneity, or what scales of structure formation might dominate this inhomogeneity metric. And perhaps the idea of a “global gauge theory” is impossible to formalize in physics-standard terms and we’d need the fields already explored in gravitational gauge theory circles — and at that point maybe this idea is old hat. But it’s fun to speculate on “what happens if we add a conservation law to spacetime compression?” — and from the perspectives of “what aesthetics in physics have proven generative”; “how do other forces work?”; and “Occam’s razor: is it simpler to assume the presence or absence of this conservation law?” I think this is a defensible move.

Edit, 3-10-13: The big bang happened. The universe expanded a lot. Everything was pretty evenly distributed. The universe was a uniform mist of plasma, light, and hydrogen, as far as the eye could see.

After 13.77 billion years, things are no longer so evenly distributed. Matter has clumped up into gas clouds, planets, stars, solar systems, galaxies, clusters, superclusters. Quantum fluctuations during the big bang made things a little uneven, and gravity did the rest.

I’ve been trying to figure out how to quantify just how clumped up matter has become. How to put a number on how gravitationally inhomogeneous the universe is. If a universe with a perfectly even mist of atoms and photons is a 1, what are we?

I have this crazy-and-probably-wrong idea that this quantity, and the amount of dark energy observed throughout cosmological history, might share some eerily similar inflection points. Moreover, these two quantities might be causally connected- e.g., an increase in gravitational inhomogeneity may cause an increase in dark energy.

But I don’t think there’s a good holistic calculation of gravitational inhomogeneity yet. And I am not a very good cosmologist.

The following is an attempt to fumble around for an analogy of why this could be the case. However, I ask theorists to focus less on the analogy and more on the simple, empirical prediction that gravitational inhomogeneity and dark energy will correlate better and better as our measurements of them improve.

Dark Energy, or the unknown force causing the accelerated expansion of the universe, is one of the prime mysteries of modern physics. The following guess as to its nature, as any new theory, is probably wrong– but it’s also elegant, consistent with the provisional data, and falsifiable. I believe it’s worth looking into.

The basic idea is to model spacetime as imperfectly compressible (contrary to the standard implicit assumption of perfect compressibility) and identify Dark Energy as the resulting and proportional ‘pushback’ connected with gravity’s compression of spacetime.

To approach this explanation via analogy– if we take spacetime to be like the surface of a balloon upon which we live, the gravitational compression from aggregated clumps of matter (stars, galaxies, black holes, etc) are like fingers pushing into the balloon. The balloon is imperfectly compressible, thus it’s natural for the balloon to bulge where it’s not being compressed. Furthermore, though its volume stays ~constant[1] as we squeeze it, the balloon (like the universe) appears to expand to any flatlanders: its total surface area increases in relation to how unevenly compression is applied.

Timeline and predictions:

The timeline of massive expansions due to Dark Energy seems to correlate with what we can guess about major thresholds in the de-homogenization of matter. We would expect a massive initial de-homogenization (and thus a massive jump in Dark Energy) right after the Big Bang due to quantum effects, then another during Big Bang nucleosynthesis, another when large-scale matter structures are able to form, and another as matter organizes into very large scale structures such as galaxies, clusters, and superclusters. Eventually, when potential gravitational entropy is exhausted, Dark Energy will cease increasing. As we improve both our models of matter aggregation, and our empirical measurements of the changing contribution of Dark Energy, I predict these timelines — the extent to which matter is clumped up, and the amount of Dark Energy in the universe — will correlate increasingly well.

Dehomogenization and Dark Energy:

(1): Initial dehomogenization due to quantum effects;

(2): Clumping based on strong/weak/EM interactions (Big Bang nucleosynthesis and EM-based dehomogenization);

(3): Gravitational clumping based on small-scale initial inhomogeneities (planet/star formation);

(4): Gravitational clumping based on larger-scale inhomogeneities (galaxy/cluster/supercluster formation).

A more precise answer would involve quantifying matter clumping computationally, and deriving exact numbers and timescales. I provide these graphs as an aide to the intuition, and particularly to illustrate inflection points.

Discussion:

– I don’t know the specific mechanism of Dark Energy. Some counterpart carrier particle to the (still theoretical) graviton? A non-localizable property of spacetime? A MOND-like modification of gravity? An emergent property of entropy? I don’t have a reasonable guess.

– This theory seems consistent with the most popular topologies posited for our universe. However, it may be more elegant under certain topologies than others.

– As an explanation-by-correlation-and-analogy, this theory is weak. As a structure which generates definite, falsifiable predictions, this theory is strong. The proof, as they say, will be in the pudding.

Footnotes:

[1] There are really three options here- that spacetime is perfectly compressible, imperfectly compressible, or perfectly incompressible. My theory merely requires that it is not perfectly compressible.

[2] This statement would not be exactly correct, as even a universe with homogeneously distributed matter would have a small dark energy component: only a universe with homogeneously distributed compression would not. But if matter was truly distributed homogeneously this component would presumably be very small and would not be a dehomogenizing force.

[3] How do we quantify how ‘clumped up’ or dehomogenized the matter in the universe is? It’s obviously a problem with a solution, but how to approach the solution isn’t obvious. One option would be to frame this issue in terms of gravitons and how long the average graviton travels before interacting. For instance, and I don’t have a lot of confidence in the following equation, but the type of formulation that might make sense might be something like this:

Within any specific range of times, the amount of Dark Energy should be equal to: Gr(a)*S(a) – Gr(h)*S(h), where

Gr(a) is the actual number of gravitational carrier particles (gravitons?) exchanged in the universe;

Gr(h) is the hypothetical number of gravitons which would be exchanged in a universe of the current size if matter and energy were distributed homogeneously (necessarily equal to or lesser than Gr(a));

S(a) is the scaling factor of how much the average graviton bends spacetime in the current universe;

S(h) is the scaling factor of how much the average graviton would bend spacetime in a hypothetical universe of the current size but where matter and energy were homogeneously distributed — presumably differing from S(a) due to differences in average graviton longevity.

 

I’ll admit my formalizations are rough. But I think the core approach elegantly arises from one assumption, and most importantly makes very specific, falsifiable predictions.

Additional Visual Aids:

The latest poll numbers are in, and I’m clearly not alone. The AP is now reporting that 81% of Americans think we’re on the wrong track. One need not look far for proximate reasons: a strange and fragile economy, huge credit card debts, the behavior of our elected officials, our election of said officials, the sad, hollow state of our public discourse, voter apathy, the general state of our media, and so forth. There are still plenty of things going right in America, but compared to our particularly exemplary history of competence, principles, and vibrant public life, something has clearly changed.

As Bob Herbert of the Times opines in today’s column, Losing Our Will,

This is the pathetic state of affairs in the U.S. as we approach the end of the first decade of the 21st century. Whatever happened to the dynamic country that flexed its muscles after World War II and gave us the G.I. Bill, the Marshall Plan, the United Nations (in a quest for peace, not war), the interstate highway system, the civil rights movement, the women’s movement, the finest higher education system the world has known, and a standard of living that was the envy of all?

This dysfunction is clearly a matter of willpower, not capacity. So what’s happening and what can we do about it?

Many (such as Herbert) simply blame President Bush. He’s certainly made a dog’s breakfast out of anything he’s touched, but I tend to see him as more of a latecoming figurehead to this national dysfunction that’s been building for quite some time.

Many would say this is a question without an answer: that when you talk about a culture getting screwed up, the dysfunction is so complex and emergent that it defies words. Similarly, a group of researchers have recently asserted that “once a society develops beyond a certain level of complexity it becomes increasingly fragile” and devotes more and more of its output to merely supporting its complexity, and as time goes on will tend to either implode or crumble in result to outside threats (they describe Rome’s downfall as such[1]). I think this is worth noting, but the ‘it just happens’ explanation rings rather hollow and unsatisfying.

Many blame the media; secular atheists; religious fundamentalists; monied interests; particular cultural quirks of the baby boomers; the winner’s curse; various academic fads and corrosive memes; various liberal movements; various conservative movements; corporations; Canadians. There’s likely some truth in some of these, but our culture has dealt with worse in the past and not become dysfunctional.

Near to my heart, we have the Change Congress movement and its description of Washington as having developed an economy of influence systematically biased toward monied interests. I think this is accurate, that it contributes to the problem I describe, and that CC has some elegant solutions.

But I would suggest there’s an embarrassingly simple yet profoundly corrosive factor underlying a non-trivial amount of America’s dysfunction: we eat too much high-fructose corn syrup.

This connection is obscure but important. To back up a bit:

High-fructose corn syrup (HFCS) has become the sweetener of choice for processed foods and sweetened drinks. It’s cheap, sweet, and flexible (i.e., easily chemically modified for more or less sweetness and has a longer shelf-life than sugar). In the past 40 years America has had the unmatched distinction of going from eating 0 to ~70lbs of the stuff per capita annually. Lately it’s appearing this has been a big mistake: the majority of the science coming in is implicating HFCS in the drastic jumps in obesity and type II diabetes of the past 40 years[2]. A common theory (though not the only one available) suggests that because HFCS can’t be broken down in the way other sugars are, it taxes these alternate pathways, puts stress on the body’s ability to pump out insulin, and most importantly and verifiably, tends to cause insulin resistance and general problems in regulating glucose levels[3][4]. It’s likely that this looms larger for younger people with still-developing physiologies, and like many health risks, the effects of HFCS are statistical and are often felt more at the aggregate level than the personal.

This is probably old news to many readers.

What I think is not obvious and is worth pointing out is how tightly coupled glucose and cognition are, and specifically, how tightly coupled glucose regulation and willpower/self-control are.

We know that glucose is the fuel of thought: our brains use up the glucose in our bloodstream as it functions. But recent results from Gailliot and Baumeister[5] highlight several things about the brain’s ‘glucose economy’- among them are:

• Thinking through complex problems, keeping focused, and resisting temptation consume a relatively large amount of brain glucose. Subconscious, ‘easy’, and habitual actions consume a relatively small amount.

• We are less able to resist temptation when we have recently resisted temptation (e.g., faced with multiple temptations, our brain runs low on glucose and has a harder time going against the grain). All tasks involving self-control seem to follow the same pattern and draw from the same reservoir, be they resisting temptation, suppressing emotion, keeping focused, and so on.

• Once depleted, drinking Kool-aid sweetened with sugar replenishes this reservoir of willpower, bringing experimental metrics of self-control up to their original levels, while drinking Kool-aid sweetened with Splenda (a sweetener that does not contain or metabolize into glucose) does not[6].

• People vary significantly in their glucose tolerance (a measure of how accurately and quickly their bodies can normalize blood sugar), and scoring low on this test is highly predictive of tendencies toward impulsive and aggressive behavior, attention deficits, moodiness, addiction, and a general lack of self-control. In effect, their bodies’ clumsiness at regulating blood glucose levels lead to having smaller reservoirs of willpower.

More commentary (with pretty graphs) at Cognitive Daily. It’s a neat experiment and a fascinating finding.

In essence, willpower is a finite quantity and it’s centrally (though not exclusively) linked to the presence of and ability to regulate glucose.

So on one hand, it’s established that HFCS tends to corrode our bodies’ ability to regulate our glucose levels. On the other, it’s becoming clear that the single most important factor in willpower is having a well-functioning glucose regulation system. It’s a minimal jump to put these hands together and assert that by consuming high-fructose corn syrup, we’re not just making ourselves fat and diabetic, but we’re also actively corroding our physiological basis for willpower and self-control.

I’m not saying this is the root cause of everything that’s wrong in America, but I am saying that, aggregated over 300 million people, it’d be odd if it hasn’t been a contributing factor to this weirdly lazy, short-sighted, apathetic, debt-ridden state parts of the country are in. Speaking from both a humanistic and a cost-benefit perspective, I think it makes a truly excruciating amount of sense to prioritize more direct study of this hypothesized connection, to stop subsidizing HFCS through corn subsidies and sugar import duties, and to figure out how to prevent such situations from happening in the future.

Notes:

One of the criticisms of HFCS is that, because it doesn’t trip the body’s normal satiation response, people tend to eat or drink more of something if it’s sweetened with HFCS vs. a more conventional sweetener. Fructose is handled differently by the body. Fructose also has a unique metabolic footprint in that it’s broken down by the liver, not absorbed through the intestine. Both of these factors contribute to insulin resistance, and thus eventual malfunction in regulating blood glucose.

However, it’s unclear how much of HFCS’s reported contribution to insulin resistance is specific to HFCS (which is usually ~55% unbound fructose and ~45% unbound glucose) and how much of it is a result of overingestion of sugar in general (e.g., table sugar, or sucrose, is one fructose molecule bound to one glucose molecule, for a total of 50% fructose… hardly a large difference).

There are open questions in the science, however– e.g., is the extra chemical bond in sucrose relevant to health? Are some of HFCS’s observed health risks associated less with its fructose content and more with the biochemistry of how it’s commonly manufactured?[7] And what about gut flora? There’s every reason from microbiology to believe high vs. low fructose diets would lead to different gut ecologies, and that even modest differences in gut flora could have huge physiological effects, but like most phenomena involving gut flora the science is still out.

Footnotes:

[1] Another theory on Rome’s downfall is that the common and semi-indiscriminate usage of lead for plumbing, utensils, medicine, cooking ingredients, and so on caused mild brain damage to enough youngsters that Rome could no longer support itself.

[2] Bray, George A; Samara Joy Nielsen and Barry M Popkin (April 2004). “Consumption of high-fructose corn syrup in beverages may play a role in the epidemic of obesity.” American Journal of Clinical Nutrition. 79(4):537-543.

[3] Faeh, David; Kaori Minehira, Jean-Marc Schwarz, Raj Periasamy, Seongsoo Park and Luc Tappy (July 2005). “Effect of fructose overfeeding and fish oil administration on hepatic de novo lipogenesis and insulin sensitivity in healthy men.” Diabetes. 54(7):1907-13.

[4] Elliott, Sharon S; Nancy L Keim, Judith S Stern, Karen Teff and Peter J Havel (November 2002). “Fructose, weight gain, and the insulin resistance syndrome.” American Journal of Clinical Nutrition. 76(5):911-922.

[5] Gailliot, M.T., Baumeister, R.F. (2007). The Physiology of Willpower: Linking Blood Glucose to Self-Control. Personality and Social Psychology Review, 11(4), 303-327. DOI: 10.1177/1088868307303030

(If you like science, go read it– it’s genuinely interesting. http://psr.sagepub.com/cgi/reprint/11/4/303 )

[6] Though this particular experiment shows a link between glucose intake and willpower, the bigger picture is that it’s more precisely a link between the body’s ability to regulate the amount of glucose in the brain and willpower. Presumably too much sugar would disrupt this regulation just as much as too little. As the authors state,

This does not entail a linear relationship between glucose and self-control, such that a person who downs a large bag of candy will become a paragon of self-discipline for the next few hours.

Indeed.

[7] Of note, the journal Environmental Health has published an (industry-disputed) study on the prevelence of mercury in high-fructose corn syrup. The takeaway seems to be twofold: one, that a significant amount of HFCS may contain mercury due to particulars in the manufacturing process. And two, that HFCS is not ‘pure’ by any means: it takes a cocktail of reagents, enzymes, and chemicals to refine corn syrup into HFCS, and at least some of this cocktail is carried over into the final product.
Washington Post synopsis.

Update, 3/14/10: A reader points me toward a NYT article entitled Blood Sugar Control Linked to Memory Decline, Study Says.

Update, 4/14/11: There’s a longform piece in the NYT, “Is Sugar Toxic?“, that explores the concept that sugar isn’t just empty calories, but is actively toxic to the body. It implicates sugar in many common ills but doesn’t single out HFCS as worse than e.g., table sugar. The choice quote:

The phrase Lustig uses when he describes this concept is “isocaloric but not isometabolic.” This means we can eat 100 calories of glucose (from a potato or bread or other starch) or 100 calories of sugar (half glucose and half fructose), and they will be metabolized differently and have a different effect on the body. The calories are the same, but the metabolic consequences are quite different.

The fructose component of sugar and H.F.C.S. is metabolized primarily by the liver, while the glucose from sugar and starches is metabolized by every cell in the body. Consuming sugar (fructose and glucose) means more work for the liver than if you consumed the same number of calories of starch (glucose). And if you take that sugar in liquid form — soda or fruit juices — the fructose and glucose will hit the liver more quickly than if you consume them, say, in an apple (or several apples, to get what researchers would call the equivalent dose of sugar). The speed with which the liver has to do its work will also affect how it metabolizes the fructose and glucose.

In animals, or at least in laboratory rats and mice, it’s clear that if the fructose hits the liver in sufficient quantity and with sufficient speed, the liver will convert much of it to fat. This apparently induces a condition known as insulin resistance, which is now considered the fundamental problem in obesity, and the underlying defect in heart disease and in the type of diabetes, type 2, that is common to obese and overweight individuals. It might also be the underlying defect in many cancers.

If what happens in laboratory rodents also happens in humans, and if we are eating enough sugar to make it happen, then we are in trouble.

Update, 8/30/11: More evidence has come out regarding the impact of gut flora on mood, emotion, and behavior(1)(2). Presumably one of the largest factors in gut flora composition is diet, particularly the types and amounts of sugars eaten. I’d categorize this “diet->gut flora->mood/behavior” link as separate from the issue of willpower, and one we can also multiply by a significant fraction of 300 million to get a stochastic change in national behavior — though one that we can’t say much about at this point.

Update, 1/16/12: A little bit more data on the gut flora-insulin resistance link: “A group of Brazilian researchers have taken a strain of mice normally known to be immune to insulin resistance, and made them insulin resistant (pre-diabetic) by changing their gut bacteria. They then gave the mice antibiotics, and by changing their gut bacteria again, reversed the process, curing them of the disease.” Paper. (via Slashdot)

Update, 4/19/12: Here’s a wonderful survey of Baumeister’s research on ego depletion (the process by which our finite willpower is spent).

Update, 11/12/12: An experimental wrinkle in Baumeister’s model: swishing a glucose solution around in ones mouth (and not swallowing) gives some of the willpower benefits of drinking it.

If that seems overly technical, I’m taking a stab at the question, “What’s so special about three dimensions? Why don’t we live in four dimensions, or two?”

The Anthropic Principle is an argument about why we seem to live in a universe which seems fine-tuned for living creatures. There are 20-plus ‘universal constants’ (for instance, the speed of light or the strength of the electromagnetic force) which contribute to how matter, energy, and space behave, and it’s been observed that if any of them were much different, life couldn’t exist–nuclei couldn’t form, planets couldn’t form, the universe would expand too quickly and fly apart, and so forth. The Anthropic Principle[1] argues that it’s not such a big surprise that everything appears fine-tuned for life: if it were any different, we wouldn’t be alive to talk about it.

It’s a bit of a strange argument, and it’s sometimes tough to pin down exactly what the Anthropic Principle is arguing for, especially since there are so many variants. It certainly shouldn’t be taken as an excuse to throw up our hands and say, so that’s why things are the way they are: the Anthropic Principle is no substitute for hard scientific knowledge about why these constants have the values they do. Used carefully, though, it’s a good intuition pump for exploring the intersection between cosmology and biology.

I’ve sketched out an attempt to generalize the Anthropic Principle to include dimensionality: essentially, I argue that our dimensionality is very fine-tuned, and that any alternate macroscopic dimensional configuration (e.g., any configuration other than our 3 spacial dimensions + 1 time dimension) would likely not give rise to life[2]. It’s clear that we don’t know all the factors and implications involved in switching our dimensionality for another, but I believe we do have enough knowledge to make certain limited but probable predictions[3]. With these predictions in mind, I’ve outlined nine arguments to support my hypothesis: five are from the realm of Physics and Physical Chemistry, three from the realm of Biochemistry and Biology, and one dealing with entropy. A major goal of the project, which I have yet to attempt, is to use these arguments to try to generalize the Drake Equation across dimensionalities.

It’s a little on the abstract side. And terribly difficult (some would say impossible). But I think the process of working toward this goal could highlight a lot of interesting things about life, intelligence, biology, biochemistry, physics, and cosmology.

[1] My frame of reference is what people call the “weak” Anthropic Principle. I disagree with the “strong” Anthropic Principle, which argues that the universe must at some point generate intelligent life such that the universe will be observed, since it gives an almost mystical, non-naturalistic primacy to observation acts by intelligent beings. Instead of tying physical laws to observation, I would rather see them tied to cosmological natural selection.

[2] The National Academy of Science has recently issued an analysis of (and call for further research on) the prerequisite chemical conditions for life. Ars Technica’s coverage of this report is an excellent place to start for anyone who wants to get up to speed on what scientists have identified as enabling conditions for life to arise (though extending some of these chemical principles/prerequisites to other dimensionalities will not be straightforward).

[3] I haven’t been able to find much published literature explicitly on this topic of physical and biochemical implications of alternate dimensionalities and I’m grateful to those people with whom I’ve discussed this.

Still, I’m drawn to science like a fish to water, and as a hobby I sometimes plan out lines of scientific inquiry I would pursue if I were a scientist and/or rich, particularly on issues which might not come up in normal science funding cycles. Every week in July I’ll be posting about one such idea. It’ll be a grab bag. Sit back and enjoy it.
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What I’d do with a research lab, part 1:
Study the effects of diet on gut flora, and the effects of gut flora on physiology and psychology

Working hypothesis: Diet significantly and predictably influences gut flora, and gut flora has a significant influence on physiology, psychology, and in aggregate, perhaps even national character.

Gut flora, as anyone who’s read any yogurt labels nowadays knows, refers to the bacteria in our guts which help us digest food and absorb nutrients. The way this is commonly put is that, just as cows have cellulose-eating bacteria in their guts which help them digest grass, we have our own bacteria, or gut flora, which help break down hard-to-digest foods like Twinkies and Big Macs. This sounds simple enough– but what common wisdom misses about these bacteria in our guts is just how widely important and frighteningly under-studied they are. We know these bacteria not only play a key role in health[1][2]- they may be the primary external factor in immune system function- but can actually influence perception[3]. We also know literally almost nothing[4] about what lives in our guts. So, while the rest of the body is fairly well mapped-out, we have this huge question mark on gut flora, with a scribbled-in note that

1. Whatever does live in our guts outnumbers the ‘human’ cells in our body about 10-to-1,
2. gut flora is terribly important to body function (perhaps on the scale of a major bodily organ),
3. gut flora likely has some influence on nearly every part of our physiology, and
4. lots of things can go wrong. But we have few reliable metrics to figure out if someone’s gut flora has gone wrong, let alone how.

A big frontier in health science is figuring out what sorts of things influence this gut flora we carry around. Scientific wisdom has us getting much of our initial flora as a baby from our mothers, largely from breast milk and the birthing process, and these initial gut ecosystems are thought to be at least minimally stable. We also get bacteria from the food we eat, both from obvious sources such as yogurt but also from bacteria that naturally grow on foods such as grains and produce. Gut flora and mouth flora are linked, as are one’s genes and gut flora (e.g., genes influence gut flora, but gut flora also influences gene expression[5]). Taking a cycle of antibiotics is thought to sort of “reroll the dice” on gut flora: most bacteria die off and the survivors must scramble to repopulate the gut before their competitors do. There is, of course, a lot of randomness in all of this.

The nuances and relative contributions of these factors are several frontiers in themselves. Scientists are also starting to explore the indirect contributions of food to our gut flora, or whether different diets stochastically give rise to different biosystems by virtue of changing the competitive landscape for bacteria in our guts. One issue that I think is on the edge of researchers’ minds is whether a significant part of America’s systemic health problems is not just that we as a nation tend to eat food that’s bad for us, but we’re eating food (e.g., high-fructose corn syrup, food additives, preservatives (particularly sulfates), low-level food-borne antibiotics and pesticides) that makes the wrong bacteria thrive in our guts.

I think that’s probably the case and, getting further afield, what I’d like to do is attempt to look into whether the changes in gut ecosystems caused by changes in eating habits and food manufacturing trends- aggregated over the 300 million people who live in America- could contribute to a stochastic change in national character. Perhaps a significant contributing factor to some of our institutional ills is the food we eat, the corresponding imbalance in gut flora which arises from eating such food, and the subtle yet powerful-in-aggregate dysfunctional personality changes that e.g., biologically-active metabolites of such non-symbiotic gut flora might cause.

I sense some reader eyebrows being raised at this point– that bacteria could influence, let alone commonly influence, personality may seem quite a stretch. But consider the case of toxoplasma gondii (among other examples): it’s a protozoan parasite that spends part of its lifecycle in cats and part in other mammals, and shows clear signs of manipulating host behavior for its own ends. Infected rats, for instance, actually seek out the scent of cat urine, since when the rat gets eaten the toxoplasma can complete its lifecycle in the cat. What’s mind-boggling, though, is that humans who show tell-tale immunological signs of a past toxoplasma infection score statistically different on personality tests than do those who have not been infected (infected men tend to score higher in paranoia, whereas for women toxoplasmosis seems to lead to higher levels of social trust and sexual promiscuity). It’s unknown how or why toxoplasma causes such subtle personality changes in humans– though likely it’s a result of many generations of toxoplasma getting progressive fitness benefits from honing its initially accidental effects on rat fear/motivation, and since rats and humans are both mammals, some of those same psychological buttons toxoplasma has evolved to exploit in rats are hooked up to things in our brains, too. The fundamental point I would take from this is that there’s ample evidence that pathological microorganisms can and do subtly affect personality[6]. Personally? I believe these external pathogenic influences on personality – real as they are – will pale in comparison to that mediated by our gut floras.

Let me be perfectly clear: whether diet-gut flora interaction could be a commonly significant or significant-in-aggregate factor in personality is mostly just an intuition, built on the connections between gut flora and health, and health and personality, how tightly coupled bacteria and their metabolites are to our bodies and the scope of functional possibilities where such metabolites might- intentionally or unintentionally- act as e.g., hormone mimics, how many nerves (100 million+) are in our guts and how connected they are to our brain, the many possible feedback mechanisms between bacteria and neurotransmitters[7], how ‘clever‘ and environmentally manipulative bacteria can be[8], and how connected I think physiology and personality are. A sore tooth can influence personality; surely something that can not only affect our nerves, but many aspects of our biochemistry and nutrition as well, may do the same. I think it’s premature to really push any specific hypothesis about this gut flora-personality connection, since a lot of the basic science isn’t there to build on. But it’s a hypothesis we should be open to, and if I were in charge of the NIH or a well-endowed charitable foundation, I’d heavily prioritize flora research in general, particularly in the context of twin studies: we know so very little about gut flora (other than that it’s important) that the expected return is extremely high[9].

Update 10/13/07:

Since posting, there have been at least two major developments on this topic:
1. A theory that the appendix functions as a “safehouse” for good bacteria, especially with respect to repopulating the body’s GI tract after diarrhea, has been gaining traction. See

Bollinger RR, Barbas AS, Bush EL, Lin SS, Parker W. 2007. Biofilms in the large bowel suggest an apparent function of the human vermiform appendix. J Theor Biol (in press) doi:10.1016/j.jtbi.2007.08.032

2. A study has linked preference for chocolate with gut flora metabolism. Specifically, people who identified themselves as “chocolate desiring” had significantly different metabolic profiles and significantly different gut flora activity profiles than those who self-identified as “chocolate indifferent”. It’s both clear that this is an important result, and extremely hard to tease apart the causality involved at this point.

Results to be published in the Nov. 2 issue of American Chemical Society’s Journal of Proteome Research (via PhysOrg).

[2] One of my reasons for being so interested in this topic is that I suffer from celiac disease, or at least have some probably-autoimmune-mediated reactions to gluten and casein, and some think celiac disease may be initially, and perhaps chronically, caused by the presence of ‘bad’ bacteria in the gut.

[3] http://www.nature.com/nm/journal/v13/n1/abs/nm1521.html
“We found that oral administration of specific Lactobacillus strains induced the expression of mu-opioid and cannabinoid receptors in intestinal epithelial cells, and mediated analgesic functions in the gut–similar to the effects of morphine. These results suggest that the microbiology of the intestinal tract influences our visceral perception.”

[4] http://www.sciencemag.org/cgi/content/abstract/312/5778/1355 – with interesting commentary by John Hawks:
“This is really important stuff — our nutrition is very dependent on these microbes, and there is every reason to think that their ecology affects our overall health status as well. And we know very little about them — heck, these guys are using the same metagenomic techniques to fine organisms in our bodies that are used to find new unidentified ocean life!” e.g., we’re forced to use metabolism and byproduct analysis because most of what lives in our guts can’t be cultured in vitro. Edit, 7/13/07: A recent paper came out in PNAS outlining a technique that may at least partially solve this problem.

[5] Hooper LV, Wong MH, Thelin A, Hansson L, Falk PG, Gordon JI. Molecular analysis of commensal host-microbial relationships in the intestine. Science 2001 Feb 2;291(5505):881-4.

[6] Gregory Cochran has argued from an angle of evolutionary fitness load and allele frequency that many things we think of as being caused by genes or behavior (e.g., rheumatoid arthritis) are probably primarily mediated by pathogens.

[7] See http://findarticles.com/p/articles/mi_m0FDN/is_2_9/ai_n6112781/pg_6 – it’s also a good survey of some other complexities in this topic.

[8] This just scratches the surface of the manipulative potential of bacteria, but one such subtle strategy used by bacteria is (briefly) explained here: http://www.physorg.com/news99220113.html
“The genes responsible for toxin production only seem to be expressed during periods of nutrient deprivation. This is consistent with the view that most disease-causing bacteria express their pathogenicity when they are hungry,” says Abraham Sonenshein, professor at the Sackler School of Graduate Biomedical Sciences at Tufts University and at Tufts University School of Medicine, at the 107th General Meeting of the American Society for Microbiology (ASM) on May 24, 2007.

[9] Another related high-return research area which I think is a little better represented in science (but should also get more funding) is examining potential pathogenic influences on personality, e.g., along the aforementioned lines of toxoplasma gondii and this previous post.

Notes:
– I got to thinking about this when musing about America’s social/political/institutional ills. Perhaps this is a longshot for trying to help explain our various institutional dysfunctions. But who knows? It could be a contributing factor. Just because the effects of gut flora are incredibly complex doesn’t mean they’re neutral when aggregated into larger contexts.
– At this point I don’t have solid predictions on what sorts of diets tend to strengthen/weaken what sorts of personalities. Perhaps the safest thing to say is that the significant variation in human personality may have gut flora as a significant contributing factor, and also that the physiological stress of coping with a non-symbiotic flora may e.g. lead to personalities more prone to addiction.
– Though there is plenty of literature documenting correlations between gut flora and various diseases, and specific diseases and personality changes, I haven’t found any literature that covers what I would like to study, which is modeling gut flora’s influence on personality without the mediating frame of a specific disease.

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