# Social neuroscience issue of Nature Neuroscience

I meant to post this earlier, but Nature Neuroscience devoted the most recent issue to Social Neuroscience.  There are quite a few good review articles to spend your time on…

# How social status affects your brain

When you get into work in the morning, you might say hi to your coworkers and complain for awhile about your boss.  Then maybe you joke with the janitor, only to flee when you see your boss headed to your desk.  Each of these interactions – as is every interaction between individuals -is deeply embedded in the context of social status.  Social status isn’t just a construct of our world, but a state of our environment that causes profound changes in the way your brain functions.

One way social status affects the brain is through serotonin; it is well known in the scientific literature that changes in serotonin level seems to directly affect perceived social status.  Whether high social status depends on high or low serotonin depends on the species; dominant individuals of species who must fight to retain social status have high serotonin levels, whereas dominant individuals of more cooperative species such as bonobos have low serotonin levels.

Issa et al. looked at social status in crayfish.  Crayfish actually form long-lasting and complex dominance hierarchies where subordinate animals give way to dominants in contests over resources.  Issa et al. took socially isolated animals and let them interact for thirty minutes a day, even though dominance was usually established within the first fifteen minutes.  They then examined the response to these individuals to a surprise touch to the back leg.  Dominant individuals always immediately turned toward the tap, presumably because they were prepared to be aggressive toward some threat.  Submissive individuals, on the other hand, always showed one of two behaviors: they either pushed backwards and then lowered their posture, or they flexed their abdomen, dropped their posture, and then moved backward.  When they recorded from a (specific) neuron that releases serotonin, they found the same kind of stereotyped response from the dominant individuals’ neurons, and the same kind of symmetric response from the subordinates’.  The authors also have a nice model suggesting that the neural circuit itself might be reconfiguring itself by modifying thresholds for firing of excitatory and inhibitory neurons.  It’s a simple result that looks true, though in the field of circuit neuroscience, the easy answer is almost never the right one…

This means that the dominant and subordinate individuals not only have different levels of serotonin, but that their neural circuitry is fundamentally different.  The authors interpret this to mean that a change in status indicates a persistent change is enacted, perhaps by modifying the amount or type of receptors.  The fact that dominance is usually established within fifteen minutes leads one to think that perhaps there is some other underlying difference; however, isolated individuals that weren’t exposed to this dominant-subordinate training acted in roughly the same manner as dominant individuals, with similar neural responses.

For the crayfish, there is probably a trade-off: dominant individuals get more resources, but must also be prepared to fight, perhaps making them more likely to be consumed by predators.  The lessons for humans is probably more complex.  Serotonin is not just linked to social status, but also depression, so it would not be surprising if low social status can literally make us ill.

Reference

Issa, F., Drummond, J., Cattaert, D., & Edwards, D. (2012). Neural Circuit Reconfiguration by Social Status Journal of Neuroscience, 32 (16), 5638-5645 DOI: 10.1523/JNEUROSCI.5668-11.2012

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# Moving and migrating

People move around.  A lot.  In our daily lives and over the longer term, we are constantly moving from place to place.  Is there a simple way to understand our mobile interaction with the world?  Albert-László Barabási has spent a substantial amount of time trying to understand this question.

A couple of years ago his lab took anonymized data from cell phone carriers which let him track movement from cell tower to cell tower.  Over a course of three months, they analyzed how predictable a person’s movement was.  They did this by measuring the entropy, or orderliness, of movement; a low entropy is very predictable while a high entropy is essentially random.  Movement entropy was large when looking at the overall number of locations a person visits at any random time, but the entropy of movement within those locations (temporally uncorrelated) was much smaller.  Even smaller was the entropy of the pattern of movements within those locations.  In fact, 93% of a person’s movements can be predicted!  And there was no significant difference between weekdays and weekends, so this isn’t just due to the fact that people spend a lot of time going to and from work.  There was also not a difference between genders, people of different age, or rural and urban environments.  But the entropy distribution did have fat tails, so even though people are generally pretty predictable, they are prone to veering off into drastically new locations.

How about migration between different regions?  Barabási’s lab looked at movement between counties.  The previous model for this migration was called a gravity model, in analogy with Newton’s law of gravity.  It tried to explain migration as a simple case of attraction based on the multiplied population of two areas, divided by some function of the distance.  However, this doesn’t do a great job of actually predicting how people move.  His lab took data from the census and developed a new model that they are calling a radiation model.  Using some slick math, they suggest that the flux of movement between two places is governed by the law:

$ = T_i \frac{m_i n_j}{(m_i + s_{ij})(m_i + n_j + s_{ij})}$

Let’s unpack what this means.  We want to know how many people move between location i and location j.  We’ll say there are $m_i$ people at location i and $n_j$ people at location j.  These places are a certain distance apart which we’ll say is $r_{ij}$.  Now there can be a lot of people living between these two places!  And that probably has something to do with migration patterns.  So we need to use the number of people living within a circular radius $r_{ij}$ of the first location i (excluding the number of people specifically at i and j), and we’ll call that number of people $s_{ij}$.  Finally, $T_i$ is just the number of commuters starting at i.

So the number of people moving between two areas is not just a function of how many people live in each area, but also how many people live in between!  And the model makes some pretty impressive predictions and uses some pretty slick math to get a simple equation.  The moral of the story here is that how people move about their spatial environment in their daily lives, how they commute between places, and how they up and move to new places is all regular and fairly predictable.

References

Song, Qu, Blumm, Barabasi.  Limits of predictability in human movement.  Science (327) 1018-1021.  DOI: 10.1126/science.1177170

Simini, Gonzalez, Maritan, Barabasi.  A universal model for mobility and migration patterns.  Nature (484) 96-100.  DOI: 10.1038/nature10856

# Why you’ll become an alcoholic unless you get more sex

One very social behavior involves a man and a woman who love each other very much (hint: I’m talking about sex).  Flies who love each other very much obviously also mate, although you may not know that they undergo a courtship ritual first – not just any ol’ fly is getting to home plate.  That’s a behavior I’ll talk more about in a future post.  What I want to talk about here is instead what happens to that unlucky guy who, know matter how hard he tries, isn’t getting any.

A recent paper looked at this very question by taking a bunch of flies, and either having one group that either had a lot of sex or were rejected.  And can I say how awesome this sounds?  Listen to the protocol: one group of male flies experienced 1-hour sessions of sexual rejection three times a day for four days.  Another group experienced six-hour sessions of mating with multiple receptive virgin females for four days.  Let’s just say that you probably couldn’t do this kind of science in people.

The flies were then given the choice between food with alcohol and food without alcohol.  When the flies were sexually satisfied, they went without the alcohol; the flies were rejected needed that extra beer.  It turns out even virgin flies choose the alcohol – though they like it less than the rejected flies – which means that it is the lack of sex that mainly influences how much they need to drink.  If these same flies are allowed to mate?  Then they don’t need the alcohol anymore!

Desire, motivation and addiction in the brain are normally associated with the neural chemical dopamine.  But in this paper they looked at a neural peptide instead.  In humans, the neural peptide Y regulates alcohol consumption, as does all kinds of stress like PTSD and early maternal separation.  The equivalent peptide in fly is neural peptide F (NPF).  When they measured the amount of NPF in these flies, they found that it matched the desire for alcohol: the sexually rejected males had the lowest amount of NPF, the virgins had a little more, and the mated males had the most.  By decreasing the amount of NPF with siRNA or artificially activating it, they were able to control how much the flies wanted the alcohol.

So what is happening in the brain in response to sex?  Sex releases this neuropeptide – NPF in flies, NPY in humans – and the peptide is rewarding!  You love it (no surprise there)!  The peptide probably sets in motion changes in the larger reward system, modifying dopamine transmission over the course of many days.  This reveals the importance of investigating how we interact with our environment and fellow creatures in order to understand how our brain really works.

Reference

Shohat-Ophir, Kaun, Azanchi, Huberlein.  Sexual deprivation increases ethanol intake in Drosophila.  Science (335) 1351-1355.  DOI: 10.1126/science.1215932