CNS 2015 in Prague, deadline extended

It seems unlikely that I’ll be able to attend CNS (the annual computational neuroscience meeting) this year (I went to the Quebec meeting last year and it was great – my posters are on figshare), but they’ve just announced that the abstract submission deadline has been extended to 1st March. It’s a cool meeting with some fun and interesting people, and Prague is swell. I feel I should be pimping it out, too, as I did the poster:

CNS 2015 Prague

Obviously the poster is now out of date with the abstract deadline there. Anyway.


My week on Biotweeps

Last Sunday I finished a week curating the Biotweeps Twitter account. Biotweeps features a different researcher each week, tweeting about their particular areas of interest. It’s a great account to follow for broadening your biology knowledge (as I’m a fake biologist mine is extremely limited). I tweeted about my PhD research, the work I’m currently doing, and some interesting projects and papers from my previous colleagues at Newcastle University (particularly the CANDO project, which aims to create an implantable device for preventing seizures).

My tweets are archived on Storify here.

Postgraduate students are rich

I used the Institute for Fiscal Studies’ “where do you fit in” calculator the other day, which, given your household size, number of dependent children, council tax and post-tax earnings, calculates your income in relation to the rest of the UK population. Though the standard research council annual stipend of £13 590 doesn’t sound much compared to what other graduates are expected to earn, it is income-tax and NI free, and as a student I pay no council tax. I also live in the City of Dreams, where the cost of living is not so high as other parts of the UK (though the calculator doesn’t look at this). What are my results?

I live with one other similarly-funded student, so I entered a combined household income of £28 000 (a few hundred extra each per year for teaching/marking), 0 children and £0 council tax. This means my household has a higher income than 66% of the population (red bar in image below).

UK income distribution histogram

UK income distribution

Not bad for students eh? If we had one dependent child aged 0-14, the calculator estimates we’d have a greater income than 52% of the population.

Other thoughts: If I lived on my own, my household income would be greater than 49% of the population. If I quit now, moved into a flat on my own and got a graduate software development job at, say £30 000 a year (pre-tax), depending on my council tax (I’ll assume ~£2 000) and assuming I was making full student loan repayments, my household income would be more than 68% of the population.


  • Research council funded PhD students are really quite well off
  • Children are expensive

Music in the brain

Well not really, but indulge the metaphor because it’s a nice description of a phenomenon observed in epileptic brains. A recent paper in the journal Epilepsia (unfortunately behind a paywall, sorry) describes a pattern of electrical activity recorded from the brains of epilepsy patients that may be helpful in predicting when seizures occur. The paper is a good example of useful cross-disciplinary work: it combines clinical, basic and computational research to arrive at a convincing mechanistic explanation for the observed electrical patterns, and proposes some hypotheses about epileptic seizures that, if correct, could lead to better treatments for patients [conflict of interest warning: one of the authors is my second supervisor].

The pattern described is an electrical rhythm that rapidly increases in frequency over a time span of about one second, which seems to happen shortly before a seizure occurs. The electrical signal recorded from the patient’s brain rapidly increases in frequency; in our musical analogy, this electrical pattern is similar to the sound pattern produced when you rapidly slide your hand up the notes of a piano, from low to high. In music, this slide is called a glissando, and the authors adopted this name to describe the pattern of brain activity. This pattern was noticed in electrocorticogram (ECoG) signals – recordings made by placing electrodes directly on the surface of the brain, usually used before epilepsy surgery to give the surgical team information on the location of the small region of brain tissue that initiates seizures (the seizure focus). After surgery, the tissue removed from the seizure focus was kept alive and studied in the lab. Recordings from this removed brain tissue also displayed the glissando pattern of activity.

The mechanism proposed by the authors is complex and derives from a lot of the previous literature on the subject of brain rhythms in epilepsy (for a technical review, see this book). Much previous work on fast rhythms, as seen in glissandi, has pointed towards the importance of electrical connections, called gap junctions, between neurons as being crucial, rather than more conventional chemical synapses. Specifically, the proposed mechanism requires gap junctions to exist between the axons of excitatory neurons (the axon being the part of the neuron that transmits electrical signals to other neurons, which then receive these signals via chemical synapses). When a neuron sends a signal down its axon in the form of an electrical spike and the axon is connected to other axons by gap junctions, under some conditions the spike can be transmitted into these other axons, causing a wide spread of excitatory signals in the neuron network. This spread is fast, as the direct electrical connection of a gap junction transmits signals more rapidly than conventional chemical synapses. A proposed property of the gap junctions between axons is that their electrical resistance decreases the more alkaline their surroundings are*. As shown in the paper, this decrease in resistance allows even faster propagation of electrical spikes between axons, so when the alkalinity of the tissue increases, the frequency of the activity increases, creating the glissando effect.

Previous clinical reports have suggested that increased alkalinity can contribute to starting seizures in some cases, and this paper proposes a mechanism for how this alkalinisation could contribute. Further experiments are required to establish this proposed mechanism as correct, but if it is proved right, it opens up some interesting possibilities for treatment in some patients. If suitable monitoring equipment could be worn, glissandi could be used to predict seizures, and local brain alkalinity controlled in response. Alternatively, drugs acting on gap junctions between excitatory cell axons could be used – though the significance of axonal gap junctions in healthy brain function is unknown.

You can read the Newcastle University press release about this research here.

*other types of gap junction apparently increase in resistance under alkaline conditions