Ah yes, I was going to write about data analysis, but I got distracted by more data analysis. Anyway, here’s a bit of information on how we measure what’s going on in the brain, and how we interpret those measurements.
The brain generates and processes information using electrical signals, as far as our current understanding goes. For example, neurons in the eye respond to light by sending electrical signals to the visual cortex (via some other brain areas), where the signals are processed, interpreted, and distributed to other parts of the brain for integration with our other senses, further interpretation etc. These signals are very small, but measurable, even from the scalp – this is called electroencephalography (EEG). Each electrode of an EEG measures contributions from hundreds of thousands, perhaps millions, of neurons, so only provides a very coarse measurement of what’s really going on. At the other end of the scale, the electrical responses of an individual neuron can be measured by using a very small electrode to attach to its cell membrane, revealing the electrical activity inside it (or even to look at the currents flowing through individual channels in the membrane). This gives you information on what a single neuron is doing, but neurons never work in isolation, so you’re missing out on a lot of information about how the rest of the neuronal network is behaving.
Various types of measurement are available to bridge this scale divide. The type that I’m working with is from Utah arrays – 3.6mm square grids of 100 small electrodes that measure electrical activity from the space around neurons. This kind of measurement is similar to an EEG in that each electrode measures activity from many neurons surrounding it, but because the electrodes are placed so close to the neurons, spikes from individual neurons can also be picked up. The smaller scale allows the construction of a detailed picture of the local brain dynamics. Utah arrays are also particularly cool, because they are one of the only types of electrode that provide information on this kind of spatial scale that have also been approved for use in humans. They have already provided previously inaccessible information about epileptic brain activity in humans, and can be used to create brain-machine interfaces.
The data I get is from less glamorous locations than behaving humans, but in future I may get my grubby paws on recordings from brain tissue that has been removed during epilepsy surgery. Currently I’m looking at how brain activity varies in space over the small scale that the Utah array provides, and trying to match a computer model to the information provided by the recordings. The idea then is to investigate aspects of the model that cannot be changed in an experiment, such as how neurons are connected together, in order to guide future experimental research into unhealthy brain activity.