Thursday, April 12, 2012

#ALGORITHMS: "Analytics Solve Brain's Mystery"

Analytics has been harnessed to plumb the mysteries of the brain, by discovering rules for the behaviors of its neural networks that now allow algorithms to mimic its behavior without duplicating every little detail, according to the École Polytechnique Fédérale de Lausanne (EPFL). Although much work remains to realize this dream, the hope is that now brain models can now be run on reasonably sized computers and yet still obtain unparalleled accuracy. R. Colin Johnson

Here is what EPFL says about its latest results: The École Polytechnique Fédérale de Lausanne has discovered rules that relate the genes that a neuron switches on and off, to the shape of that neuron, its electrical properties and its location in the brain.

Using data mining analytics EPFL's senior researcher on the project, Henry Markram, now believes reasonably sized models can predict the fundamental structure and functions of the brain. Since every aspect of the brain need not be modeled, the new technique greatly reduces the complexity of models of the brain, enabling them to be modeled using conventional computer resources, which opens the door to "predictive biology," the Holy Grail of the EPFL's Human Brain Project.

Within a cortical column, the basic processing unit of the mammalian brain, there are roughly 300 different neuronal types. These types are defined both by their anatomical structure and by their electrical properties, and their electrical properties are in turn defined by the combination of ion channels they present—the tiny pores in their cell membranes through which electrical current passes, which make communication between neurons possible.
Scientists would like to be able to predict, based on a minimal set of experimental data, which combination of ion channels a neuron presents.

They know that genes are often expressed together, perhaps because two genes share a common promoter—the stretch of DNA that allows a gene to be transcribed and, ultimately, translated into a functioning protein—or because one gene modifies the activity of another. The expression of certain gene combinations is therefore informative about a neuron’s characteristics, and Georges Khazen and co-workers hypothesized that they could extract rules from gene expression patterns to predict those characteristics.

The researchers took a dataset that Markram and others had collected a few years ago, in which they recorded the expression of 26 genes encoding ion channels in different neuronal types from the rat brain. They also had data classifying those types according to a neuron’s morphology, its electrophysiological properties and its position within the six, anatomically distinct layers of the cortex. They found that, based on the classification data alone, they could predict those previously measured ion channel patterns with 78 per cent accuracy. And when they added in a subset of data about the ion channels to the classification data, as input to their data-mining program, their analytics accuracy was boosted to 87 per cent for the more commonly occurring neuronal types.

According to team member, Felix Schürmann, the increased accuracy of their results shows that it is now possible for analytics to mine rules from a subset of data and use them to improve results without having to measure every aspect of a behavior. Once the rules have been validated in similar, but independently collected datasets, they can now be used to predict the entire complement of functions--here the ion channels presented by a given neuron--based simply on data about that neuron’s morphology, its electrical behavior and a few key genes that it expresses.

The researchers now hope to use analytics to derive such rules that express the roles of different genes in regulating transcription processes. The teams reasoning is that if rules exist for ion channels, they are also likely to exist for other aspects of brain organization. For example, the researchers believe it will be possible to predict where synapses are likely to form in neural networks, based on information about the ratio of neuronal types in that network. Knowledge of such rules could therefore usher in a new era of predictive biology, and accelerate progress towards a better understanding of the brain, as well as more manageable models the brain.
Further Reading