Functional imaging and connectivity

In the exercise this week, we conducted a little EEG experiment. So, let’s talk a bit about where the signal comes from.

Non-invasive functional brain imaging can’t detect the activity of a single neuron. The electromagnetic signals measured in EEG and MEG, and the blood flow changes detectable by fMRI are formed because of approximately simultaneous activation of many neurons close to each other.

So, why do mental activities so often activate a larger area in the brain, making them detectable by imaging? It’s a question of efficiency. The processes in brain are complicated and usually require activity of more than one neuron. And placing those neurons close to each other reduces the needed total axon length, saving time and energy spent on the action potentials traveling between them.

The selection of the efficient organization happens on two levels: evolution and individual development. Of course, the selection processes happening in the individual development have themselves developed through evolution. The first form of these processes is the placement of the different types of cells and the formation of the axons. This happens mainly before birth, and is based (among other things) chemical signals that different cells emit around them to guide each other, and gene activation/deactivation that makes the cell types different.

Later, both before and after birth, learning processes happen. These processes change the connections between neurons. The simplest model of these processes is Hebbian learning: “neurons that fire together wire together”. This kind of learning (in addition to being important for memory) amplifies the connection distributions that result from geometry. For example, one reason that ocular dominance columns are formed in the brain might be, that it just is more efficient to pass messages from one eye to one area and from the other eye to another area, and Hebbian learning then makes this contrast stronger.

So, together these many kinds of processes make brain structure be both very efficient and, luckily for us scientists, easier to measure and understand.

Posted by Mikko Luukinen

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