Describing the Energy Brain
Neurons are constantly receiving and sending information. Hundreds of chemical messages are arriving from other neurons in its networks at any moment. In response, an electro-chemical charge builds up in the cell body until it reaches a critical level. Each time it fires down the trunks and branches of its axon tree, the pulse triggers chemical messages to all outgoing connections. There are billions of neurons and trillions of links—synapses–in a brain. Most of them fire several times a second or more. It’s an electro-chemical chaos.
However, when neurons pool their activity and fire jointly, the result is measurable. An EEG provides a real-time reflection of these pooled bursts.
We’ll discuss the energy brain in terms of Frequency / Location / Linkage / Control. How much energy it makes—when and where it makes it–how effectively areas operate together and share information.—how flexible and stable it is.
Measuring energy Levels: Frequencies/Amplitudes/Power
The measure of energy is frequency—how many times/second the neuron fires. Frequency is measured in Hertz (Hz). One Hz is 1 beat per second. A neuron firing 9 times per second is producing a frequency of 9 Hz. Each firing must metabolize oxygen and glucose. Neurons don’t burn fat.
It’s said that, with just 3% of body weight, the brain uses 25% of the oxygen and more than half the glucose in the body. The more efficiently it operates, the better.
In fact, one of the things that clearly differentiates peak brains is their tendency to operate most of their neuron pools, most of the time (at least during performance) in an idling, auto-pilot state. They demonstrate a clear ability to activate when processing is required—until the task is completed—then to return to the resting/ready state.
In simplest terms, the lower the frequency, the lower the energy level. It takes less energy to fire 2 times a second than to fire 12.
The second dimension of measuring energy–how many pools are firing at each frequency–is amplitude. EEG amplitude is measured in microvolts (u)–millionths of a volt. In simplest terms, the more pools firing at a frequency, the greater the amplitude. 5-million neurons pooled at 9Hz, for example, produces a greater amplitude than 2-million.
Power is a related measure of relative size of groups firing at each frequency. Power is amplitude squared. It is used in gathering normative QEEG’s, but rarely in training.
Frequency and amplitude can be useful in identifying the overall character of a brain—fast-dominant, slow-dominant, capable of shifting smoothly and efficiently, etc.
More specifically, though, we also recognize that different areas of the brain have different architecture, functions and energy patterns. Like musicians in an orchestra, they do different things and do them in different ways to create a harmonious whole. Brains produce very different patterns with eyes closed, eyes-open resting or at task. We can measure how a brain activates at task—or doesn’t; what happens when eyes are closed or open. We can also compare within frequencies between the left and right hemispheres, and front and back halves of the brain.
These measures correlate with a person’s emotional state and ability to handle routine or novel situations, to relax and be still, etc. When and where the brain cells are making a certain frequency gives us powerful hints toward where and what to train.
When neurons fire in sync with others, their signal appears more strongly on the EEG. But there is a functional effect as well. As we’ll see later, depending on the frequency, areas of the brain may function independently or lock together. Synchrony among pools of neurons when they are idling is very efficient. Linkage between pools at other frequencies can indicate efficient cooperation and communication.
Flexibility / Stability
Brain activation patterns do tend to be stable, but when they become rigid, the brain is cut off from some of its available functions. All energy patterns are good for some things, not for others. None is good or bad. But when a brain is unable to shift, it operates far from its peak. Too much control keeps me from shifting patterns based on the situation. Too little keeps me from sustaining an appropriate pattern long enough to make it useful.
Before we can learn to read patterns in the brain, we need to start with the alphabet—the frequency categories generally used in discussing EEG.