The human brain is the ultimate control center of the entire body and because there is no higher regulating center, it only can achieve its task because it is highly self-organized and –regulated. But how is such a self-regulation achieved by the many billions of interconnected, electrically excitable nerve cells? Due to the enormous progress that has been made by the brain research in the last decades, the question arises whether the existing model of over- and under-arousal now is outdated? Furthermore, how does this model fit to the present models emphasizing slow cortical potentials (ILF)? And what about Default Mode Networks?
The long existing model of over- and under-arousal is not yet outdated. On the contrary, the performance-arousal curve still is a good model of self-regulation. In response to a demand, a healthy brain easily finds the optimal arousal-level on its own, i.e. without inappropriate motor activity or the influence of coffee, alcohol or medication. These all are hallmarks of a good self-regulation ability.
The extent to which the brain is busy with self-regulatory processes is shown by the fact that the difference in energy consumption between a resting situation and full effort is less than five percent. Thereby, the brain consumes more than a quarter of the total body energy and three-quarters of the blood sugar.
We can therefore state a confirmation of the old brain models of excitation level adjustment, which have been further refined and supplemented over time. Only, there is no longer the nice imaginary linear relationship between reward frequency and level of arousal. (Strictly speaking, we already had to discard this relationship at the times of the frequency band training).
That is, because the development of even more effective neurofeedback training lead us to the transition to report even slower processes back to the brain, which did not fit any more to the old ideas of high frequency = high arousal and low frequency = low arousal.
At the same time we recognized that the brain responded even stronger, the more subtle the EEG signal is returned in the feedback animations and the fewer a patient is asked to participate to the training by solving special tasks (“the rocket should fly as fast as possible”). Today we even don’t give any instruction at all, except to watch the feedback animations.
Thus, how does all of this fit together? With Neurofeedback we are still dealing with dysregulation and its improvement towards a healthy self-regulated brain. But it is obvious that the brain, by the method we are using to feedback its recorded EEG activity, easily recognizes its influence on the feedback and starts to interact with the feedback process. Because we are the ones who determine via the signal processing, what feedback signal the brain is receiving, we also are in control of what sort of electrical potential differences the brain is producing on its outer surface in order to change the feedback animations.
Because we perform bipolar recordings, the activity on two sites of a whole neuronal network is reported to the brain. Or in other words, by choosing certain electrode positions the affected neuronal network is defined and via the feedback signals its contributors are driven to produce an activity that results in such potential differences on the two sites of the brain underneath the electrodes. Due to such changes, the neuronal network is reconfigured and thereby brought to an increased capability of auto-regulation.
“Default Mode Networks” are an intriguing discovery and they fit very well to the hypothesis above. The results of fMRI measurements let researchers to believe that quite a number of such neuronal networks exist in the human brain (for a review see Raichle, M. (2011): The Restless Brain, Brain Connectivity, Vol. 1, p3-12). One interesting element of such networks are certain “nodes” – brain areas that have attracted attention because of their synchronous activity, which they showed in imaging studies. It is intriguing to us that the sites of these nodes correspond very well to the electrode positions our group has found the most effective in neurofeedback training of the various mental dysregulation conditions. (On this aspect of the Default Mode Networks, Siegfried Othmer had given a whole lecture on our advance Neurofeedback course in May 2014).
Yet, of course, all this is only a hypothesis. But we can affirm that a.) The development of our training method in concordance with this hypothesis has let to faster and stronger Neurofeedback effects (which might somehow confirm the hypothesis) and b.) Our consideration suits well with the previous findings on Default Mode Networks.
However, it is still merely more than a hypothesis. We still have to keep in mind that we are measuring a very tiny voltage signal, which is often superimposed by large electrode drifts, and which is recorded about two finger widths above its origin site on the brains cortex. While doing so, we interact with a complex, self-organized and neuronal plasticity-showing network, from which we believe to know surprisingly much, but might know almost nothing. It has to be kept in mind, that until twenty years ago the idea of neuronal plasticity had yet to be developed.
We like to follow an empiric approach to develop our Neurofeedback protocols on the basis of qualitative evidence criteria and believe this to be the best method towards an effective and ethically justifiable Neurofeedback procedure. Thus, we only release and recommend a Neurofeedback protocol after it has a proven history within our own clinic network of hundreds of successful Neurofeedback sessions. In parallel to these findings, we fine-tune our hypothesis and adjust the following developmental steps on the current results.
Modern Neurofeedback is a therapeutically challenging process. Patients are too different individuals as to apply similar training protocols and expect similar effects. Furthermore, the modern training approaches are too effective as to leave a patient unattended or to consider to treat patients en masse or even take the responsibility for home use.
An advantage of our method is its strong and fast effectivity and the equivalent immediate responses from the patient or the patient’s environment (often already during a session). This allows us to adjust the training parameter in an iterative process. Thereby, our Neurofeedback approach represent a powerful tool in therapy, but cannot be considered as a therapy replacement.