STEM editor: Francesca Farina
The human brain has a remarkable capacity to learn from feedback. During daily life as we interact with our environment the brain processes the consequences of our actions, and uses this ‘feedback’ in order to update its stored representations or ‘blueprints’ for how to perform certain behaviours optimally. This learning-by-feedback process occurs regardless of whether we are consciously aware of it or not.
The more interesting implication of this process is that the brain can also ‘learn from itself’, forming the basis of the ‘neurofeedback’ phenomenon.
Basically, if we stick an electrode on the head to record the brain’s electrical rhythms (or ‘waves’), the brain can learn to change the rhythm simply by watching feedback displayed on a computer screen. Because we know that the presence of particular types of brain rhythms can be beneficial or detrimental depending on the context and the task being performed, the ability to volitionally change them may have useful applications for enhancing human performance and treating pathological patterns of brain activity.
In recent years neurofeedback has, however, earned itself a bad reputation in scientific circles. This is mainly due to the premature commercialisation of the technique, which is now being ‘sold’ as a treatment for clinical disorders – for which the research evidence is currently still lacking – and even for home use to alleviate symptoms of stress, migraine, depression, anxiety, and essentially any other complaint you can think of! The problem with all of this is that we, as scientists, understand very little about the brain rhythms in the first place; Where do they come from? What do they mean? Are they simply a by-product of other ongoing brain processes, or does the rhythm itself set the ‘state’ of a particular brain region, enhancing or inhibiting its processing capabilities?
In my own research, I am currently working towards bridging this gap, by trying to make the connection between fundamental brain mechanisms, behaviours, and their associated electrical rhythms or brain ‘states’.
By training people to put their brain into different ‘states’, we were – for the first time – able to glimpse how brain rhythms directly influence these states in humans. We focused on the motor cortex, the part of the brain that controls movement, because there is a vast ongoing debate in the literature concerning whether changing the state of this region has implications for movement rehabilitation following stroke or other brain injury. Some argue that if the motor cortex is in a more ‘excitable’ state, traditional stroke rehabilitation therapies have enhanced effectiveness, compared to when the same region is more ‘inhibited’. Brain stimulation directly targeting the motor cortex has been used in the past in an attempt to achieve this more plastic, excitable state, but with mixed success and small effects that have proven difficult to reproduce.
In our investigation we used brain stimulation in a non-traditional way to achieve robust bidirectional changes in the state of the motor cortex. Transcranial magnetic stimulation (TMS) can be used to measure the excitability (state) of the motor system. By applying a magnetic pulse to the skull over the exact location in the brain that controls the finger, a response can be measured in finger muscles that is referred to as a motor-evoked potential (MEP). The size of the MEP tells us how excitable the system is. We developed a form of neurofeedback training where the size of each MEP was displayed to participants on screen, and they were rewarded for either large, or small MEPs by positive auditory feedback and a dollar symbol. This type of neurofeedback mobilizes learning mechansims in the brain, as participants develop mental strategies and observe the consequences of their thought processes upon the state of their motor system. Over a period of 5 days, participants were able to make their MEPs significantly bigger or smaller, by changing the excitatory/inhibitory state of the motor cortex.
Our next question was, how exactly is this change of state being achieved in the brain? Are electrical brain rhythms changing in the motor cortex to mediate the changing brain state? Using this new tool to change brain state experimentally, we asked participants to return for one final training session, this time while we recorded their brain rhythms (using EEG) during the TMS-based neurofeedback. This revealed that when the motor cortex was more excitable, there was a significant local increase in high frequency (gamma) brainwaves (between 30-50Hz). By contrast, higher alpha waves (8-14Hz) were associated with a more ‘inhibited’ brain state, but were not as influential in setting the excitability of the motor cortex as the gamma waves
The implications of these findings are twofold. Firstly, having a tool to robustly change the excitatory/inhibitory balance of the motor cortex gives us experimental control over this process, and thus opens several doors for new fundamental scientific research into the neural mechanisms that determine the state of the motor system. Secondly, this approach may have future clinical potential, as a non-invasive and non-pharmacological way to ‘prime’ the motor cortex in advance of movement rehabilitation therapy, by putting the brain in a state that is more receptive to re-learning motor skills. As the training is straightforward, pain free and enjoyable for the participant, we believe that this approach may pave the way for a new wave of research using neurofeedback in place of traditional electrical brain stimulation, as a scientific tool and an adjunct to commonly used stroke rehabilitation practices.