What is Neurofeedback?
Neurofeedback is a biofeedback method that either displays real-time EEG data, or reacts to real-time EEG data (see Neurofeedback in 200 Words). This enables users to learn how to self-regulate their brains. Many people in public society have never even heard the word, nor do they understand what applications it has to offer.
Neurofeedback can be described as a technique used to train the brain’s electrical activity that utilizes concepts and strategies of biofeedback. Biofeedback and neurofeedback are similar in the aspect that they are both determined to operate through classical/operant conditioning mechanisms. These systems train the brain and body to more adequately regulate itself by implementing aids, such as real-time video, audio, and tactile information, that relay its measured electrical activity. This specific activity is typically assessed by placing electrodes on the surface of the body (constituting as biofeedback) or the head (constituting as neurofeedback).
In short, neurofeedback is method through which your brain is trained to bring its electrical activity back to a healthy, normalized level. The brain waves are measured in hertz (Hz), or the number of waves per second, using an EEG (or an electroencephalogram). The rhythm of the brain activity correlates with different types of frequency bands and stages of wakefulness that occur in an individual. For instance, if waves reach up to 4 Hz, these are called delta waves and the individual resides in a slow-wave sleep state. At 4-8 Hz, these are called theta waves and the individual is in a drowsy or inattentive state. Alpha waves occur between 8-12 Hz and are associated with a relaxed, wakeful state, while beta waves are between 12-30 Hz and constitute as an active, attentive state .
Neurofeedback and ADHD
Neurofeedback is considered as an experimental treatment for ADHD. The particular waves we are interested in are found over the sensorimotor cortex of the brain – they are specifically called low beta activity (12-15 Hz) . This is called the sensorimotor rhythm (SMR) and this SMR is particularly relevant to individuals with ADHD. SMR amplitude increases when related sensory-motor areas are in states of immobility. The amplitude decreases when correlated motor areas are in the midst of performing motor tasks. The summation of this description is essentially that SMR is a way to measure motor inhibition – the SMR is strongest during the most inhibition and is weakest during the least. There are various specific wave patterns, which are referred to as event-related potentials (ERPs). ERPs can also be identified and studied using an EEG. These event-related potentials are actually the brain’s underlying sensory and cognitive processing that occur in the form of an electrical representation. This electrical representation is the brain’s way of responding or reacting to some stimulus or event .
An important group of ERPs are called slow cortical potentials (SCPs), which reflect the excitation threshold of large accumulations of cortical cells by directing current shifts of the EEG. When these currents shift in a negative direction, they are called the contingent negative variation (CNV) – this means that a reduction of the excitation threshold has been indicated and is speculated to correlate with cognitive preparation along with increased cortical activation of a network . On the other hand, shifts in the positive direction demonstrate an increase of the excitation threshold and is related to inhibiting activation. Research conducted on SCPs has shown that children with inattention and hyperactivity have reduced cortical negativity (also termed a “deviant CNV) during cognitive preparation . This conclusion suggests that the lack of engaging in SCP networks contributes to inadequate performance.
It has been theorized that core symptoms of ADHD, such as inattention, hyperactivity, and impulsivity, are correlated with this slow-wave activity and reduced cortical negativity . Neuroimaging techniques also propose that ADHD is associated with areas of the brain accountable for sustaining attention, motor control and behavioral planning, such as smaller/potentially underaroused frontal lobes and other areas notably related. Assessments performed using positron emission tomography (PET) and single-photon emission computed tomography relay that there is also reduced blood flow and metabolism. This proposes electrophysiologic underarousal over central-midline and frontal cortical regions in roughly 80%-90% of those with ADHD .
A multitude of patients with ADHD experience more slow-wave power, notably theta (at 3.5-8 Hz) during resting EEG spectral analyses than normal controls. Not only this, but they also experience less beta power (12-20 Hz) – this can most likely be explained by underarousal of the patient’s central nervous system. In animal testing, higher beta frequencies (>15 Hz) are correlated with focusing on a task or another scenario that requires attention; lower beta frequencies (12-15 Hz) are correlated with calm immobility . The way the neurofeedback system works is that it reduces the amount of theta waves (associated with inattention) and increases the amount of beta waves (associated with attention). These methods have been further developed into video games and more interactive forms of animation. Studying the way medications affect the human EEG in those who have ADHD is also invaluable, as it portrays how it is possible to affect brain waves with the same accuracy and similar mechanism, but by using the process of neurofeedback instead. The research performed on this system and analyzing the changes related to positive medication responses carry over and contribute to the idea that brain waves can be learned consciously .
Though this idea of treatment may seem novel, the knowledge of it has been around for quite some time. Neurofeedback was first reported as a treatment for ADHD back in 1976, and many studies have been conducted since analyzing its efficacy. A meta-analysis of the efficacy of neurofeedback treatment in ADHD portrayed the treatment to be considered “efficacious and specific” for ADHD “with a large effect size for inattention and impulsivity and a medium effect size for hyperactivity.” This suggests that, in this particular study, hyperactivity is likely most sensitive to treatment factors that are non-specific .
Another study, comprised of 102 children with ADHD (ranging from ages 8 to 12 years old), had its subjects undergo a randomized assignment of either participating in a neurofeedback training group or participating in a computerized attention skill training group. All children performed a total of 36 sessions and trained within two blocks of approximately four weeks each. Treatments consisted of two blocks of 18 sessions, which were performed as nine double sessions of about 2 * 50 minutes each, separated by a small break . The combined neurofeedback treatment was made up of one block of theta/beta wave training and one of SCP training. Placebo scales were integrated to set controls for satisfaction and expectation of the treatment for the parents. Several behavior rating scales (i.e. the German ADHD rating scale, FBB-HKS) were incorporated into the assessment of the pre-training, intermediate, and post-training sessions.
The system design involved was SAM (Self-Regulation and Attention Management), which was used for neurofeedback training and was designed by the study group. By using animations, the children were able to play a sort of computer game to stay engaged as well as have their brain activity monitored . SCP monitoring involved finding ways to either direct a ball upward (negativity trials) or downward (positivity trials), while at the same time the bar representing theta activity was reduced and a bar representing beta activity was increased.
The theta/beta trials initially lasted about 5 minutes, and were then extended later on to a duration of 10 minutes (baseline values were determined at the start of each session for 3 minutes). Overall, about 120 trials of these SCP training sessions were performed. In addition to this neurofeedback training, every child in this group was also encouraged to practice their focus outside of the sessions . For instance, they were encouraged to choose one particular activity each day to concentrate on while at home or in different scenarios (i.e. reading, playing football, etc.).
In the end, parents rated improvements in the FBB-HKS score total and additionally in the inattention and hyperactivity/impulsivity subscales for both theta/beta and SCP training . Trends statistically displayed that there was improvement in the FBB-HKS scores of the participants, particularly when theta/beta training was done prior to SCP training . Conclusively, this study determined neurofeedback to be a potentially clinically effective portion of treatment for children with ADHD.
In southern Germany, another study that was performed in 2003 set up a 3 month-long EEG feedback program and compared its effects on cortical SMR (12-15 Hz) and beta activity (15-18 Hz) with those of stimulant medication. There were a total of 34 subjects between the ages of 8-12 years. Twenty-two were assigned to the neurofeedback group, while twelve were assigned to the methylphenidate (Ritalin) group (choosing of the groups was based on the preference of the parents). Subjects were chosen based on these main characteristics: a diagnosis of ADHD based on interviews with parents and children using DSM-IV criteria, had a Weschler intelligence quotient of >80, and were at least one standard subscore (<85) on the Test of Variables of Attention (TOVA) . Also, none of the subjects had experienced any prior treatment for their ADHD before beginning the study .
Neurofeedback and methylphenidate groups were treated in the program for 12 weeks at about three sessions a week in the afternoon (excluding weekends). The EEG recorded values from an electrode placed at either C3 or C4 (referenced to linked earlobes, or a mastoid ground electrode). C4 and SMR were used in subjects that fell in the hyperactive-impulsive subtype of ADHD, while C3 and beta1 frequencies were used in subjects that were primarily inattentive subtypes . Children with a combined subtype were treated half the time like the hyperactive-impulsive subtype, and the other half like the inattentive subtype. The point in this type of neurofeedback training was to increase the power in SMR or beta1 bands (or “reward bands”) while at the same time decreasing the power in the theta and beta2 bands (or “inhibit bands”). Sessions typically consisted of 30 to 60 minutes of some visual and auditory feedback, the baseline was determined by the first 2 minutes at the beginning of the session .
When subjects achieved defined goals consistently in their training, their reward thresholds increased in difficulty. For instance, subjects played a Pacman-like game where their icons had to “eat” through a maze of dots. The speed and brightness of the icon was determined by the power in the reward bands (between 12-15 or 15-18 Hz). The higher the power was, the brighter and faster the icon would be. When the reward criteria was met, an audiovisual signal (a beeping noise and another increasing its value) indicated scores. On the other hand, when power in the inhibit bands increased over its threshold (4-7 or 22-30 Hz) then the icon would freeze and the screen would turn black. Once the subject had maneuvered his/her icon to the end of the maze, there would be a performance bar chart available so the subject could visually see how well he/she did in the session.
Both groups’ progress was based on the subscales of the TOVA and the d2 Attention Endurance Test. Both groups showed improvements in all categories. This finding demonstrates that neurofeedback can be used as an effective treatment for behavioral deficits of ADHD for parents who choose non-pharmacological treatments for their child.
Not only can neurofeedback provide insight about those with ADHD and be used to help improve attention deficit, but it can also help us determine behavioral disorders, such as autism, by analyzing underlying brain patterns . By utilizing machine learning methods and applying them to the fMRI patterns of 17 adults with high-functioning autism, it is possible to study the alterations in neural representations of social concepts in these individuals. Not only this, but these alterations can also contribute as neurocognitive markers in those with autism.
The 17 adult individuals were studied comparably next to a matching control group – both groups were scanned while thinking about 16 different social interactions. The subjects were given 8 social interaction verbs to think about (such as insult, hate, adore, compliment, etc.) and were asked to consider each word from two perspectives (either being the recipient or agent of the action). Total, there were six presentations for 16-item blocks .
The most significant finding was a neural representation factor in self-representation, which is evident in posterior midline regions of the brain . The factor was near-absent in the autism group and was pretty much only present for those who belonged in the control group. The degree of alteration in a subject with autism’s neural representation of “self” is related to the cingulum bundle, or a section of brain connective anatomy that joins regions that are correlated with the representation of “self” (the frontal and posterior midline make up these areas of the brain).
Behavior is also correlated with the degree of alteration, which is done by measuring face processing with a Benton Facial Recognition Test, as well as many other tests. This gives a layered account that connects the brain anatomy, neural activity, and behavior related with a subject with autism’s thoughts about specific social interactions. Overall, this method was able to classify individuals as either belonging to the autism or control group with 97% accuracy (made correct predictions for 33 of 34 cases) based on their fMRI neurocognitive markers .
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