Catherine L. McHugh, MA, BCBA, LBA and Thomas Zane, PhD, BCBA-D
Department of Applied Behavioral Science, University of Kansas

Is there science behind thatAutism is diagnosed through a variety of assessments based on observable behaviors that an individual may exhibit. According to the Centers for Disease Control and Prevention (CDC, 2021), diagnosing autism can be difficult since there are no medical tests to definitively determine if a child has autism. That is, there is no blood test or physical scan that can diagnose it. Although recent research on brain form and functioning is aimed at finding characteristics of the brain that might help in diagnosis and early detection, there is still no fail-proof way to use medical tests for the diagnosis of autism (CDC, 2021).

The literature on autism treatment is vast, and there exist a plethora of interventions targeting various challenges for this diagnostic category. Some researchers have been analyzing brain waves in various groups of the population in an attempt to identify differences and perhaps change the waves in an effort to reduce symptoms such as depression, anxiety, and inattention. This “neurofeedback” (NF) approach is a neurologically-based therapy in which auditory or visual stimuli (singly or together) are presented to change patterns of neuronal activity (van Hoogdalem, et al., 2021; Wang, et al., 2013). One form, electroencephalogram (EEG) neurofeedback, is a type of biofeedback that provides real-time information about the electrical activity of the brain in the form of visual or auditory stimuli delivered through binaural beat sounds transmitted through earphones connected to a headband that includes EEG sensors, allowing individuals to learn to regulate aspects of the brain’s electrical activity (Carrick et al., 2018). This particular type of neurofeedback has revealed differences among individuals with ASD, with some individuals found to have had diminished connectivity between various cortical areas and reduced hemispheric differences (e.g., Hashemian & Pourghassem, 2014). The binaural beats produce a perceptual phenomenon that occurs when two slightly different frequencies are presented separately to the left and right ears resulting in the listeners perceiving a single tone that varies in amplitude at a frequency equal to the frequency difference between the two tones (Carrick et al., 2018). EEG NF studies in ASD have targeted core deficits such as social behavior and communication as well as associated deficits such as sustained attention and cognitive flexibility (e.g., Coben, Linden, & Myers, 2010). Kouijzer et al. (2009) stated that neurofeedback referred to a form of operant conditioning of electrical brain activity in which desirable activity is rewarded and undesirable activity is not. The authors suggested that neurofeedback inhibits theta waves and rewards beta waves and these may have specific value for individuals with autism but there are no functional explanations that currently exist to explain the neural mechanisms (Kouijzer et al., 2009). This complex treatment involves very technical language with results that are difficult to interpret. Therefore, the purpose of this paper is to summarize some of the studies on neurofeedback and its impacts on autism symptoms with the goal of answering the question, “Is there science behind that?”

Current Research on Treatment 

Only a few small EEG NF studies in ASD have included important aspects of clinical trial design such as randomization and some form of blinding. For example, Kouijzer et al. published four studies from 2009 to 2013 on neurofeedback and autism including preliminary findings, results post treatment, and a follow-up study. The preliminary study was aimed at determining if the researchers could teach participants to control theta brain waves and results suggest that 60% of the children were successful at demonstrating control of these particular type of brain waves. They also state that parents reported a significant improvement in reciprocal social interactions and communication skills, relative to parents in the control group. The next study assessed executive functioning, cognitive flexibility, communication, and attention control skills pre- and post-neurofeedback (Kouijzer et al., 2009). They used the Children’s Communication Checklist with the control and intervention group and AUTI-R with the intervention group only. The results suggest that the test group demonstrated changes in several domains where the control group did not. Finally, Kouijzer et al. (2010) conducted a long-term (12-month follow up) study on the effects of neurofeedback treatment in autism. Results of this study suggest that improvements maintained with the exception of some communication skills (Kouijzer et al., 2010). A study by Carrick et al. (2018) measured frequency bands and included questionnaires including the Autism Treatment Evaluation Checklist, The Social Responsiveness Scale-Second Edition, The Behavior Rating Inventory of Executive Function, The Autism Behavior Checklist (ABC) and Questions About Behavioral Function (QABF) to determine if neurofeedback had any impact on autism symptoms. Reported results include three frequency bands demonstrating statistically significant changes pre- and post-treatment for the active group only. With regards to the other outcomes, they report there was a statistically significant change for both active and control groups according to Autism Treatment Evaluation Checklist, where both groups showed improvement in various areas of the Behavior Rating Inventory of Executive Function. Only the active group had statistically significant differences in the ABC and QABF and no change occurred for either group in the Social Responsiveness Scale. The authors suggested that neurofeedback may result in positive changes in the neuro-regulation of children with autism. However, there were some potential limitations that lower the confidence in the causal relationship between neurofeedback and changes in neuro-regulation. For example, a total of 83 individuals were enrolled in the study, but only 34 completed both pre- and post-measures. Perhaps the individuals who didn’t complete these measures would have reacted differently to the neurofeedback procedures. The tentative connection between neurofeedback and changes in neural activity has been replicated in a few other studies (e.g., LaMarca et al., 2018).

Issues with the current research 

At first glance, the results of these studies seem promising. However, there are some issues with these studies that are worth noting. First, Kouijzer et al. (2009; 2010) included participants with an IQ of above 90; meaning, these participants did not have an intellectual disability. Therefore, although they had a diagnosis of autism, they did not have any intellectual deficits that sometimes coincide with an autism diagnosis. This calls into question the generalizability of the results to others on the spectrum. Steiner et al. (2014) conducted a pilot study on the feasibility of neurofeedback for children with autism and found similar results. They and others (e.g., LaMarca, et al., 2018) suggested that neurofeedback may be feasible to conduct with children with “high functioning autism and attention difficulties.”

Another issue is that the outcome measures used in these studies may not be the most sensitive measure of autism symptoms. For example, The Behavior Rating Inventory of Executive Function is not a formal diagnostic tool but it can inform diagnosis and help develop and guide therapy. Similarly, the Autism Behavior Checklist provides information on non-adaptive behaviors but is not intended as a diagnostic tool. Finally, the Questions about Behavioral Function assessment is not a diagnostic tool. It is an indirect assessment that provides contextual information about a specific behavior and is not specific to autism.

In addition to these issues, there are two review papers on neurofeedback and autism that are worth discussing. First, Hurt et al. (2014) reviewed the literature on neurofeedback on specific groups, one being children with autism, and based on the results of the studies they reviewed, they recommend more research be done on neurofeedback with this population. Second, they say that neurofeedback should not replace evidence-based practice in the treatment of autism including applied behavior analysis, medication management for comorbid behavior problems, speech and occupational therapy, and education interventions.

Holtmann et al. (2011) reviewed the literature on the effectiveness of neurofeedback as a treatment for autism and found that the existing literature at the time did not support the use of neurofeedback in the treatment of autism. Further, they suggest that the studies that did report positive findings may have been demonstrating an improvement in co-morbid attention deficit hyperactivity disorder (ADHD) rather than the symptoms of autism. However, they do note that these interpretations of the literature may be limited because the studies available include a small sample size, short duration (no long- term data), variable diagnostic criteria and some insufficient control interventions. Finally, Coben et al., (2010) conducted a review on a variety of interventions for the treatment of autism symptoms. Based on the literature, they concluded that the results may seem promising but further advancement is required to demonstrate efficacy.

Future Research

Given that this treatment appears to have limited to no risks to the individual, future researchers could continue to evaluate this treatment to determine if there is an impact on the core symptoms of autism or co-existing conditions (e.g., ADHD, anxiety, depression) commonly associated with autism. However, there are a few considerations that we recommend. First, researchers should use appropriate pre- and post-treatment measures for the symptoms/behaviors being targeted to provide the reader with more information about how exactly the treatment impacted the individual. Second, they should attempt to replicate the treatment with individuals with a variety of different skills and abilities across the spectrum to assess the extent of maintenance and generalization of any results. Third, it might be beneficial for these researchers to determine a way to increase the ecological validity (e.g., applicability to daily life) of the treatment. Even if it does prove to be useful, the technology is likely not easily accessible to the population at large, due to the cost of equipment and intense effort of implementation (see Carrick et al., 2018, for the intensity of treatment), which may lead to disparities in access across socioeconomic lines. Fourth, there is a need for larger studies (for adequate power) that include randomization, some form of blinding (usually sham treatment of some type), and use of appropriate statistical methods, including controlling for multiple comparisons when necessary. Finally, it may be helpful for future researchers to disclose more information about their participants, including any other treatments or interventions they may have presently or previously been exposed to, so that these potential confounds can be discussed. In the current literature, this is unclear.

Final Thoughts

Given the existing literature, there is a great deal more research required to make conclusions about the effectiveness of neurofeedback as a treatment for autism. One positive attribute of neurofeedback is that it is non-invasive; therefore, the possible risk to the individual appears to be low. However, the current results are mixed and there is limited ecological validity. Most families would likely not have access to the technology to use this treatment. Before neurofeedback approaches is added to the menu of autism treatments that have solid empirical research to support their effectiveness (i.e., evidenced-based practice), researchers must more formally study this basic approach, using measurement and design conditions that allow for confidence in any causal relationship between neurofeedback strategies and improvement in any autism-related symptom.

References

Carrick, F. R., Pagnacco, G., Hankir, A., Abdulrahman, M., Zamanm R., Kalambaheti, E. R., Barton, D. A., Link. P. E., & Oggero, E. (2018). The treatment of autism spectrum disorder with auditory neurofeedback: A randomized placebo controlled trial using the Mente autism device. Frontiers in Neurology, 9, 1-19. https://doi.org/10.3389/fneur.2018.00537

Centers for Disease Control and Prevention, 2021. Screening and Diagnosis of Autism Spectrum Disorder. https://www.cdc.gov/ncbddd/autism/screening.html

Coben, R., Linden, M., & Myers, T. E. (2010). Neurofeedback for autistic spectrum disorder: A review of the literature. Applied Psychophysiology and Biofeedback, 35, 83-105. http://doi.org/10.1007/s10484-009-9177-y

Hashemian, M., & Pourghassem, H. (2014). Diagnosing autism spectrum disorders based on EEG analysis: A survey. Neurophysiology, 46(2), 183-195)

Holtmann, M., Steiner, S., Hohmann, S., Poustka, L., Banaschewski, T., & Bolte, S. (2011). Neurofeedback in autism spectrum disorders. Developmental Medicine & Child Neurology, 53(11), 986-93. http://doi.org/10.1111/j.1469-8749.2011.04043.x

Hurt, E., Arnold, L. E., & Lofthouse, N. (2014). Quantitative EEG neurofeedback for the treatment of pediatric attention-deficit/hyperactivity disorder, autism spectrum disorders, learning disorders, and epilepsy. Child and Adolescent Psychiatrics clinics of North America, 23, 465-486. http://dx.doi.org/10.1016/j.chc.2014.02.001

Kouijzer, M. E. J., de Moor, J. M. H., Gerrits, B. J. L., Congedo, M., & van Schie, H. T. (2009). Neurofeedback improves executive functioning in children with autism spectrum disorders. Research in Autism Spectrum Disorders, 3, 145-162. http://doi.org/10.1016/j.rasd.2008.05.001

Kouijzer, M. E. J., de Moor, J. M. H., Gerrits, Buitlaar, J. K., & van Schie, H. T. (2009). Long-term effects of neurofeedback treatment in autism. Research in Autism Spectrum Disorders, 3, 496-501. http://doi.org/10.1016/j.rasd.2008.10.003

Kouijzer, M. E. J., van Schie, H. T., de Moor, J. M. H., Gerrits, B. J. L., & Buitelaar, J. K. (2010). Neurofeedback treatment in autism. Preliminary findings in behavioral, cognitive, and neurophysiological functioning. Research in Autism Spectrum Disorders, 4, 386-399. http://doi.org/10.1016/j.rasd.2009.10.007

Kouijzer, M. E. J., van Schie, H. T., Gerrits, B. J. L., Buitelaar, J. K., & de Moor, J. M. H. (2013). Is EEG-biofeedback an Effective Treatment in Autism Spectrum Disorders? A Randomized Controlled Trial. Applied Psychophysiology and Biofeedback, 38(1), 17-28. doi:10.1007/s10484-012-9204-3

LaMarca, K., Gevirtz, R., Lincoln, A.J., Pineda, J. A. (2018). Facilitating neurofeedback in children with autism and intellectual Impairments using TAGteach. Journal of Autism and Developmental Disorders, 48, 2090–2100 (2018). https://doi.org/10.1007/s10803-018-3466-4

Steiner, N. J., Frenette, E., Hynes, C., Pisarik. E., Tomasetti, K., Perrin, E. C., & Rene, K. (2014). A pilot feasibility study of neurofeedback for children with autism. Applied Psychophysiology and Biofeedback, 39, 99-107. http://doi.org/1007/s1-484-014-9241-1

Van Hooddalem, L. E., Feijs, H. M. E., Bramer, W. M., Ismail, S. Y., & van Dongen, J. D. M. (2021). The effectiveness of neurofeedback therapy as an alternative treatment for autism spectrum disorders in children: A systematic review. Journal of Psychophysiology, 35(2), 102-115.

Wang, J., Barstein, J., Ethridge, L. E., Mosconi, M. W., Takarae, Y., & Sweeney, J. A. (2013). Resting state EEG abnormalities in autism spectrum disorders. Journal of neurodevelopmental disorders, 5(1), 24. https://doi.org/10.1186/1866-1955-5-24

Citation for this article:

McHugh, C. L., & Zane, T. (2022). EEG neurofeedback and autism: Is there science behind that? Science in Autism Treatment, 19(4).

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