The Role of Neurofeedback in Managing Menopausal Symptoms

Menopause is becoming more and more a focus of society. At a mean age of around 50, most women experience first menopause symptoms, which can vary according to the cultural, familiar and social background, but also according to the general health condition and well-being (Parazzini, 2006; Hu et al., 1999). According to the National Institute of Aging, menopause symptoms are reported to last around 2- 8 years with a large variety depending on genetics, but also external factors such as ethnicity, culture, lifestyle and environment (National Institute on Aging, 2024). 

Symptoms are manifold and embrace hot flushes, night sweat, vaginal dryness, sleep disturbance, depression, anxiety, memory loss, fatigue, concentration disorder, mood changes, headache, joint pains and weight gain (Makara-Studzińśka et al., 2014). Around 50 million women annually go globally into menopause, which shows the high prevalence of this condition (Massart et al., 2001). A standard treatment to alleviate symptoms is hormone replacement therapy (Patel & Dhillo, 2021), but also the uptake of medication to ease symptoms like headaches, sleep disturbance or depression is commonly used. All of them can cause side effects, so alternative methods that help without adverse effects are needed. 

One of the main causes of menopausal symptoms is the decrease in estrogen levels in the female body. The release of estrogen is controlled in the brain as well as the brain interacts also with it. It was shown that there are several brain regions that respond to estrogen such as the hypothalamus, the neocortex, but also hippocampus and brainstem, so a change in the estrogen levels also has an influence on the brain functions (Morrison et al., 2006), resulting in several symptoms with neurological origin.

Therefore the application of Neurofeedback, which can help to improve the brain's self-regulatory capacity, seems to be a promising approach. Several studies have shown that Neurofeedback can help with various of the above mentioned symptoms. In a publication with three case studies, it was shown that ILF Neurofeedback can help to alleviate symptoms of depression (Grin-Yatsenko et al., 2018). In addition, Virtual Reality ILF Neurofeedback has been used to address centralized pain accompanied by insomnia (Orakpo et al., 2022). A multicenter study involving 196 clients in an outpatient setting applied ILF Neurofeedback over approximately 30 sessions per participant, with continuous performance tests indicating improved attention (Schneider et al., 2021). Anxiety disorders, which encompass a wide range of symptoms, have also been explored in relation to Neurofeedback. A review on anxiety and depression highlighted the positive impact of Neurofeedback on these conditions (Hammond, 2005). Fatigue—commonly experienced by (post-)cancer patients and increasingly recognized as a component of Post-Covid syndrome—has also been a focus of recent research. A pilot study with 16 participants found that Neurofeedback may help alleviate fatigue-related symptoms. 

These studies show that Neurofeedback can be an effective therapy for a variety of conditions. Since many of these indications—such as depression, anxiety, insomnia, and fatigue—are also common symptoms during menopause, Neurofeedback holds promise as a supportive treatment option for menopausal women as well. Especially ILF Neurofeedback seems to be a promising method as it is a symptom based approach. 

Interested in more insights? 

Dr. Dawn Harris, founder and CEO of Kedras Clinics, has been working successfully with Neurofeedback for years. In an article, she shares how neurofeedback can also be used in dealing with symptoms of menopause. 

Sources:

Grin-Yatsenko, V., Othmer, S., Ponomarev, V., Evdokimov, S., Konoplev, Y., & Kropotov, J. (2018). Infra-Low Frequency Neurofeedback in Depression: Three case studies. NeuroRegulation, 5(1), 30–42. https://doi.org/10.15540/nr.5.1.30

Hammond, D. C. (2005). Neurofeedback treatment of depression and anxiety. Journal of Adult Development, 12(2–3), 131–137. https://doi.org/10.1007/s10804-005-7029-5

Hu, F. B., Grodstein, F., Hennekens, C. H., Colditz, G. A., Johnson, M., Manson, J. E., Rosner, B., & Stampfer, M. J. (1999). Age at natural menopause and risk of cardiovascular disease. Archives of Internal Medicine, 159(10), 1061. https://doi.org/10.1001/archinte.159.10.1061

Makara-Studzińśka, M. T., Kryś-Noszczyk, K. M., & Jakiel, G. (2014). Epidemiology of the symptoms of menopause – an intercontinental review. Menopausal Review, 3, 203–211. https://doi.org/10.5114/pm.2014.43827

Massart, F., Reginster, J. Y., & Brandi, M. L. (2001). Genetics of menopause-associated diseases. Maturitas, 40(2), 103–116. https://doi.org/10.1016/s0378-5122(01)00283-3

Morrison, J. H., Brinton, R. D., Schmidt, P. J., & Gore, A. C. (2006). Estrogen, menopause, and the aging brain: How basic neuroscience can inform hormone therapy in women. Journal of Neuroscience, 26(41), 10332–10348. https://doi.org/10.1523/jneurosci.3369-06.2006

National Institute on Aging (2024).  What is Menopause. https://www.nia.nih.gov/health/menopause/what-menopause#:~:text=Symptoms%20related%20to%20menopause%20can,culture%2C%20lifestyle%2C%20and%20environment.

Orakpo, N., Yuan, C., Olukitibi, O., Burdette, J., & Arrington, K. (2022). Does virtual reality feedback at Infra-Low frequency improve centralized pain with comorbid insomnia while mitigating risks for sedative use disorder?: a case report. Frontiers in Human Neuroscience, 16. https://doi.org/10.3389/fnhum.2022.915376

Parazzini, F. (2006). Determinants of age at menopause in women attending menopause clinics in Italy. Maturitas, 56(3), 280–287. https://doi.org/10.1016/j.maturitas.2006.09.003

Patel, B., & Dhillo, W. S. (2021). Menopause review: Emerging treatments for menopausal symptoms. Best Practice & Research Clinical Obstetrics & Gynaecology, 81, 134–144. https://doi.org/10.1016/j.bpobgyn.2021.10.010

Schneider, H., Riederle, J., & Seuss, S. (2021). Therapeutic Effect of Infra-Low-Frequency Neurofeedback Training on Children and Adolescents with ADHD. In Artificial intelligence. https://doi.org/10.5772/intechopen.97938