How to use neuroscience to maintain cognitive function while aging?
Harnessing the power of neuroscience to help achieve early detection and prevention of neurodegenerative illnesses and cognitive decline relating to aging.
Catching It Early
When it comes to aging, the brain is the number one organ we want to take care of. So many of us have seen loved ones suffer from cognitive decline and neurodegenerative disease that we are motivated more than ever to have a brain that supports us as we age.
Based on the study done by Prince et al. in 2013, the number for neurodegenerative disease is expected to double every 20 years, therefore 66 million people could be affected by dementia in 2030.
According to a study published by Petersen et al. in 2001 Clinical progression comes much later and proceeds from a preclinical state, through mild cognitive impairment to dementia.
Research conducted by Gauthier et al. in 2006 suggested that annually, around 10–15% of patients with MCI could convert to Alzheimer’s disease dementia and over half of all converters are likely to do so within 5 years.
Neurodegenerative diseases such as Alzheimer’s Disease (AD) and dementia are challenging conditions to address because there are often no known ways to detect them reliably before the symptoms start to appear. But imagine if we could have some ways to detect and address Alzheimer’s, dementia, and Mild Cognitive Impairment (MCI) before symptoms even start and do something proactive about it. That's the exciting promise of EEG-based technology and neurofeedback as emerging tools that show great potential for early detection and prevention of diseases like Alzheimer's and protect your cognitive function.
Early Signs of Alzheimer’s with EEG
It turns out that our brain’s electrical patterns, picked up by EEG sensors, can reveal a lot about our cognitive health. Specifically, scientists are finding that certain EEG patterns can signal Mild Cognitive Impairment (MCI)—often an early warning sign for Alzheimer's. Let’s explore some key EEG markers:
Brain Wave Changes: Normally, our brains exhibit specific patterns of electrical activity. Research shows that in early Alzheimer's, there's increased activity in slow brain waves (delta and theta) and reduced activity in faster ones (alpha and beta). This shift hints at disrupted memory and cognitive processing.
Connectivity Issues: How brain regions communicate is crucial for healthy cognitive function. Studies have found disrupted connections between brain areas in those developing Alzheimer's are linked to symptoms that would later appear. EEG coherence metrics—how synchronized these regions are—can help effectively separate healthy aging from early signs of cognitive decline.
Event-Related Potentials (ERPs): These are brain reactions to specific events or stimuli. ERP changes can highlight early memory problems even before obvious symptoms emerge, making them valuable for early Alzheimer’s detection.
Supporting Cognitive Resilience Through Neurofeedback
Neurofeedback isn't just all about detection—it’s a proactive tool that can help individuals enhance cognitive function and potentially delay Alzheimer’s onset and progression. Think of it as training your brain to stay healthy through real-time feedback on its own activity.
Here's how neurofeedback can help make a difference:
Improving Cognitive Health: By training to enhance the production of specific brain waves, neurofeedback can boost memory, attention, and emotional regulation. For instance, studies show that training to enhance alpha or beta wave activity can significantly improve memory and attention in people with early cognitive concerns.
Promoting Brain Plasticity: Neurofeedback training can also help maintain neuroplasticity—the brain's remarkable ability to reorganize itself by forming new connections. Strengthening neuroplasticity can help protect the brain against cognitive decline related to aging.
Promising Neurofeedback Protocols for Alzheimer’s Prevention
Several neurofeedback protocols are showing promise in delaying or reducing the risk of Alzheimer's:
SMR-Theta Training: This approach increases sensorimotor rhythm (SMR) and decreases theta waves. Research indicates this method can enhance memory and cognitive function significantly, helping reduce the impact and delay onset for Alzheimer’s and MCI.
Peak Alpha Frequency Training: Enhancing peak alpha activity promotes alertness and cognitive flexibility, crucial for maintaining sharp cognitive performance.
Theta/Beta Ratio Regulation: Lowering this ratio helps boost attention and emotional control, both essential for maintaining cognitive function in aging adults.
Beta Wave Enhancement: Targeting specific bands of beta waves can improve executive functions such as working memory and attention, directly addressing cognitive deficits common in early onset Alzheimer's patients.
Gamma-Band Synchronization: Boosting gamma activity may support memory and attention, providing another promising avenue for neurofeedback-based cognitive enhancement.
Future Thoughts
Neurofeedback isn't a magic bullet, but its potential and role in early detection and prevention is exciting. By spotting Alzheimer's early and training the brain to strengthen its cognitive functions, neurofeedback offers hope for delaying or even preventing Alzheimer’s Disease. Continued research and clinical application could make EEG-based neurofeedback a cornerstone of proactive cognitive health strategies effective on many neurodegenerative illnesses such as Alzheimer’s and dementia.
How Can I Learn More
Divergence offers a suite of powerful tools to help providers deliver neurofeedback both in-person and remotely. Our tools can help detect markers that are relevant to neurodegenerative illnesses, as well as offer training options to help maintain peak cognitive function.
If you would like to learn more about how to use neurofeedback to help your patients and clients improve their cognitive function and fight Alzheimer’s and MCI, get in touch with us at info@divergenceneuro.com or visit www.divergenceneuro.com
References:
Andrade et al. (2022) Andrade et al. "EEG-neurofeedback for promoting neuromodulation in the elderly: evidence from a double-blind study" (2022) https://doi.org/10.1101/2022.09.26.509227
Cejnek, M., Vyšata, O., Vališ, M., & Bukovský, I. (2021). Novelty detection-based approach for alzheimer’s disease and mild cognitive impairment diagnosis from eeg. Medical & Biological Engineering & Computing, 59(11-12), 2287-2296. https://doi.org/10.1007/s11517-021-02427-6
Cissé, A., Farahat, Z., Zrira, N., Benmiloud, I., Abdi, B., & Ngote, N. (2024). Eeg-based alzheimer's detection using power spectral density, tsallis entropy, amplitude features, and svm classification.. https://doi.org/10.21203/rs.3.rs-5312646/v1
Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R. C., Ritchie, K., Broich, K., et al. (2006). Mild cognitive impairment. Lancet 367, 1262–1270. doi: 10.1016/S0140-6736(06)68542-5
Fischer, M., Zibrandtsen, I., Høgh, P., & Musaeus, C. (2023). Systematic review of eeg coherence in alzheimer’s disease. Journal of Alzheimer S Disease, 91(4), 1261-1272. https://doi.org/10.3233/jad-220508
Huang, W., Wu, W., Lucas, M., Huang, H., Wen, Z., & Li, Y. (2023). Neurofeedback training with an electroencephalogram-based brain-computer interface enhances emotion regulation. Ieee Transactions on Affective Computing, 14(2), 998-1011. https://doi.org/10.1109/taffc.2021.3134183
Jang, J., Kim, J., Park, G., Kim, H., Jung, E., Cha, J., … & Yoo, H. (2019). Beta wave enhancement neurofeedback improves cognitive functions in patients with mild cognitive impairment. Medicine, 98(50), e18357. https://doi.org/10.1097/md.0000000000018357
Jiao, B., Li, R., Zhou, H., Qing, K., Liu, H., Pan, H., … & Shen, L. (2023). Neural biomarker diagnosis and prediction to mild cognitive impairment and alzheimer’s disease using eeg technology. Alzheimer S Research & Therapy, 15(1). https://doi.org/10.1186/s13195-023-01181-1
Kluetsch, R., Ros, T., Théberge, J., Frewen, P., Calhoun, V., Schmahl, C., … & Lanius, R. (2013). Plastic modulation of ptsd resting‐state networks and subjective wellbeing by eeg neurofeedback. Acta Psychiatrica Scandinavica, 130(2), 123-136. https://doi.org/10.1111/acps.12229
Lavy, Y., Dwolatzky, T., Kaplan, Z., Guez, J., & Todder, D. (2021). Mild cognitive impairment and neurofeedback: a randomized controlled trial. Frontiers in Aging Neuroscience, 13. https://doi.org/10.3389/fnagi.2021.657646
Li, X. (2024). The potential and prospects of sleep biomarkers in early alzheimer's disease diagnosis. Theoretical and Natural Science, 29(1), 63-68. https://doi.org/10.54254/2753-8818/29/20240730
Liu, F., Fuh, J., Peng, C., & Yang, A. (2020). Phenotyping neuropsychiatric symptoms profiles of alzheimer’s disease using cluster analysis on eeg power.. https://doi.org/10.1101/2020.08.20.20179051
Marlats, F., Bao, G., Chevallier, S., Boubaya, M., Djabelkhir-Jemmi, L., Wu, Y., … & Azabou, É. (2020). Smr/theta neurofeedback training improves cognitive performance and eeg activity in elderly with mild cognitive impairment: a pilot study. Frontiers in Aging Neuroscience, 12. https://doi.org/10.3389/fnagi.2020.00147
Marlats, F., Djabelkhir-Jemmi, L., Azabou, É., Boubaya, M., Pouwels, S., & Rigaud, A. (2019). Comparison of effects between smr/delta-ratio and beta1/theta-ratio neurofeedback training for older adults with mild cognitive impairment: a protocol for a randomized controlled trial. Trials, 20(1). https://doi.org/10.1186/s13063-018-3170-x
Ouchani, M., Gharibzadeh, S., Jamshidi, M., & Amini, M. (2021). A review of methods of diagnosis and complexity analysis of alzheimer’s disease using eeg signals. Biomed Research International, 2021(1). https://doi.org/10.1155/2021/5425569
Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., et al. (2001). Current concepts in mild cognitive impairment. Arch. Neurol. 58, 1985–1992. doi: 10.1001/archneur.58.12.1985
Prince, M., Bryce, R., Albanese, E., Wimo, A., Ribeiro, W., and Ferri, C. P. (2013). The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement. 9, 63e–75e. doi:10.1016/j.jalz.2012.11.007
Su, R., Li, X., Liu, Y., Cui, W., Xie, P., & Han, Y. (2021). Evaluation of the brain function state during mild cognitive impairment based on weighted multiple multiscale entropy. Frontiers in Aging Neuroscience, 13. https://doi.org/10.3389/fnagi.2021.625081
Vilou, I., Varka, A., Parisis, D., Afrantou, T., & Ioannidis, P. (2023). Eeg-neurofeedback as a potential therapeutic approach for cognitive deficits in patients with dementia, multiple sclerosis, stroke and traumatic brain injury. Life, 13(2), 365. https://doi.org/10.3390/life13020365
Xia, J., Mazaheri, A., Segaert, K., Salmon, D., Harvey, D., Shapiro, K., … & Olichney, J. (2020). Event-related potential and eeg oscillatory predictors of verbal memory in mild cognitive impairment. Brain Communications, 2(2). https://doi.org/10.1093/braincomms/fcaa213


