EEG as a Scalable Tool for Early Detection of Alzheimer’s Disease
We have published a significant study in the Journal of Dementia and Alzheimer’s Disease, demonstrating the potential of electroencephalography (EEG) to reliably track changes across the Alzheimer’s disease (AD) spectrum. Our findings suggest that analyzing brain activity through EEG-based connectivity patterns could provide a cost-effective and noninvasive method for earlier diagnosis and intervention in AD.
Key Findings
Our study analyzed EEG recordings from over 1,000 individuals, spanning healthy controls, those with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and individuals diagnosed with AD. Using sophisticated graph theory analysis, we observed a clear trend: as cognitive decline progressed, there was a progressive increase in theta-band connectivity and a decrease in alpha and beta band connectivity. Notably, theta-band betweenness centrality emerged as the most effective EEG metric for distinguishing between different stages of AD. The classification accuracy we achieved using these EEG metrics was comparable to that of advanced machine learning models, highlighting the potential of EEG as a simpler and more accessible diagnostic tool.
Why This Matters
Early Detection: Our research supports the use of EEG to identify subtle signs of cognitive decline even before clinical symptoms become apparent, particularly in the early SCD and MCI stages. This opens doors for earlier interventions that could potentially slow disease progression.
Accessibility: EEG is a portable and affordable technology, making it feasible for widespread use in primary care settings and remote or rural areas where access to more expensive neuroimaging techniques might be limited.
Cost-Effectiveness: Earlier diagnosis and intervention facilitated by EEG could lead to reduced long-term care demands and alleviate the significant financial burden on families and healthcare systems associated with advanced AD.
Our Recommendations for Future Action
To further advance the impact of our findings, we recommend launching pilot EEG-screening programs within community health centers and memory clinics to rigorously evaluate the real-world applicability of this approach. Simultaneously, it is crucial to establish clear guidelines and standardized protocols for the utilization of EEG connectivity metrics in routine clinical assessments, ensuring consistent and reliable interpretation. Finally, a vital step involves investing in the training of healthcare professionals and building the necessary infrastructure to seamlessly integrate EEG-based analysis into standard care pathways for individuals experiencing cognitive decline.
Read the Full Paper
The complete research article, titled “Exploring Functional Brain Networks in Alzheimer’s Disease Using Resting State EEG Signals,” by Oikonomou, V.P.; Georgiadis, K.; Lazarou, I.; Nikolopoulos, S.; Kompatsiaris, I.; and the PREDICTOM Consortium, is available in the Journal of Dementia and Alzheimer’s Disease: https://doi.org/10.3390/jdad2020012
