How qEEG band power ratios can help in early Alzheimer′s detection
Last week we teased how power ratios between frequency bands can have be used to help diagnose and follow the stage of different diseases.
Research has shown that neurodegeneration induces a decrease in high frequency waves (gamma, beta and alpha) and an increase in lower frequency waves (delta and theta), which leads to high_frec/low_frec ratios being a powerful biomarker for early stage detection of Alzheimer's Disease (AD), even before clinical symptoms occur, or when symptoms are insufficient to draw clear conclusions. This biomarkers still need to be considered in combination with other relevant parameters, such as age.
Here are some articles on this topic, focusing on different bands, but drawing similar results.
The power change has been shown to appear way before other symptoms, allowing for early reaction and treatment.
The tool to assess these metric is the quantitative EEG analysis and goes way back. However, some critical functionality is still lacking today in most available software:
-?????????Power ratios between bands.
-?????????Controlled and intuitive artifact removal process, updating relevant metrics.
Our analysis shows how artifact removal significantly impacts band power ratios, as shown by the following data for different channels (each channel analyzed over different signals):
What this means in practice is that analysis based on “unclean” bands will yield considerably inaccurate results.
Comment and reach out to us for further discussion on this topic or what additional information you think is useful to be drawn from the qEEG.