Back

Multinational qEEG developmental surfaces

Hu, S.; Ngulugulu, A.; Bosch-Bayard, J.; Bringas-Vega, M. L.; Valdes-Sosa, P. A.

2019-12-27 neuroscience
10.1101/2019.12.20.883991 bioRxiv
Show abstract

The quantitative electroencephalogram (qEEG) is a diagnostic method based on the spectral features of the resting state EEG. The departure of spectral features from normality is gauged by the z transform with respect to the age-adjusted mean and deviation of normative databases - known as the developmental equations/surfaces. However, the extent to which the data collected from different countries with various equipment require separate developmental equations remains unanswered. Here, we analyzed the EEG of 535 subjects from 3 countries, Switzerland, the USA and Cuba. The EEG power spectra of all samples were log transformed and their relations to the covariables ( age, frequency, country and individual) were analyzed using the linear mixed effects model. We found that the origin country of the subjects did not play a significant effect on the log spectra, even without interactions with other independent variables, whereas, age and frequency were highly significant. To estimate the developmental surfaces in greater detail, we carried out kernel regression (lowess) in two dimensions of log-age and frequency. We found two main phenomena: 1) slow rhythms ({delta}, {theta}) predominated in the lower ages and then decreased with a tendency to disappear at higher ages; 2) rhythm was absent at lower ages, but gradually appeared more relevant in occipital and parietal regions, and increased with aging with an increasing centering frequency of rhythm. We consider both phenomena as an expression of healthy neurodevelopmental and maturation related to age. It is the first study of multinational qEEG developmental surfaces accounting for country. The results demonstrate the possibility of creating international qEEG norms since the individual and age variability are much larger than the specific factors like country, or the technology employed device.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.