Back

Measurements of EEG Alpha Peak Frequencies Over the Lifespan: Validating Target Ranges on an In-Clinic Platform

Oakley, D. S.; Joffe, D.; Arese Lucini, F.; Palermo, F.

2021-10-08 neuroscience
10.1101/2021.10.06.463353 bioRxiv
Show abstract

BackgroundPeak individual alpha frequencies (IAF) extracted from an EEG exam can provide novel sources of information regarding brain function. This information can help measure and track changes in cognition arising from conditions such as concussion or unhealthy aging. Objective 1To validate a method for combining eyes-closed EEG with eyes-closed audio P300 ERP in order to streamline testing times involved in IAF extraction. Objective 2To validate age-stratified target ranges of IAF collected in clinic against published research Objective 3To validate the stability of IAF for data collected in routine clinical settings. ParticipantsTwo thousand twenty-five subjects aged 13-90. MethodsEEG with audio P300 was collected as part of a health screening exam for studies through Colorado University, Childrens Hospital Colorado, Boone Heart Institute, WAVi Co., and various clinics alongside other clinical evaluations. Results(1) No differences were seen between IAF extracted during an eyes-closed resting and the P300 protocol. (2) The age-related AF trends measured in clinic match the age-related trends from previous research. (3) IAF remained stable over the course of 0-2 years in a test-retest dataset. ConclusionIn-clinic measures of peak EEG frequency corroborate the age-related trends of published research taken over the last several decades and IAF. These results also confirm that IAF is a stable trait, making it useful for within-person longitudinal tracking. By following changes in IAF over time, deviations from normal CNS functioning, such as onset or progression of disease, can be monitored.

Matching journals

The top 9 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.