Back

Normative characterization of age-related periodic and aperiodic activity in resting-state clinical EEG recordings

Leroy, S.; Bublitz, V. K.; von Dincklage, F.; Antonenko, D.; Fleischmann, R.

2024-06-19 neurology
10.1101/2024.06.18.24308910 medRxiv
Show abstract

ObjectiveNon-invasive oscillatory brain stimulation techniques target the oscillatory activity of the brain and have several potential therapeutic applications in the clinical context. The aim of these analyses from clinical routine EEG recordings was to provide normative values of physiological age-related oscillatory (periodic) and non-rhythmic (aperiodic) activity. MethodsWe analyzed 532 EEGs of patients between 8 and 92 years of age. EEG segments were preprocessed, and the power spectrum was computed using a multitaper method. We decomposed the power spectrum into periodic (peak power, frequency, and bandwidth) and aperiodic (intercept and exponent) components. Linear regression models were used to investigate age-related changes in these parameters. ResultsWe observed significant global age-related changes in the periodic alpha (- 0.015 Hz/year) and gamma (+ 0.013 to + 0.031Hz/year) peak frequency as well as in the aperiodic exponent (- 0.003 to - 0.004 V2/Hz/year). In the other parameters there were solely regional or no significant age-related changes. ConclusionsDecomposing the power spectrum into periodic and aperiodic components allows for the characterization of age-related changes. SignificanceThis study provides the first spectrum-wide normative characterization of age-related changes in periodic and aperiodic activity, relevant for non-invasive brain stimulation with alternating current targeting ongoing oscillatory activity. Highlights- Alpha peak frequency decreases with age, while gamma peak frequency accelerates. - Age-related changes in alpha and theta power result from a flattening of the aperiodic slope, not decreased oscillatory activity - Decomposing the EEG spectrum into periodic and aperiodic activity is essential when characterizing ongoing oscillatory activity

Matching journals

The top 6 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.