A neurophysiological basis for aperiodic EEG and the background spectral trend

Area of Science:

• Neuroscience
• Computational Neuroscience
• Anesthesiology

Background:

• Electroencephalograms (EEGs) exhibit both rhythmic and broadband fluctuations.
• The neural basis of broadband EEG signals, often appearing as a 1/f trend, is not well understood.
• Rhythmic EEG activity is attributed to network oscillations, but broadband components remain unexplained.

Purpose of the Study:

• To investigate the neural basis of broadband EEG signals using biophysical modeling.
• To determine if aperiodic neural activity contributes to broadband EEG features.
• To assess the impact of broadband changes on the quantification of rhythmic EEG activity and anesthetic effects.

Main Methods:

• Biophysical modeling of neural activity to simulate EEG signals.
• Recording human EEGs during propofol administration (a GABA receptor agonist).
• Analyzing spectral changes in EEG data and applying a model to correct for broadband effects.

Main Results:

• Biophysical models demonstrated that aperiodic neural activity can generate detectable scalp potentials and shape broadband EEG features without significantly altering rhythm quantification.
• Propofol administration induced broadband EEG changes consistent with its known effects on GABA receptors.
• Model-based correction for broadband changes revealed a unique increase in delta power shortly after loss of consciousness.

Conclusions:

• Aperiodic neural activity is a significant determinant of broadband EEG signals.
• Broadband EEG features can be modulated by factors like anesthesia and may confound traditional EEG interpretations.
• The study provides a method to correct for broadband confounds, enabling more accurate quantification of EEG rhythms, such as the increase in delta power during anesthesia-induced unconsciousness.