Topic: neural power spectra

Recent research has unveiled the aperiodic component of neural power spectra as a promising non-invasive biomarker for the balance between excitatory and inhibitory neural processes (E-I balance), assessable through electroencephalography (EEG) and magnetoencephalography (MEG). Through simulations and in vivo local field potential assessments, it has been demonstrated that a reduced slope of the aperiodic component indicates an excitatory state, whereas an increased slope suggests an inhibitory state. Nonetheless, the effectiveness of the aperiodic slope as a marker for E-I balance in somatose

Neural power spectra in the human brain consist of rhythmic oscillations, transient bursts and background 1/f like activity. Several cognitive processes have been linked to neural oscillations, which are thought to be a central mechanism of information flow across brain regions. In this research, we develop a new method based on source reconstructed magnetoencephalography (MEG) resting human brain data to isolate rhythmic episodes from the neural signal. To ensure the detection of genuine oscillatory activity across brain volumes, only local maximum peaks in the power spectra are selected. Pea

This study contributes to the investigation of true oscillatory activity within neurophysiological data, amidst the backdrop of aperiodic neural activity. Donoghue et al. (2020) showed that in MEEG narrowband power peaks can be distorted due to interactions with the aperiodic component of the power spectrum, complicating interpretations of oscillatory peaks. We validated the separation of aperiodic and oscillatory components in EEG data from three conditions — eyes open, eyes closed, and waiting for visual tasks —using IRASA (Irregular-Resampling Auto-Spectral Analysis, Wen & Liu, 2016) an

In the analysis of neuro-electrophysiological recordings, recent developments have emphasized that as well as the commonly analyzed rhythmic or oscillatory activity, there is also prominent arrhythmic, or aperiodic activity. The distinction between periodic and aperiodic activity is important – methodologically because these two components can be conflated by common analysis approaches that do not explicitly consider the different forms of the data, and scientifically, as the two features have distinct interpretations in terms of the underlying circuit dynamics and physiological properties. In