Characterizing endogenous delta oscillations in human MEG

Abstract

Rhythmic activity in the delta frequency range (0.5–3 Hz) is a prominent feature of brain dynamics. Here, we examined whether spontaneous delta oscillations, as found in invasive recordings in awake animals, can be observed in non-invasive recordings performed in humans with magnetoencephalography (MEG). In humans, delta activity is commonly reported when processing rhythmic sensory inputs, with direct relationships to behaviour. However, rhythmic brain dynamics observed during rhythmic sensory stimulation cannot be interpreted as an endogenous oscillation. To test for endogenous delta oscillations we analysed human MEG data during rest. For comparison, we additionally analysed two conditions in which participants engaged in spontaneous finger tapping and silent counting, arguing that internally rhythmic behaviours could incite an otherwise silent neural oscillator. A novel set of analysis steps allowed us to show narrow spectral peaks in the delta frequency range in rest, and during overt and covert rhythmic activity. Additional analyses in the time domain revealed that only the resting state condition warranted an interpretation of these peaks as endogenously periodic neural dynamics. In sum, this work shows that using advanced signal processing techniques, it is possible to observe endogenous delta oscillations in non-invasive recordings of human brain dynamics.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

“…Alternatively, the state space representation used here in OCA provides a convenient and compact way to perform multichannel time-domain modeling and analysis [61]. The OCA modelling approach decouples the oscillatory dynamics and the spatial mixing pattern effectively by estimating one set of parameters for each discovered oscillation and the associated spatial mixing matrix, thereby avoiding the need for cross–spectral density matrices and frequency parameters for individual channels altogether [62]. Another major advantage of OCA over global coherence analysis is that OCA automatically identifies the dominant coherent oscillations across the channels based on its probabilistic generative model and Bayesian learning approach.…”

Sensory temporal sampling in time: an integrated model of the TSF and neural noise hypothesis as an etiological pathway for dyslexia

“…Higher range gamma oscillations > 50 Hz have been shown to underlie higher-level cognitive and perceptual processes, such as feature binding and attention (Başar et al, 2000 ; Ray et al, 2008 ; Fries, 2009 ; Friese et al, 2012 ; Rudo-Hutt, 2015 ). In contrast, the delta and theta bands (1–4 and 4–10 Hz, respectively) are both confined to narrower ranges with greater consensus than the gamma band, although variation and overlap still exists (Goswami, 2011 ; Harmony, 2013 ; Karakaş, 2020 ; Gunasekaran et al, 2023 ). For these reasons, the specific definitions for frequency ranges used by the authors of a given paper will be made explicit when their results are discussed.…”

Sensory temporal sampling in time: an integrated model of the TSF and neural noise hypothesis as an etiological pathway for dyslexia

“…Higher range gamma oscillations > 50 Hz have been shown to underlie higher-level cognitive and perceptual processes, such as feature binding and attention (Başar et al, 2000;Ray et al, 2008;Fries, 2009;Friese et al, 2012;Rudo-Hutt, 2015). In contrast, the delta and theta bands (1-4 Hz and 4-10 Hz, respectively) are both confined to narrower ranges with greater consensus than the gamma band, although variation and overlap still exists (Goswami, 2011;Harmony, 2013;Karakaş, 2020;Gunasekaran et al, 2023). For these reasons, the specific definitions for frequency ranges used by the authors of a given paper will be made explicit when their results are discussed.…”