Discovering frequency sensitive thalamic nuclei from EEG microstate informed resting state fMRI

Abstract

Microstates (MS), the fingerprints of the momentarily and time-varying states of the brain derived from electroencephalography (EEG), are associated with the resting state networks (RSNs). However, using MS fluctuations along different EEG frequency bands to model the functional MRI (fMRI) signal has not been investigated so far, or elucidated the role of the thalamus as a fundamental gateway and a putative key structure in cortical functional networks. Therefore, in the current study, we used MS predictors in standard frequency bands to predict blood oxygenation level dependent (BOLD) signal fluctuations. We discovered that multivariate modeling of BOLD-fMRI using six EEG-MS classes in eight frequency bands strongly correlated with thalamic areas and large-scale cortical networks. Thalamic nuclei exhibited distinct patterns of correlations for individual MS that were associated with specific EEG frequency bands. Anterior and ventral thalamic nuclei were sensitive to the beta frequency band, medial nuclei were sensitive to both alpha and beta frequency bands, and posterior nuclei such as the pulvinar were sensitive to delta and theta frequency bands. These results demonstrate that EEG-MS informed fMRI can elucidate thalamic activity not directly observable by EEG, which may be highly relevant to understand the rapid formation of thalamocortical networks.

Fluctuations of spontaneous EEG topographies predict disease state in relapsing-remitting multiple sclerosis

“…Because scalp EEG does not record thalamic activity, such alterations cannot be confirmed by EEG recordings alone. In a recent combined EEG/fMRI study, Schwab et al (2015) demonstrated selective BOLD activity in specific parts of the thalamus with respect to different microstate classes in specific frequency bands, meaning that thalamic activity indeed participates in the microstate fluctuations. However, in this study, thalamic contributions to the microstate fluctuations were found for all microstate classes except class A.…”

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

“…One may further argue that these latter processes that implement communication itself may take place on spatial scales that are mostly below the resolution of scalp EEG data and typically need to be resolved by recording local field potentials (van Kerkoerle et al, 2014). The fact that combined EEG-fMRI data has shown that the topographic appearance of specific transient states of EEG synchronization (that are assumingly cortical) covaried with the spatial distribution of thalamic activity (Schwab et al, 2015) may further support this view, since the thalamus is a well-known pace-maker for cortical cycles of M A N U S C R I P T…”

“…The topographic time frequency decomposition of the EEG (Koenig et al, 2001) (Schwab et al, 2015), indicating that different classes of states of synchronized cortical oscillation exhibited BOLD correlates in partially separate sub-regions of the thalamus. However, other researchers have criticized this approach, as the use of wavelets may alleviate but not eliminate non-stationarities in the data.…”

“…When such methods are augmented by dictionary-learning procedures, one may again obtain a discrete set of transiently active functional brain states, which are now defined in time, frequency, and phase Studer et al, 2006). Interestingly, a recent study based on combined EEG and fMRI measurements indicated that the activity of particular transient networks integrated by synchronous oscillations of cortical neurons was correlated with the BOLD signal in particular subregions of the thalamus in a frequency-specific manner (Schwab et al, 2015). The involvement of thalamo-cortical loops (Lopes da Silva, 1991) may thus be an important link between the typical oscillations observed using EEG, the overarching pattern of synchronization as assessed via microstate analyses, and the cognitive correlates of spectral and microstate features of resting-state EEG data.…”

A Change-point Analysis Method for Single-trial Study of Simultaneous EEG-fMRI of Auditory/Visual Oddball Task

“…Furthermore, it is believed that the frequency-band components of EEG signal have important implications on different cognitive processes. EEG is usually divided to five frequency-bands: δ-band (0.5-4 Hz), θ-band (4-8 Hz) α-band (8-14 Hz), β-band (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and γ-band (30-100 Hz) [7]. Both amplitudes (powers) and phases of these frequency-band components carry information about cognitive processes inside brain [8]- [26].…”