Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

Li, Zhong‐Ming; Zwart, Jacco A. de; Chang, Catie; Qi, Dawei; Gelderen, Peter van; Duyn, Jeff H.

doi:10.1093/cercor/bht164

Cited by 19 publications

(31 citation statements)

References 60 publications

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“…The scale-free component is arrhythmic and not confined to any specific time scale (Yamamoto et al, 1991); its power spectrum follows a power-law distribution, shown as a descending line on the log-log plot (Miller et al, 2009;He, 2014). In contrast, the oscillatory component contains rhythms indicative of neural dynamics with characteristic time scales (Buzsáki and Draguhn, 2004); it typically exhibits one or multiple spectral peaks at specific frequencies. Based on their distinct temporal and spectral characteristics, we were able to separate the scale-free and oscillatory components in the power spectrum of the neurophysiological signal.…”

Section: Resultsmentioning

confidence: 99%

“…fMRI has been used increasingly to uncover large-scale neural networks with correlated spontaneous activity in the absence of any overt task (Biswal et al, 1995). These resting-state networks (RSNs) mostly appear modular (Van Dijk et al, 2010), arise from structural connections (Greicius et al, 2009), persist across behavioral states , and resemble task activa-tion patterns (Smith et al, 2009), thereby being recognized to report on the brain's intrinsic functional organization (Fox and Raichle, 2007;Power et al, 2011;Yeo et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal

Wen

2016

J. Neurosci.

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101

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Spontaneous activity observed with resting-state fMRI is used widely to uncover the brain's intrinsic functional networks in health and disease. Although many networks appear modular and specific, global and nonspecific fMRI fluctuations also exist and both pose a challenge and present an opportunity for characterizing and understanding brain networks. Here, we used a multimodal approach to investigate the neural correlates to the global fMRI signal in the resting state. Like fMRI, resting-state power fluctuations of broadband and arrhythmic, or scale-free, macaque electrocorticography and human magnetoencephalography activity were correlated globally. The power fluctuations of scale-free human electroencephalography (EEG) were coupled with the global component of simultaneously acquired resting-state fMRI, with the global hemodynamic change lagging the broadband spectral change of EEG by ϳ5 s. The levels of global and nonspecific fluctuation and synchronization in scale-free population activity also varied across and depended on arousal states. Together, these results suggest that the neural origin of global resting-state fMRI activity is the broadband power fluctuation in scale-free population activity observable with macroscopic electrical or magnetic recordings. Moreover, the global fluctuation in neurophysiological and hemodynamic activity is likely modulated through diffuse neuromodulation pathways that govern arousal states and vigilance levels.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal

Wen

2016

J. Neurosci.

Self Cite

101

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“…Indeed, beta rhythms are associated with activation of the primary somatosensory and motor cortices (Liu et al 2014;Ritter et al 2009). We found delta/theta frequency tuning in the pulvinar, as visualized in map D and F. This finding contrasts with the finding that the pulvinar can act as a posterior alpha generator (Liu et al 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, cortical maps of spectral EEG signatures during spontaneous fMRI signals were created and used to determine clusters of brain areas that represent prominent functional subdivisions (Liu et al 2014). …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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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.

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“…imultaneous acquisition of functional magnetic resonance imaging (fMRI) in combination with electroencephalography (EEG), electrocorticography (ECoG), local field potentials (LFP), and single or multi-unit activity (SUA/MUA) holds great potential to bridge brain activity across spatial and temporal scales [1]- [10]. Despite its scientific premise and clinical This potential, concurrent electrophysiological (EP) and MRI acquisition is challenging, since the MRI apparatus presents a hostile environment for recording bioelectric signals [3], [5], [8], [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive and Wireless Recordings of Electrophysiological Signals during Concurrent Magnetic Resonance Imaging

Mandal

Babaria

Cao

et al. 2018

Preprint

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Strong electromagnetic fields that occur during functional magnetic resonance imaging (fMRI) presents a challenging environment for concurrent electrophysiological recordings. Here, we present a miniaturized, wireless platform -"MR-Link" (Multimodal Recording Link) that provides a hardware solution for simultaneous electrophysiological and fMRI signal acquisition. The device detects the changes in the electromagnetic field during fMRI to synchronize amplification and sampling of electrophysiological signals with minimal artifacts. It wirelessly transmits the recorded data at a frequency detectable by the MR-receiver coil. The transmitted data is readily separable from MRI in the frequency domain. To demonstrate its efficacy, we used this device to record electrocardiograms and somatosensory evoked potential during concurrent fMRI scans. The device minimized the fMRI-induced artifacts in electrophysiological data and wirelessly transmitted the data back to the receiver coil without compromising fMRI signal quality. The device is compact (22 mm dia., 2gms) and can be placed within the MR-bore to precisely synchronize with fMRI. Therefore, MR-Link offers an inexpensive system by eliminating the need for amplifiers with a high dynamic range, high-speed sampling, additional storage or synchronization hardware for electrophysiological signal acquisition. It is expected to enable a broader range of applications of simultaneous fMRI and electrophysiology in animals and humans.

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Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

Cited by 19 publications

References 60 publications

Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal

Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal

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

Adaptive and Wireless Recordings of Electrophysiological Signals during Concurrent Magnetic Resonance Imaging

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