A study of the brain's resting state based on alpha band power, heart rate and fMRI

Munck, Jan C. de; Gonçalves, S.I.; Faes, T.J.C.; Kuijer, Joost P.A.; Pouwels, Petra J. W.; Heethaar, R.M.; Silva, F.H. Lopes da

doi:10.1016/j.neuroimage.2008.04.244

Cited by 102 publications

(80 citation statements)

References 40 publications

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“…Several studies have shown that physiological noise related to respiration and heart-rate variability correlates with cognitive tasks (Birn et al, 1999;Birn et al, 2009;Ent et al, 2014;Park et al, 2014;Vlemincx et al, 2011) and elicits prolonged negative correlations with BOLD signals -especially in midline areas (Birn et al, 2009;de Munck et al, 2008;Ent et al, 2014;Shmueli et al, 2007). Furthermore, task related changes in physiology can produce BOLD signal changes in vascular networks mimicking neuronal network activations (Bright et al, 2015).…”

Section: Task Related Motion Causing Event Related Effectsmentioning

confidence: 99%

Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement

et al. 2016

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Simultaneous EEG-fMRI provides an increasingly attractive research tool to investigate cognitive processes with high temporal and spatial resolution. However, artifacts in EEG data introduced by the MR-scanner still remain a major obstacle. This study employing commonly used artifact correction steps shows that head motion, one overlooked major source of artifacts in EEG-fMRI data, can cause plausible EEG effects and EEG-BOLD correlations. Specifically, low frequency EEG (<20 Hz) is strongly correlated with in-scanner movement. Accordingly, minor head motion (<0.2 mm) induces spurious effects in a twofold manner: Small differences in task-correlated motion elicit spurious low frequency effects, and, as motion concurrently influences fMRI data, EEG-BOLD correlations closely match motion-fMRI correlations. We demonstrate these effects in a memory encoding experiment showing that obtained theta power (~3-7 Hz) effects and channel-level theta-BOLD correlations reflect motion in the scanner. These findings highlight an important caveat that needs to be addressed by future EEG-fMRI studies.

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Section: Task Related Motion Causing Event Related Effectsmentioning

confidence: 99%

Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement

et al. 2016

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“…Only NT floating-point numbers need to be stored, as opposed to N 2 numbers for the complete matrix R. Similarly, the number of multiplications is reduced from N 2 to 2NT. One of the benefits of this formulation is that by using the projection matrix P, it can be generalized to definitions of R where other confounding signals are removed from the data, such as breathing, heartbeat, and mean image intensity variability (De Munck et al, 2008).…”

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confidence: 99%

Fast Eigenvector Centrality Mapping of Voxel-Wise Connectivity in Functional Magnetic Resonance Imaging: Implementation, Validation, and Interpretation

et al. 2012

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Eigenvector centrality mapping (ECM) has recently emerged as a measure to spatially characterize connectivity in functional brain imaging by attributing network properties to voxels. The main obstacle for widespread use of ECM in functional magnetic resonance imaging (fMRI) is the cost of computing and storing the connectivity matrix. This article presents fast ECM (fECM), an efficient algorithm to estimate voxel-wise eigenvector centralities from fMRI time series. Instead of explicitly storing the connectivity matrix, fECM computes matrix-vector products directly from the data, achieving high accelerations for computing voxel-wise centralities in fMRI at standard resolutions for multivariate analyses, and enabling high-resolution analyses performed on standard hardware. We demonstrate the validity of fECM at cluster and voxel levels, using synthetic and in vivo data. Results from synthetic data are compared to the theoretical gold standard, and local centrality changes in fMRI data are measured after experimental intervention. A simple scheme is presented to generate time series with prescribed covariances that represent a connectivity matrix. These time series are used to construct a 4D dataset whose volumes consist of separate regions with known intra-and inter-regional connectivities. The fECM method is tested and validated on these synthetic data. Resting-state fMRI data acquired after real-versus-sham repetitive transcranial magnetic stimulation show fECM connectivity changes in resting-state network regions. A comparison of analyses with and without accounting for motion parameters demonstrates a moderate effect of these parameters on the centrality estimates. Its computational speed and statistical sensitivity make fECM a good candidate for connectivity analyses of multimodality and high-resolution functional neuroimaging data.

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“…It is unclear to which degree non-neuronal processes affect the fMRI signal, as different physiological signals such as respiration and cardiac pulse rate were shown to be correlated with restingstate oscillations (Biswal et al, 1996;Power et al, 2017), which raised concerns that RSN oscillations may be unrelated to neuronal information processing, but rather constitute an epiphenomenon (Birn et al, 2006;de Munck et al, 2008;Shmueli et al, 2007;Yuan et al, 2013). The interpretation and handling of these signal modulations is therefore hotly debated and they are often considered as artefactual and removed from fMRI studies (Birn et al, 2006;Chang and Glover, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, intracellular recordings showed that inhibitory events are inseparable from excitatory events, resulting in an ongoing excitation-inhibition balance (E/I balance) (Isaacson and Scanziani, 2011;Okun and Lampl, 2008). The significance of the a-rhythm is underscored by strong negative correlations between ongoing a-band power fluctuation and resting-state fMRI amplitude fluctuation (de Munck et al, 2008;Feige et al, 2005;Goldman et al, 2002;Moosmann et al, 2003). Lastly, despite wide-spread interest in critical dynamics (Bak, 2013), the key determinants of emergent power-law scaling, a signal pattern that is ubiquitous in nature and commonly observed in neural activity, are unclear (Beggs and Timme, 2012;Markovic´and Gros, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…(c) On a longer time scale (<0.25 Hz), oscillations of firing rates are inversely related to a-band power fluctuations (adapted from Haegens et al, 2011). (d) Model simulations suggest a mechanism that transforms a-band power oscillations into fMRI oscillations, predicting subject-specific resting-state fMRI time series, corresponding spatial network patterns and the inverse correlation between a-power and fMRI (adapted from de Munck et al, 2008. Reprinted with permission of Elsevier.).…”

Section: Introductionmentioning

confidence: 99%

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Inferring multi-scale neural mechanisms with brain network modelling

Schirner

McIntosh

Jirsa

et al. 2017

Preprint

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The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between arhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

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A study of the brain's resting state based on alpha band power, heart rate and fMRI

Cited by 102 publications

References 40 publications

Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement

Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement

Fast Eigenvector Centrality Mapping of Voxel-Wise Connectivity in Functional Magnetic Resonance Imaging: Implementation, Validation, and Interpretation

Inferring multi-scale neural mechanisms with brain network modelling

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