Electrophysiological correlates of non-stationary BOLD functional connectivity fluctuations (original) (raw)
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Questions and controversies in the study of time-varying functional connectivity in resting fMRI
Network Neuroscience
The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods which estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights which can be gained from this promising new research area...
On the nature of resting fMRI and time-varying functional connectivity
2018
The brain is a complex dynamical system composed of many interacting sub-regions. Knowledge of how these interactions reconfigure over time is critical to a full understanding of the brain's functional architecture, the neural basis of flexible cognition and behavior, and how neural systems are disrupted in psychiatric and neurological illness. The idea that we might be able to study neural and cognitive dynamics through analysis of neuroimaging data has catalyzed substantial interest in methods which seek to estimate moment-to-moment fluctuations in functional connectivity (often referred to as "dynamic" or time-varying connectivity; TVC). At the same time, debates have emerged regarding the application of TVC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive relevance of resting TVC. These and other unresolved issues complicate the interpretation of resting TVC findings and limit the insights which can be gained from this otherwise promising research area. This article reviews the current resting TVC literature in light of these issues. We introduce core concepts, define key terms, summarize current controversies and open questions, and present a forward-looking perspective on how resting TVC analyses can be rigorously applied to investigate a wide range of questions in cognitive and systems neuroscience.
Can apparent resting state connectivity arise from systemic fluctuations?
Frontiers in Human Neuroscience, 2015
It is widely accepted that the fluctuations in resting state blood oxygenation level dependent (BOLD) functional MRI (fMRI) reflect baseline neuronal activation through neurovascular coupling; this data is used to infer functional connectivity in the human brain during rest. Consistent activation patterns, i.e., resting state networks (RSN) are seen across groups, conditions, and even species. In this study, we show that some of these patterns can also be generated from the dynamic, systemic, non-neuronal physiological low frequency oscillations (sLFOs) in the BOLD signal alone. We have previously used multimodal imaging to demonstrate the wide presence of the same sLFOs in the brain (BOLD) and periphery with different time delays. This study shows that these sLFOs from BOLD signals alone can give rise to stable spatial patterns, which can be detected during resting state analyses. We generated synthetic resting state data for 11 subjects based only on subject-specific, dynamic sLFO information obtained from resting state data using concurrent peripheral optical imaging or a novel recursive procedure. We compared the results obtained by performing a group independent component analysis (ICA) on this synthetic data (i.e., the result from simulation) to the results obtained from analysis of the real data. ICA detected most of the eight well-known RSNs, including visual, motor, and default mode networks (DMNs), in both the real and the synthetic data sets. These findings suggest that RSNs may reflect, to some extent, vascular anatomy associated with systemic fluctuations, rather than neuronal connectivity.
Correcting for Non-stationarity in BOLD-fMRI Connectivity Analyses
Frontiers in Neuroscience, 2021
In this work fMRI BOLD datasets are shown to contain slice-dependent non-stationarities. A model containing slice-dependent, non-stationary signal power is proposed to address time-varying signal power during BOLD data acquisition. The impact of non-stationary power on functional MRI connectivity is analytically derived, establishing that pairwise connectivity estimates are scaled by a function of the time-varying signal power, with magnitude upper bound by 1, and that the variance of sample correlation is increased, thereby inducing spurious connectivity. Consequently, we make the observation that time-varying power during acquisition of BOLD timeseries has the propensity to diminish connectivity estimates. To ameliorate the impact of non-stationary signal power, a simple correction for slice-dependent non-stationarity is proposed. Our correction is analytically shown to restore both signal stationarity and, subsequently, the integrity of connectivity estimates. Theoretical results...
On wakefulness fluctuations as a source of BOLD functional connectivity dynamics
Scientific Reports, 2017
Human brain dynamics and functional connectivity fluctuate over a range of temporal scales in coordination with internal states and environmental demands. However, the neurobiological significance and consequences of functional connectivity dynamics during rest have not yet been established. We show that the coarse-grained clustering of whole-brain dynamic connectivity measured with magnetic resonance imaging reveals discrete patterns (dynamic connectivity states) associated with wakefulness and sleep. We validate this using EEG in healthy subjects and patients with narcolepsy and by matching our results with previous findings in a large collaborative database. We also show that drowsiness may account for previous reports of metastable connectivity states associated with different levels of functional integration. This implies that future studies of transient functional connectivity must independently monitor wakefulness. We conclude that a possible neurobiological significance of dynamic connectivity states, computed at a sufficiently coarse temporal scale, is that of fluctuations in wakefulness. The dynamics of neural populations in the human brain leads to a continuously changing landscape of interactions with cell assemblies forming and dissolving either spontaneously or in coordination with sensory stimulation 1, 2. Such rich dynamics have been postulated as a mechanism underlying the binding of information and crucial for cognitive operations 3, 4. The non-invasive monitoring of spontaneous brain activity using functional magnetic resonance imaging (fMRI) has revealed complex dynamics exhibiting non-trivial large-scale organization into networks of regions commonly co-activated during task performance, termed resting state networks (RSN) 5-7. The so-called ultra-slow (0.01-0.1 Hz) fluctuations of the blood oxygenation level-dependent (BOLD) signal in fMRI data are a well-observed phenomenon. Yet, their biological significance is still under debate 8. These ultra-slow fluctuations result in dynamic inter-areal coordination. When attempting to understand the nature of the slow fluctuations, the study of such coordinated activity will be more informative than the study of individual time courses alone. Hence, we consider the temporal evolution of functional connectivity as measured with fMRI 9-12 as key in the search to the interpretation of slow BOLD signal oscillations. Dynamic functional connectivity is commonly computed as the time series of temporal correlations between BOLD signals during short sliding windows 9, 10. As potential contributing factors to the variability in connectivity, studies have established artifacts due to the sliding window method itself 13, 14 , relatively short scanning length 15 , head movement 16 and physiological noise 17. Beyond these, a neurobiological origin of fMRI dynamic functional connectivity is supported by its association with neural oscillations quantified using simultaneous electroencephalography (EEG) 18-22. Furthermore, dynamic functional connectivity relates to ongoing cognition 23 , daydreaming 24 , and levels of conscious awareness 25, 26. A series of recent independent reports provides converging evidence of transient global states of connectivity (dynamic connectivity states) 27 associated with different degrees of functional integration and cognitive performance 28-31. It has been speculated that, at a certain temporal scale, these states could relate to fluctuations in
Journal of Neuroscience, 2014
Over the last decade, synchronized resting-state fluctuations of blood oxygenation level-dependent (BOLD) signals between remote brain areas [so-called BOLD resting-state functional connectivity (rs-FC)] have gained enormous relevance in systems and clinical neuroscience. However, the neural underpinnings of rs-FC are still incompletely understood. Using simultaneous positron emission tomography/ magnetic resonance imaging we here directly investigated the relationship between rs-FC and local neuronal activity in humans.
Human Brain Mapping, 2008
Recent studies have demonstrated large amplitude spontaneous fluctuations in functional-MRI (fMRI) signals in humans in the resting state. Importantly, these spontaneous fluctuations in blood-oxygenation-level-dependent (BOLD) signal are often synchronized over distant parts of the brain, a phenomenon termed functional-connectivity. Functional-connectivity is widely assumed to reflect interregional coherence of fluctuations in activity of the underlying neuronal networks. Despite the large body of human imaging literature on spontaneous activity and functional-connectivity in the resting state, the link to underlying neural activity remains tenuous. Through simultaneous fMRI and intracortical neurophysiological recording, we demonstrate correlation between slow fluctuations in BOLD signals and concurrent fluctuations in the underlying locally measured neuronal activity. This correlation varied with time-lag of BOLD relative to neuronal activity, resembling a traditional hemodynamic response function with peaks at 6 s lag of BOLD signal. The correlations were reliably detected when the neuronal signal consisted of either the spiking rate of a small group of neurons, or relative power changes in the multi-unit activity band, and particularly in the local field potential gamma band. Analysis of correlation between the voxel-by-voxel fMRI time-series and the neuronal activity measured within one cortical site showed patterns of correlation that slowly traversed cortex. BOLD fluctuations in widespread areas in visual cortex of both hemispheres were significantly correlated with neuronal activity from a single recording site in V1. To the extent that our V1 findings can be generalized to other cortical areas, fMRI-based functional-connectivity between remote regions in the resting state can be linked to synchronization of slow fluctuations in the underlying neuronal signals.
Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, " dynamic global signal regression " (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional " static " global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.
Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state
Neuroscience Letters, 2011
Recent neuroimaging studies have demonstrated that the spontaneous brain activity reflects, to a large extent, the same activation patterns measured in response to cognitive and behavioral tasks. This correspondence between activation and rest has been explored with a large repertoire of computational methods, ranging from analysis of pairwise interactions between areas of the brain to the global brain networks yielded by independent component analysis. In this paper we describe an alternative method based on the averaging of the BOLD signal at a region of interest (target) triggered by spontaneous increments in activity at another brain area (seed). The resting BOLD event triggered averages ("rBeta") can be used to estimate functional connectivity at resting state. Using two simple examples, here we illustrate how the analysis of the average response triggered by spontaneous increases/decreases in the BOLD signal is sufficient to capture the aforementioned correspondence in a variety of circumstances. The computation of the non linear response during rest here described allows for a direct comparison with results obtained during task performance, providing an alternative measure of functional interaction between brain areas.