Stereotypical modulations in dynamic functional connectivity explained by changes in BOLD variance (original) (raw)

Resting state networks in empirical and simulated dynamic functional connectivity

NeuroImage, 2017

It is well-established that patterns of functional connectivity (FC)-measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging-are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions ("communities") that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations.

Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network

Frontiers in physics, 2017

Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33 ± 6 years, 8 F/12 M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the continuous execution of a working memory n-back task. We found that task execution impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cin...

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

Dynamic reorganization of human resting-state networks during visuospatial attention

Proceedings of the National Academy of Sciences, 2015

Fundamental problems in neuroscience today are understanding how patterns of ongoing spontaneous activity are modified by task performance and whether/how these intrinsic patterns influence task-evoked activation and behavior. We examined these questions by comparing instantaneous functional connectivity (IFC) and directed functional connectivity (DFC) changes in two networks that are strongly correlated and segregated at rest: the visual (VIS) network and the dorsal attention network (DAN). We measured how IFC and DFC during a visuospatial attention task, which requires dynamic selective rerouting of visual information across hemispheres, changed with respect to rest. During the attention task, the two networks remained relatively segregated, and their general pattern of within-network correlation was maintained. However, attention induced a decrease of correlation in the VIS network and an increase of the DAN→VIS IFC and DFC, especially in a top-down direction. In contrast, within the DAN, IFC was not modified by attention, whereas DFC was enhanced. Importantly, IFC modulations were behaviorally relevant. We conclude that a stable backbone of within-network functional connectivity topography remains in place when transitioning between resting wakefulness and attention selection. However, relative decrease of correlation of ongoing "idling" activity in visual cortex and synchronization between frontoparietal and visual cortex were behaviorally relevant, indicating that modulations of resting activity patterns are important for task performance. Higher order resting connectivity in the DAN was relatively unaffected during attention, potentially indicating a role for simultaneous ongoing activity as a "prior" for attention selection.

Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity

PLoS computational biology, 2015

Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, ...

Dynamic brain functional connectivity modulated by resting-state networks

Brain structure & function, 2013

Studies of large-scale brain functional connectivity using the resting-state functional magnetic resonance imaging have advanced our understanding of human brain functions. Although the evidence of dynamic functional connectivity is accumulating, the variations of functional connectivity over time have not been well characterized. In the present study, we aimed to associate the variations of functional connectivity with the intrinsic activities of resting-state networks during a single resting-state scan by comparing functional connectivity differences between when a network had higher and lower intrinsic activities. The activities of the salience network, default mode network (DMN), and motor network were associated with changes of resting-state functional connectivity. Higher activity of the salience network was accompanied by greater functional connectivity between the fronto-parietal regions and the DMN regions, and between the regions within the DMN. Higher DMN activity was associated with less connectivity between the regions within the DMN, and greater connectivity between the regions within the fronto-parietal network. Higher motor network activity was correlated with greater connectivity between the regions within the motor network, and smaller connectivity between the DMN regions and fronto-parietal regions, and between the DMN regions and the motor regions. In addition, the whole brain network modularity was positively correlated with the motor network activity, suggesting that the brain is more segregated as sub-systems when the motor network is intrinsically activated. Together, these results demonstrate the association between the resting-state connectivity variations and the intrinsic activities of specific networks, which can provide insights on the dynamic changes in large-scale brain connectivity and network configurations.

Electrophysiological signatures of resting state networks in the human brain

Proceedings of the National Academy of Sciences, 2007

Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus-or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.

Resting-State Brain Organization Revealed by Functional Covariance Networks

PLoS ONE, 2011

Background: Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN) and structural covariance network (SCN) have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization.

Linking functional connectivity and dynamic properties of resting-state networks

Scientific reports, 2017

Spontaneous brain activity is organized into resting-state networks (RSNs) involved in internally-guided, higher-order mental functions (default mode, central executive and salience networks) and externally-driven, specialized sensory and motor processing (auditory, visual and sensorimotor networks). RSNs are characterized by their functional connectivity in terms of within-network cohesion and between-network integration, and by their dynamic properties in terms of synchrony and metastability. We examined the relationship between functional connectivity and dynamic network features using fMRI data and an anatomically constrained Kuramoto model. Extrapolating from simulated data, synchrony and metastability across the RSNs emerged at coupling strengths of 5 ≤ k ≤ 12. In the empirical RSNs, higher metastability and synchrony were respectively associated with greater cohesion and lower integration. Consistent with their dual role in supporting both sustained and diverse mental operati...