Key role of coupling, delay, and noise in resting brain fluctuations.
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ABSTRACT:
SUBMITTER: Deco G
PROVIDER: S-EPMC2690605 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe origin of low frequency cerebral hemodynamic fluctuations (CHF) in the resting state remains unknown. Breath-by breath O2-CO2 exchange ratio (bER) has been reported to correlate with the cerebrovascular response to brief breath hold challenge at the frequency range of 0.008-0.03Hz in healthy adults. bER is defined as the ratio of the change in the partial pressure of oxygen (ΔPO2) to that of carbon dioxide (ΔPCO2) between end inspiration and end expiration. In this study, we aimed to investigate the contribution of respiratory gas exchange (RGE) metrics (bER, ΔPO2 and ΔPCO2) to low frequency CHF during spontaneous breathing.MethodsTwenty-two healthy adults were included. We used transcranial Doppler sonography to evaluate CHF by measuring the changes in cerebral blood flow velocity (ΔCBFv) in bilateral middle cerebral arteries. The regional CHF were mapped with blood oxygenation level dependent (ΔBOLD) signal changes using functional magnetic resonance imaging. Temporal features and frequency characteristics of RGE metrics during spontaneous breathing were examined, and the simultaneous measurements of RGE metrics and CHF (ΔCBFv and ΔBOLD) were studied for their correlation.ResultsWe found that the time courses of ΔPO2 and ΔPCO2 were interdependent but not redundant. The oscillations of RGE metrics were coherent with resting state CHF at the frequency range of 0.008-0.03Hz. Both bER and ΔPO2 were superior to ΔPCO2 in association with CHF while CHF could correlate more strongly with bER than with ΔPO2 in some brain regions. Brain regions with the strongest coupling between bER and ΔBOLD overlapped with many areas of default mode network including precuneus and posterior cingulate.ConclusionAlthough the physiological mechanisms underlying the strong correlation between bER and CHF are unclear, our findings suggest the contribution of bER to low frequency resting state CHF, providing a novel insight of brain-body interaction via CHF and oscillations of RGE metrics.
| S-EPMC7505589 | biostudies-literature
Project description:How do intrinsic brain dynamics interact with processing of external sensory stimuli? We sought new insights using functional magnetic resonance imaging to track spatiotemporal activity patterns at the whole brain level in lightly anesthetized mice, during both resting conditions and visual stimulation trials. Our results provide evidence that quasiperiodic patterns (QPPs) are the most prominent component of mouse resting brain dynamics. These QPPs captured the temporal alignment of anticorrelation between the default mode (DMN)- and task-positive (TPN)-like networks, with global brain fluctuations, and activity in neuromodulatory nuclei of the reticular formation. Specifically, the phase of QPPs prior to stimulation could significantly stratify subsequent visual response magnitude, suggesting QPPs relate to brain state fluctuations. This is the first observation in mice that dynamics of the DMN- and TPN-like networks, and particularly their anticorrelation, capture a brain state dynamic that affects sensory processing. Interestingly, QPPs also displayed transient onset response properties during visual stimulation, which covaried with deactivations in the reticular formation. We conclude that QPPs appear to capture a brain state fluctuation that may be orchestrated through neuromodulation. Our findings provide new frontiers to understand the neural processes that shape functional brain states and modulate sensory input processing.
| S-EPMC7869084 | biostudies-literature
Project description:Resting- or baseline-state low-frequency (0.01-0.2 Hz) brain activity is observed in fMRI, EEG, and local field potential recordings. These fluctuations were found to be correlated across brain regions and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow resting-state fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (<0.05 Hz) activity could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra- and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 cotransporter. In the network model simulating resting awake-like brain state, we observed infra-slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons, and local field potentials. Holding K+ concentration constant prevented generation of the infra-slow fluctuations. The amplitude and peak frequency of this activity were modulated by the Na+/K+ pump, AMPA/GABA synaptic currents, and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state activity observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are reported as the resting-state fluctuations in fMRI and EEG recordings.
| S-EPMC6042137 | biostudies-literature
Project description:The amplitude of low-frequency fluctuation (ALFF) measures low-frequency oscillations of the blood-oxygen-level-dependent signal, characterizing local spontaneous activity during the resting state. ALFF is a commonly used measure for resting-state functional magnetic resonance imaging (rs-fMRI) in numerous basic and clinical neuroscience studies. Using a test-retest rs-fMRI dataset consisting of 21 healthy subjects and three repetitive scans, we found that several key brain regions with high ALFF intensities (or magnitude) had poor reliability. Such regions included the posterior cingulate cortex, the medial prefrontal cortex in the default mode network, parts of the right and left thalami, and the primary visual and motor cortices. The above finding was robust with regard to different sample sizes (number of subjects), different scanning parameters (repetition time) and variations of test-retest intervals (i.e., intra-scan, intra-session, and inter-session reliability), as well as with different scanners. Moreover, the qualitative, map-wise results were validated further with a region-of-interest-based quantitative analysis using "canonical" coordinates as reported previously. Therefore, we suggest that the reliability assessments be incorporated in future ALFF studies, especially for the brain regions with a large ALFF magnitude as listed in our paper. Splitting single data into several segments and assessing within-scan "test-retest" reliability is an acceptable alternative if no "real" test-retest datasets are available. Such evaluations might become more necessary if the data are collected with clinical scanners whose performance is not as good as those that are used for scientific research purposes and are better maintained because the lower signal-to-noise ratio may further dampen ALFF reliability.
| S-EPMC4460034 | biostudies-literature
Project description:The increase in blood flow evoked by synaptic activity is essential for normal brain function and underlies functional brain imaging signals. Nitric oxide, a vasodilator released by NMDA receptor activation, is critical for the flow increase, but the factors linking NMDA receptor activity to nitric oxide-dependent hyperemia are poorly understood. Here, we show that tissue plasminogen activator (tPA), a serine protease implicated in NMDA receptor signaling, is required for the flow increase evoked by somatosensory stimulation. tPA acts by facilitating neuronal nitric oxide release, but this effect does not involve enhancement of NMDA currents or the associated intracellular Ca(2+) rise. Rather, the evidence suggests that tPA controls NMDA-dependent nitric oxide synthesis by influencing the phosphorylation state of neuronal nitric oxide synthase. These findings unveil a previously unrecognized role of tPA in vital homeostatic mechanisms coupling NMDA receptor signaling with nitric oxide synthesis and local cerebral perfusion.
| S-EPMC2242693 | biostudies-literature
Project description:We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson's correlation to estimate functional connectivity implies that the range of fluctuations of functional connections over short time-scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are unexpectedly strong or weak. We apply this method to data recorded from N=80 participants, and show that the number of unexpectedly strong/weak connections fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with default mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form distinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivity.
| S-EPMC4755785 | biostudies-literature
Project description:Resting-state (rs) fMRI has been widely used to examine brain-wide large-scale spatiotemporal architectures, known as resting-state networks (RSNs). Recent studies have focused on the temporally evolving characteristics of RSNs, but it is unclear what temporal characteristics are reflected in the networks. To address this issue, we devised a novel method for voxel-based visualization of spatiotemporal characteristics of rs-fMRI with a time scale of tens of seconds. We first extracted clusters of dominant activity-patterns using a region-of-interest approach and then used these temporal patterns of the clusters to obtain voxel-based activation patterns related to the clusters. We found that activation patterns related to the clusters temporally evolved with a characteristic temporal structure and showed mutual temporal alternations over minutes. The voxel-based representation allowed the decoding of activation patterns of the clusters in rs-fMRI using a meta-analysis of functional activations. The activation patterns of the clusters were correlated with behavioral measures. Taken together, our analysis highlights a novel approach to examine brain activity dynamics during rest.
| S-EPMC9878402 | biostudies-literature
Project description:Stochasticity is a hallmark of cellular processes, and different classes of genes show large differences in their cell-to-cell variability (noise). To decipher the sources and consequences of this noise, we systematically measured pairwise correlations between large numbers of genes, including those with high variability. We find that there is substantial pathway variability shared across similarly regulated genes. This induces quantitative correlations in the expression of functionally related genes such as those involved in the Msn2/4 stress response pathway, amino-acid biosynthesis, and mitochondrial maintenance. Bioinformatic analyses and genetic perturbations suggest that fluctuations in PKA and Tor signaling contribute to pathway-specific variability. Our results argue that a limited number of well-delineated "noise regulons" operate across a yeast cell and that such coordinated fluctuations enable a stochastic but coherent induction of functionally related genes. Finally, we show that pathway noise is a quantitative tool for exploring pathway features and regulatory relationships in un-stimulated systems.
| S-EPMC3327736 | biostudies-literature
Project description:BackgroundStochastic fluctuations in molecular numbers have been in many cases shown to be crucial for the understanding of biochemical systems. However, the systematic study of these fluctuations is severely hindered by the high computational demand of stochastic simulation algorithms. This is particularly problematic when, as is often the case, some or many model parameters are not well known. Here, we propose a solution to this problem, namely a combination of the linear noise approximation with optimisation methods. The linear noise approximation is used to efficiently estimate the covariances of particle numbers in the system. Combining it with optimisation methods in a closed-loop to find extrema of covariances within a possibly high-dimensional parameter space allows us to answer various questions. Examples are, what is the lowest amplitude of stochastic fluctuations possible within given parameter ranges? Or, which specific changes of parameter values lead to the increase of the correlation between certain chemical species? Unlike stochastic simulation methods, this has no requirement for small numbers of molecules and thus can be applied to cases where stochastic simulation is prohibitive.ResultsWe implemented our strategy in the software COPASI and show its applicability on two different models of mitogen-activated kinases (MAPK) signalling -- one generic model of extracellular signal-regulated kinases (ERK) and one model of signalling via p38 MAPK. Using our method we were able to quickly find local maxima of covariances between particle numbers in the ERK model depending on the activities of phospho-MKKK and its corresponding phosphatase. With the p38 MAPK model our method was able to efficiently find conditions under which the coefficient of variation of the output of the signalling system, namely the particle number of Hsp27, could be minimised. We also investigated correlations between the two parallel signalling branches (MKK3 and MKK6) in this model.ConclusionsOur strategy is a practical method for the efficient investigation of fluctuations in biochemical models even when some or many of the model parameters have not yet been fully characterised.
| S-EPMC3814289 | biostudies-literature
Project description:Background and purposeThe BOLD signal amplitude as a response to a hypercapnia stimulus is commonly used to assess cerebrovascular reserve. Despite recent advances, the implementation remains cumbersome and alternative ways to assess hemodynamic impairment are desirable. Resting-state BOLD signal fluctuations (rsBOLD) have been proposed however data on its sensitivity and dependence on baseline venous cerebral blood volume (vCBV) is limited. The primary aim of this study was to compare the effect sizes of resting-state and hypercapnia induced BOLD signal changes in the detection of hemodynamic impairment. The second aim of the study was to assess the dependence of BOLD signal variability on vCBV.Materials and methodsFifteen patients with internal carotid artery occlusive disease and 15 matched healthy controls were included in this study. The BOLD signal was derived from a dual-echo gradient-echo echo-planar sequence during hypercapnia (HC) and hyperoxia (HO) gas modulations. BOLD (fractional) amplitude of low frequency fluctuations ((f)ALFF) was compared to HC-BOLD, BOLD response delays derived from time delay analysis and ΔBOLD in response to progressively increasing HC. Effect sizes (i.e. the standard mean difference between patients and controls) were calculated. HO-BOLD was used to estimate vCBV, and its contribution to the variability in rsBOLD signal was evaluated.ResultsThe effect sizes of ALFF and fALFF (0.61 and 0.72) were lower than the effect sizes related to hypercapnia-based hemodynamic assessment analysis; 1.62, 1.56 and 0.90 for HC-BOLD, BOLD response delays and ΔBOLD in response to progressively increasing HC. A moderate relation was found between (f)ALFF and HC-BOLD in controls (R2 of 0.61 and 0.42), but this relation decreased in patients (R2 of 0.33 and 0.15). (f)ALFF did not differ between patients and controls whereas HC-BOLD did (p < 0.005). The ΔBOLD response to progressively increasing HC was significantly different in between patients and controls for ΔEtCO2 values ≥ 2 mmHg (at +2 mmHg F(1, 18) = 5.85, p = 0.026). Up to 31% and 53% of the variance in the ALFF and HC-BOLD spatial distribution could be explained by HO-BOLD.ConclusionALFF and fALFF demonstrated a moderate effect size to detect hemodynamic impairment whereas the effect size was large for methods employing a hypercapnia-based vascular stress stimulus. Based on our analysis of BOLD signal change as a response to a progressively increasing hypercapnia stimulus we can argue that a hypercapnia stimulus of at least 2 mmHg above baseline EtCO2 is necessary to evaluate hemodynamic impairment. We also demonstrated that a substantial amount of information imbedded in the rsBOLD and HC-BOLD was explained by HO-BOLD. HO-BOLD can serve as a proxy for vCBV and this thus indicates that one should be careful when adopting these techniques in disease cases with compromised CBV.
| S-EPMC6057274 | biostudies-literature