Investigating intrinsic connectivity networks using simultaneous BOLD and CBF measurements

AbstractWhen the sensory cortex is stimulated and directly receiving afferent input, modulations can also be observed in the activity of other brain regions comprising spatially distributed, yet intrinsically connected networks, suggesting that these networks support brain function during task performance. Such networks can exhibit subtle or unpredictable task responses which can pass undetected by conventional general linear modelling (GLM). Additionally, the metabolic demand of these networks in response to stimulation remains incompletely understood. Here, we recorded concurrent BOLD and CBF measurements during median nerve stimulation (MNS) and compared GLM analysis with independent component analysis (ICA) for identifying the spatial, temporal and metabolic properties of responses in the primary sensorimotor cortex (S1/M1), and in the default mode (DMN) and fronto-parietal (FPN) networks. Excellent spatial and temporal agreement was observed between the positive BOLD and CBF responses to MNS detected by GLM and ICA in contralateral S1/M1. Values of the change in cerebral metabolic rate of oxygen consumption (Δ%CMRO2) and the Δ%CMRO2/Δ%CBF coupling ratio were highly comparable when using either GLM analysis or ICA to extract the contralateral S1/M1 responses, validating the use of ICA for estimating changes in CMRO2. ICA identified DMN and FPN network activity that was not detected by GLM analysis. Using ICA, spatially coincident increases/decreases in both BOLD and CBF signals to MNS were found in the FPN/DMN respectively. Calculation of CMRO2 changes in these networks during MNS showed that the Δ%CMRO2/Δ%CBF ratio is comparable between the FPN and S1/M1 but is larger in the DMN than in the FPN, assuming an equal value of the parameter M in the DMN, FPN and S1/M1. This work suggests that metabolism-flow coupling may differ between these two fundamental brain networks, which could originate from differences between task-positive and task-negative fMRI responses, but might also be due to intrinsic differences between the two networks

Resisting Sleep Pressure:Impact on Resting State Functional Network Connectivity

In today's 24/7 society, sleep restriction is a common phenomenon which leads to increased levels of sleep pressure in daily life. However, the magnitude and extent of impairment of brain functioning due to increased sleep pressure is still not completely understood. Resting state network (RSN) analyses have become increasingly popular because they allow us to investigate brain activity patterns in the absence of a specific task and to identify changes under different levels of vigilance (e.g. due to increased sleep pressure). RSNs are commonly derived from BOLD fMRI signals but studies progressively also employ cerebral blood flow (CBF) signals. To investigate the impact of sleep pressure on RSNs, we examined RSNs of participants under high (19 h awake) and normal (10 h awake) sleep pressure with three imaging modalities (arterial spin labeling, BOLD, pseudo BOLD) while providing confirmation of vigilance states in most conditions. We demonstrated that CBF and pseudo BOLD signals (measured with arterial spin labeling) are suited to derive independent component analysis based RSNs. The spatial map differences of these RSNs were rather small, suggesting a strong biological substrate underlying these networks. Interestingly, increased sleep pressure, namely longer time awake, specifically changed the functional network connectivity (FNC) between RSNs. In summary, all FNCs of the default mode network with any other network or component showed increasing effects as a function of increased 'time awake'. All other FNCs became more anti-correlated with increased 'time awake'. The sensorimotor networks were the only ones who showed a within network change of FNC, namely decreased connectivity as function of 'time awake'. These specific changes of FNC could reflect both compensatory mechanisms aiming to fight sleep as well as a first reduction of consciousness while becoming drowsy. We think that the specific changes observed in functional network connectivity could imply an impairment of information transfer between the affected RSNs

Caffeine-Induced Global Reductions in Resting-State BOLD Connectivity Reflect Widespread Decreases in MEG Connectivity.

In resting-state functional magnetic resonance imaging (fMRI), the temporal correlation between spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal from different brain regions is used to assess functional connectivity. However, because the BOLD signal is an indirect measure of neuronal activity, its complex hemodynamic nature can complicate the interpretation of differences in connectivity that are observed across conditions or subjects. For example, prior studies have shown that caffeine leads to widespread reductions in BOLD connectivity but were not able to determine if neural or vascular factors were primarily responsible for the observed decrease. In this study, we used source-localized magnetoencephalography (MEG) in conjunction with fMRI to further examine the origins of the caffeine-induced changes in BOLD connectivity. We observed widespread and significant (p < 0.01) reductions in both MEG and fMRI connectivity measures, suggesting that decreases in the connectivity of resting-state neuro-electric power fluctuations were primarily responsible for the observed BOLD connectivity changes. The MEG connectivity decreases were most pronounced in the beta band. By demonstrating the similarity in MEG and fMRI based connectivity changes, these results provide evidence for the neural basis of resting-state fMRI networks and further support the potential of MEG as a tool to characterize resting-state connectivity

Global signal modulation of single-trial fMRI response variability: effect on positive vs negative BOLD response relationship

In functional magnetic resonance imaging (fMRI), the relationship between positive BOLD responses (PBRs) and negative BOLD responses (NBRs) to stimulation is potentially informative about the balance of excitatory and inhibitory brain responses in sensory cortex. In this study, we performed three separate experiments delivering visual, motor or somatosensory stimulation unilaterally, to one side of the sensory field, to induce PBR and NBR in opposite brain hemispheres. We then assessed the relationship between the evoked amplitudes of contralateral PBR and ipsilateral NBR at the level of both single-trial and average responses. We measure single-trial PBR and NBR peak amplitudes from individual time-courses, and show that they were positively correlated in all experiments. In contrast, in the average response across trials the absolute magnitudes of both PBR and NBR increased with increasing stimulus intensity, resulting in a negative correlation between mean response amplitudes. Subsequent analysis showed that the amplitude of single-trial PBR was positively correlated with the BOLD response across all grey-matter voxels and was not specifically related to the ipsilateral sensory cortical response. We demonstrate that the global component of this single-trial response modulation could be fully explained by voxel-wise vascular reactivity, the BOLD signal standard deviation measured in a separate resting-state scan (resting state fluctuation amplitude, RSFA). However, bilateral positive correlation between PBR and NBR regions remained. We further report that modulations in the global brain fMRI signal cannot fully account for this positive PBR-NBR coupling and conclude that the local sensory network response reflects a combination of superimposed vascular and neuronal signals. More detailed quantification of physiological and noise contributions to the BOLD signal is required to fully understand the trial-by-trial PBR and NBR relationship compared with that of average responses

Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

EEG and fMRI are important tools in cognitive and clinical neuroscience. Combined EEGfMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological-haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals, and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (“EEG-fMRI mapping”), or exploring a range of EEGderived quantities to determine which best explain co-localised BOLD fluctuations (“local EEG-fMRI coupling”). While reviewing studies of different forms of brain activity (epileptic and non-epileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG-fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations

Optimized simultaneous ASL and BOLD functional imaging of the whole brain

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106990/1/jmri24273.pd

Physiologically informed dynamic causal modeling of fMRI data

AbstractThe functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses — such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal process and offer new ways of inferring changes in local neuronal activity and effective connectivity from fMRI