We present the first evidence for vascular regulation driving fMRI signals in specific functional brain networks. Using concurrent neuronal and vascular stimuli, we collected 30 BOLD fMRI datasets in 10 healthy individuals: a working memory task, flashing checkerboard stimulus, and CO 2 inhalation challenge were delivered in concurrent but orthogonal paradigms. The resulting imaging data were averaged together and decomposed using independent component analysis, and three “neuronal networks” were identified as demonstrating maximum temporal correlation with the neuronal stimulus paradigms: Default Mode Network, Task Positive Network, and Visual Network. For each of these, we observed a second network component with high spatial overlap. Using dual regression in the original 30 datasets, we extracted the time-series associated with these network pairs and calculated the percent of variance explained by the neuronal or vascular stimuli using a normalized R 2 parameter. In each pairing, one network was dominated by the appropriate neuronal stimulus, and the other was dominated by the vascular stimulus as represented by the end-tidal CO 2 time-series recorded in each scan. We acquired a second dataset in 8 of the original participants, where no CO 2 challenge was delivered and CO 2 levels fluctuated naturally with breathing variations. Although splitting of functional networks was not robust in these data, performing dual regression with the network maps from the original analysis in this new dataset successfully replicated our observations. Thus, in addition to responding to localized metabolic changes, the brain’s vasculature may be regulated in a coordinated manner that mimics (and potentially supports) specific functional brain networks. Multi-modal imaging and advances in fMRI acquisition and analysis could facilitate further study of the dual nature of functional brain networks. It will be critical to understand network-specific vascular function, and the behavior of a coupled vascular-neural network, in future studies of brain pathology.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used technique for mapping the brain’s functional architecture, so delineating the main sources of variance comprising the signal is crucial. Low frequency oscillations (LFO) that are not of neural origin, but which are driven by mechanisms related to cerebral autoregulation (CA), are present in the blood-oxygenation-level-dependent (BOLD) signal within the rs-fMRI frequency band. In this study we use a MR compatible device (Caretaker, Biopac) to obtain a non-invasive estimate of beat-to-beat mean arterial pressure (MAP) fluctuations concurrently with rs-fMRI at 3T. Healthy adult subjects ( n = 9; 5 male) completed two 20-min rs-fMRI scans. MAP fluctuations were decomposed into different frequency scales using a discrete wavelet transform, and oscillations at approximately 0.1 Hz show a high degree of spatially structured correlations with matched frequency fMRI fluctuations. On average across subjects, MAP fluctuations at this scale of the wavelet decomposition explain ∼2.2% of matched frequency fMRI signal variance. Additionally, a simultaneous multi-slice multi-echo acquisition was used to collect 10-min rs-fMRI at three echo times at 7T in a separate group of healthy adults ( n = 5; 5 male). Multiple echo times were used to estimate the R 2 ∗ decay at every time point, and MAP was shown to strongly correlate with this signal, which suggests a purely BOLD (i.e., blood flow related) origin. This study demonstrates that there is a significant component of the BOLD signal that has a systemic physiological origin, and highlights the fact that not all localized BOLD signal changes necessarily reflect blood flow supporting local neural activity. Instead, these data show that a proportion of BOLD signal fluctuations in rs-fMRI are due to localized control of blood flow that is independent of local neural activity, most likely reflecting more general systemic autoregulatory processes. Thus, fMRI is a promising tool for studying flow changes associated with cerebral autoregulation with high spatial resolution.

A fast emerging technique for studying human resting state networks (RSNs) is based on spontaneous temporal fluctuations in neuronal oscillatory power, as measured by magnetoencephalography. However, it has been demonstrated recently that this power is sensitive to modulations in arterial CO2 concentration. Arterial CO2 can be modulated by natural fluctuations in breathing pattern, as might typically occur during the acquisition of an RSN experiment. Here, we demonstrate for the first time the fine-scale dependence of neuronal oscillatory power on arterial CO2 concentration, showing that reductions in alpha, beta, and gamma power are observed with even very mild levels of hypercapnia (increased arterial CO2). We use a graded hypercapnia paradigm and participant feedback to rule out a sensory cause, suggesting a predominantly physiological origin. Furthermore, we demonstrate that natural fluctuations in arterial CO2, without administration of inspired CO2, are of a sufficient level to influence neuronal oscillatory power significantly in the delta-, alpha-, beta-, and gamma-frequency bands. A more thorough understanding of the relationship between physiological factors and cortical rhythmicity is required. In light of these findings, existing results, paradigms, and analysis techniques for the study of resting-state brain data should be revisited.SIGNIFICANCE STATEMENT In this study, we show for the first time that neuronal oscillatory power is intimately linked to arterial CO2 concentration down to the fine-scale modulations that occur during spontaneous breathing. We extend these results to demonstrate a correlation between neuronal oscillatory power and spontaneous arterial CO2 fluctuations in awake humans at rest. This work identifies a need for studies investigating resting-state networks in the human brain to measure and account for the impact of spontaneous changes in arterial CO2 on the neuronal signals of interest. Changes in breathing pattern that are time locked to task performance could also lead to confounding effects on neuronal oscillatory power when considering the electrophysiological response to functional stimulation.

Functional magnetic resonance imaging (fMRI) techniques in which the blood oxygenation level dependent (BOLD) and cerebral blood flow (CBF) response to a neural stimulus are measured, can be used to estimate the fractional increase in the cerebral metabolic rate of oxygen consumption (CMRO2) that accompanies evoked neural activity. A measure of neurovascular coupling is obtained from the ratio of fractional CBF and CMRO2 responses, defined as n, with the implicit assumption that relative rather than absolute changes in CBF and CMRO2 adequately characterise the flow-metabolism response to neural activity. The coupling parameter n is important in terms of its effect on the BOLD response, and as potential insight into the flow-metabolism relationship in both normal and pathological brain function. In 10 healthy human subjects, BOLD and CBF responses were measured to test the effect of baseline perfusion (modulated by a hypercapnia challenge) on the coupling parameter n during graded visual stimulation. A dual-echo pulsed arterial spin labelling (PASL) sequence provided absolute quantification of CBF in baseline and active states as well as relative BOLD signal changes, which were used to estimate CMRO2 responses to the graded visual stimulus. The absolute CBF response to the visual stimuli were constant across different baseline CBF levels, meaning the fractional CBF responses were reduced at the hyperperfused baseline state. For the graded visual stimuli, values of n were significantly reduced during hypercapnia induced hyperperfusion. Assuming the evoked neural responses to the visual stimuli are the same for both baseline CBF states, this result has implications for fMRI studies that aim to measure neurovascular coupling using relative changes in CBF. The coupling parameter n is sensitive to baseline CBF, which would confound its interpretation in fMRI studies where there may be significant differences in baseline perfusion between groups. The absolute change in CBF, as opposed to the change relative to baseline, may more closely match the underlying increase in neural activity in response to a stimulus.

There is increasing interest in exploring the use of functional MRI neurofeedback (fMRI-NF) as a therapeutic technique for a range of neurological conditions such as stroke and Parkinson's disease (PD). One main therapeutic potential of fMRI-NF is to enhance volitional control of damaged or dysfunctional neural nodes and networks via a closed-loop feedback model using mental imagery as the catalyst of self-regulation. The choice of target node/network and direction of regulation (increase or decrease activity) are central design considerations in fMRI-NF studies. Whilst it remains unclear whether the primary motor cortex (M1) can be activated during motor imagery, the supplementary motor area (SMA) has been robustly activated during motor imagery. Such differences in the regulation potential between primary and supplementary motor cortex are important because these areas can be differentially affected by a stroke or PD, and the choice of fMRI-NF target and grade of self-regulation of activity likely have substantial influence on the clinical effects and cost effectiveness of NF-based interventions. In this study we therefore investigated firstly whether healthy subjects would be able to achieve self-regulation of the hand-representation areas of M1 and the SMA using fMRI-NF training. There was a significant decrease in M1 neural activity during fMRI-NF, whereas SMA neural activity was increased, albeit not with the predicated graded effect. This study has important implications for fMRI-NF protocols that employ motor imagery to modulate activity in specific target regions of the brain and to determine how they may be tailored for neurorehabilitation.