This study aims to improve the method used to produce cerebrovascular reactivity (CVR) maps by MRI. Previous methods have used a standard boxcar presentation of carbon dioxide (CO 2 ). Here this is replaced with a sinusoidally modulated CO 2 stimulus. This allowed the use of Fourier analysis techniques to measure both the amplitude and phase delay of the BOLD CVR response, and hence characterize the arrival sequence of blood to different regions of the brain. This characterization revealed statistically significant relative delays between regions of the brain (ANOVA < 0.0001). In addition, post hoc comparison showed that the frontal (P < 0.001) and parietal (P 5 0.004) lobes reacted earlier than the occipital lobe. Cerebrovascular reactivity (CVR) mapping using MRI is increasingly used to assess the effects of cerebrovascular conditions such as atherosclerotic steno-occlusive disease (1,2) and Moyamoya (3). CVR is conventionally mapped by administering CO 2 mixed with air through an open oxygen face mask. In early measurements, this stimulus was applied in an interleaved fashion with blocks of air, while BOLD-weighted images and measurements of end-tidal PCO 2 (PETCO 2 ) are acquired (4,5). This PETCO 2 time-course is then regressed against the BOLD data on a pixel-by-pixel basis to create CVR maps showing the strength of the correlation (2,6). The underlying assumption in generating maps in this way is that the CVR response occurs in all parts of the brain simultaneously. However, if the change in PCO 2 carried by the blood arrived in various areas of the brain at different times, or if there were a delayed vascular response to this change in PCO 2 in some brain regions, then the correlation between the BOLD signal and the PETCO 2 , and thus CVR, could be underestimated. Furthermore, variations in the arrival time of blood or response time of the vasculature, in different areas of the brain may provide useful diagnostic information.The aim of this study is to improve upon the existing BOLD CVR methodology by acquiring potentially useful information about the regional delays in the BOLD CVR response while minimizing errors due to any regional delays. This is achieved by combining a sinusoidally varying carbon dioxide stimulus with Fourier analysis techniques. To apply an accurate sinusoidal stimulus, a computerized gas blender was used along with a modelbased algorithm for prospective targeting and control of end-tidal PO 2 (PETO 2 ) and PCO 2 (7). This algorithm allows more accurate and independent control of PETO 2 and PETCO 2 than that achieved by simply presenting CO 2 mixed with air. This enables arbitrarily shaped changes in PETCO 2 to be administered, such as the sinusoid used here. This stimulus delivery can then be coupled with Fourier analysis techniques, as used in retinotopic mapping (8). This analysis method was chosen as synchronization of the MRI, and PETCO 2 data is not required, as it is the frequency of the signal that is detected, reducing confounding effects of inaccurate synchronization. In ...

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.

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