Dynamic Magnetic Resonance Vascular Fingerprinting During Hypercapnia for Quantitative and Multiparametric Cerebrovascular Reactivity Measures.

Magnetic resonance fingerprinting (MRF) represents a potential paradigm shift in MR image acquisition, reconstruction, and analysis using computational biophysical modelling in parallel to image acquisition. Its flexibility allows for examination of cerebrovascular metrics through MR vascular fingerprinting (MRvF), and this has been extended even further to produce quantitative cerebral blood volume (CBV), microvascular vessel radius, and tissue oxygen saturation (SO2) maps of the whole brain simultaneously every few seconds. This allows for observation of rapid physiological changes like cerebrovascular reactivity (CVR), which is the ability of vessels to dilate in response to a vasoactive stimulus. Here we demonstrated a novel protocol in which a rapid, spin- and gradient-echo pulse sequence allowed for dynamic, and simultaneous acquisition of MRvF and blood oxygen level dependent (BOLD) measures. By combining this with a tailored hypercapnic (5% CO2) breathing paradigm we were able to show how these quantitative CBV, radius, and SO2 parameters changed in response to a stimulus and directly compare those to a colocalized, traditionally used BOLD CVR. We also compared these measures to another traditionally utilized technique in cerebral blood flow CVR from an arterial spin labelling sequence. These imaging, processing, and analysis techniques will allow for further investigation into the magnitude and rate of CVR based on BOLD and MRvF-based metrics and enable investigations to better understand vascular function in healthy aging and cerebrovascular diseases.Clinical Relevance- The development of dynamic magnetic resonance vascular fingerprinting has the potential to enable rapid, quantitative, and multiparametric functional imaging biomarkers of cerebrovascular diseases like vascular cognitive impairment, dementia, and Alzheimer's disease.

Characterizing systemic physiological effects on the blood oxygen level dependent signal of resting-state fMRI in time-frequency space using wavelets.

Systemic physiological dynamics, such as heart rate variability (HRV) and respiration volume per time (RVT), are known to account for significant variance in the blood oxygen level dependent (BOLD) signal of resting-state functional magnetic resonance imaging (rsfMRI). However, synchrony between these cardiorespiratory changes and the BOLD signal could be due to neuronal (i.e., autonomic activity inducing changes in heart rate and respiration) or vascular (i.e., cardiorespiratory activity facilitating hemodynamic changes and thus the BOLD signal) effects and the contributions of these effects may differ spatially, temporally, and spectrally. In this study, we characterize these brain-body dynamics using a wavelet analysis in rapidly sampled rsfMRI data with simultaneous pulse oximetry and respiratory monitoring of the Human Connectome Project. Our time-frequency analysis across resting-state networks (RSNs) revealed differences in the coherence of the BOLD signal and heartbeat interval (HBI)/RVT dynamics across frequencies, with unique profiles per network. Somatomotor (SMN), visual (VN), and salience (VAN) networks demonstrated the greatest synchrony with both systemic physiological signals when compared to other networks; however, significant coherence was observed in all RSNs regardless of direct autonomic involvement. Our phase analysis revealed distinct frequency profiles of percentage of time with significant coherence between BOLD and systemic physiological signals for different phase offsets across RSNs, suggesting that the phase offset and temporal order of signals varies by frequency. Lastly, our analysis of temporal variability of coherence provides insight on potential influence of autonomic state on brain-body communication. Overall, the novel wavelet analysis enables an efficient characterization of the dynamic relationship between cardiorespiratory activity and the BOLD signal in spatial, temporal, and spectral dimensions to inform our understanding of autonomic states and improve our interpretation of the BOLD signal.