Quantifying the Microvascular Origin of BOLD-fMRI from First Principles with Two-Photon Microscopy and an Oxygen-Sensitive Nanoprobe

The blood oxygenation level-dependent (BOLD) contrast is widely used in functional magnetic resonance imaging (fMRI) studies aimed at investigating neuronal activity. However, the BOLD signal reflects changes in blood volume and oxygenation rather than neuronal activity per se. Therefore, understanding the transformation of microscopic vascular behavior into macroscopic BOLD signals is at the foundation of physiologically informed noninvasive neuroimaging. Here, we use oxygen-sensitive two-photon microscopy to measure the BOLD-relevant microvascular physiology occurring within a typical rodent fMRI voxel and predict the BOLD signal from first principles using those measurements. The predictive power of the approach is illustrated by quantifying variations in the BOLD signal induced by the morphological folding of the human cortex. This framework is then used to quantify the contribution of individual vascular compartments and other factors to the BOLD signal for different magnet strengths and pulse sequences.National Institutes of Health (U.S.) (Grant P41RR14075)National Institutes of Health (U.S.) (Grant R01NS067050)National Institutes of Health (U.S.) (Grant R01NS057198)National Institutes of Health (U.S.) (Grant R01EB000790)American Heart Association (Grant 11SDG7600037)Advanced Multimodal NeuroImaging Training Program (R90DA023427

Regional quantification of cerebral venous oxygenation from MRI susceptibility during hypercapnia

There is an unmet medical need for noninvasive imaging of regional brain oxygenation to manage stroke, tumor, and neurodegenerative diseases. Oxygenation imaging from magnetic susceptibility in MRI is a promising new technique to measure local venous oxygen extraction fraction (OEF) along the cerebral venous vasculature. However, this approach has not been tested in vivo at different levels of oxygenation. The primary goal of this study was to test whether susceptibility imaging of oxygenation can detect OEF changes induced by hypercapnia, via CO[subscript 2] inhalation, within selected a priori brain regions. Ten healthy subjects were scanned at 3 T with a 32-channel head coil. The end-tidal CO[subscript 2] (ETCO[subscript 2]) was monitored continuously and inspired gases were adjusted to achieve steady-state conditions of eucapnia (41 ± 3 mm Hg) and hypercapnia (50 ± 4 mm Hg). Gradient echo phase images and pseudo-continuous arterial spin labeling (pcASL) images were acquired to measure regional OEF and CBF respectively during eucapnia and hypercapnia. By assuming constant cerebral oxygen consumption throughout both gas states, regional CBF values were computed to predict the local change in OEF in each brain region. Hypercapnia induced a relative decrease in OEF of − 42.3% in the straight sinus, − 39.9% in the internal cerebral veins, and approximately − 50% in pial vessels draining each of the occipital, parietal, and frontal cortical areas. Across volunteers, regional changes in OEF correlated with changes in ETCO[subscript 2]. The reductions in regional OEF (via phase images) were significantly correlated (P < 0.05) with predicted reductions in OEF derived from CBF data (via pcASL images). These findings suggest that susceptibility imaging is a promising technique for OEF measurements, and may serve as a clinical biomarker for brain conditions with aberrant regional oxygenation

Bold fMRI identifies limbic, paralimbic, and cerebellar activation during air hunger

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The pulsatility volume index: An indicator of cerebrovascular compliance based on fast magnetic resonance imaging of cardiac and respiratory pulsatility

The influence of cardiac activity on the viscoelastic properties of intracranial tissue is one of the mechanisms through which brain-heart interactions take place, and is implicated in cerebrovascular disease. Cerebrovascular disease risk is not fully explained by current risk factors, including arterial compliance. Cerebrovascular compliance is currently estimated indirectly through Doppler sonography and magnetic resonance imaging (MRI) measures of blood velocity changes. In order to meet the need for novel cerebrovascular disease risk factors, we aimed to design and validate an MRI indicator of cerebrovascular compliance based on direct endogenous measures of blood volume changes. We implemented a fast non-gated two-dimensional MRI pulse sequence based on echo-planar imaging (EPI) with ultra-short repetition time (approx. 30-50 ms), which stepped through slices every approximately 20 s. We constrained the solution of the Bloch equations for spins moving faster than a critical speed to produce an endogenous contrast primarily dependent on spin volume changes, and an approximately sixfold signal gain compared with Ernst angle acquisitions achieved by the use of a 90? flip angle. Using cardiac and respiratory peaks detected on physiological recordings, average cardiac and respiratory MRI pulse waveforms in several brain compartments were obtained at 7 Tesla, and used to derive a compliance indicator, the pulsatility volume index (pVI). The pVI, evaluated in larger cerebral arteries, displayed significant variation within and across vessels. Multi-echo EPI showed the presence of significant pulsatility effects in both S0 and T∗2 signals, compatible with blood volume changes. Lastly, the pVI dynamically varied during breath-holding compared with normal breathing, as expected for a compliance indicator. In summary, we characterized and performed an initial validation of a novel MRI indicator of cerebrovascular compliance, which might prove useful to investigate brain-heart interactions in cerebrovascular disease and other disorders

FDG PET Parkinson’s disease-related pattern as a biomarker for clinical trials in early stage disease

Background: The development of therapeutic interventions for Parkinson disease (PD) is challenged by disease complexity and subjectivity of symptom evaluation. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects. Objectives: We sought to independently evaluate the potential utility of the PDRP as a biomarker for clinical trials of early-stage PD. Methods: Two machine learning approaches (Scaled Subprofile Model (SSM) and NPAIRS with Canonical Variates Analysis) were performed on FDG-PET scans from 17 healthy controls (HC) and 23 PD patients. The approaches were compared regarding discrimination of HC from PD and relationship to motor symptoms. Results: Both classifiers discriminated HC from PD (p < 0.01, p < 0.03), and classifier scores for age- and gender- matched HC and PD correlated with Hoehn & Yahr stage (R2 = 0.24, p < 0.015) and UPDRS (R2 = 0.23, p < 0.018). Metabolic patterns were highly similar, with hypometabolism in parieto-occipital and prefrontal regions and hypermetabolism in cerebellum, pons, thalamus, paracentral gyrus, and lentiform nucleus relative to whole brain, consistent with the PDRP. An additional classifier was developed using only PD subjects, resulting in scores that correlated with UPDRS (R2 = 0.25, p < 0.02) and Hoehn & Yahr stage (R2 = 0.16, p < 0.06). Conclusions: Two independent analyses performed in a cohort of mild PD patients replicated key features of the PDRP, confirming that FDG-PET and multivariate classification can provide an objective, sensitive biomarker of disease stage with the potential to detect treatment effects on PD progression. Keywords: Parkinson, FDG PET, PDRP, Classifier, Biomarke

Intrinsic Functional Connectivity As a Tool For Human Connectomics: Theory, Properties, and Optimization

Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics—high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2–12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics

The relationship of anxiety disorders, anxiety sensitivity and pulmonary dysfunction with dyspnea-related distress and avoidance.

Little is known about factors that mediate the relationship between anxiety and respiratory-related distress and disability. We hypothesized that elevations in anxiety sensitivity would be associated with greater severity of dyspnea, greater dyspnea-related avoidance, and poorer subjective assessment of health in patients with dyspnea referred for pulmonary function testing, regardless of objective evidence of pulmonary dysfunction. A total of 182 consecutive patients receiving pulmonary function tests to evaluate dyspnea were screened with a patient-rated Primary Care Evaluation of Mental Disorders and completed the Anxiety Sensitivity Index and questionnaires assessing symptom severity and avoidance. Anxiety Sensitivity Index score predicted more severe subjective dyspnea and greater dyspnea-related avoidance, even after adjustment for anxiety disorders and pulmonary dysfunction. Despite some limitations, these data provide preliminary support that strategies to identify, measure, and address high levels of anxiety sensitivity should be examined to reduce subjective distress and improve functioning for patients with dyspnea