Dataset Information

Hemodynamic mechanisms underlying elevated oxygen extraction fraction (OEF) in moyamoya and sickle cell anemia patients.

ABSTRACT: Moyamoya is a bilateral, complex cerebrovascular condition characterized by progressive non-atherosclerotic intracranial stenosis and collateral vessel formation. Moyamoya treatment focuses on restoring cerebral blood flow (CBF) through surgical revascularization, however stratifying patients for revascularization requires abilities to quantify how well parenchyma is compensating for arterial steno-occlusion. Globally elevated oxygen extraction fraction (OEF) secondary to CBF reduction may serve as a biomarker for tissue health in moyamoya patients, as suggested in patients with sickle cell anemia (SCA) and reduced oxygen carrying capacity. Here, OEF was measured (TRUST-MRI) to test the hypothesis that OEF is globally elevated in patients with moyamoya (n = 18) and SCA (n = 18) relative to age-matched controls (n = 43). Mechanisms underlying the hypothesized OEF increases were evaluated by performing sequential CBF-weighted, cerebrovascular reactivity (CVR)-weighted, and structural MRI. Patients were stratified by treatment and non-parametric tests applied to compare study variables (significance: two-sided P < 0.05). OEF was significantly elevated in moyamoya participants (interquartile range = 0.38-0.45) compared to controls (interquartile range = 0.29-0.38), similar to participants with SCA (interquartile range = 0.37-0.45). CBF was inversely correlated with OEF in moyamoya participants. Elevated OEF was only weakly related to reductions in CVR, consistent with basal CBF level, rather than vascular reserve capacity, being most closely associated with OEF.

SUBMITTER: Watchmaker JM

PROVIDER: S-EPMC6125968 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Hemodynamic mechanisms underlying elevated oxygen extraction fraction (OEF) in moyamoya and sickle cell anemia patients.

Watchmaker Jennifer M JM Juttukonda Meher R MR Davis Larry T LT Scott Allison O AO Faraco Carlos C CC Gindville Melissa C MC Jordan Lori C LC Cogswell Petrice M PM Jefferson Angela L AL Kirshner Howard S HS Donahue Manus J MJ

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20161228 9

Moyamoya is a bilateral, complex cerebrovascular condition characterized by progressive non-atherosclerotic intracranial stenosis and collateral vessel formation. Moyamoya treatment focuses on restoring cerebral blood flow (CBF) through surgical revascularization, however stratifying patients for revascularization requires abilities to quantify how well parenchyma is compensating for arterial steno-occlusion. Globally elevated oxygen extraction fraction (OEF) secondary to CBF reduction may serve ...[more]

PMID: 28029271

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Project description:Sickle cell anaemia is a monogenetic disorder with a high incidence of stroke. While stroke screening procedures exist for children with sickle cell anaemia, no accepted screening procedures exist for assessing stroke risk in adults. The purpose of this study is to use novel magnetic resonance imaging methods to evaluate physiological relationships between oxygen extraction fraction, cerebral blood flow, and clinical markers of cerebrovascular impairment in adults with sickle cell anaemia. The specific goal is to determine to what extent elevated oxygen extraction fraction may be uniquely present in patients with higher levels of clinical impairment and therefore may represent a candidate biomarker of stroke risk. Neurological evaluation, structural imaging, and the non-invasive T2-relaxation-under-spin-tagging magnetic resonance imaging method were applied in sickle cell anaemia (n = 34) and healthy race-matched control (n = 11) volunteers without sickle cell trait to assess whole-brain oxygen extraction fraction, cerebral blood flow, degree of vasculopathy, severity of anaemia, and presence of prior infarct; findings were interpreted in the context of physiological models. Cerebral blood flow and oxygen extraction fraction were elevated (P < 0.05) in participants with sickle cell anaemia (n = 27) not receiving monthly blood transfusions (interquartile range cerebral blood flow = 46.2-56.8 ml/100 g/min; oxygen extraction fraction = 0.39-0.50) relative to controls (interquartile range cerebral blood flow = 40.8-46.3 ml/100 g/min; oxygen extraction fraction = 0.33-0.38). Oxygen extraction fraction (P < 0.0001) but not cerebral blood flow was increased in participants with higher levels of clinical impairment. These data provide support for T2-relaxation-under-spin-tagging being able to quickly and non-invasively detect elevated oxygen extraction fraction in individuals with sickle cell anaemia with higher levels of clinical impairment. Our results support the premise that magnetic resonance imaging-based assessment of elevated oxygen extraction fraction might be a viable screening tool for evaluating stroke risk in adults with sickle cell anaemia.

Project description:Magnetic resonance imaging (MRI) offers the possibility to non-invasively map the brain's metabolic oxygen consumption (CMRO2), which is essential for understanding and monitoring neural function in both health and disease. However, in depth study of oxygen metabolism with MRI has so far been hindered by the lack of robust methods. One MRI method of mapping CMRO2 is based on the simultaneous acquisition of cerebral blood flow (CBF) and blood oxygen level dependent (BOLD) weighted images during respiratory modulation of both oxygen and carbon dioxide. Although this dual-calibrated methodology has shown promise in the research setting, current analysis methods are unstable in the presence of noise and/or are computationally demanding. In this paper, we present a machine learning implementation for the multi-parametric assessment of dual-calibrated fMRI data. The proposed method aims to address the issues of stability, accuracy, and computational overhead, removing significant barriers to the investigation of oxygen metabolism with MRI. The method utilizes a time-frequency transformation of the acquired perfusion and BOLD-weighted data, from which appropriate feature vectors are selected for training of machine learning regressors. The implemented machine learning methods are chosen for their robustness to noise and their ability to map complex non-linear relationships (such as those that exist between BOLD signal weighting and blood oxygenation). An extremely randomized trees (ET) regressor is used to estimate resting blood flow and a multi-layer perceptron (MLP) is used to estimate CMRO2 and the oxygen extraction fraction (OEF). Synthetic data with additive noise are used to train the regressors, with data simulated to cover a wide range of physiologically plausible parameters. The performance of the implemented analysis method is compared to published methods both in simulation and with in-vivo data (n=30). The proposed method is demonstrated to significantly reduce computation time, error, and proportional bias in both CMRO2 and OEF estimates. The introduction of the proposed analysis pipeline has the potential to not only increase the detectability of metabolic difference between groups of subjects, but may also allow for single subject examinations within a clinical context.

Dataset Information

Hemodynamic mechanisms underlying elevated oxygen extraction fraction (OEF) in moyamoya and sickle cell anemia patients.

Publications

Hemodynamic mechanisms underlying elevated oxygen extraction fraction (OEF) in moyamoya and sickle cell anemia patients.

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