Aliya Gifford

Characterizing Active and Inactive Brown Adipose Tissue in Adult Humans Using PET-CT and MR Imaging

Activated brown adipose tissue (BAT) plays an important role in thermogenesis and whole body metabolism in mammals. Positron emission tomography (PET)-computed tomography (CT) imaging has identified depots of BAT in adult humans, igniting scientific interest. The purpose of this study is to characterize both active and inactive supraclavicular BAT in adults and compare the values to those of subcutaneous white adipose tissue (WAT). We obtained [(18)F]fluorodeoxyglucose ([(18)F]FDG) PET-CT and magnetic resonance imaging (MRI) scans of 25 healthy adults. Unlike [(18)F]FDG PET, which can detect only active BAT, MRI is capable of detecting both active and inactive BAT. The MRI-derived fat signal fraction (FSF) of active BAT was significantly lower than that of inactive BAT (means ± SD; 60.2 ± 7.6 vs. 62.4 ± 6.8%, respectively). This change in tissue morphology was also reflected as a significant increase in Hounsfield units (HU; -69.4 ± 11.5 vs. -74.5 ± 9.7 HU, respectively). Additionally, the CT HU, MRI FSF, and MRI R2* values are significantly different between BAT and WAT, regardless of the activation status of BAT. To the best of our knowledge, this is the first study to quantify PET-CT and MRI FSF measurements and utilize a semiautomated algorithm to identify inactive and active BAT in the same adult subjects. Our findings support the use of these metrics to characterize and distinguish between BAT and WAT and lay the foundation for future MRI analysis with the hope that some day MRI-based delineation of BAT can stand on its own.

Human brown adipose tissue depots automatically segmented by positron emission tomography/computed tomography and registered magnetic resonance images.

Reliably differentiating brown adipose tissue (BAT) from other tissues using a non-invasive imaging method is an important step toward studying BAT in humans. Detecting BAT is typically confirmed by the uptake of the injected radioactive tracer 18F-Fluorodeoxyglucose (18F-FDG) into adipose tissue depots, as measured by positron emission tomography/computed tomography (PET-CT) scans after exposing the subject to cold stimulus. Fat-water separated magnetic resonance imaging (MRI) has the ability to distinguish BAT without the use of a radioactive tracer. To date, MRI of BAT in adult humans has not been co-registered with cold-activated PET-CT. Therefore, this protocol uses 18F-FDG PET-CT scans to automatically generate a BAT mask, which is then applied to co-registered MRI scans of the same subject. This approach enables measurement of quantitative MRI properties of BAT without manual segmentation. BAT masks are created from two PET-CT scans: after exposure for 2 hr to either thermoneutral (TN) (24 °C) or cold-activated (CA) (17 °C) conditions. The TN and CA PET-CT scans are registered, and the PET standardized uptake and CT Hounsfield values are used to create a mask containing only BAT. CA and TN MRI scans are also acquired on the same subject and registered to the PET-CT scans in order to establish quantitative MRI properties within the automatically defined BAT mask. An advantage of this approach is that the segmentation is completely automated and is based on widely accepted methods for identification of activated BAT (PET-CT). The quantitative MRI properties of BAT established using this protocol can serve as the basis for an MRI-only BAT examination that avoids the radiation associated with PET-CT.

Canine body composition quantification using 3 tesla fat–water MRI

To test the hypothesis that a whole‐body fat–water MRI (FWMRI) protocol acquired at 3 Tesla combined with semi‐automated image analysis techniques enables precise volume and mass quantification of adipose, lean, and bone tissue depots that agree with static scale mass and scale mass changes in the context of a longitudinal study of large‐breed dogs placed on an obesogenic high‐fat, high‐fructose diet.

Whole‐body continuously moving table fat–water MRI with dynamic B0 shimming at 3 Tesla

The purpose of this work was to develop a rapid and robust whole‐body fat–water MRI (FWMRI) method using a continuously moving table (CMT) with dynamic field corrections at 3 Tesla.

Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, the linear mixed model and the recently proposed logistic mixed model, perform poorly; they produce large type I error rates when used to analyze unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE (Scalable and Accurate Implementation of GEneralized mixed model), provides accurate P values even when case-control ratios are extremely unbalanced. SAIGE uses state-of-art optimization strategies to reduce computational costs; hence, it is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 samples from white British participants with European ancestry for > 1,400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.SAIGE (Scalable and Accurate Implementation of GEneralized mixed model) is a generalized mixed model association test that can efficiently analyze large data sets while controlling for unbalanced case-control ratios and sample relatedness, as shown by applying SAIGE to the UK Biobank data for > 1,400 binary phenotypes.

Automated pipeline to analyze non-contact infrared images of the paraventricular nucleus specific leptin receptor knock-out mouse model

Evidence of leptin resistance is indicated by elevated leptin levels together with other hallmarks of obesity such as a defect in energy homeostasis.1 As obesity is an increasing epidemic in the US, the investigation of mechanisms by which leptin resistance has a pathophysiological impact on energy is an intensive field of research.2 However, the manner in which leptin resistance contributes to the dysregulation of energy, specifically thermoregulation,3 is not known. The aim of this study was to investigate whether the leptin receptor expressed in paraventricular nucleus (PVN) neurons plays a role in thermoregulation at different temperatures. Non-contact infrared (NCIR) thermometry was employed to measure surface body temperature (SBT) of nonanesthetized mice with a specific deletion of the leptin receptor in the PVN after exposure to room (25 °C) and cold (4 °C) temperature. Dorsal side infrared images of wild type (LepRwtwt/sim1-Cre), heterozygous (LepRfloxwt/sim1-Cre) and knock-out (LepRfloxflox/sim1-Cre) mice were collected. Images were input to an automated post-processing pipeline developed in MATLAB to calculate average and maximum SBTs. Linear regression was used to evaluate the relationship between sex, cold exposure and leptin genotype with SBT measurements. Findings indicate that average SBT has a negative relationship to the LepRfloxflox/sim1-Cre genotype, the female sex and cold exposure. However, max SBT is affected by the LepRfloxflox/sim1-Cre genotype and the female sex. In conclusion this data suggests that leptin within the PVN may have a neuroendocrine role in thermoregulation and that NCIR thermometry combined with an automated imaging-processing pipeline is a promising approach to determine SBT in non-anesthetized mice.

Progress toward automatic classification of human brown adipose tissue using biomedical imaging

Brown adipose tissue (BAT) is a small but significant tissue, which may play an important role in obesity and the pathogenesis of metabolic syndrome. Interest in studying BAT in adult humans is increasing, but in order to quantify BAT volume in a single measurement or to detect changes in BAT over the time course of a longitudinal experiment, BAT needs to first be reliably differentiated from surrounding tissue. Although the uptake of the radiotracer 18F-Fluorodeoxyglucose (18F-FDG) in adipose tissue on positron emission tomography (PET) scans following cold exposure is accepted as an indication of BAT, it is not a definitive indicator, and to date there exists no standardized method for segmenting BAT. Consequently, there is a strong need for robust automatic classification of BAT based on properties measured with biomedical imaging. In this study we begin the process of developing an automated segmentation method based on properties obtained from fat-water MRI and PET-CT scans acquired on ten healthy adult subjects.

Correlations between quantitative fat-water magnetic resonance imaging and computed tomography in human subcutaneous white adipose tissue.

Abstract. Beyond estimation of depot volumes, quantitative analysis of adipose tissue properties could improve understanding of how adipose tissue correlates with metabolic risk factors. We investigated whether the fat signal fraction (FSF) derived from quantitative fat–water magnetic resonance imaging (MRI) scans at 3.0 T correlates to CT Hounsfield units (HU) of the same tissue. These measures were acquired in the subcutaneous white adipose tissue (WAT) at the umbilical level of 21 healthy adult subjects. A moderate correlation exists between MRI- and CT-derived WAT values for all subjects, R2=0.54, p<0.0001, with a slope of −2.6, (95% CI [−3.3,−1.8]), indicating that a decrease of 1 HU equals a mean increase of 0.38% FSF. We demonstrate that FSF estimates obtained using quantitative fat–water MRI techniques correlate with CT HU values in subcutaneous WAT, and therefore, MRI-based FSF could be used as an alternative to CT HU for assessing metabolic risk factors.