Melanie Ganz

A High-Resolution In Vivo Atlas of the Human Brain's Serotonin System

The serotonin (5-hydroxytryptamine, 5-HT) system modulates many important brain functions and is critically involved in many neuropsychiatric disorders. Here, we present a high-resolution, multidimensional, in vivo atlas of four of the human brains 5-HT receptors (5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4) and the 5-HT transporter (5-HTT). The atlas is created from molecular and structural high-resolution neuroimaging data consisting of positron emission tomography (PET) and magnetic resonance imaging (MRI) scans acquired in a total of 210 healthy individuals. Comparison of the regional PET binding measures with postmortem human brain autoradiography outcomes showed a high correlation for the five 5-HT targets and this enabled us to transform the atlas to represent protein densities (in picomoles per milliliter). We also assessed the regional association between protein concentration and mRNA expression in the human brain by comparing the 5-HT density across the atlas with data from the Allen Human Brain atlas and identified receptor- and transporter-specific associations that show the regional relation between the two measures. Together, these data provide unparalleled insight into the serotonin system of the human brain. SIGNIFICANCE STATEMENT We present a high-resolution positron emission tomography (PET)- and magnetic resonance imaging-based human brain atlas of important serotonin receptors and the transporter. The regional PET-derived binding measures correlate strongly with the corresponding autoradiography protein levels. The strong correlation enables the transformation of the PET-derived human brain atlas into a protein density map of the serotonin (5-hydroxytryptamine, 5-HT) system. Next, we compared the regional receptor/transporter protein densities with mRNA levels and uncovered unique associations between protein expression and density at high detail. This new in vivo neuroimaging atlas of the 5-HT system not only provides insight in the human brains regional protein synthesis, transport, and density, but also represents a valuable source of information for the neuroscience community as a comparative instrument to assess brain disorders.

Automatic Segmentation of Polyps in Colonoscopic Narrow-Band Imaging Data

Colorectal cancer is the third most common type of cancer worldwide. However, this disease can be prevented by detection and removal of precursor adenomatous polyps during optical colonoscopy (OC). During OC, the endoscopist looks for colon polyps. While hyperplastic polyps are benign lesions, adenomatous polyps are likely to become cancerous. Hence, it is a common practice to remove all identified polyps and send them to subsequent histological analysis. But removal of hyperplastic polyps poses unnecessary risk to patients and incurs unnecessary costs for histological analysis. In this paper, we develop the first part of a novel optical biopsy application based on narrow-band imaging (NBI). A barrier to an automatic system is that polyp classification algorithms require manual segmentations of the polyps, so we automatically segment polyps in colonoscopic NBI data. We propose an algorithm, Shape-UCM, which is an extension of the gPb-OWT-UCM algorithm, a state-of-the-art algorithm for boundary detection and segmentation. Shape-UCM solves the intrinsic scale selection problem of gPb-OWT-UCM by including prior knowledge about the shape of the polyps. Shape-UCM outperforms previous methods with a specificity of 92%, a sensitivity of 71%, and an accuracy of 88% for automatic segmentation of a test set of 87 images.

Abdominal aortic calcification quantified by the Morphological Atherosclerotic Calcification Distribution (MACD) index is associated with features of the metabolic syndrome

BackgroundAbdominal aortic calcifications (AAC) predict cardiovascular mortality. A new scoring model for AAC, the Morphological Atherosclerotic Calcification Distribution (MACD) index may contribute with additional information to the commonly used Aortic Calcification Severity (AC24) score, when predicting death from cardiovascular disease (CVD). In this study we investigated associations of MACD and AC24 with traditional metabolic-syndrome associated risk factors at baseline and after 8.3 years follow-up, to identify biological parameters that may account for the differential performance of these indices.MethodsThree hundred and eight healthy women aged 48 to 76 years, were followed for 8.3 ± 0.3 years. AAC was quantified using lumbar radiographs. Baseline data included age, weight, blood pressure, blood lipids, and glucose levels. Pearson correlation coefficients were used to test for relationships.ResultsAt baseline and across all patients, MACD correlated with blood glucose (r2 = 0.1, P< 0.001) and to a lesser, but significant extent with traditional risk factors (p < 0.01) of CVD. In the longitudinal analysis of correlations between baseline biological parameters and the follow-up calcification assessment using radiographs we found LDL-cholesterol, HDL/LDL, and the ApoB/ApoA ratio significantly associated with the MACD (P< 0.01). In a subset of patients presenting with calcification at both baseline and at follow-up, all cholesterol levels were significantly associated with the MACD (P< 0.01) index. AC24 index was not correlated with blood parameters.ConclusionPatterns of calcification identified by the MACD, but not the AC24 index, appear to contain useful biological information perhaps explaining part of the improved identification of risk of cardiovascular death of the MACD index. Correlations of MACD but not the AC24 with glucose levels at baseline suggest that hyperglycemia may contribute to unique patterns of calcification indicated by the MACD.

Distribution, size, shape, growth potential and extent of abdominal aortic calcified deposits predict mortality in postmenopausal women

BackgroundAortic calcification is a major risk factor for death from cardiovascular disease. We investigated the relationship between mortality and the composite markers of number, size, morphology and distribution of calcified plaques in the lumbar aorta.Methods308 postmenopausal women aged 48-76 were followed for 8.3 ± 0.3 years, with deaths related to cardiovascular disease, cancer, or other causes being recorded. From lumbar X-rays at baseline the number (NCD), size, morphology and distribution of aortic calcification lesions were scored and combined into one Morphological Atherosclerotic Calcification Distribution (MACD) index. The hazard ratio for mortality was calculated for the MACD and for three other commonly used predictors: the EU SCORE card, the Framingham Coronary Heart Disease Risk Score (Framingham score), and the gold standard Aortic Calcification Severity score (AC24) developed from the Framingham Heart Study cohorts.ResultsAll four scoring systems showed increasing age, smoking, and raised triglyceride levels were the main predictors of mortality after adjustment for all other metabolic and physical parameters. The SCORE card and the Framingham score resulted in a mortality hazard ratio increase per standard deviation (HR/SD) of 1.8 (1.51-2.13) and 2.6 (1.87-3.71), respectively. Of the morphological x-ray based measures, NCD revealed a HR/SD >2 adjusted for SCORE/Framingham. The MACD index scoring the distribution, size, morphology and number of lesions revealed the best predictive power for identification of patients at risk of mortality, with a hazard ratio of 15.6 (p < 0.001) for the 10% at greatest risk of death.ConclusionsThis study shows that it is not just the extent of aortic calcification that predicts risk of mortality, but also the distribution, shape and size of calcified lesions. The MACD index may provide a more sensitive predictor of mortality from aortic calcification than the commonly used AC24 and SCORE/Framingham point card systems.

Distribution, size, and shape of abdominal aortic calcified deposits and their relationship to mortality in postmenopausal women

Abdominal aortic calcifications (AACs) correlate strongly with coronary artery calcifications and can be predictors of cardiovascular mortality. We investigated whether size, shape, and distribution of AACs are related to mortality and how such prognostic markers perform compared to the state-of-the-art AC24 marker introduced by Kauppila. Methods. For 308 postmenopausal women, we quantified the number of AAC and the percentage of the abdominal aorta that the lesions occupied in terms of their area, simulated plaque area, thickness, wall coverage, and length. We analysed inter-/intraobserver reproducibility and predictive ability of mortality after 8-9 years via Cox regression leading to hazard ratios (HRs). Results. The coefficient of variation was below 25% for all markers. The strongest individual predictors were the number of calcifications (HR = 2.4) and the simulated area percentage (HR = 2.96) of a calcified plaque, and, unlike AC24 (HR = 1.66), they allowed mortality prediction also after adjusting for traditional risk factors. In a combined Cox regression model, the strongest complementary predictors were the number of calcifications (HR = 2.76) and the area percentage (HR = −3.84). Conclusion. Morphometric markers of AAC quantified from radiographs may be a useful tool for screening and monitoring risk of CVD mortality.

Cerebellar heterogeneity and its impact on PET data quantification of 5-HT receptor radioligands

In the quantification of positron emission tomography (PET) radiotracer binding, a commonly used method is reference tissue modeling (RTM). RTM necessitates a proper reference and a ubiquitous choice for G-protein coupled receptors is the cerebellum. We investigated regional differences in uptake within the grey matter of the cerebellar hemispheres (CH), the cerebellar white matter (CW), and the cerebellar vermis (CV) for five PET radioligands targeting the serotonin system. Furthermore, we evaluated the impact of choosing different reference regions when quantifying neocortical binding. The PET and MR images are part of the Cimbi database: 5-HT1AR ([11C]CUMI-101, n = 8), 5-HT1BR ([11C]AZ10419369, n = 36), 5-HT2AR ([11C]Cimbi-36, n = 29), 5-HT4R ([11C]SB207145, n = 59), and 5-HTT ([11C]DASB, n = 100). We employed SUIT and FreeSurfer to delineate CV, CW, and CH and quantified mean standardized uptake values (SUV) and nondisplaceable neocortical binding potential (BPND). Statistical difference was assessed with paired nonparametric two-sided Wilcoxon signed-rank tests and multiple comparison corrected via false discovery rate. We demonstrate significant radioligand specific regional differences in cerebellar uptake. These differences persist when using different cerebellar regions for RTM, but the influence on the neocortical BPND is small. Nevertheless, our data highlight the importance of validating each radioligand carefully for defining the optimal reference region.

A Bayesian Framework for Automated Cardiovascular Risk Scoring on Standard Lumbar Radiographs

We present a fully automated framework for scoring a patients risk of cardiovascular disease (CVD) and mortality from a standard lateral radiograph of the lumbar aorta. The framework segments abdominal aortic calcifications for computing a CVD risk score and performs a survival analysis to validate the score. Since the aorta is invisible on X-ray images, its position is reasoned from 1) the shape and location of the lumbar vertebrae and 2) the location, shape, and orientation of potential calcifications. The proposed framework follows the principle of Bayesian inference, which has several advantages in the complex task of segmenting aortic calcifications. Bayesian modeling allows us to compute CVD risk scores conditioned on the seen calcifications by formulating distributions, dependencies, and constraints on the unknown parameters. We evaluate the framework on two datasets consisting of 351 and 462 standard lumbar radiographs, respectively. Promising results indicate that the framework has potential applications in diagnosis, treatment planning, and the study of drug effects related to CVD.

Cerebral serotonin transporter measurements with [11C]DASB: A review on acquisition and preprocessing across 21 PET centres

Positron Emission Tomography (PET) imaging has become a prominent tool to capture the spatiotemporal distribution of neurotransmitters and receptors in the brain. The outcome of a PET study can, however, potentially be obscured by suboptimal and/or inconsistent choices made in complex processing pipelines required to reach a quantitative estimate of radioligand binding. Variations in subject selection, experimental design, data acquisition, preprocessing, and statistical analysis may lead to different outcomes and neurobiological interpretations. We here review the approaches used in 105 original research articles published by 21 different PET centres, using the tracer [11C]DASB for quantification of cerebral serotonin transporter binding, as an exemplary case. We highlight and quantify the impact of the remarkable variety of ways in which researchers are currently conducting their studies, while implicitly expecting generalizable results across research groups. Our review provides evidence that the foundation for a given choice of a preprocessing pipeline seems to be an overlooked aspect in modern PET neuroscience. Furthermore, we believe that a thorough testing of pipeline performance is necessary to produce reproducible research outcomes, avoiding biased results and allowing for better understanding of human brain function.

An Improved Optimization Method for the Relevance Voxel Machine

In this paper, we will re-visit the Relevance Voxel Machine (RVoxM), a recently developed sparse Bayesian framework used for predicting biological markers, e.g., presence of disease, from high-dimensional image data, e.g., brain MRI volumes. The proposed improvement, called IRVoxM, mitigates the shortcomings of the greedy optimization scheme of the original RVoxM algorithm by exploiting the form of the marginal likelihood function. In addition, it allows voxels to be added and deleted from the model during the optimization. In our experiments we show that IRVoxM outperforms RVoxM on synthetic data, achieving a better training cost and test root mean square error while yielding sparser models. We further evaluated IRVoxMs performance on real brain MRI scans from the OASIS data set, and observed the same behavior - IRVoxM retains good prediction performance while yielding much sparser models than RVoxM.

Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos

We propose a statistical generative shape model for archipelago-like structures. These kind of structures occur, for instance, in medical images, where our intention is to model the appearance and shapes of calcifications in x-ray radio graphs. The generative model is constructed by (1) learning a patch-based dictionary for possible shapes, (2) building up a time-homogeneous Markov model to model the neighbourhood correlations between the patches, and (3) automatic selection of the model complexity by the minimum description length principle. The generative shape model is proposed as a probability distribution of a binary image where the model is intended to facilitate sequential simulation. Our results show that a relatively simple model is able to generate structures visually similar to calcifications. Furthermore, we used the shape model as a shape prior in the statistical segmentation of calcifications, where the area overlap with the ground truth shapes improved significantly compared to the case where the prior was not used.