Glia Imaging Differentiates Multiple System Atrophy from Parkinson's Disease: A Positron Emission Tomography Study with [C-11]PBR28 and Machine Learning Analysis

Background The clinical diagnosis of multiple system atrophy (MSA) is challenged by overlapping features with Parkinson's disease (PD) and late-onset ataxias. Additional biomarkers are needed to confirm MSA and to advance the understanding of pathophysiology. Positron emission tomography (PET) imaging of the translocator protein (TSPO), expressed by glia cells, has shown elevations in MSA. Objective In this multicenter PET study, we assess the performance of TSPO imaging as a diagnostic marker for MSA.Methods We analyzed [C-11]PBR28 binding to TSPO using imaging data of 66 patients with MSA and 24 patients with PD. Group comparisons were based on regional analysis of parametric images. The diagnostic readout included visual reading of PET images against clinical diagnosis and machine learning analyses. Sensitivity, specificity, and receiver operating curves were used to discriminate MSA from PD and cerebellar from parkinsonian variant MSA. Results We observed a conspicuous pattern of elevated regional [C-11]PBR28 binding to TSPO in MSA as compared with PD, with "hotspots" in the lentiform nucleus and cerebellar white matter. Visual reading discriminated MSA from PD with 100% specificity and 83% sensitivity. The machine learning approach improved sensitivity to 96%. We identified MSA subtype-specific TSPO binding patterns. Conclusions We found a pattern of significantly increased regional glial TSPO binding in patients with MSA. Intriguingly, our data are in line with severe neuroinflammation in MSA. Glia imaging may have potential to support clinical MSA diagnosis and patient stratification in clinical trials on novel drug therapies for an alpha-synucleinopathy that remains strikingly incurable. </p

Development, validation and application of advanced neuroimaging analysis tools for in vivo neuroreceptor studies

Positron emission tomography (PET) is an imaging technology, which can be used to study neuroreceptors in the human brain in vivo. The technique estimates the regional binding of radiolabelled ligands to neuroreceptors and the data are commonly displayed as images showing the distribution of radioactivity in the brain volume. A traditional image analysis approach builds upon reduction of noise in a region of interest (ROI) by averaging radioactivity in the volume elements (voxels) of the ROI. This approach is efficient to improve the reliability of the time curves but does not allow for a detailed analysis of the entire brain. To obtain detailed three-dimensional maps of binding parameters in brain, novel approaches have been developed during recent years. The aim of the present thesis project is to examine and validate the repertoire of advanced computerised tools used to obtain parametric maps of receptor binding in basic and clinical neuroscience research. In addition, the increasing number of suitable PET radioligands, targeted for different neuroreceptor systems, calls for approaches that allow for a combined analysis of multiple receptor systems. A parametric mapping approach using wavelet filtering was evaluated in a crossvalidation design. Data from PET-studies on regional D2/D3 dopamine and 5-HT1A serotonin receptor binding in the human brain were used to compare the binding potential (BP) estimates of the wavelet-based approach and other parametric imaging approaches to the ROI-based graphical Logan analysis which was used as a reference. The approach using three-dimensional wavelet filtering was noise-tolerant and yielded BP maps with regional averages closely matching the reference values. Overall, the wavelet-based approach seemed to provide the most valid and reliable estimates across regions with a wide range of receptor densities. However, there was some loss of resolution, which may be critical for analysis of binding in small anatomical regions. Another set of parametric mapping approaches is similar to the ROI-based analyses in the sense that signal averaging is used to reduce noise. However, these approaches do not average the time-activity curves (TAC s) of spatially adjacent voxels but that of voxels having a TAC with a similar shape. A process was developed to classify voxels into a large number of groups (clusters) and thus to obtain an average TAC for voxels with a similar TAC. The classification was performed using an artificial neural network model, called the growing adaptive neural gas (GANG), which was developed as part of the thesis. Parameter estimation was performed on the average TAC s and the parameters were then back-projected to the original spatial locations of the voxels thereby providing 3D parametric maps. The approach was applied to PET images measuring D2/D3 receptor binding. The results indicate that the approach can be used to effectively reduce noise. The created parametric maps were highly detailed and the binding distribution was consistent with parametric images obtained with previous approaches. Novel technical approaches are required in combined analyses of multiple neuroreceptor systems. Such approaches have to be capable of operating on very large parametric image datasets. An initial step is the development of exploratory data-mining tools, which provide guidance as to the structure of complex multi-individual, multi-receptor datasets. For this task, an unsupervised and unbiased data-mining tool was developed and proposed. The tool includes a GANG-based clustering of multi-receptor data. The proposed approach was tested on a dataset containing BP maps of the serotonin transporter and the 5-HT1A receptors obtained in the same individuals. The outputs of the method were multi-receptor maps with potential to reveal complex relationships and tendencies in a dataset with several ligands. Such maps may have value in clinical research on multi-receptor interactions and pattern changes in the human brain. In conclusion, the present thesis has examined and extended a methodological platform that allows for additional gain of information from routinely generated data in PET studies on neuroreceptor binding. The results support application of parametric image analysis in basic and clinical research

Reliability of volumetric and surface-based normalisation and smoothing techniques for PET analysis of the cortex : A test-retest analysis using [11C]SCH-23390

Parametric voxelwise analysis is a commonly used tool in neuroimaging, as it allows for identification of regions of effects in the absence of a strong a-priori regional hypothesis by comparing each voxel of the brain independently. Due to the inherent imprecision of single voxel measurements, spatial smoothing is performed to increase the signal-to-noise ratio of single-voxel estimates. In addition, smoothing compensates for imprecisions in anatomical registration, and allows for the use of cluster-based statistical thresholding. Smoothing has traditionally been applied in three dimensions, without taking the tissue types of surrounding voxels into account. This procedure may be suitable for subcortical structures, but is problematic for cortical regions for which grey matter often constitutes only a small proportion of the smoothed signal. New methods have been developed for cortical analysis in which voxels are sampled to a surface, and smoothing is restricted to neighbouring regions along the cortical grey matter in two dimensions. This procedure has recently been shown to decrease intersubject variability and bias of PET data. The aim of this study was to compare the variability, bias and test-retest reliability of volumetric and surface-based methods as they are applied in practice. Fifteen healthy young males were each measured twice using the dopamine D1 receptor radioligand [11C]SCH-23390, and analyses were performed at the level of individual voxels and vertices within the cortex. We found that surface-based methods yielded higher BPND values, lower coefficient of variation, less bias, better reliability and more precise estimates of parametric binding. All in all, these results suggest that surface-based methods exhibit superior performance to volumetric approaches for voxelwise analysis of PET data, and we advocate for their use when a ROI-based analysis is not appropriate

Serotonin 1B receptor density mapping of the human brainstem using positron emission tomography and autoradiography

The serotonin 1B (5-HT1B) receptor has lately received considerable interest in relation to psychiatric and neurological diseases, partly due to findings based on quantification using Positron Emission Tomography (PET). Although the brainstem is an important structure in this regard, PET radioligand binding quantification in brainstem areas often shows poor reliability. This study aims to improve PET quantification of 5-HT1B receptor binding in the brainstem.Volumes of interest (VOIs) were selected based on a 3D [3H]AZ10419369 Autoradiography brainstem model, which visualized 5-HT1B receptor distribution in high resolution. Two previously developed VOI delineation methods were tested and compared to a conventional manual method. For a method based on template data, a [11C]AZ10419369 PET template was created by averaging parametric binding potential (BPND) images of 52 healthy subjects. VOIs were generated based on a predefined volume and BPND thresholding and subsequently applied to test-retest [11C]AZ10419369 parametric BPND images of 8 healthy subjects. For a method based on individual subject data, VOIs were generated directly on each individual parametric image.Both methods showed improved reliability compared to a conventional manual VOI. The VOIs created with [11C]AZ10419369 template data can be automatically applied to future PET studies measuring 5-HT1B receptor binding in the brainstem

Low background and high contrast PET imaging of amyloid-β with [11C]AZD2995 and [11C]AZD2184 in Alzheimer’s disease patients

PURPOSE: The aim of this study was to evaluate AZD2995 side by side with AZD2184 as novel PET radioligands for imaging of amyloid-β in Alzheimer’s disease (AD). METHODS: In vitro binding of tritium-labelled AZD2995 and AZD2184 was studied and compared with that of the established amyloid-β PET radioligand PIB. Subsequently, a first-in-human in vivo PET study was performed using [(11)C]AZD2995 and [(11)C]AZD2184 in three healthy control subjects and seven AD patients. RESULTS: AZD2995, AZD2184 and PIB were found to share the same binding site to amyloid-β. [(3)H]AZD2995 had the highest signal-to-background ratio in brain tissue from patients with AD as well as in transgenic mice. However, [(11)C]AZD2184 had superior imaging properties in PET, as shown by larger effect sizes comparing binding potential values in cortical regions of AD patients and healthy controls. Nevertheless, probably due to a lower amount of nonspecific binding, the group separation of the distribution volume ratio values of [(11)C]AZD2995 was greater in areas with lower amyloid-β load, e.g. the hippocampus. CONCLUSION: Both AZD2995 and AZD2184 detect amyloid-β with high affinity and specificity and also display a lower degree of nonspecific binding than that reported for PIB. Overall [(11)C]AZD2184 seems to be an amyloid-β radioligand with higher uptake and better group separation when compared to [(11)C]AZD2995. However, the very low nonspecific binding of [(11)C]AZD2995 makes this radioligand potentially interesting as a tool to study minute levels of amyloid-β. This sensitivity may be important in investigating, for example, early prodromal stages of AD or in the longitudinal study of a disease modifying therapy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00259-012-2322-6) contains supplementary material, which is available to authorized users

Glia Imaging Differentiates Multiple System Atrophy from Parkinson's Disease:A Positron Emission Tomography Study with [¹¹C]PBR28 and Machine Learning Analysis

Background The clinical diagnosis of multiple system atrophy (MSA) is challenged by overlapping features with Parkinson's disease (PD) and late-onset ataxias. Additional biomarkers are needed to confirm MSA and to advance the understanding of pathophysiology. Positron emission tomography (PET) imaging of the translocator protein (TSPO), expressed by glia cells, has shown elevations in MSA. Objective In this multicenter PET study, we assess the performance of TSPO imaging as a diagnostic marker for MSA. Methods We analyzed [C-11]PBR28 binding to TSPO using imaging data of 66 patients with MSA and 24 patients with PD. Group comparisons were based on regional analysis of parametric images. The diagnostic readout included visual reading of PET images against clinical diagnosis and machine learning analyses. Sensitivity, specificity, and receiver operating curves were used to discriminate MSA from PD and cerebellar from parkinsonian variant MSA. Results We observed a conspicuous pattern of elevated regional [C-11]PBR28 binding to TSPO in MSA as compared with PD, with "hotspots" in the lentiform nucleus and cerebellar white matter. Visual reading discriminated MSA from PD with 100% specificity and 83% sensitivity. The machine learning approach improved sensitivity to 96%. We identified MSA subtype-specific TSPO binding patterns. Conclusions We found a pattern of significantly increased regional glial TSPO binding in patients with MSA. Intriguingly, our data are in line with severe neuroinflammation in MSA. Glia imaging may have potential to support clinical MSA diagnosis and patient stratification in clinical trials on novel drug therapies for an alpha-synucleinopathy that remains strikingly incurable. (c) 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Societ