The mismatch in the spatial resolution of Arterial Spin Labeling (ASL) MRI perfusion images and the anatomy of functionally distinct tissues in the brain leads to a partial volume effect (PVE),... Show moreThe mismatch in the spatial resolution of Arterial Spin Labeling (ASL) MRI perfusion images and the anatomy of functionally distinct tissues in the brain leads to a partial volume effect (PVE), which in turn confounds the estimation of perfusion into a specific tissue of interest such as gray or white matter. This confound occurs because the image voxels contain a mixture of tissues with disparate perfusion properties, leading to estimated perfusion values that reflect primarily the volume proportions of tissues in the voxel rather than the perfusion of any particular tissue of interest within that volume. It is already recognized that PVE influences studies of brain perfusion, and that its effect might be even more evident in studies where changes in perfusion are co-incident with alterations in brain structure, such as studies involving a comparison between an atrophic patient population vs control subjects, or studies comparing subjects over a wide range of ages. However, the application of PVE correction (PVEc) is currently limited and the employed methodologies remain inconsistent. In this article, we outline the influence of PVE in ASL measurements of perfusion, explain the main principles of PVEc, and provide a critique of the current state of the art for the use of such methods. Furthermore, we examine the current use of PVEc in perfusion studies and whether there is evidence to support its wider adoption. We conclude that there is sound theoretical motivation for the use of PVEc alongside conventional, 'uncorrected', images, and encourage such combined reporting. Methods for PVEc are now available within standard neuroimaging toolboxes, which makes our recommendation straightforward to implement. However, there is still more work to be done to establish the value of PVEc as well as the efficacy and robustness of existing PVEc methods. Show less
The prevalence of obesity, defined as a body mass index (BMI) > 30 kg/m2, is increasing to epidemic proportions. In 2014, 11% of men and 15% of women worldwide were obese. Thus, more than... Show moreThe prevalence of obesity, defined as a body mass index (BMI) > 30 kg/m2, is increasing to epidemic proportions. In 2014, 11% of men and 15% of women worldwide were obese. Thus, more than half a billion adults worldwide are classed as obese. The fundamental cause of obesity is an imbalance between energy intake (excessive intake of energy-dense foods) and energy expenditure (reduced physical activity). People with obesity are at risk for a range of chronic conditions including cardiovascular disease (CVD) and nonalcoholic fatty liver disease (NAFLD). Furthermore, obesity is a major risk factor for the development of type 2 diabetes, which is one of the most common chronic diseases in nearly all countries. According to the World Health Organization, the global prevalence of diabetes in 2014 was estimated to be 9%, of which 90% was comprised of type 2 diabetes. This thesis focuses on cardiovascular and cerebral dimensions and function in people with obesity and type 2 diabetes. State-of-the-art imaging techniques are used to investigate links between the heart, liver, abdominal fat, and brain to elucidate parts of the complex relationships between these organs. Show less
Schouten, T.M.; Koini, M.; Vos, F. de; Seiler, S.; Grond, J. van der; Lechner, A.; ... ; Rombouts, S.A.R.B. 2016
Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease.... Show moreMagnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification. (C) 2016 The Authors. Published by Elsevier Inc. Show less
Schouten, T.M.; Koini, M.; Vos, F. de; Seiler, S.; Van der Grond, J.; Lechner, A.; ... ; Rombouts, S.A.R.B. 2016
The aims of this thesis were to gain insight into specific disease processes in Huntington__s Disease (HD) and to identify biomarkers. To achieve these aims, cognitive functioning, structural brain... Show moreThe aims of this thesis were to gain insight into specific disease processes in Huntington__s Disease (HD) and to identify biomarkers. To achieve these aims, cognitive functioning, structural brain characteristics and intrinstic functional brain connectivity of premanifest and early HD subjects were examined. Cortical, subcortical and the intermediate white matter brain tissue shows evidence of structural and functional decline. We found evidence that disease processes, such as altered metabolism, excessive iron accumulation and cell loss, play a role in the changes. We conclude that changes occur throughout the brain from the earliest disease phase onwards. Hence, both premanifest and manifest HD should not be regarded as a disorder of the basal ganglia, but as a disease affecting the whole brain. Candidate biomarkers that have the potential to objectively reflect the early changes and the progressive nature of the disease are measures of subcortical atrophy, integrity of white matter pathways and of intrinsic functional brain connectivity. Iron, creatine, and N-acetylaspartate concentrations in the caudate nucleus and putamen may prove to be useful as markers of disease state for objectifying transitional disease processes from premanifest to manifest HD. Visuospatial working memory could be applied as a state marker for stage two HD. Show less
