Imaging of Age-related Brain Changes: A Population-based Approach

The objective of the studies described in this thesis was to investigate with magnetic resonance imaging (MRI) brain changes that may function as preclinical imaging markers for neurodegenerative and cerebrovascular disease. For this goal, advanced MRI techniques were applied in the Rotterdam Scan Study, a large population-based brain imaging study among middle-aged and elderly persons. We studied the prevalence and distribution of age-related brain changes on MRI, investigated associated risk factors and related these brain changes to cognitive functioning. We found that cerebral microbleeds were present in 1 in 5 persons over age of 60. This prevalence is much higher than reported previously, which in part may be explained by the use of a more sensitive MRI sequence. Furthermore, we showed that risk factors for microbleeds varied according to the location of microbleeds in the brain. By measuring cerebral blood flow, we assessed that persons with low total brain perfusion had significantly more white matter lesions compared to those with high total brain perfusion. This suggests that tissue hypoperfusion may contribute to white matter lesion pathogenesis. Microstructural integrity within white matter lesions or normal-appearing white matter was associated with cognitive function, even when taking into account volume of white matter lesions and white matter atrophy. This indicates that the deleterious effect of white matter changes on cognition not only depends on lesion burden or amount of atrophy, but also on characteristics that are not easily evaluated by conventional MRI. The studies described in this thesis have identified several age-related brain changes that have potential to serve as imaging markers for neurodegenerative or cerebrovascular disease

Cerebral microbleeds: Spatial distribution implications

Cerebral microbleeds are considered an imaging marker of cerebral small vessel disease. The location of microbleeds is thought to reflect the underlying pathology. Microbleeds in the deep and infratentorial region are thought to reflect hypertensive arteriopathy whereas lobar microbleeds are associated clinically with cerebral amyloid angiopathy (CAA). Aside from patient populations, microbleeds are frequently observed in seemingly asymptomatic populations. Moreover, many elderly, both in clinical and preclinical populations, have multiple coexisting pathologies in their brains, which complicates the interpretation of cerebral microbleeds, especially early in the clinical course. In this commentary, we discuss the influence of the strongest genetic risk factor for CAA, Apolipoprotein E (APOE), in the spatial distribution of microbleeds, and we additionally address issues in interpretation and implication of the location of microbleeds in clinical and asymptomatic populations

Neuroimaging in Dementia: More than Typical Alzheimer Disease

Alzheimer disease (AD) is the most common cause of dementia. The prevailing theory of the underlying pathology assumes amyloid accumulation followed by tau protein aggregation and neurodegeneration. However, the current antiamyloid and antitau treatments show only variable clinical efficacy. Three relevant points are important for the radiologic assessment of dementia. First, besides various dementing disorders (including AD, frontotemporal dementia, and dementia with Lewy bodies), clinical variants and imaging subtypes of AD include both typical and atypical AD. Second, atypical AD has overlapping radiologic and clinical findings with other disorders. Third, the diagnostic process should consider mixed pathologies in neurodegeneration, especially concurrent cerebrovascular disease, which is frequent in older age. Neuronal loss is often present at, or even before, the onset of cognitive decline. Thus, for effective emerging treatments, early diagnosis before the onset of clinical symptoms is essential to slow down or stop subsequent neuronal loss, requiring molecular imaging or plasma biomarkers. Neuroimaging, particularly MRI, provides multiple imaging parameters for neurodegenerative and cerebrovascular disease. With emerging treatments for AD, it is increasingly important to recognize AD variants and other disorders that mimic AD. Describing the individual composition of neurodegenerative and cerebrovascular disease markers while considering overlapping and mixed diseases is necessary to better understand AD and develop efficient individualized therapies

Neuroimaging in Dementia:More than Typical Alzheimer Disease

Alzheimer disease (AD) is the most common cause of dementia. The prevailing theory of the underlying pathology assumes amyloid accumulation followed by tau protein aggregation and neurodegeneration. However, the current antiamyloid and antitau treatments show only variable clinical efficacy. Three relevant points are important for the radiologic assessment of dementia. First, besides various dementing disorders (including AD, frontotemporal dementia, and dementia with Lewy bodies), clinical variants and imaging subtypes of AD include both typical and atypical AD. Second, atypical AD has overlapping radiologic and clinical findings with other disorders. Third, the diagnostic process should consider mixed pathologies in neurodegeneration, especially concurrent cerebrovascular disease, which is frequent in older age. Neuronal loss is often present at, or even before, the onset of cognitive decline. Thus, for effective emerging treatments, early diagnosis before the onset of clinical symptoms is essential to slow down or stop subsequent neuronal loss, requiring molecular imaging or plasma biomarkers. Neuroimaging, particularly MRI, provides multiple imaging parameters for neurodegenerative and cerebrovascular disease. With emerging treatments for AD, it is increasingly important to recognize AD variants and other disorders that mimic AD. Describing the individual composition of neurodegenerative and cerebrovascular disease markers while considering overlapping and mixed diseases is necessary to better understand AD and develop efficient individualized therapies.</p

Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia

Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uniand bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, "bi-modal comparison plots" show the distribution of uni-and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using "worm plots". Group differences in connections are examined with an existing visualization, the "connectogram". These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the "Statistical Analysis of Minimum cost path based Structural Connectivity" method and the average fractional anisotropy along the fiber. The functional measures were Pearson's correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies

Ethical framework for the detection, management and communication of incidental findings in imaging studies, building on an interview study of researchers' practices and perspectives

BACKGROUND: As thousands of healthy research participants are being included in small and large imaging studies, it is essential that dilemmas raised by the detection of incidental findings are adequately handled. Current ethical guidance indicates that pathways for dealing with incidental findings should be in place, but does not specify what such pathways should look like. Building on an interview study of researchers’ practices and perspectives, we identified key considerations for the set-up of pathways for the detection, management and communication of incidental findings in imaging research. METHODS: We conducted an interview study with a purposive sample of researchers (n = 20) at research facilities across the Netherlands. Based on a qualitative analysis of these interviews and on existing guidelines found in the literature, we developed a prototype ethical framework, which was critically assessed and fine-tuned during a two-day international expert meeting with bioethicists and representatives from large population-based imaging studies from the United Kingdom, Germany, Sweden and Belgium (n = 14). RESULTS: Practices and policies for the handling of incidental findings vary strongly across the Netherlands, ranging from no review of research scans and limited feedback to research participants, to routine review of scans and the arrangement of clinical follow-up. Respondents felt that researchers do not have a duty to actively look for incidental findings, but they do have a duty to act on findings, when detected. The principle of reciprocity featured prominently in our interviews and expert meeting. CONCLUSION: We present an ethical framework that may guide researchers and research ethics committees in the design and/or evaluation of appropriate pathways for the handling of incidental findings in imaging studies. The framework consists of seven steps: anticipation of findings, information provision and informed consent, scan acquisition, review of scans, consultation on detected abnormalities, communication of the finding, and further clinical management and follow-up of the research participant. Each of these steps represents a key decision to be made by researchers, which should be justified not only with reference to costs and/or logistical considerations, but also with reference to researchers’ moral obligations and the principle of reciprocity

Transfer learning improves supervised image segmentation across imaging protocols

The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%