Many neurological diseases, especially those occurring at older age, have a long subclinical phase during which a person is asymptomatic and does not seek medical attention. As a consequence, once symptoms manifest, in many instances the pathologic changes caused by the disease process are already advanced and mostly irreversible. To study disease in the asymptomatic stage, population-based studies are of great importance. Medical imaging applied in these studies, or ‘population imaging’ can, non- or minimally-invasively, show the changes that occur in the human body that may reflect either early disease, intermediate factors or risk indicators of disease.
1. To understand the rationale and general design of population neuroimaging studies.
2. To learn how information derived from population neuroimaging can be translated to clinical practice.
3. To realize opportunities and challenges of the “population to practice”-approach.
Observational cohort studies following large population of heathy individuals for occurrence of disease are an optimal setting to study determinants and risk factors for disease. Neuro-epidemiologic studies have traditionally focused on studying these associations treating the pathway in between risk factor and outcome as a ‘black box’. With the availability of non-invasive, advanced neuroimaging techniques, it has become possible to directly study brain changes occurring in this ‘black box’. This importantly enables us to unravel pathways of disease, find new markers of disease or identify subjects at risk of disease.
Imaging in such population-based studies is also called ‘population imaging’: “the large-scale application and analysis of medical images in controlled population cohorts”. This lecture discusses the rationale of population neuroimaging, the various ways to extract visual or quantitative information from these images, and the implications for understanding etiology, disease prediction and clinical impact.
Important strengths of population imaging are its quantitative nature, providing reproducible and objective measures which can be used as surrogates for an aspect of the disease, or to monitor disease progression. The preclinical changes that can be assessed with imaging enable us to study pathways of disease, investigate how tissue changes mediate the relation between causal factors and outcomes, and study how various markers interact in disease development.
Key factors of population imaging that should be understood is that its strength lies not only in the depth and detail of the imaging studies, but even more so in combining imaging data with a wide variety of other measurements in the study participants. Because these studies are usually initiated in healthy persons, imaging should be minimally or non-invasive. These study designs are not efficient for studying rare diseases, as a large enough number of outcomes in needed for meaningful inferences. Finally, due to the large amounts of data collected, not only in a numerical sense but also the complexity and depth of the information (especially when combined with other rich datasets such as genomics or metabolomics), these are considered ‘big data’ with associated challenges for handling, storage and processing.
Examples will be given of how population imaging increases our understanding of how disease develops, helps us build improved prediction models, find new biomarkers or simply understand what is ‘normal’ as a reference for clinical practice. Challenges such as dealing with unexpected findings are mentioned, as well as those related to the fact that population neuroimaging is a fast progressing field, with a growing need for collaboration, harmonization and standardization among studies.