The scientific challenge

Neurodegenerative pathologies, such as Alzheimer’s disease, are a major public health issue. Despite an unprecedented research effort and numerous trials, there is currently no disease-modifying treatment for these diseases. This failure may be partly attributed to pharmacological research which could not propose efficient molecules. However, there is converging evidence that this may be due also to the selection of patients in clinical trials. The current criteria for the inclusion of patients in clinical trials may thus not be sufficient for the discovery of disease-modifying treatments. Indeed, inclusion is currently based on clinical criteria which leads to important limitations:

• Clinical signs and symptoms are the latest manifestation of neurodegenerative diseases and brain damage probably starts many years before. Treatments may thus not be efficient at this stage of the disease while they could have been earlier.
• Clinical diagnoses are not reliable enough. For instance, the sensitivity and specificity of clinical diagnosis of Alzheimer’s disease based on established consensus criteria are of only about 70-80% compared to histopathological confirmation. Furthermore, clinical measures embed subjective aspects and have a limited reproducibility and are thus not ideal to track disease progression.
• Clinical diagnoses are not precise enough and only partially overlap with pathological processes. Common clinical symptoms may be associated with different pathological substrates and biological causes.

It is thus crucial to supplement clinical examinations with biomarkers that can detect and track the progression of pathological processes in the living patient. This has potentially very important implications for the development of new treatments of neurodegenerative disorders as it would help: i) identifying patients with a given pathology at the earliest (ideally preclinical) stage of the disease, for inclusion in clinical trials; ii) providing measures to monitor the efficacy of treatments.

The interplay of biological processes that lead from abnormal protein accumulation to neuronal loss and cognitive dysfunction is not fully understood. In this context, neuroimaging biomarkers and statistical methods to study large datasets can play a pivotal role to better understand the pathophysiology of neurodegenerative disorders and to increase the chances of success of clinical trials. There is increasing evidence suggesting that anatomical alterations precede cognitive impairment and clinical diagnosis. The discovery of new anatomical biomarkers could thus have a major impact on clinical trials by allowing inclusion of patients at a very early stage, at which treatments are the most likely to be effective. Besides, anatomical measures are more stable than cognitive scales and could thus provide more effective markers of efficacy. Furthermore, the correlation of imaging markers to clinical variables shall allow to better understand the relationships between anatomical alterations and cognitive impairment. Correlations with genetic variables could allow determining subgroups of patients with common anatomical and genetic characteristics. This could ultimately result in a refinement of the taxonomy of neurodegenerative disorders and increase the chances of success of clinical trials by targeting more homogeneous populations.

This endeavour requires addressing several methodological challenges. First, it is necessary to robustly extract anatomical structures and quantitative measures from brain images (MRI, PET). A second challenge is to model anatomical shapes and their alterations. In particular, it is crucial to be able to represent in the same framework different types of anatomical structures such as the cortex, subcortical structures and white matter tracts, crossing different spatial scales. Furthermore, one needs to develop not only static but dynamic models of the evolution of anatomical alterations. Finally, one needs to develop statistical methods to integrate anatomical and genetic information into a common framework.