Developing New MRI Connectome Biomarkers of Disease Outcome for Amyotrophic Lateral Sclerosis (ALS)

2016  -  Utrecht, Netherlands

Organizations

University Medical Center Utrecht

Project description

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disease with a median survival of 3 to 5 years after symptom onset. A considerable proportion (30%) of patients live shorter (<2 years) while others (10%) live for 10 or more years with the disease. Also in terms of symptom development and disease course there is large and unexplained variability among patients. Finding the biological underpinnings of this variation is of the utmost importance for individual patients, as it will help to predict their disease course and expected survival. Using advanced MRI techniques subtle differences in cortical thickness and brain connectivity have been observed in the brains of ALS patients as compared with healthy control subjects. In parallel, post-mortem investigations have shown patterns of misfolded proteins to be present in the ALS brain. Here, we set out to develop sensitive and objective measures of disease stage (biomarkers) based on the ‘connectome’, the brain’s anatomical network as reconstructed from MRI scans. Combining connectome data with patterns of misfolded proteins in state-of-the art prediction models, we aim to predict disease course and survival for the individual patient.

Relevance to the acceleration of therapeutics for neurodegenerative diseases of aging

Biomarkers are needed as sensitive outcome measures in future clinical trials capturing weak, yet potentially important therapeutic effects that would otherwise remain undetected. The current standard to measure a patient’s disability, the ALS functional rating scale (ALSFRS-R), is a relatively coarse scale based on a questionnaire, and performs poorly as a marker for brain involvement. Accurate biomarkers are therefore highly sought after. Furthermore, biomarkers that predict disease stage and course of the disease for the individual are instrumental in the selection and inclusion of comparable groups of patients in clinical trials to test the efficacy of new potential treatments. More homogeneous patient group enhance a trial’s sensitivity such that even subtle treatment responses can be detected.

Anticipated outcome

Investigating patterns of disease spread on the basis of MRI scans, we aim to extract predictive features which we expect to be within one’s anatomical brain network of neural connections, i.e. one’s connectome. With the subject-specific connectome the main goal is to go from group-level patient/control descriptives to personalized patient characterization. Applying simulation models to high-resolution MR images, we will test different mechanisms of disease spread across the brain. From these models of spread we will develop and assess potential new imaging biomarkers in our ALS cohort acquired for staging and prognostication of individual patients.