An image‐based model of brain volume biomarker changes in Huntington's disease

• Published in: Annals of clinical and translational neurology Vol. 5; no. 5; pp. 570 - 582
• Main Authors: Wijeratne, Peter A., Young, Alexandra L., Oxtoby, Neil P., Marinescu, Razvan V., Firth, Nicholas C., Johnson, Eileanoir B., Mohan, Amrita, Sampaio, Cristina, Scahill, Rachael I., Tabrizi, Sarah J., Alexander, Daniel C.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.05.2018; John Wiley and Sons Inc
• Subjects: Biomarkers; Huntingtons disease
• ISSN: 2328-9503; 2328-9503
• DOI: 10.1002/acn3.558

Objective Determining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine‐grained model of temporal progression of Huntington's disease from premanifest through to manifest stages. Methods We employ a probabilistic event‐based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track‐HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides. Results The model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross‐validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow‐up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers. Interpretation We used a data‐driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event‐based model, to provide new insight into Huntington's disease progression and to support fine‐grained patient stratification for future precision medicine in Huntington's disease.