Structural magnetic resonance imaging (sMRI) and a disease progression model researchers developed can improve prediction of onset of Huntington disease (HD), according to a study findings published in Neurology Genetics.
Variability in patients, lack of a common reference timeline, and measurement noise have made it challenging to identify a timeline for the progression of HD, which may help prognosis. Measurement of sMRI at multiple time points could provide biomarkers for the neurodegenerative disease. In the current study, the researchers tested and trained a Gaussian process progression model (GPPM) on the TRACK-HD and PREDICT-HD data sets, which are the largest available for the disease.
The researchers trained GPPM using volumes of brain regions of 284 individuals in the TRACK-HD data set and 159 individuals in the PREDICT-HD data set. The brain regions included were the 4 main regions of the cerebral cortex and those involved in HD pathology (caudate, putamen, pallidum, ventricles, thalamus proper, and sensory motor).
The researchers discovered that the largest (about 18% to 22%) and earliest brain atrophy (about 2 years before typical abnormalities) occurred in the striatum (caudate, pallidum, and putamen). Change was gradual in the main regions of the brain over 11 years (about 7% to 16%).
GPPM (root mean square error 4.5 years, maximum error 7.9 years) had smaller spread compared with survival model (root mean square error 6.6 years, maximum error 18.2 years.
Study limitations included sole analysis of MRI measurements, inability to stratify by individuals’ rate of change, and not including the whole natural history of HD.
The researchers believe their findings can provide an informative method when choosing brain imaging markers to track HD progression. For example, for tracking acceleration earlier in HD, they suggest the striatum is the most suitable. For tracking acceleration later on in disease progression, the sensory motor and lateral ventricles are better markers.
“As such, our approach has potential application in clinical trial design, where it could be used to identify the most suitable marker at the most suitable time to observe the effect of a given intervention. Moreover, we demonstrate that GPPM can be used to predict clinical onset of HD more accurately than the current state of the art,” the researchers said.
They added, “this supports the use of sMRI, when combined with GPPM, for potentially informing prognosis in clinical practice and stratification in the clinical trial design.”
Wijeratne PA, Garbarino S, Gregory S, et al. Revealing the timeline of structural MRI changes in premanifest to manifest Huntington disease. Neurol Genet. Published online October 12, 2021. doi: 10.1212/NXG.0000000000000617