Brain imaging data don’t improve Parkinson’s prediction models

Clinical assessments beat brain scans for forecasting long-term trajectory

Written by Marisa Horak, MS | Aug. 25, 2026

• Brain imaging data do not improve computer models used to predict long-term Parkinson’s disease progression.
• Incorporating MRI scans and machine learning models sometimes reduced the accuracy of motor symptom predictions.
• A small set of baseline clinical assessments remains the most effective way to forecast long-term cognitive and motor trajectories.

Brain imaging data are not very useful for computer models predicting the long-term trajectory of Parkinson’s disease, a study showed.

Researchers found that computer models aimed at predicting Parkinson’s progression were not meaningfully better when imaging data were included. In fact, some models became less accurate when imaging data were added.

The findings suggest that the best way to predict long-term Parkinson’s trajectories is with a small set of clinical assessments, the researchers said.

The study, “Baseline clinical features outperform structural MRI in predicting rapid cognitive and motor decline in Parkinson’s disease,” was published in npj Parkinson’s Disease.

Parkinson’s is a neurological disease that causes both motor and nonmotor symptoms. The disease is progressive, meaning these symptoms tend to worsen over time. But Parkinson’s doesn’t progress at the same rate for everyone, and there isn’t a reliable way to predict which patients will experience faster disease progression.