# Brain structural MRI marker for predicting conversion to Parkinson’s disease in individuals with prodromal symptoms

## TL;DR

This study uses brain MRI and clinical markers to predict who with early Parkinson’s symptoms will develop the disease.

## Contribution

Combining MRI brain structure with non-motor clinical markers improves PD conversion prediction accuracy.

## Key findings

- An MRI-based classifier identified PD-like brain patterns in 21 individuals, including all future converters.
- Combining MRI with olfactory dysfunction achieved 100% sensitivity and 90% balanced accuracy in predicting PD conversion.
- Multimodal prediction outperformed individual markers and supports early identification of high-risk individuals.

## Abstract

During the prodromal stage of Parkinson’s disease (PD), brain structural alterations precede clinical diagnosis and offer opportunities for early detection. We investigated whether combining clinical non-motor markers with an MRI-based brain structural marker could enhance predictive performance for PD conversion.

Individuals with prodromal symptoms (n = 46, 63.5 ± 7.6 years, 24 males) were selected from the Parkinson’s Progression Markers Initiative dataset. We developed a machine learning classifier to identify individuals with brain structural patterns similar to PD based on cortical thickness and white matter integrity. Its predictive performance for PD conversion was assessed alone and combined with clinical non-motor markers such as rapid eye movement sleep behavior disorder and olfactory dysfunction.

Six individuals converted to PD within 4 years. The MRI marker classified 21 individuals as having PD-like brain patterns, including all six converters. When combined with olfactory dysfunction, the approach achieved optimal performance with 100% sensitivity, 80% specificity, and 90% balanced accuracy, outperforming individual markers and other combinations.

MRI-quantified brain structural similarity to PD, particularly when combined with olfactory assessment, significantly enhances prediction of PD conversion in individuals with prodromal symptoms. This accessible, multimodal approach could facilitate early identification of high-risk individuals for targeted interventions and clinical trials.