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January 24, 2026American Journal of Neuroradiology0 citationsOpen Access

Machine Learning-Driven Approach to Identify Freezing of Gait in Individuals with Parkinson’s Disease Using Conventional Structural MRI and Clinical Measures

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GRGaurav N. RathiAGAlan J. GardnerJLJason K. Longhurst

Key Points

  • To assess the predictive ability of cortical area measures derived from MRI for freezing of gait in individuals with Parkinson’s disease.
  • Analyzed MRI data using FreeSurfer to measure cortical areas in 27 regions of interest.
  • Applied a linear SVM model for prediction of freezing of gait.
  • Conducted assessments based on conventional structural MRI and clinical measures.
  • Achieved an area under the curve (AUC) of 0.71 for predicting freezing of gait.
  • Findings suggest cortical morphology may play a role in freezing of gait risk.
  • Results are preliminary and require validation with larger data sets.

Abstract

Our results demonstrate that FreeSurfer-derived cortical area measures from 27 key regions across the frontal, temporal, parietal, and occipital lobes can moderately predict FOG in PD (AUC = 0.71) using a linear SVM model. While preliminary, our work outlines an MRI-based analytical approach that may inform future external validation efforts and contribute to understanding the potential role of cortical morphology in PD-FOG risk. However, given the limited sample size and constrained independent testing cohort, these findings should be interpreted as exploratory and warrant replication in larger, multi-center studies.

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Cite This Study

Rathi et al. (2026) studied this question.

synapsesocial.com/papers/6974610cbb9d90c67120ae25https://doi.org/10.3174/ajnr.a9171
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