Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures

Kevin P. Nguyen, Vyom Raval, Alex Treacher, Cooper Mellema, Fang Frank Yu, Marco C. Pinho, Rathan M. Subramaniam, Richard B. Dewey, Albert A. Montillo

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Predictive biomarkers of Parkinson's Disease progression are needed to expedite neuroprotective treatment development and facilitate prognoses for patients. This work uses measures derived from resting-state functional magnetic resonance imaging, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF), to predict an individual's current and future severity over up to 4 years and to elucidate the most prognostic brain regions. Methods: ReHo and fALFF are measured for 82 Parkinson's Disease subjects and used to train machine learning predictors of baseline clinical and future severity at 1 year, 2 years, and 4 years follow-up as measured by the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Predictive performance is measured with nested cross-validation, validated on an external dataset, and again validated through leave-one-site-out cross-validation. Important predictive features are identified. Results: The models explain up to 30.4% of the variance in current MDS-UPDRS scores, 55.8% of the variance in year 1 scores, and 47.1% of the variance in year 2 scores (p < 0.0001). For distinguishing high and low-severity individuals at each timepoint (MDS-UPDRS score above or below the median, respectively), the models achieve positive predictive values up to 79% and negative predictive values up to 80%. Higher ReHo and fALFF in several regions, including components of the default motor network, predicted lower severity across current and future timepoints. Conclusion: These results identify an accurate prognostic neuroimaging biomarker which may be used to better inform enrollment in trials of neuroprotective treatments and enable physicians to counsel their patients.

Original languageEnglish (US)
Pages (from-to)44-51
Number of pages8
JournalParkinsonism and Related Disorders
Volume85
DOIs
StatePublished - Apr 2021

Keywords

  • Functional MRI
  • Machine learning
  • Neuroimaging
  • Parkinson's disease
  • Prognosis

ASJC Scopus subject areas

  • Neurology
  • Geriatrics and Gerontology
  • Clinical Neurology

Fingerprint Dive into the research topics of 'Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures'. Together they form a unique fingerprint.

Cite this