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

Publication ,  Journal Article
Nguyen, KP; Raval, V; Treacher, A; Mellema, C; Yu, FF; Pinho, MC; Subramaniam, RM; Dewey, RB; Montillo, AA
Published in: Parkinsonism Relat Disord
April 2021

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.

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Published In

Parkinsonism Relat Disord

DOI

EISSN

1873-5126

Publication Date

April 2021

Volume

85

Start / End Page

44 / 51

Location

England

Related Subject Headings

  • Severity of Illness Index
  • Reproducibility of Results
  • Prognosis
  • Parkinson Disease
  • Neurology & Neurosurgery
  • Nerve Net
  • Middle Aged
  • Male
  • Magnetic Resonance Imaging
  • Machine Learning
 

Citation

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Nguyen, K. P., Raval, V., Treacher, A., Mellema, C., Yu, F. F., Pinho, M. C., … Montillo, A. A. (2021). Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures. Parkinsonism Relat Disord, 85, 44–51. https://doi.org/10.1016/j.parkreldis.2021.02.026
Nguyen, Kevin P., Vyom Raval, Alex Treacher, Cooper Mellema, Fang Frank Yu, Marco C. Pinho, Rathan M. Subramaniam, Richard B. Dewey, and Albert A. Montillo. “Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures.Parkinsonism Relat Disord 85 (April 2021): 44–51. https://doi.org/10.1016/j.parkreldis.2021.02.026.
Nguyen KP, Raval V, Treacher A, Mellema C, Yu FF, Pinho MC, et al. Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures. Parkinsonism Relat Disord. 2021 Apr;85:44–51.
Nguyen, Kevin P., et al. “Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures.Parkinsonism Relat Disord, vol. 85, Apr. 2021, pp. 44–51. Pubmed, doi:10.1016/j.parkreldis.2021.02.026.
Nguyen KP, Raval V, Treacher A, Mellema C, Yu FF, Pinho MC, Subramaniam RM, Dewey RB, Montillo AA. Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures. Parkinsonism Relat Disord. 2021 Apr;85:44–51.
Journal cover image

Published In

Parkinsonism Relat Disord

DOI

EISSN

1873-5126

Publication Date

April 2021

Volume

85

Start / End Page

44 / 51

Location

England

Related Subject Headings

  • Severity of Illness Index
  • Reproducibility of Results
  • Prognosis
  • Parkinson Disease
  • Neurology & Neurosurgery
  • Nerve Net
  • Middle Aged
  • Male
  • Magnetic Resonance Imaging
  • Machine Learning