Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures.
Parkinsonism Relat Disord
; 85: 44-51, 2021 04.
Article
in En
| MEDLINE
| ID: mdl-33730626
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Parkinson Disease
/
Magnetic Resonance Imaging
/
Cerebellum
/
Cerebral Cortex
/
Disease Progression
/
Functional Neuroimaging
/
Machine Learning
/
Default Mode Network
/
Nerve Net
Type of study:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Aspects:
Patient_preference
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Journal:
Parkinsonism Relat Disord
Journal subject:
NEUROLOGIA
Year:
2021
Document type:
Article
Affiliation country: