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Neuroimage Clin. 2019 Aug 19;24:101986. doi: 10.1016/j.nicl.2019.101986. [Epub ahead of print]

Assessment of a prognostic MRI biomarker in early de novo Parkinson's disease.

Author information

1
Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.
2
Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden.
3
Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
4
Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montréal, QC, Canada.
5
Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada. Electronic address: alain.dagher@mcgill.ca.

Abstract

BACKGROUND:

Commonly used neuroimaging biomarkers in Parkinson's disease (PD) are useful for diagnosis but poor at predicting outcomes. We explored whether an atrophy pattern from whole-brain structural MRI, measured in the drug-naïve early stage, could predict PD prognosis.

METHODS:

362 de novo PD patients with T1-weighted MRI (n = 222 for the main analysis, 140 for the validation analysis) were recruited from the Parkinson's Progression Markers Initiative (PPMI). We investigated a previously identified PD-specific network atrophy pattern as a potential biomarker of disease severity and prognosis. Progression trajectories of motor function (MDS-UPDRS-part III), cognition (Montreal Cognitive Assessment (MoCA)), and a global composite outcome measure were compared between atrophy tertiles using mixed effect models. The prognostic value of the MRI atrophy measure was compared with 123I ioflupane single photon emission computed tomography, the postural-instability-gait-disturbance score, and cerebrospinal fluid markers.

FINDINGS:

After 4.5 years follow-up, PD-specific atrophy network score at baseline significantly predicted change in UPDRS-part III (r = -0.197, p = .003), MoCA (r = 0.253, p = .0002) and global composite outcome (r = -0.249, p = .0002). Compared with the 3rd tertile (i.e. least atrophy), the tertile with the highest baseline atrophy (i.e. the 1st tertile) had a 3-point annual faster progression in UPDRS-part III (p = .012), faster worsening of posture-instability gait scores (+0.21 further annual increase, p < .0001), faster decline in MoCA (-0.74 further annual decline in MoCA, p = .0372) and a + 0.38 (p = .0029) faster annual increase in the global composite z-score. All findings were replicated in a validation analysis using 1.5T MRI. Receiver operating characteristic analysis confirmed the superiority of the MRI biomarker, although it had modest AUC values (0.63). By comparison, the other biomarkers were limited in their ability to predict prognosis either in the main or validation analysis.

INTERPRETATION:

A PD-specific network atrophy pattern predicts progression of motor, cognitive, and global outcome in PD, and is a better predictor of prognosis than any of the other tested biomarkers. Therefore, it has potential as a prognostic biomarker for clinical trials of early PD.

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