Scientific publications

Automated Neuromelanin Imaging as a Diagnostic Biomarker for Parkinson's Disease

Castellanos G(1), Fernández-Seara MA, Lorenzo-Betancor O, Ortega-Cubero S, Puigvert M, Uranga J, Vidorreta M, Irigoyen J, Lorenzo E, Muñoz-Barrutia A, Ortiz-de-Solorzano C, Pastor P, Pastor MA [SP].

(1) Neuroimaging Laboratory, University of Navarra, Pamplona, Spain; CIBERNED, Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, Instituto de Salud Carlos III, Madrid, Spain.

Magazine: Movement Disorders

Date: Mar 15, 2015

Neurology [SP]

ABSTRACT

We aimed to analyze the diagnostic accuracy of an automated segmentation and quantification method of the SNc and locus coeruleus (LC) volumes based on neuromelanin (NM)-sensitive MRI (NM-MRI) in patients with idiopathic (iPD) and monogenic (iPD) Parkinson's disease (PD).

Thirty-six patients (23 idiopathic and 13 monogenic PARKIN or LRRK2 mutations) and 37 age-matched healthy controls underwent 3T-NM-MRI. SNc and LC volumetry were performed using fully automated multi-image atlas segmentation. The diagnostic performance to differentiate PD from controls was measured using the area under the curve (AUC) and likelihood ratios based on receiver operating characteristic (ROC) analyses.

We found a significant reduction of SNc and LC volumes in patients, when compared to controls. ROC analysis showed better diagnostic accuracy when using SNc volume than LC volume. Significant differences between ipsilateral and contralateral SNc volumes, in relation to the more clinically affected side, were found in patients with iPD (P = 0.007). Contralateral atrophy in the SNc showed the highest power to discriminate PD subjects from controls (AUC, 0.93-0.94; sensitivity, 91%-92%; specificity, 89%; positive likelihood ratio: 8.4-8.5; negative likelihood ratio: 0.09-0.1 at a single cut-off point). Interval likelihood ratios for contralateral SNc volume improved the diagnostic accuracy of volumetric measurements. SNc and LC volumetry based on NM-MRI resulting from the automated segmentation and quantification technique can yield high diagnostic accuracy for differentiating PD from health and might be an unbiased disease biomarker. 

CITATION Mov Disord. 2015 Mar 15. doi: 10.1002/mds.26201.

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