Accuracy to Differentiate Mild Cognitive Impairment in Parkinson’s Disease Using Cortical Features
Mild cognitive impairment (MCI) is common in Parkinson’s Disease (PD) patients and it is key to predict the development of dementia. There is not report of discriminant accuracy for MCI using based-surface cortical morphometry. This study used Cortical-Thickness (CT) combined to Local-Gyrification-Index (LGI) to assess discriminant accuracy for MCI stages in PD. Sixty-four patients with idiopathic PD and nineteen healthy controls (HC) were analyzed. CT and LGI were estimated using Freesurfer software. Principal Component Analysis and Lineal Discriminant Analysis (LDA) assuming a common diagonal covariance matrix (or Naive-Bayes classifier) was used with cross-validation leave-one-subject-out scheme. Accuracy, sensibility and specificity were reported to different classification analysis. CT combined to LGI limited revealed the best discrimination with accuracy of 82,98%, sensitivity of 85.71% and specificity of 80.77%. A validation process using independent and more heterogeneous data set and further longitudinal studies, are necessary to confirm our results.
KeywordsNaive-Bayes classifier PCA Accuracy Parkinson’s disease MCI Cortical Thickness Cortical Folding LGI MRI Surface-based morphometry
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