Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson's Disease.

Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson's Disease. Int J Neural Syst. 2020 Aug 12;:2050044 Authors: Castillo-Barnes D, Martinez-Murcia FJ, Ortiz A, Salas-Gonzalez D, RamÍrez J, Górriz JM Abstract Finding new biomarkers to model Parkinson's Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD but also as a way to measure quantitatively the loss of dopaminergic neurons mainly concentrated at substantia nigra. Within this context, this work presented here tries to provide a set of imaging features based on morphological characteristics extracted from I[Formula: see text]-Ioflupane SPECT scans to discern between HC and PD participants in a balanced set of [Formula: see text] scans from Parkinson's Progression Markers Initiative (PPMI) database. These features, obtained from isosurfaces of each scan at different intensity levels, have been classified through the use of classical Machine Learning classifiers such as Support-Vector-Machines (SVM) or Naïve Bayesian and compared with the results obtained using a Multi-Layer Perceptron (MLP). The proposed system, based on a Mann-Whitney-Wilcoxon U-Test for feature selection and the SVM approach, yielded a [Formula: see text] balanced accuracy when the performance was evaluated using a [Formula: see text]-fold cross-validation. This proves the reliability of these bi...
Source: International Journal of Neural Systems - Category: Neurology Authors: Tags: Int J Neural Syst Source Type: research