Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson's Disease
International Journal of Neural Systems, ISSN: 1793-6462, Vol: 30, Issue: 9, Page: 2050044
2020
- 24Citations
- 27Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations24
- Citation Indexes24
- 24
- Captures27
- Readers27
- 27
- Mentions1
- News Mentions1
- 1
Most Recent News
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 PubMed: 32787634 Submit Comment
Article Description
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[123]-Ioflupane SPECT scans to discern between HC and PD participants in a balanced set of 386 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 97.04% balanced accuracy when the performance was evaluated using a 10-fold cross-validation. This proves the reliability of these biomarkers, especially those related to sphericity, center of mass, number of vertices, 2D-projected perimeter or the 2D-projected eccentricity, among others, but including both internal and external isosurfaces.
Bibliographic Details
World Scientific Pub Co Pte Lt
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