Structural neuroimaging as clinical predictor: a review of machine learning applications

Autor:	Mateos-Pérez, José María, Dadar, Mahsa, Lacalle-Aurioles, María, Iturria-Medina, Yasser, Zeighami, Yashar, Evans, Alan C.
Rok vydání:	2018
Předmět:	Quantitative Biology - Quantitative Methods
Druh dokumentu:	Working Paper
DOI:	10.1016/j.nicl.2018.08.019
Popis:	In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields. Comment: 70 pages, 3 figures. First two authors share first authorship
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1806.03512 Zobrazit plný text záznamu View this record from Arxiv