BundleMAP: Anatomically localized classification, regression, and hypothesis testing in diffusion MRI
作者：
Highlights：
• Manifold learning is used to achieve a joint parametrization of fiber bundles from diffusion MRI.
• Diffusion parameters can be plotted along the bundle.
• Anatomically localized and interpretable features are extracted.
• Increased accuracy for supervised classification and regression is demonstrated.
• Increased power for hypothesis testing is shown.
摘要
Highlights•Manifold learning is used to achieve a joint parametrization of fiber bundles from diffusion MRI.•Diffusion parameters can be plotted along the bundle.•Anatomically localized and interpretable features are extracted.•Increased accuracy for supervised classification and regression is demonstrated.•Increased power for hypothesis testing is shown.
论文关键词：Disease detection,Manifold learning,Support vector machines,Classification,Regression,Fiber tracking,Diffusion MRI
论文评审过程：Received 31 January 2016, Revised 23 August 2016, Accepted 21 September 2016, Available online 21 September 2016, Version of Record 27 November 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.020
