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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/neurohacking
课程评论：没有评论
课程名称：R语言神经黑客基础 课程概述：神经黑客是指如何使用R编程语言及其相关包对神经成像数据进行操作、处理和分析。本课程重点关注公开可用的结构性磁共振成像（MRI）数据，讨论了诸如不均匀性校正、图像配准和图像可视化等概念。 课程结束后，学员将能够： - 读取/写入NIfTI（神经成像信息技术计划）格式的脑部图像 - 可视化并探索这些图像 - 执行不均匀性校正、脑部提取和图像配准（在受试者内或与模板对齐） 课程大纲： 1. **引言** 描述课程的基本内容和目标。 2. **神经成像：格式与可视化** 讨论脑部图像的不同格式以及常见的磁共振成像（MRI）扫描技术。 3. **图像处理** 讲解处理脑部MRI数据的步骤，包括不均匀性校正、脑部提取（或头颅去除）及各种图像配准技术。 4. **扩展图像处理** 探讨不同类型的配准及如何处理多序列MRI扫描，并介绍简化处理过程的包装函数。同时覆盖脑部图像数据的交互式探索及T1加权图像的组织水平（白质/灰质和脑脊液）分割。 通过本课程，学员将掌握R语言在神经成像数据分析中的实际应用技能。
Name:Introduction
Description:
Name:Neuroimaging: Formats and Visualization
Description:In this section, we will discuss different formats that brain images come in, as well as some of the commonly done magnetic resonance imaging (MRI) scans.
Name:Image Processing
Description:In this section, we will discuss the steps done to process brain MRI data. We will discuss inhomogeneity correction, brain extraction or skull stripping, and various image registration techniques.
Name:Extended Image Processing
Description:In this section, we will discuss the different types of registration and how one would go through processing a multi-sequence MRI scan, as well as wrapper functions that make the process much easier. We also cover interactive exploration of brain image data and tissue-level (white/gray matter and cerebrospinal fluid (CSF)) segmentation from a T1-weighted image.
Neurohacking describes how to use the R programming language (https://cran.r-project.org/) and its associated package to perform manipulation, processing, and analysis of neuroimaging data. We focus on publicly-available structural magnetic resonance imaging (MRI). We discuss concepts such as inhomogeneity correction, image registration, and image visualization. By the end of this course, you will be able to: Read/write images of the brain in the NIfTI (Neuroimaging Informatics Technology Initiative) format Visualize and explore these images Perform inhomogeneity correction, brain extraction, and image registration (within a subject and to a template).
