About this course: 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).
Created by: Johns Hopkins University
Taught by: Dr. Elizabeth Sweeney , Rice Academy Postdoctoral Fellow
Biostatistics
Taught by: Ciprian M. Crainiceanu, Professor
Biostatstics
Taught by: John Muschelli III , Assistant Scientist
Biostatistics
| Level | Intermediate |
| Commitment | 4 weeks of study, 2-4 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Introduction
3 videos, 1 reading, 1 practice quiz
- Video: Meet the Team
- Reading: Syllabus
- Video: Introduction
- Video: Structural MRI and R
- Practice Quiz: Theme 1 Quiz
WEEK 2
Neuroimaging: Formats and Visualization
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.
7 videos
- Video: Data Structures and Operations
- Video: The NIfTI Format
- Video: Basic Visualization 1
- Video: Basic Visualization 2
- Video: Basic Data Manipulation
- Video: Transformations and Smoothing
- Video: Basic MRI Contrasts
Graded: Theme 2 Lecture 1 Quiz
Graded: Theme 2 Lecture 2 Quiz
Graded: Theme 2 Lecture 3 Quiz
Graded: Theme 2 Lecture 4 Quiz
Graded: Theme 2 Lecture 5 Quiz
Graded: Theme 2 Lecture 6 Quiz
WEEK 3
Image Processing
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.
9 videos
- Video: Preprocessing: An Overview
- Video: Registration
- Video: fslr: System Requirements
- Video: Bias Field Correction Using fslr
- Video: Brain Extraction using fslr
- Video: Image Registration Using fslr
- Video: Installing ANTsR
- Video: Basic Data Manipulation with ANTsR
- Video: Pre-Processing with ANTSR
Graded: Theme 3 Quiz 1
Graded: Theme 3 Quiz 2
Graded: Theme 3 Quiz 3
Graded: Theme 3 Quiz 4
Graded: Theme 3 Quiz 5
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Graded: Theme 3 Quiz 8
Graded: Theme 3 Quiz 9
WEEK 4
Extended Image Processing
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.
11 videos, 1 reading
- Video: Advanced Brain Image Processing
- Video: Co-Registration using fslr
- Video: Co-Registration using ANTsR
- Video: Wrapper Functions for Processing Pipelines
- Video: Across-Visit Co-Registration of T1 Images
- Video: Rigid Registration to a Template
- Video: Affine Registration of T1 to Template
- Video: Nonlinear Registration of T1 to Template
- Video: Getting ROI Anatomic Information from Non-Linear Registration
- Video: Tissue-Level Segmentation
- Video: Papayar: Interactively Viewing Images in R
- Reading: Papayar Script
Graded: Theme 4 Quiz 1
Graded: Theme 4 Quiz 2
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Graded: Theme 4 Quiz 9
Graded: Theme 4 Quiz 10
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Ratings and Reviews
Perfect! Precise to the subject and provides a lot of hands-on details, which is, unfortunately, something that most other similar courses don't do. Absolutely loved it!
It was a really good course, thank you!
Great course....medical imaging techniques at its best :)
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LP
Very comprehensive step-by-step introduction to imaging analyses using R. Also includes helpful information on the nature of files and processes. I am not sure that I will do my imaging analyses using R but still found this to be a very useful introduction to the topic.