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Ciprian M. Crainiceanu,
Dr. Elizabeth Sweeney ,
and
John Muschelli III
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.
Read more
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.
Read more
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).
Register for this course and see more details by visiting:
OpenCourser.com/course/8lmt59/introduction
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What's inside
Syllabus
Introduction
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.
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Syllabus
Introduction
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.
Read more
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.
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides foundational skills in data manipulation, processing, and analysis for neuroimaging research
Covers essential neuroimaging techniques such as inhomogeneity correction, brain extraction, and image registration
Leverages the widely used R programming language and associated neuroimaging packages
Taught by experienced instructors with expertise in neuroimaging research
Applicable for both academic and industry settings where neuroimaging data analysis is required
May require additional prerequisites in programming and neuroimaging concepts for optimal learning experience
Save this course
Save Introduction to Neurohacking In R to your list so you can find it easily later:
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Reviews summary
Hands-on intro to neurohacking with r
learners say that this hands-on course is an excellent introduction to neuroimaging in R. The in-depth explanations cover the basics of R and fsl, antr. However, some students found the initial software and library setup challenging and express that the pace of some lectures could be slowed down.
Detailed coverage of core concepts.
"Love the in depth explanations of the R!"
"excelllent course building you up with basics using R and fsl, antr"
"Very Informative. I suggest all people who are seeking to gain knowledge about image processing should definitely check this out."
Practical exercises and real-world applications.
"very hands on"
"This course helped me a lot with my thesis project, I didn't know that you can use R for Medical Image Processing."
Some lectures could be slower paced.
"I learned a huge amount of material. I feel confident in "R". My only suggestion is to slow down some of the lectures a bit."
"Great course! Just please delete the 10-second introduction before each video. It makes the most horrible sound. Other than that, amazing course"
Difficulties with software and library installation.
"Only problem was the difficulty at first with the software and libraries stubbornly refusing to get setup."
"The process of downloading packages on the virtual machine was very tiring."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Introduction to Neurohacking In R with these
activities:
Review: An Introduction to Neuroimaging
Show steps
Review this book as a foundational reference text to provide a comprehensive understanding of neuroimaging techniques and their applications.
View
Clinical Neuroanatomy: Brain Circuitry and Its...
on Amazon
Show steps
-
Read the introductory chapters
-
Focus on sections relevant to the course
-
Take notes and highlight key concepts
Form a study group
Show steps
Join or form a study group with classmates to review course materials, discuss concepts, and prepare for assessments.
Browse courses on
Collaboration
Show steps
-
Find classmates willing to participate
-
Establish a regular meeting schedule
-
Prepare discussion topics and questions
Run code examples
Show steps
Run through the code examples in the course materials to ingrain the syntax of the R programming language.
Browse courses on
Programming
Show steps
-
Locate the code example
-
Run the code example
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice image manipulation
Show steps
Complete hands-on exercises and challenges to develop proficiency in image manipulation techniques using the R programming language.
Browse courses on
Image Processing
Show steps
-
Obtain sample brain images
-
Write R code to perform image manipulation tasks
-
Experiment with different parameters and settings
Create diagrams
Show steps
Create diagrams of the brain to visualize and understand the concepts of inhomogeneity correction, image registration, and image segmentation.
Show steps
-
Choose a brain image
-
Use an image processing tool to create a diagram
-
Annotate the diagram with relevant information
Review: An Introduction to Neuroimaging
Show steps
Review this book as a foundational reference text to provide a comprehensive understanding of neuroimaging techniques and their applications.
View
Clinical Neuroanatomy: Brain Circuitry and Its...
on Amazon
Show steps
Show steps
Show steps
- Read the introductory chapters
- Focus on sections relevant to the course
- Take notes and highlight key concepts
Form a study group
Show steps
Join or form a study group with classmates to review course materials, discuss concepts, and prepare for assessments.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find classmates willing to participate
- Establish a regular meeting schedule
- Prepare discussion topics and questions
Run code examples
Show steps
Run through the code examples in the course materials to ingrain the syntax of the R programming language.
Browse courses on
Programming
Show steps
Show steps
Show steps
- Locate the code example
- Run the code example
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice image manipulation
Show steps
Complete hands-on exercises and challenges to develop proficiency in image manipulation techniques using the R programming language.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Obtain sample brain images
- Write R code to perform image manipulation tasks
- Experiment with different parameters and settings
Create diagrams
Show steps
Create diagrams of the brain to visualize and understand the concepts of inhomogeneity correction, image registration, and image segmentation.
Show steps
Show steps
Show steps
- Choose a brain image
- Use an image processing tool to create a diagram
- Annotate the diagram with relevant information
Career center
Learners who complete Introduction to Neurohacking In R will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists play a key role in developing and applying neuroimaging techniques for understanding brain function and behavior. This course would build a foundation in image processing, analysis, and visualization techniques, which are essential for Data Scientists. Understanding inhomogeneity correction, brain extraction, and image registration will be particularly relevant to this role.
Statistician
Statistician
Statisticians are involved in the collection, analysis, interpretation, and presentation of data. In neuroimaging, Statisticians play a key role in developing statistical methods for analyzing neuroimaging data. This course would provide a strong foundation in statistical concepts and techniques that are relevant to neuroimaging.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make predictions. In neuroimaging, Quantitative Analysts use these techniques to analyze neuroimaging data to identify patterns and relationships. This course would provide a strong foundation in the mathematical and statistical techniques used in neuroimaging.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and implement machine learning algorithms to solve real-world problems. In neuroimaging, Machine Learning Engineers develop algorithms for analyzing and classifying neuroimaging data. This course would provide a strong foundation in machine learning algorithms and techniques that are relevant to neuroimaging.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. In neuroimaging, Software Engineers develop software for acquiring, processing, and analyzing neuroimaging data. This course would provide a strong foundation in software development principles and practices that are relevant to neuroimaging.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers apply engineering principles and techniques to solve problems in biology and medicine. In neuroimaging, Biomedical Engineers develop and use neuroimaging technologies for diagnosing and treating brain disorders. This course would provide a strong foundation in the engineering principles and techniques used in neuroimaging.
Neuroscientist
Neuroscientist
Neuroscientists study the nervous system, including the brain. They use a variety of techniques to study the structure and function of the brain, including neuroimaging. This course would provide a strong foundation in the principles and techniques of neuroimaging, which are essential for Neuroscientists.
Radiologist
Radiologist
Radiologists use imaging techniques, such as MRI, to diagnose and treat diseases. In neuroimaging, Radiologists use MRI to diagnose and treat brain disorders. This course would provide a strong foundation in the principles and techniques of MRI, which are essential for Radiologists.
Neurologist
Neurologist
Neurologists diagnose and treat disorders of the nervous system, including the brain. They use a variety of techniques to diagnose and treat brain disorders, including neuroimaging. This course would provide a strong foundation in the principles and techniques of neuroimaging, which are essential for Neurologists.
Pediatrician
Pediatrician
Pediatricians diagnose and treat diseases in children, including brain disorders. They use a variety of techniques to diagnose and treat brain disorders, including neuroimaging. This course may provide a foundation in the principles and techniques of neuroimaging, which can be helpful for Pediatricians.
Psychiatrist
Psychiatrist
Psychiatrists diagnose and treat mental disorders, including those that affect the brain. They use a variety of techniques to diagnose and treat mental disorders, including neuroimaging. This course may provide a foundation in the principles and techniques of neuroimaging, which can be helpful for Psychiatrists.
Neuropsychologist
Neuropsychologist
Neuropsychologists study the relationship between the brain and behavior. They use a variety of techniques to study the brain and behavior, including neuroimaging. This course may provide a foundation in the principles and techniques of neuroimaging, which can be helpful for Neuropsychologists.
For more career information including salaries, visit:
OpenCourser.com/course/8lmt59/introduction
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Neurohacking In R.
Provides a comprehensive overview of neuroscience, including the structure and function of the brain, the development of the nervous system, and the neural basis of behavior.
Provides a comprehensive overview of probabilistic machine learning, including supervised and unsupervised learning.
Provides a comprehensive overview of pattern recognition and machine learning, including supervised and unsupervised learning, and deep learning.
Save
Provides a comprehensive overview of deep learning, including convolutional neural networks and recurrent neural networks.
A textbook that provides a comprehensive overview of the principles of neuroimaging, including both theoretical and practical aspects. Useful for gaining a strong foundation in neuroimaging.
Provides a comprehensive overview of data science, including data cleaning, transformation, modeling, and evaluation.
Provides a comprehensive overview of neuropsychology, a field that studies the relationship between brain function and behavior. It covers a wide range of topics, including the assessment of neuropsychological disorders and the treatment of neuropsychological disorders
A clinical guide to neuroimaging, covering a wide range of neurological and psychiatric disorders. Useful for gaining an understanding of the clinical applications of neuroimaging.
Provides a comprehensive overview of Python for data analysis, including data manipulation, visualization, and machine learning.
Provides a comprehensive overview of statistical learning methods. It covers topics such as linear regression, logistic regression, tree-based methods, and support vector machines.
Save
A popular science book that provides a fascinating overview of the human brain and its functions. Useful for gaining a general understanding of the brain and its relation to neuroimaging.
Provides a comprehensive overview of statistical methods for fMRI data analysis. It covers topics such as experimental design, data preprocessing, and statistical modeling.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8lmt59/introduction
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Effort
4 weeks of study, 2-4 hours/week
Level
Intermediate
Via
Coursera
Institution
Johns Hopkins University
Instructors
Ciprian M. Crainiceanu
Dr. Elizabeth Sweeney
John Muschelli III
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Neuroscience and Neuroimaging. It's comprised of four courses:
- Principles of fMRI 2
- Introduction to Neurohacking In R (viewing)
- Fundamental Neuroscience for Neuroimaging
- Principles of fMRI 1
Good to know
Provides foundational skills in data manipulation, processing, and analysis for neuroimaging research
Covers essential neuroimaging techniques such as inhomogeneity correction, brain extraction, and image registration
Leverages the widely used R programming language and associated neuroimaging packages
Taught by experienced instructors with expertise in neuroimaging research
Applicable for both academic and industry settings where neuroimaging data analysis is required
May require additional prerequisites in programming and neuroimaging concepts for optimal learning experience
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