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Martin Lindquist, PhD, MSc
and
Tor Wager, PhD
Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from many disciplines. Psychologists, statisticians, physicists, computer scientists, neuroscientists, medical researchers, behavioral scientists, engineers, public health researchers, biologists, and others are coming together to advance our understanding of the human mind and brain. This course covers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data, including psychological inference, MR Physics, K Space, experimental design, pre-processing of fMRI data, as well as Generalized Linear Models (GLM’s). A book related to the class can be found here: https://leanpub.com/principlesoffmri.
Register for this course and see more details by visiting:
OpenCourser.com/course/zsvzgn/principles
What's inside
Syllabus
Week 1
This week we will introduce fMRI, and talk about data acquisition and reconstruction.
Week 2
This week we will discuss the fMRI signal, experimental design and pre-processing.
Read more
Syllabus
Week 1
This week we will introduce fMRI, and talk about data acquisition and reconstruction.
Week 2
This week we will discuss the fMRI signal, experimental design and pre-processing.
Read more
Week 3
This week we will discuss the General Linear Model (GLM).
Week 4
The description goes here
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Well-suited for individuals interested in the neuroscience, psychology, medical research, and data science fields
Taught by experts in the field of neuroimaging, Martin Lindquist and Tor Wager
Covers fundamental concepts of fMRI, including experimental design, data acquisition, and analysis
Involves hands-on exercises and provides resources for further learning
Introduces specialized software and tools used in fMRI research
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Reviews summary
fmri theory and analysis
According to students, Principles of fMRI 1 is an engaging and well-structured course that provides a largely positive learning experience. Learners say that the course is a deep dive into theoritical principles and analysis within fMRI. Students describe the content as challenging yet foundational, leading to deep understanding of fMRI concepts. This course is recommended for individuals with a background in statistics, mathematics, or neuroscience, as some knowledge is assumed.
Instructors are engaging and knowledgeable.
"High-quality information brought to the point."
"Lectures were easy to follow."
"Instructors were great."
Course focuses on theoretical understanding of fMRI.
"Excellent introduction to fMRI."
"Taken in concert, the lectures and the reading are thorough if complex."
"A pretty challenging but interesting course."
Course content is challenging but provides a deep dive into theoretical principles of fMRI.
"Very interesting with challenging yet foundational information."
"The information covered in this course provides a deep understanding."
"Really lots of new knowledge."
Assumes knowledge of statistics and mathematics.
"The course is a good theoritical introduction to the concept of fMRI analysis. However, there is a lot of room for upgrading its content."
"Many times I felt that the course assumed that the readers has advance knowledge with respect to mathematical notations and statistical notations."
"This course seems to require more background knowledge when it comes to experimentation and statistics if you want to really understand it."
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 Principles of fMRI 1 with these
activities:
Khan Academy: Statistics and probability review
Show steps
Brush up on foundational skills to better understand the statistical measures and analyses taught in this course.
Browse courses on
Statistics
Show steps
-
Access the videos and exercises on the Khan Academy website.
-
Complete the 'Probability' and 'Statistics' units.
-
Review key concepts such as mean, median, mode, and standard deviation.
-
Practice solving problems involving data analysis and interpretation.
-
Take the practice quizzes to check your understanding.
Practice fMRI data analysis with Neuroimaging in Python Pipeline (NiPype)
Show steps
Gain hands-on experience working with real fMRI data and solidify your understanding of the analysis techniques covered in the course.
Show steps
-
Install NiPype and its dependencies.
-
Download a sample fMRI dataset from an open-source repository.
-
Follow a NiPype tutorial or documentation to preprocess and analyze the data.
-
Explore the results and interpret the findings.
-
Troubleshoot any errors or issues encountered during the process.
Compile a curated list of fMRI resources.
Show steps
Organize and share valuable resources related to fMRI, serving as a valuable reference for your learning journey and the community.
Browse courses on
Compilation
Show steps
-
Identify and gather relevant resources such as articles, tutorials, datasets, and software tools.
-
Categorize and annotate the resources based on topic and difficulty level.
-
Create a user-friendly format for sharing, such as a website, blog post, or online repository.
-
Promote and share the compiled resources within the fMRI community.
-
Regularly update and maintain the compilation to ensure its relevance and usefulness.
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow tutorials on advanced fMRI analysis techniques.
Show steps
Expand your knowledge and skills by exploring specialized fMRI analysis methods through guided tutorials.
Browse courses on
Advanced Techniques
Show steps
-
Identify specific analysis techniques you want to learn.
-
Search for reputable tutorials or online courses on those techniques.
-
Follow the instructions and complete the exercises provided in the tutorials.
-
Apply the learned techniques to your own fMRI data or publicly available datasets.
-
Share your findings and discuss with others in online forums or research groups.
fMRI Research Proposal
Show steps
Develop a research proposal for an fMRI study based on your interests, demonstrating your comprehension of the methodologies and applications of fMRI.
Browse courses on
Research Proposal
Show steps
-
Identify a research question or hypothesis.
-
Review existing literature and design a study protocol.
-
Select appropriate fMRI techniques and analysis methods.
-
Create a timeline and budget for the proposed research.
-
Write a compelling research proposal that clearly outlines your study design, expected outcomes, and significance.
Assist in a research laboratory using fMRI.
Show steps
Obtain practical experience in an fMRI research environment, applying the knowledge and techniques learned in the course to real-world projects.
Show steps
-
Contact research laboratories and inquire about volunteer opportunities.
-
Attend an orientation or training session to familiarize yourself with the laboratory procedures.
-
Assist with data collection, preprocessing, or analysis tasks under the guidance of a mentor.
-
Participate in team meetings and discussions to gain insights into the research process.
-
Contribute to the laboratory's research goals and present your findings at conferences or meetings.
Khan Academy: Statistics and probability review
Show steps
Brush up on foundational skills to better understand the statistical measures and analyses taught in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Access the videos and exercises on the Khan Academy website.
- Complete the 'Probability' and 'Statistics' units.
- Review key concepts such as mean, median, mode, and standard deviation.
- Practice solving problems involving data analysis and interpretation.
- Take the practice quizzes to check your understanding.
Practice fMRI data analysis with Neuroimaging in Python Pipeline (NiPype)
Show steps
Gain hands-on experience working with real fMRI data and solidify your understanding of the analysis techniques covered in the course.
Show steps
Show steps
Show steps
- Install NiPype and its dependencies.
- Download a sample fMRI dataset from an open-source repository.
- Follow a NiPype tutorial or documentation to preprocess and analyze the data.
- Explore the results and interpret the findings.
- Troubleshoot any errors or issues encountered during the process.
Compile a curated list of fMRI resources.
Show steps
Organize and share valuable resources related to fMRI, serving as a valuable reference for your learning journey and the community.
Browse courses on
Compilation
Show steps
Show steps
Show steps
- Identify and gather relevant resources such as articles, tutorials, datasets, and software tools.
- Categorize and annotate the resources based on topic and difficulty level.
- Create a user-friendly format for sharing, such as a website, blog post, or online repository.
- Promote and share the compiled resources within the fMRI community.
- Regularly update and maintain the compilation to ensure its relevance and usefulness.
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow tutorials on advanced fMRI analysis techniques.
Show steps
Expand your knowledge and skills by exploring specialized fMRI analysis methods through guided tutorials.
Browse courses on
Advanced Techniques
Show steps
Show steps
Show steps
- Identify specific analysis techniques you want to learn.
- Search for reputable tutorials or online courses on those techniques.
- Follow the instructions and complete the exercises provided in the tutorials.
- Apply the learned techniques to your own fMRI data or publicly available datasets.
- Share your findings and discuss with others in online forums or research groups.
fMRI Research Proposal
Show steps
Develop a research proposal for an fMRI study based on your interests, demonstrating your comprehension of the methodologies and applications of fMRI.
Browse courses on
Research Proposal
Show steps
Show steps
Show steps
- Identify a research question or hypothesis.
- Review existing literature and design a study protocol.
- Select appropriate fMRI techniques and analysis methods.
- Create a timeline and budget for the proposed research.
- Write a compelling research proposal that clearly outlines your study design, expected outcomes, and significance.
Assist in a research laboratory using fMRI.
Show steps
Obtain practical experience in an fMRI research environment, applying the knowledge and techniques learned in the course to real-world projects.
Show steps
Show steps
Show steps
- Contact research laboratories and inquire about volunteer opportunities.
- Attend an orientation or training session to familiarize yourself with the laboratory procedures.
- Assist with data collection, preprocessing, or analysis tasks under the guidance of a mentor.
- Participate in team meetings and discussions to gain insights into the research process.
- Contribute to the laboratory's research goals and present your findings at conferences or meetings.
Career center
Learners who complete Principles of fMRI 1 will develop knowledge and skills
that may be useful to these careers:
Medical Physicist
Medical Physicist
As a Medical Physicist, you will research, develop, and apply advanced technologies for the treatment of patients. This course is a good fit if you are interested building a foundation in the physical principles of fMRI. The topics covered in the course will be useful for those looking to enter this field as it provides a foundation in the field of physics.
Neuroscientist
Neuroscientist
As a Neuroscientist, you will investigate the structure and function of the nervous system. This course is a good fit if you are interesting in understanding the neural basis of cognition and behavior as the course will help you build a foundation in fMRI analysis techniques.
Physicist
Physicist
As a Physicist, you will conduct research and develop theories in the field of physics. This course is a good fit if you are interested in the physical principles of fMRI. It will help you build a foundation in the field of physics and prepare you for research in the field.
Medical Researcher
Medical Researcher
As a Medical Researcher, you will research and develop new treatments and cures for diseases. This course is a good fit if you are interested in using fMRI to develop new medical treatments.
Neurologist
Neurologist
As a Neurologist, you will diagnose, manage, and treat disorders of the central nervous system. Those seeking to enter this field may find this course helpful as it will help build a foundation in fMRI and related techniques, which can be used to study and treat disorders of the brain.
Neurosurgeon
Neurosurgeon
As a Neurosurgeon, you will diagnose and treat disorders of the nervous system. Those seeking to enter this field may find this course helpful as it will help build a foundation in fMRI and related techniques, which can be used to study and treat disorders of the brain.
Psychologist
Psychologist
As a Psychologist, you will study the mind and behavior. This course may be helpful as it will help you build a foundation in the field of psychology and prepare you to conduct psychological research using fMRI.
Behavioral Scientist
Behavioral Scientist
As a Behavioral Scientist, you will study the behavior of humans and animals. This course may be useful as it will help you build a foundation in the field of behavioral science and prepare you to conduct behavioral research using fMRI.
Biomedical Engineer
Biomedical Engineer
As a Biomedical Engineer, you will apply engineering principles to the development of medical devices and treatments. This course may be useful for someone seeking to enter this field as the course covers the physics and engineering principles of fMRI.
Biologist
Biologist
As a Biologist, you will study the structure, function, and development of living organisms. This course may be helpful as it will help you build a foundation in the field of biology and prepare you to conduct biological research using fMRI.
Radiologist
Radiologist
As a Radiologist, you will use imaging techniques to diagnose and treat diseases. This course may be useful for someone seeking to enter this field as some of the techniques covered in the course are uses by Radiologists.
Statistician
Statistician
As a Statistician, you will collect, analyze, interpret, and present data. This course may be useful for someone seeking to enter this field as the course covers statistical techniques used in fMRI analysis.
Engineer
Engineer
As an Engineer, you will design, build, and operate machines, systems, and structures. This course may be useful as it covers some of the engineering principles used in fMRI.
Public Health Researcher
Public Health Researcher
As a Public Health Researcher, you will investigate the factors that affect the health of populations. This course may be useful for someone seeking to enter this field as they may need to use fMRI to study the effects of public health interventions.
Computer Scientist
Computer Scientist
As a Computer Scientist, you will research, design, and develop computer systems and software. The topics covered in the course will be useful to those seeking to work in medical imaging.
For more career information including salaries, visit:
OpenCourser.com/course/zsvzgn/principles
Reading list
We've selected 27 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
Principles of fMRI 1.
This textbook provides a comprehensive and rigorous treatment of statistical mechanics, with a focus on the fundamental principles and their applications in a variety of fields.
This textbook provides a comprehensive and rigorous treatment of statistical mechanics, covering both classical and quantum systems. It valuable resource for students and researchers working in statistical physics and related fields.
This textbook provides a modern and comprehensive treatment of statistical mechanics, with a focus on molecular simulation techniques. It valuable resource for students and researchers working in computational physics and related fields.
This textbook provides a broad and comprehensive treatment of statistical mechanics, with a focus on applications in physics, chemistry, and biology. It valuable resource for students and professionals in these fields.
Save
Provides a comprehensive overview of fMRI, from its physical principles to its applications in research and clinical settings. It is an excellent resource for students and researchers who want to gain a broad understanding of the field.
Provides a solid background in linear algebra, which is essential for understanding the mathematical foundations of fMRI data analysis. Covers topics such as vectors, matrices, least squares, and singular value decomposition, making it a useful resource for both beginners and those seeking a deeper understanding.
This classic textbook provides a comprehensive overview of thermodynamics and thermostatistics, with a focus on the fundamental principles and their applications. It valuable reference for students and professionals in physics, chemistry, and engineering.
Save
Provides a comprehensive overview of the mathematical concepts and techniques used in neuroscience, including linear algebra, calculus, probability, and statistics. It is written in a clear and accessible style, making it an excellent choice for students and researchers with a limited mathematical background.
Save
This textbook provides a comprehensive and modern treatment of statistical physics and thermodynamics, with a focus on the fundamental principles and their applications in a variety of fields.
Save
This textbook provides a comprehensive and modern treatment of statistical mechanics, with a focus on the applications in chemistry and biology.
Provides an extensive, detailed, modern, and comprehensive treatment of the principles and practice of fMRI. It also touches on the historical and background work that led to fMRI. This book is comprehensive but is useful as a reference tool more so than as a primary text.
This textbook provides a concise and accessible introduction to statistical mechanics, with a focus on the fundamental principles. It valuable resource for students and professionals in physics, chemistry, and engineering.
This textbook provides a comprehensive and accessible introduction to statistical physics, with a focus on the fundamental principles and their applications in a variety of fields.
This textbook provides a concise and accessible introduction to statistical mechanics, with a focus on the fundamental principles and their applications in a variety of fields.
An introductory resource to the anatomy and functions of the human brain. Will be useful for those looking for background knowledge of the neuroanatomy and terminology commonly used in the field of fMRI.
This textbook provides foundational knowledge of statistical thermodynamics. Concepts are explained in a concise and accessible manner, with many mathematical equations simplified to make them easier to understand for students with varied backgrounds.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. Covers topics such as supervised learning, unsupervised learning, and Bayesian modeling, which are essential for understanding the advanced techniques used in fMRI data analysis.
Save
Covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. Provides insights into the latest advancements in deep learning and its applications in various fields, including neuroimaging.
This textbook provides a concise and accessible introduction to statistical mechanics, with a focus on the applications in physics.
Covers a lot of the same material as the course (and the book that is already linked in the course resources). It suitable introductory text and would also serve well as a reference tool.
Provides an introduction to cognitive neuroscience, exploring the relationship between the brain and cognitive processes such as perception, attention, memory, and language. Useful for understanding the cognitive functions that are often investigated using fMRI.
Though technical and difficult to read, this book is considered the seminal reference text for SPM, a widely used software tool for fMRI.
Is useful background reading for this course. Though most of this book does not deal with fMRI directly, it does give good, broad background on how the brain works.
Useful reference for analyzing data from fMRI studies. It would be more useful to supplement the course than to replace it.
Covers advanced statistical models for analyzing longitudinal data, which is commonly encountered in fMRI studies. Provides a strong foundation for understanding the statistical methods used to analyze fMRI data over time.
Provides a comprehensive overview of the challenges and opportunities involved in analyzing neural time series data.
Provides a detailed overview of the emerging field of connectomics, which seeks to understand the structural and functional connectivity of the brain.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zsvzgn/principles
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Level
Introductory
Via
Coursera
Institution
Johns Hopkins University
Instructors
Martin Lindquist, PhD, MSc
Tor Wager, PhD
Language
English
Transcripts
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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
- Fundamental Neuroscience for Neuroimaging
- Principles of fMRI 1 (viewing)
Good to know
Well-suited for individuals interested in the neuroscience, psychology, medical research, and data science fields
Taught by experts in the field of neuroimaging, Martin Lindquist and Tor Wager
Covers fundamental concepts of fMRI, including experimental design, data acquisition, and analysis
Involves hands-on exercises and provides resources for further learning
Introduces specialized software and tools used in fMRI research
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