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Course Catalog

140.682.01 PRINCIPLES AND METHODS OF FUNCTIONAL NEUROIMAGING I

Department: Biostatistics
Term: 2nd term
Credits: 4 credits
Contact: Martin Lindquist
Academic Year: 2013 - 2014
Course Instructors:
Description:

Introduces the principles of functional magnetic resonance imaging (fMRI) as applied to human subjects research. Presents a theoretical overview of human fMRI research and includes key aspects of the design, data collection, processing, analysis and publication of a human subjects fMRI experiment. Focuses on describing all aspects of an fMRI study from the initial experimental design, through data collection and pre-processing, to statistical analysis. Describes the goals and limitations for fMRI studies, the data format and how it is processed prior to statistical analysis. Focuses on preforming individual subject and group level univariate statistical analysis of fMRI data with appropriate thresholding and multiple comparison correction. Weekly labs provide a practical application of these concepts to sample datasets and prepares students for the analysis of fMRI data.

Learning Objective(s):
Upon successfully completing this course, students will be able to:
Describe key aspects of fMRI experimental design, and design and prepare a human subjects fMRI experiment.
Explain the specific methods, source of MR signal, goals and limitations and research design issues for fMRI studies.
Import and pre-process fMRI data including slice-timing correction, motion correction and registration.
Perform individual subject and group-level univariate statistical analysis of fMRI data with appropriate thresholding and multiple comparison correction.
Critically evaluate research methods and results of human subjects fMRI studies in published literature.

Methods of Assessment: Lab assignments (50%) and take-home final exam (50%)
Location: East Baltimore
Class Times:
  • Monday 3:30 - 4:50
  • Wednesday 3:30 - 4:50
Lab Times:
  • Friday 10:30 - 11:50
Enrollment Minimum: 10
Instructor Consent: No consent required
Prerequisite:

An introductory statistics class and a working knowledge of regression

Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail