BME 234 Neuroimaging Data Analysis (2017-2018)

BME 234 Neuroimaging Data Analysis

(Not required for any major. Elective for BME-G.)
Catalog Data:

BME 234 Neuroimaging Data Analysis (Credit Units: 4) This course provides knowledge and understanding of recent techniques for the analysis of healthy and pathological structure and function in neuroimaging data. Graduate students only. (Design units: 0)

Required Textbook:
None
Recommended Textbook:
None
References:

A list of journal publications will be provided.

Coordinator:
Frithjof Kruggel
Relationship to Student Outcomes
No student outcomes specified.
Course Learning Outcomes. Students will:

1. Be able to identify data analysis problems typical in neuroimaging

2. Be able to develop strategies for data analysis

3. Be able to descibe the use and limitations fo specific modeling approaches

4. Be able to interpret results of neuroimaging techniques

5. Be able to demonstrate use of tools for data analysis

6. Be able to demonstrate knowledge of the design, implementation, testing and usage of computer algorithms to analyze neuroimages.

Prerequisites by Topic

Basic math, statistics and neuroanatomy.

Lecture Topics:
  • Review of neuroimaging methods: aMRI, fMRI, EEGm MEG, optical imaging
  • Layout of a basic data analysis strategy
  • Review of current concepts in statistical data analysis
  • Morphometry: describing the human brain, brain atlases
  • Strategies for ERP/ERF analysis, source localization
  • Strategies for fMRI analysis, model-based and exploratory approaches
  • Description and analysis of functional networks
Class Schedule:

Meets for 3 hours of lecture each week for 10 weeks.

Computer Usage:
  • Students have the option of implementing and teting alogrithms in the SIP lab.
Laboratory Projects:
Professional Component
Design Content Description
Approach:
Lectures:
Laboratory Portion:
Grading Criteria:
  • Seminar project: 70%
  • Discussion: 30%
  • Total: 100%
Estimated ABET Category Content:

Mathematics and Basic Science: 0.0 credit units

Computing: 0.0 credit units

Engineering Topics: 0.0 credit units

Engineering Science: 0.0 credit units

Engineering Design: 0.0 credit units

Prepared:
February 22, 2017
Senate Approved:
April 25, 2014
Approved Effective:
2014 Fall Qtr