The CCBBI encourages students, postdocs, and research scientists interested in neuroimaging to take the following courses:

Psych 5425: Introduction to Neuroimaging Analysis 

  • Next Offered: Spring 2024
  • Course Number: 5425 (Graduate Class # 34627, Undergraduate Class # 34626)
  • Class Time: Tue/Thu 12:45-2:05 
  • Class Location: TBD

Description: The main content of the course is the introduction to the principle and basics of functional magnetic resonance imaging data analysis, mainly in FSL. The course also includes brief introduction of fMRI-related physiological basics and fMRI experimental design.

Instructor:  Dr. Ruchika Prakash

Dr. Ruchika Prakash, CCBBI Associate Director


Office: 62 Psychology Building
Office Hours: By appointment

Psych 6650: Seminar in Advanced fMRI Analysis 

  • Next Offered: Fall 2023
  • Course Number: 6650
  • Class Time: Wed 2:00-4:45 pm 
  • Class Location: PS 217

Description: This course is for students who have already completed an intro-level fMRI course and/or have prior fMRI research experience. We will discuss a range of intermediate/advanced fMRI issues and analysis techniques, such as fMR-adaptation, retinotopic mapping, multi-voxel pattern analysis, functional connectivity, representational similarity analysis, real-time fMRI, etc. Discussions may also include topics such as comparing analysis software packages, preprocessing decisions, choosing proper statistical comparisons, and interpretation of fMRI data. The format of the course will be seminar-style; we will read and discuss papers using each technique, and brainstorm how to apply and improve them in our own experiments. 

Instructor:  Prof. Julie Golomb, PhD

Photo of Dr. Julie Golomb


Office: 201 Lazenby Hall
Office Hours: By appointment

 Psych 7897: Seminar in Computational Social Science & Social Neuroscience 

  • Next Offered: Spring 2024
  • Course Number: 7897
  • Class Time: Tue 1:00pm - 2:50pm 
  • Class Location: TBD

Description: Over the last decade there has been an increasing trend towards the adoption of tools and methods from the field of computer science and statistical learning to increase our understanding of how the mind and brain process knowledge of our selves and of others. This seminar will cover recent findings in this area as well as serve as an introduction to the scientific computing tools and methods that are becoming increasingly critical for understanding social phenomena. The format of the course will be split between a discussion of the current literature (theory, findings, and methods) and hands-on practical exercises in the application of machine-learning and data-driven approaches to open datasets in the social sciences and/or social cognitive neuroscience using open-source Python based tools for scientific computing (e.g., Jupyter, SciPy, Scikit-learn) and the analysis of behavioral and neuroimaging data (PyMVPA and NiLearn).

Instructor:  Prof. Dylan Wagner, PhD

Dr. Dylan Wagner


Office: 140H Lazenby Hall
Office Hours: By appointment