Resting State Brain Networks

							Resting State Brain Networks

Organizers:
Bharat Biswal, UMDNJ
Yu-Feng Zang, Hangzhou Normal University

This course is designed to teach users how to design, analyze, and interpret resting state brain
connectivity. Due to its increasing popularity, a large number of investigators are collecting MRI
data from healthy and clinical subjects during rest. A novelty of this course will be that actual
data from a large study will be used to show the user, all points of the study. In the first part of
the course, users will be taught how to design an experiment for a resting state study. The
importance of initial instruction given and the subject’s behavioral and physiological parameters
including satiety, and emotional state on the baseline signal will be discussed. In the second
part, pre-processing and post-processing steps their relative advantages and disadvantages will
be demonstrated. During this process, their software implementation will also be demonstrated.
In the third part, data integration with other clinical and connectivity measures including DTI will
also be shown.

Learning Objectives: Having completed the course, participants will be able to:
    1. Design a resting state study, with full knowledge as to how the various behavioral or
       physiological states would affect RSFC;
    2. Understand the sources of variation both within and between subjects. Also, they will
       be aware of the various pre-processing methods used, including their advantages and
       dis-advantages;
    3. Generate various measures of connectivity, including seed-based, data driven
       approached including ICA/PCA, aggregate properties including ALFF, small world, etc.
       Different software implementation including AFNI, FSL, REST, GIFT and CONN will be
       covered;
    4. Methods to integrate the RSFC results with other measures including DTI, EEG, and
       other measures will also be covered; and
    5. Analyzing Single subject and Group level analysis will be performed.

Target Audience. This course is designed for neuroimaging practitioners interested in resting
state fMRI studies.

Course Schedule

8:00-8:05       Introduction
                Yu-Feng Zang, Hangzhou Normal University

8:05-8:40       Biophysical Mechanisms and Artifactual Signals
                Bharat Biswal, UMDNJ

8:40-9:15       Pre-Processing Steps and Considerations
                Christian Windischberger, Medical University of Vienna

9:15-9:50       Analysis: ICA
                Christian Beckmann, Imperial College
9:50-10:25    Analysis: Seed-Based Correlation and Other Novel Developments
              Ziad Saad, National Institute of Health

10:25-10:35   Break

10:35-11:10   Analysis: Granger Causality and Other SEM
              Xiaoping Hu, Georgia Institute of Technology

11:10-11:45   Analysis: Network Approaches
              Yong He, Beijing Normal University

11:45-12:25   Applications: Overview
              Mike Milham, Child Mind Institute

12:25-13:25   Lunch

13:25-14:00   Applications: Development
              Vinod Menon, Stanford University

14:00-14:35   Applications: Imaging Genetics
              Yu-Feng Zang, Hangzhou Normal University

14:35-15:10   Multimodal Integration: Combining DTI and fcMRI
              Ching-Po Lin, National Yang-Ming University

15:10-15:25   Break

15:25-16:00   Multimodal Integration: Integrating Intracranial Electrodes and Diffusion
              Tractography to Study Resting State Networks
              Timothy Ellmore, University of Texas

16:00-16:45   Case Study: Single Subject and Group Analysis
              Suril Gohel and Xin Di, UMDNJ

16:45-17:00   Limitation of Resting State Studies
              Bharat Biswal, UMDNJ