Overview
This session will cover introductory topics in fMRI, including the basics of brain physiology and the origins of the fMRI signal, the effective design of fMRI paradigms, the analysis of fMRI data, and will provide an introduction to resting-state fMRI and wider applications of fMRI in basic and clinical neuroscience.
Target Audience
This course is intended for clinicians and basic science researchers, including MRI physicists, who wish to understand the fundamentals of functional MRI. This course does not assumes previous knowledge of fMRI.
Educational Objectives
Upon completion of this course, participants should be able to:
- Understand the origins of the fMRI signal;
- Describe the main data acquisition methods;
- Design a robust paradigm for an fMRI study;
- Understand the basic pre-processing steps required to avoid unwanted noise;
- Construct and apply an appropriate data analysis model;
- Understand how resting state fMRI can be used to examine networks in the brain;
and
- Provide examples of basic and clinical neuroscience applications of fMRI.
|
08:00
|
The Physiological Basis of the fMRI Signal 
Clarisse I. Mark1
1Queen's University, Centre for Neuroscience
Studies, Kingston, Ontario, Canada
While BOLD fMRI represents an invaluable tool to map
brain function, it does not measure neural activity
directly; rather, it reflects changes in blood
oxygenation resulting from the relative balance between
cerebral oxygen metabolism (through neural activity) and
oxygen supply (through cerebral blood flow and volume).
As such, there are cases in which BOLD signals might be
dissociated from neural activity, leading to misleading
results. The emphasis of this course is to develop a
critical perspective for interpreting BOLD results,
through a comprehensive consideration of BOLD’s
metabolic and vascular underpinnings.
|
08:30
|
Data Acquisition Considerations
Fa-Hsuan Lin 1
1National Taiwan University
- EPI favors high bandwidth acquisitions to reduce
susceptibility artifacts.
- fMRI acquisition methods critically depend on
the targeted spatiotemporal resolution.
- The spatiotemporal resolution of fMRI can be
optimized by a combination of k-space trajectory
design, receiver coil array, and reconstruction
algorithm.
- Sequences using spin-echo or gradient-echo, the
echo time, and the flip angle can tune the
sensitivity of fMRI acquisitions.
- Physiological noise is a dominant noise source
in high-field fMRI experiments.
- Care must be taken to get the best shimming and
to minimize motion as well as acoustic
noise/vibration.
|
09:00
|
Paradigm Design
Jeroen C.W. Siero1
1Radiology, University Medical Center
Utrecht, Utrecht, Netherlands
A presention on fMRI paradigm design for students and
researchers with no or limited experience in setting up
BOLD fMRI studies in terms of paradigm (task) design
|
09:30
|
Break & Meet the Teachers |
10:00
|
Pre-Processing of fMRI Data
Stephen Strother1
1Rotman Research/Medical Biophysics, Baycrest/University
of Toronto, Toronto, ON, Canada
The target audience is researchers and clinicians with
limited to no experience with fMRI imaging. As a result
of this presentation the audience will know (i) what
fMRI pre-processing is, and why it is important, (ii)
the basic pre-processing steps and software packages
available for implementing them, (iii) how to choose
pre-processing steps for different data sets and
experimental paradigms, and (iv) about recent
developments in automated optimization of pre-processing
of fMRI data.
|
10:30
|
Analyzing Data Using the General Linear Model
Lars Kasper1,2
1Institute for Biomedical Engineering,
University of Zurich and ETH Zurich, Zurich,
Switzerland, 2Translational
Neuromodeling Unit, IBT, University of Zurich and ETH
Zurich, Zurich, Switzerland
The general linear model (GLM) is the most common
framework for analyzing task-based fMRI data. In this
talk, we motivate its use from the precarious
contrast-to-noise situation of fMRI, which requires not
only modeling (or fitting) of experimental factors and
confounds, but also statistical assessment of their
significance in the presence of an irreducible noise
floor. The presentation will feature analyses of
simulated and measured fMRI data to highlight GLM
parameter estimation as well as statistical inference
(t-, F-tests) and its representation in Statistical
Parametric Maps. Finally, limitations of the GLM and
intricacies are discussed, e.g. correlated regressors or
multiple comparison correction, to enable its proper use
in practice.
|
11:00
|
Introduction to Resting-State fMRI & Functional Connectivity
Thomas Yeo1
1National University of SIngapore
In this education workshop, I will motivate the use of
resting-state fMRI (rs-fMRI) and functional connectivity
to study the human brain. I will also present example
studies that use rs-fMRI as a tool to investigate brain
organization, disorder and behavior. I will conclude
with some existing challenges about rs-fMRI.
|
11:30
|
Example Applications of fMRI in Basic & Clinical
Neuroscience - Permission Withheld
Kai-Hsiang Chuang1,2
1Queensland Brain Institute, The University
of Queensland, Brisbane, Australia, 2Centre
for Advanced Imaging, The University of Queensland,
Brisbane, Australia
Both task-based and resting-state fMRI have been widely
used to understand the functional organization of the
brain. Both techniques have also been applied in
patients for guiding neurosurgery, distinguishing
disease phenotypes, supporting clinical management, and
evaluating treatment response. Nonetheless, several
technical and pathophysiological issues will need to be
considered for clinical fMRI.
|
12:00
|
Adjournment & Meet the
Teachers |
|