ORGANIZERS: Jay J. Pillai, M.D. & Joshua S. Shimony, M.D., Ph.D.
Saturday, 22 April 2017
||08:15 - 12:15
||Jay Pillai, Benedikt Poser
Skill Level: Advanced
Slack Channel: #e_diff_perf_fmri
Session Number: WE03
This session will cover introductory and intermediate topics in BOLD fMRI acquisition and analysis including basic task based and resting state fMRI analysis using general linear model and data-driven analysis approaches, respectively, as well as a couple of topics in advanced functional connectivity and network analysis.
Cognitive neuroscientists, neuroradiologists, clinicians and imaging scientists who currently utilize fMRI and for MR physicists and engineers developing new fMRI methodologies. This course assumes basic knowledge of fMRI and a working knowledge of neuroscience, MR data acquisition, and basic analysis methods.
Upon completion of this course, participants should be able to:
-Prepare data-driven analyses of fMRI data;
-Explain the static and dynamic factors that give rise to observed functional connectivity patterns;
-Employ network analytic approaches to functional connectivity data;
-Recognize basic BOLD data acquisition considerations;
-Perform pre-processing steps for BOLD data analysis; and
-Recall and be able to employ the general linear model for task fMRI analysis.
|BOLD Data Acquisition Considerations
Through a series of complex processes, under the umbrella term of neurovascular coupling, neuronal activity ultimately manifests as a signal change in an MR image via the blood-oxygenation level dependent (BOLD) contrast. Functional MRI (fMRI) capitalises on this contrast mechanism to infer neuronal activity from BOLD contrast variation in a time series, typically acquired while the participant engages in a task. This approach has proved valuable in furthering our understanding of the working of the human brain. Here, issues pertinent to acquiring data with sufficiently high sensitivity to detect such changes are considered, e.g. susceptibility effects, physiological noise and approaches facilitating high spatio-temporal resolution.
Functional MRI has become a standard technique for exploring brain function, however this imaging modality is not a direct measure of neural activity. This course introduces the source of Blood Oxygenation Level Dependent (BOLD) contrast and the physiological mechanisms that drive the haemodynamic response to neural activity. The limitations and challenges of using blood as a surrogate for brain function are discussed, particularly in cohorts with differing cerebrovascular physiology. Potential solutions involving additional imaging modalities and complementary MRI contrast mechanisms may enable accurate understanding of the neuro-vascular processes underlying BOLD fMRI.
|General Linear Model Analysis of Task Based fMRI Data
The general linear model (GLM) is one of the most commonly utilized statistical platform that is currently used in analyzing task-based fMRI data. In this talk we will introduce the general over view and basic concepts of GLM and how it is used in this very specific application of clinical neuroimaging. We will briefly review the history of introduction of GLM into the fMRI community and later use some examples to demonstrate the utility in analyzing fMRI data. In the end we will discuss some of its limitations.
|Introduction to Resting State Functional Connectivity
|Break & Meet the Teachers
|Data Driven & Exploratory Analyses
Independent component analysis (ICA) has grown to be a widely used and continually developing staple for analyzing fMRI functional connectivity data. In this paper we discuss some key observations and assumptions regarding ICA and also key new applications of ICA to brain imaging data.
|Dynamic Functional Connectivity
Dynamic functional connectivity (DFC) is the study of time-varying changes in functional interactions between brain regions. This talk will describe DFC methods along with the challenges involved in such analyses. We will also highlight results demonstrating associations between DFC and independently acquired measures of behavior, physiology, and neural activity, and will discuss the potential for DFC features to serve as clinical biomarkers.
This talk provides an introduction to network analysis of functional MRI, with an emphasis on the use of graph theory for understanding distinct aspects of brain organisation and dynamics.