Month: January 2010

February 4, 2010

Brain Imaging Series Lecture: Rhodri Cusack (Cambridge) 3pm Center for Advanced Brain Imaging Conference Room

Rhodri Cusack
Programme Leader Track
MRC Cognition and Brain Sciences Unit (CBU)
15 Chaucer Road
Cambridge

Lecture Title: “Using Dynamically Adaptive Imaging to Characterize the Representations Underlying Perception, Imagery and Short-term Memory.”

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Feb 11, 2010

Young Innovators in Biomedical Engineering seminar series:  Dr. Rasmus Birn (U. Wisconsin). 11am, February 11, 2010, Emory Cardiology Conference Room (317), Woodruff Memorial Building, Emory.

Note there is a free shuttle from Georgia Tech to Emory

An increasing number of fMRI studies are looking not only at the activation of certain brain areas, but also at the connections between regions. A measure of ‘functional connectivity’ can be inferred from the correlation of fluctuations in time, particularly those occurring at low temporal frequencies (<0.1Hz) - the hypothesis being that these signal fluctuations reflect synchronized variations in the neuronal activity of a network of regions. However, there are several challenges facing us in order to make the best use of this technique. First, there are many nonneuronal processes that can cause the fMRI signal fluctuations of two regions to be correlated, including cardiac pulsation, breathing changes, and subject motion. An accurate mapping of neuronal connections with fMRI therefore requires that these confounds be addressed. In addition, functional connectivity is often measured under quite different experimental conditions, complicating the interpretation of what precisely is giving rise to correlated fluctuations. In this talk, I will present my latest research focused on understanding and reducing correlated nonneuronal fluctuations (in particular those induced by changes in respiration), as well as determining the sources of connectivity differences between a patient population (adolescents wit Download PDF announcment

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January 13, 2010

Brain Imaging Mini workshops:  David V. Smith and John A. Clithero (Duke U). 3pm Center for Advanced Brain Imaging Conference Room

John A. Clithero
Graduate Student, Huettel Lab
Department of Economics
Duke University
john.clithero@duke.edu

Workshop description:
Multivariate pattern analysis of neuroimaging data.

Workshop description:
Analyzing distributed patterns of brain activation using multivariate pattern analysis (MVPA) has become a popular approach to study functional magnetic resonance imaging (fMRI) data. This workshop will include both an overview of the methodology behind MVPA and an introduction to a new analysis package, PyMVPA (http://www.pymvpa.org/). PyMVPA, one of several freely available MVPA packages, is a Python module intended to ease pattern classification analyses of large datasets. Using recent examples from the literature, we will first outline what steps are required to suitably conduct MVPA on fMRI data, including feature selection, classifiers, sensitivity analysis, and data visualization. We will then explore how Python and PyMVPA can be used for each of these steps.

David V. Smith
Graduate Student, Huettel Lab
Department of Psychology & Neuroscience
Duke University
david.v.smith@duke.edu

Workshop title:

Using FSL for basic and advanced neuroimaging analyses

Workshop description:

Analysis techniques for functional magnetic resonance imaging (fMRI) have become increasingly complex over the past two decades, and several software packages have been developed to assist in analyzing these data. In this workshop, we will use FSL (http://www.fmrib.ox.ac.uk/fsl/), to analyze high-resolution fMRI data. We will first demonstrate how to preprocess data and implement B0 field maps to correct distortions arising from susceptibility artifacts in EPI data. Basic analyses will be performed using the general linear model (GLM); parametric extensions of these analyses will also be explored. Additionally, we will discuss advanced analysis techniques, including functional connectivity (e.g., PPI) and model-free analyses (e.g., ICA). Using model-free analysis methods, we will show examples of how this particular technique can be incorporated into preprocessing algorithms to remove spurious signals before statistical analyses.

Download David’s talk, documents and scripts
Download John’s talk Johns scripts are also loaded onto the Ubuntu Desktop in CABI 114.

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