Functional MRI User's Guide

Total Page:16

File Type:pdf, Size:1020Kb

Functional MRI User's Guide Functional MRI User's Guide Michael A. Yassa ● The Division of Psychiatric Neuroimaging ● ● Department of Psychiatry and Behavioral Sciences ● ● The Johns Hopkins School of Medicine ● ● Baltimore, MD ● 1 Document written in OpenOffice.org Writer 2.0 by Sun Microsystems Publication date: June 2005 (1st edition) Online versions available at http://pni.med.jhu.edu/intranet /fmriguide/ Acknowledgments: This document relies heavily on expertise and advice from the following individuals and/or groups: John Ashburner, Karl Friston, and Will Penny (FIL-UCL: London), Kalina Christoff (UBC: Canada), Matthew Brett (MRC-CBU: Cambridge), and Tom Nichols (SPH-UMichigan, Ann Arbor). Some portions of this document are adapted or copied verbatim from other sources, and are referenced as such. Supplemental Reading: Frackowiak RS, Friston K, Frith C, Dolan RJ, Price CJ, Zeki S, Ashburner J, & Perchey G (2004). Human Brain Function, 2nd edition, Elsevier Academic Press, San Diego, CA. Huettel SA, Song AW, McCarthy, G. (2004) Functional Magnetic Resonance Imaging. Sinaur Associates, Sunderland, MA. 2 Table of Contents Magnetic Resonance Physics..............................................................................6 How the MR Signal is Generated.............................................................................6 The BOLD Contrast Mechanism..............................................................................8 Hemodynamic Modeling.........................................................................................10 Signal and Noise in fMRI........................................................................................12 Thermal Noise.......................................................................................................12 Cardiac and respiratory artifacts............................................................................12 N/2 Ghost..............................................................................................................12 Subject motion.......................................................................................................12 Draining veins........................................................................................................13 Scanner drift..........................................................................................................13 Susceptibility artifacts............................................................................................13 Experimental Design...........................................................................................14 Cognitive subtractions ...........................................................................................14 Cognitive Conjunctions...........................................................................................14 Parametric Designs................................................................................................14 Multi-factorial Designs............................................................................................15 Optimizing fMRI Studies.........................................................................................15 Signal Processing..................................................................................................15 Confounding Factors.............................................................................................15 Control task............................................................................................................16 Latent (hidden) factor.............................................................................................16 Randomization and Counterbalancing...................................................................16 Nonlinear Hemodynamic Effects............................................................................16 Epoch (Blocked) and Event-Related Designs .......................................................17 Spatial and Temporal Pre-Processing...............................................................18 Overview................................................................................................................18 Raw Data ...............................................................................................................18 Getting Started.......................................................................................................18 Requirements.........................................................................................................19 Hardware Requirements........................................................................................19 Software Requirements.........................................................................................19 Software Set-up......................................................................................................19 The SPM Environment...........................................................................................20 Data Transfer from Godzilla...................................................................................20 Volume Separation and Analyze headers .............................................................21 Buffer Removal.......................................................................................................24 Slice Timing Correction (For event-related data)...................................................24 To Correct or Not to Correct..................................................................................24 Philips Slice Acquisition Order...............................................................................25 Which Slice to Use as a Reference Slice...............................................................25 Timing Parameters.................................................................................................26 Rigid-Body Registration (Correction for Head Motion)...........................................26 3 Creating a Mean Image.........................................................................................26 Realignment...........................................................................................................27 Anatomical Co-registration (Optional)....................................................................29 Co-registering Whole Brain Volumes.....................................................................30 Co-registering Partial Brain Volumes.....................................................................30 Spatial Normalization to Standard Space..............................................................30 Correcting Scan Orientation..................................................................................31 Normalization Defaults...........................................................................................31 Normalization to a Standard EPI Template............................................................32 Gaussian Smoothing.............................................................................................33 Summary of Pre-processing Steps........................................................................34 Statistical Analysis using the General Linear Model.......................................35 Modeling and Inference in SPM.............................................................................35 Model Specification and the SPM Design Matrix ..................................................35 Setting Up fMRI Defaults.......................................................................................36 Model Specification................................................................................................36 Estimating a Specified Model................................................................................39 Global Intensity Normalization................................................................................40 Temporal Filtering...................................................................................................40 Results and Statistical Inference............................................................................42 Contrast Specification............................................................................................42 Thresholding and Inference ..................................................................................43 Rejecting the Null Hypothesis.................................................................................43 Type I Error (Multiple Comparison Correction).......................................................44 Spatial Extent Threshold (Cluster analysis) ...........................................................46 Viewing Results using Maximum Intensity Projection ...........................................46 Small Volume Correction and Regional Hypotheses.............................................48 Extracting Results and Talairach Labeling.............................................................48 Time-Series Extraction and Local Eigenimage Analysis .......................................49 Plotting Responses and Parameter Estimates......................................................50 Anatomical Overlays..............................................................................................53 Editing, Printing and Exporting SPM output...........................................................55 Region of Interest (ROI) Analyses......................................................................56 Anatomical vs. Functional
Recommended publications
  • A Riemannian Framework for Shape Analysis of Subcortical Brain Structures
    A Riemannian Framework for Shape Analysis of Subcortical Brain Structures A dissertation presented to the faculty of the Russ College of Engineering and Technology of Ohio University In partial fulfillment of the requirements for the degree Doctor of Philosophy Shuisheng Xie August 2013 © 2013 Shuisheng Xie. All Rights Reserved. 2 This dissertation titled A Riemannian Framework for Shape Analysis of Subcortical Brain Structures by SHUISHENG XIE has been approved for the School of Electrical Engineering and Computer Science and the Russ College of Engineering and Technology by Jundong Liu Associate Professor of Electrical Engineering and Computer Science Dennis Irwin Dean, Russ College of Engineering and Technology 3 Abstract XIE, SHUISHENG, Ph.D., August 2013, Electrical Engineering and Computer Science A Riemannian Framework for Shape Analysis of Subcortical Brain Structures (106 pp.) Director of Dissertation: Jundong Liu Measuring the volumetric and morphological changes of brain structures in MR images can provide a reliable basis for diagnosing and studying Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI). Currently, the prevailing morphometric analysis frameworks are voxel-based morphometry (VBM), deformation-based morphometry (DBM) and tensor-based morphometry (TBM), which all involve a low-order spatial normalization procedure to project the computed voxel tissue map or motion field into a standard space. Shape and deformation information in local areas tend to be smoothed or even wiped out during the averaging process. In order to address the drawbacks of VBM/DBM/TBM, a novel local shape-based morphometry (SBM) framework on individual structures is developed in this dissertation, to measure the difference among a collection of anatomical images for individual subjects.
    [Show full text]
  • A Semi-Automated Workflow Solution for Multimodal Neuroimaging
    Brain Informatics (2016) 3:1–15 DOI 10.1007/s40708-015-0026-y A semi-automated workflow solution for multimodal neuroimaging: application to patients with traumatic brain injury Koon-Pong Wong . Marvin Bergsneider . Thomas C. Glenn . Vladimir Kepe . Jorge R. Barrio . David A. Hovda . Paul M. Vespa . Sung-Cheng Huang Received: 2 November 2015 / Accepted: 16 November 2015 / Published online: 1 December 2015 Ó The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Traumatic brain injury (TBI) is a major cause 15O-water (0–10 min) and 18F-FDDNP (0–6 min) for of mortality and morbidity, placing a significant financial measuring cerebral blood flow in patients with TBI. burden on the healthcare system worldwide. Non-invasive neuroimaging technologies have been playing a pivotal Keywords Traumatic brain injury (TBI) Á Cerebral blood role in the study of TBI, providing important information flow (CBF) Á Magnetic resonance imaging (MRI) Á for surgical planning and patient management. Advances in 15O-water Á 18F-FDDNP Á Positron emission tomography understanding the basic mechanisms and pathophysiology (PET) Á Spatial normalization of the brain following TBI are hindered by a lack of reli- able image analysis methods for accurate quantitative assessment of TBI-induced structural and pathophysiolog- 1 Introduction ical changes seen on anatomical and functional images obtained from multiple imaging modalities. Conventional Traumatic brain injury (TBI) is an important public health region-of-interest (ROI) analysis based on manual labeling and socio-economic problem throughout the world. It is of brain regions is time-consuming and the results could be one of the most common causes of death and long-term inconsistent within and among investigators.
    [Show full text]
  • Spatial Normalization of Diffusion Tensor MRI Using Multiple Channels
    NeuroImage 20 (2003) 1995–2009 www.elsevier.com/locate/ynimg Spatial normalization of diffusion tensor MRI using multiple channels Hae-Jeong Park,a,b Marek Kubicki,a,b Martha E. Shenton,a,b Alexandre Guimond,c Robert W. McCarley,a Stephan E. Maier,d Ron Kikinis,b Ferenc A. Jolesz,d and Carl-Fredrik Westinb,* a Clinical Neuroscience Division, Laboratory of Neuroscience, Boston VA Health Care System-Brockton Division, Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA. b Surgical Planning Laboratory, MRI Division, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA. c Center for Neurological imaging, MRI Division, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA. d Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA. Received 10 March 2003; revised 30 July 2003; accepted 5 August 2003 Abstract Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components.
    [Show full text]
  • Handbook of Functional MRI Data Analysis
    Handbook of Functional MRI Data Analysis Functionalmagnetic resonance imaging (fMRI) has become the most popularmethod for imaging brain function. Handbook of Functional MRI Data Analysis provides a comprehensive and practical introduction to the methods used for fMRI data analysis. Using minimal jargon, this book explains the concepts behind processing fMRI data, focusing on the techniques that are most commonly used in the field. This book provides background about the methods employed by common data analysis packages including FSL, SPM, and AFNI. Some of the newest cutting-edge techniques, including pattern classification analysis, connectivity modeling, and resting state network analysis, are also discussed. Readers of this book, whether newcomers to the field or experienced researchers, will obtain a deep and effective knowledge of how to employ fMRI analysis to ask scientific questions and become more sophisticated users of fMRI analysis software. Dr. Russell A. Poldrack is the director of the Imaging Research Center and professor of Psychology and Neurobiology at the University of Texas at Austin. He has published more than 100 articles in the field of cognitive neuroscience, in journals including Science, Nature, Neuron, Nature Neuroscience, and PNAS. He is well known for his writings on how neuroimaging can be used to make inferences about psychologicalfunction, as wellasfor his research using fMRI and other imaging techniques to understand the brain systems that support learning and memory, decision making, and executive function. Dr. Jeanette A. Mumford is a research assistant professor in the Department of Psychology at the University of Texas at Austin. Trained in biostatistics, her research has focused on the development and characterization of new methods for statistical modeling and analysis of fMRI data.
    [Show full text]
  • Manual Computational Anatomy Toolbox - CAT12
    Manual Computational Anatomy Toolbox - CAT12 Quick Start Guide 2 Version Information 4 Introduction and Overview 10 Getting Started 10 Download and Installation 10 Starting the Toolbox 11 Basic VBM Analysis (Overview) 11 Overview of CAT12 Processing 14 CAT12 Major Processing Steps 14 CAT12 Processing Steps in Detail 15 Basic VBM Analysis (detailed description) 17 Preprocessing Data 17 First Module: Segment Data 17 Second Module: Display slices (optionally) 18 Third Module: Estimate Total Intracranial Volume (TIV) 19 Fourth Module: Check sample 19 Fifth Module: Smooth 21 Building the Statistical Model 22 Two-sample T-Test 23 Full Factorial Model (for a 2x2 Anova) 24 Multiple Regression (Linear) 25 Multiple Regression (Polynomial) 26 Full Factorial Model (Interaction) 27 Full Factorial Model (Polynomial Interaction) 28 Estimating the Statistical Model 29 1 Checking for Design Orthogonality 29 Defining Contrasts 31 Special Cases 36 CAT12 for longitudinal data 36 Optional Change of Parameters for Preprocessing 39 Preprocessing of Longitudinal Data 39 Longitudinal Data in One Group 40 Longitudinal Data in Two Groups 42 Longitudinal Data in Two Groups with interaction of covariate by group 44 Adapting the CAT12 workflow for populations such as children 48 Customized Tissue Probability Maps 48 Customized Dartel- or Shooting-template 49 Other variants of computational morphometry 53 Deformation-based morphometry (DBM) 53 Surface-based morphometry (SBM) 54 Region of interest (ROI) analysis 57 Additional information on native, normalized and modulated volumes 58 Naming convention of output files 60 Calling CAT from the UNIX command line 62 Technical information 63 CAT12 Citation 66 References 68 2 Quick Start Guide VBM data ● Segment data using defaults (use Segment Longitudinal Data for longitudinal data).
    [Show full text]