Using fMRI to investigate (original) (raw)

HST. 583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis, Fall 2006

2006

In this analysis lab you will examine time-domain signals from fMRI datasets acquired using different spatial resolutions, imaging rates, and RF receive coils. You will use a MATLAB ®-based program called Dview to examine these signals. Note: Although not essential, a little experience with and understanding of the basics of MATLAB ® will help in these exercises. Sources of MATLAB ® help and documentation include Manipulating Matrices, which is part of the MATLAB ® 6.x documentation. If you don't understand a command in the MATLAB ® code fragments you will see below, you can use the help command to get information on it. The main objectives of the data acquisition and analysis labs are: • Familiarization of students with a typical functional MRI scanning environment, from data acquisition to offline visualization and analysis. • Acquisition and examination of image data from a phantom (inert test sample) to investigate image intensity non-uniformity, spatial and temporal noise from instrumental sources, and RF receive coil properties. • Acquisition and examination of human data to gain familiarity with 3D anatomic visualization using cross-sectional images and compare physiological and instrumental sources of noise.

Practical Aspects of Functional MRI (NMR Task Group# 8)

Medical Physics, 2002

Practical aspects of functional MRI (NMR Task Group #8). [Medical Physics 29, 1892 (2002)]. Ronald R. Price, Jerry Allison, Richard J. Massoth, Geoffrey D. Clarke, Dick J. Drost. Abstract. Functional MR imaging (fMRI) based upon ...

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Frontiers in Neuroscience, 2016

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

Functional MRI experiments: acquisition, analysis and interpretation of data

European Neuropsychopharmacology, 2002

Functional MRI is widely used to address basic and clinical neuroscience questions. In the key domains of fMRI experiments, i.e. acquisition, processing and analysis, and interpretation of data, developments are ongoing. The main issues are sensitivity for changes in fMRI signal that are associated with brain function, and the design of tasks with which brain functions are invoked. In this paper we address these issues, in terms of strengths, weaknesses and future developments. Acquisition of data is commonly achieved with techniques that measure blood oxygen level-dependent (BOLD) signal changes. Although the mechanisms that affect BOLD signal are complex and not well understood, fMRI yields results that agree with known functional topography. Sensitivity for task-related brain activity is expected to benefit from technological advances in acquisition, i.e. SENSE or parallel imaging, and higher field scanners (3 T). Data analysis is geared towards modelling sources of signal variation, i.e. reducing noise in the data time-series, and the cerebrovascular response to task-related changes in neuronal activity. Analytical algorithms such as connectivity and component analysis contribute to the extraction of meaningful information from fMRI datasets. The choice of tasks, and consequently of the statistical evaluation procedures, is best guided by the specific questions that are formulated a priori. The future is expected to bring more sophisticated questions, and tasks that allow for accurate modelling of involved brain functions. An example of a hypothesis-driven experiment is presented, where we investigated whether practise of a working memory task caused a shift in the neuronal representation of working memory or not. 

Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques, SECOND EDITION

Functional magnetic resonance imaging (fMRI) has become a standard tool for mapping the working brain's activation patterns, both in health and in disease. It is an interdisciplinary field and crosses the borders of neuroscience, psychology, psychiatry, radiology, mathematics, physics, and engineering. Developments in techniques, procedures and our understanding of this field are expanding rapidly. In this second edition of Introduction to Functional Magnetic Resonance Imaging, Richard Buxtona leading authority on fMRIprovides an invaluable guide to how fMRI works, from introducing the basic ideas and principles to the underlying physics and physiology. He covers the relationship between fMRI and other imaging techniques and includes a guide to the statistical analysis of fMRI data. This book will be useful both to the experienced neuroscientist, and the clinician or researcher with no previous knowledge of the technology.

Elements of Functional Neuroimaging 1 ELEMENTS OF FUNCTIONAL NEUROIMAGING

There has been explosive interest in the use of brain imaging to study cognitive and affective processes in recent years. Examine , for example, to see the dramatic rise in numbers of publications using positron emission tomography (PET) and functional Magnetic Resonance Imaging (fMRI) from 1985 to 2004. A recent surge in integrative empirical work that combines data from human performance, neuroimaging, neuropsychology, and psychophysiology provides a more comprehensive, but more complex, view of the human brain-mind than ever before. Because the palette of evidence from which researchers draw is larger, there is an increasing need to for cross-disciplinary integration and education. Our goal in this chapter is to provide an introduction to the growing field of neuroimaging research, including a brief survey of important issues and new directions.

Essentials of Functional Neuroimaging

Handbook of Neuroscience for the Behavioral Sciences, 2009

Functional magnetic resonance imaging: Measuring versus estimating

2007

Brain imaging techniques largely spread in neuroscience literature. Due to initial technical limitations such as the very low signal-to-noise ratio, group experiments became the rule. This fact, together with the wide use of standard brains to localize the activations, lead several experimenters to the wrong idea that the brain can be described by a Cartesian coordinate system, neglecting at the same time the importance of individual neuroanatomy. My commentary on the paper by Devlin and Poldrack reinforces their reminder that it is necessary to deal with anatomy. Moreover, it adds some considerations on the relevance of single subjects studies and on the importance of the BOLD intensity signal, which should be used to describe brain activity together with the most used statistical tools.

A Primer on Functional Magnetic Resonance Imaging

Neuropsychology Review, 2007

In this manuscript, basic principles of functional magnetic resonance imaging (fMRI) are reviewed. In the first section, two intrinsic mechanisms of magnetic resonance image contrast related to the longitudinal and transverse components of relaxing spins and their relaxation rates, T 1 and T 2 , are described. In the second section, the biophysical mechanisms that alter the apparent transverse relaxation time, T * 2 , in blood oxygenation level dependent (BOLD) studies and the creation of BOLD activation maps are discussed. The physiological complexity of the BOLD signal is emphasized. In the third section, arterial spin labeling (ASL) measures of cerebral blood flow are presented. Arterial spin labeling inverts or saturates the magnetization of flowing spins to measure the rate of delivery of blood to capillaries. In the fourth section, calibrated fMRI, which uses BOLD and ASL to infer alterations of oxygen utilization during behavioral activation, is reviewed. The discussion concludes with challenges confronting studies of individual cases.

Using fMRI to investigate (original) (raw)

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