Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook of Functional MRI Data Analysis. Handbook of Functional MRI Data Analysis. Handbook of .. pp i-iv. Access. PDF ; Export citation 5 - Statistical modeling: Single subject analysis. pp Request PDF on ResearchGate | Handbook of Functional MRI Data Analysis | Functional magnetic resonance imaging (fMRI) has become the most popular.
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Handbook of Functional. MRI Data Analysis. Russell A. Poldrack. University of Texas at Austin, Imaging Research Center. Jeanette A. Mumford. University of. The general linear model is an important tool in many fMRI data analyses. including correlations, one-sample t-tests, two-sample t-tests, analysis of variance. Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging of brain function. The Handbook of Functional MRI Data.
The input data set must have been preprocessed using some standard preprocessing pipeline. The experimental design must be randomized and must ensure exchangeability across trials.
Here, the null hypothesis is that the user-defined contrast is not positive.
A more detailed description follows. As in the group-level case, a bilateral filter is subsequently applied to the z-map, and its histogram is used to estimate Fz. Exchangeability Special care must be taken to ensure exchangeability of the permutations.
Volumes i. Here, we propose to use random permutations of task labels that leave the temporal structure of the data intact In each random permutation, every trial receives a new task label that is randomly selected from the pool of all task labels. Note that the task labels are exchangeable provided the experimental design is properly randomized and the inter-trial distances are large enough so that the trials are independent.
Subsequently, a new hemodynamic model is fitted and a z-map computed to which the bilateral filter is applied. In experiments with multiple runs, task labels are only permuted within the same run so that exchangeability is ensured. For more on random permutations in this setting, see refs.
As before, the histograms of the permuted maps are used to estimate the null distribution F0, and by Eq. A generic implementation that generalizes to other domains is also available. In its generic form, LISA expects two files as input. The first file contains a non-permuted input map. The second file contains a list of permuted maps generated with the same test statistic after applying a random permutation.
In this case, the input into LISA may be a map of classifier weights Permuted maps are generated by shuffling the subjects or task labels and computing a weight map in every permutation.
LISA will then produce a map in which each voxel has an FDR score showing the statistical significance of the feature weight.
In the following, we assess the validity and effectiveness of the LISA algorithm using large public databases and supercomputing. Specifically, we compare the reproducibility of results under repeated sampling from a large cohort of subjects and the sample sizes needed to achieve statistical significance against several widely used methods.
Furthermore, we analyze the spatial precision in ultrahigh field data. To ensure the validity of LISA, we estimate the false positive rates when applied to null models, and we estimate statistical power using simulations. We use the HCP data as a benchmark because it is widely known and contains a large number of data sets needed for validation purposes. We focused on the motor and the emotion task, using minimally preprocessed data of unrelated participants.
In the motor task, we investigated the left-hand finger tapping condition. We will discuss this issue in more detail later on. We used group-level onesample t-tests ignoring the within-subject variance. As a first step, we used the full cohort of all subjects to obtain a reference map Fig.
High power entails an increased likelihood that statistically significant results reflect true effects so that this map can serve as a highly reliable reference The colors in the left image panel a represent reproducibility scores, i. The underlying blue areas show the reference map which was derived using all subjects. The corresponding cumulative histograms panel b show that the reproducibility scores are considerably higher for LISA.
For example, in the emotion task, LISA has detected 11, voxels consistently in at least 60 of the tests. We generated maps showing the reproducibility per voxel across tests Fig.
When comparing the results, we found that LISA exceeded the reproducibility scores of the other methods considerably. We also compared the clusters found by each method where clusters are defined as connected components of the resulting activation maps. We made the following observations. First, LISA not only detected more voxels, but also entire clusters that were ignored by the other methods. A new book by Jenkinson and Chappell, both at the University of Oxford and experienced teachers of MRI analysis, aims to instruct students and researchers who are new to the field of neuroimaging research.
Chapter 1 of Introduction to Neuroimaging Analysis provides a brief overview of the main MRI modalities, walks the reader through the several steps of a first, imaginary neuroimaging study and gives a concise introduction to MR physics and scanner hardware.
The rest of the book discusses quantitative techniques of MRI analysis. Chapter 3, providing a concise and clear summary of structural, functional, and diffusion imaging pipelines, is the heart of the book. If you are a newbie to MRI analysis, you would want to read this chapter, and read it several times to absorb the many concepts introduced here. Additional chapters on brain extraction, registration as well as motion and distortion correction present a comprehensive discussion of major steps in data preprocessing.
Finally, chapter 7 illustrates a more advanced method, surface-based analysis of structural and functional data. The book is accompanied by additional chapters on the Neuroimaging Primers website.
The General Linear Model for Neuroimaging appendix is a very well-written and illustrated introduction to the centerpiece of statistical analysis of MRI.
The unique feature of this book is the use of Example Boxes that provide practical knowledge and skills for MRI analysis. Many of these boxes are linked to separate web pages on the Neuroimaging Primers website. These web pages start with instructions how to install and run FSL, the authors' favorite neuroimaging software package of which Mark Jenkinson is principal developer. Additional web pages cover, for example, analysis of structural MRI such as tissue-type segmentation , diffusion tensor imaging such as tractography and task-based functional MRI.
We ran several of these tutorials and all worked well. The data downloaded from the tutorial web page not only include the original data set, but also FSL's results folder. This allows you to check and comprehend the results of every single analysis introduced on the web pages without running the analysis yourself or even installing FSL. These tutorials are ideal for homework, they are written with the student in mind and easy to follow. If the main focus of your course is task-based functional MRI, you may need more detail and you may want to add recent review articles or chapters of the established fMRI textbooks see below to the reading list.
The main text of the book and the online appendices are software-independent. The original and the analyzed data sets on the web pages can be inspected by any nifti file viewer. The web page on MRI artifacts, e.