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Computational Medical Image Analysis
"... Functional magnetic resonance imaging (fMRI) is a prime example of multidisciplinary research. Without the beautiful physics of MRI, there would not be any images to look at in the first place. To obtain images of good quality, it is necessary to fully understand the concepts of the frequency domain ..."
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Functional magnetic resonance imaging (fMRI) is a prime example of multidisciplinary research. Without the beautiful physics of MRI, there would not be any images to look at in the first place. To obtain images of good quality, it is necessary to fully understand the concepts of the frequency domain. The analysis of fMRI data requires understanding of signal processing, statistics and knowledge about the anatomy and function of the human brain. The resulting brain activity maps are used by physicians, neurologists, psychologists and behaviourists, in order to plan surgery and to increase their understanding of how the brain works. This thesis presents methods for realtime fMRI and nonparametric fMRI analysis. Realtime fMRI places high demands on the signal processing, as all the calculations have to be made in realtime in complex situations. Realtime fMRI can, for example, be used for interactive brain mapping. Another possibility is to change the stimulus that is given to the subject, in realtime, such that the brain and the computer can work together to solve
doi:10.1155/2011/627947 Research Article Fast Random Permutation Tests Enable Objective Evaluation of Methods for SingleSubject fMRI Analysis
"... Copyright © 2011 Anders Eklund et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Parametric statistical methods, such as Z, t, ..."
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Copyright © 2011 Anders Eklund et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Parametric statistical methods, such as Z, t,andFvalues, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of nonparametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in singlesubject fMRI analysis. In this work, it is shown how the computational power of costefficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutationbased approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level. 1.
Comparing fMRI Activity Maps from GLM and CCA at the Same Significance Level by Fast Random Permutation Tests on the GPU
"... Abstract—Parametric statistical methods are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed t ..."
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Abstract—Parametric statistical methods are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. In this work it is shown how the computational power of the Graphics Processing Unit (GPU) can be used to speedup nonparametric tests, such as random permutation tests. With random permutation tests it is possible to calculate significance thresholds for any test statistics. As an example, fMRI activity maps from the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level. I.
Multiple testing corrections, nonparametric methods, and Random Field Theory
, 2012
"... I provide a selective review of the literature on the multiple testing problem in fMRI. By drawing connections with the older modalities, PET in particular, and how software implementations have tracked (or lagged) behind theoretical developments, my narrative aims to give the methodological researc ..."
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I provide a selective review of the literature on the multiple testing problem in fMRI. By drawing connections with the older modalities, PET in particular, and how software implementations have tracked (or lagged) behind theoretical developments, my narrative aims to give the methodological researcher a historical perspective on this important aspect of fMRI data analysis.
An Overview of WaveletBased
"... The measurement of brain activity in a noninvasive way is an essential element in modern neurosciences. Modalities such as electroencephalography (EEG) and magnetoencephalography (MEG) recently gained interest, but two classical techniques remain predominant. One of them is positron emission tomogra ..."
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The measurement of brain activity in a noninvasive way is an essential element in modern neurosciences. Modalities such as electroencephalography (EEG) and magnetoencephalography (MEG) recently gained interest, but two classical techniques remain predominant. One of them is positron emission tomography (PET), which is costly and lacks temporal resolution but allows the design of tracers for specific tasks; the other main one is functional magnetic resonance imaging (fMRI), which is more affordable than PET from a technical, financial, and ethical point of view, but which suffers from poor contrast and low signaltonoise ratio (SNR). For this reason, advanced methods have been devised to perform the statistical analysis of fMRI data. The bloodoxygenleveldependent (BOLD) signal, discovered by [1] in the 1990s and later elucidated in [2], has
Research Article Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data
"... which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional fo ..."
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which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional ttest in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. 1.
Research Article Evaluation of SecondLevel Inference in fMRI Analysis
, 2015
"... Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We investigate the impact of decisions in the secondlevel (i.e., over subjects) inferential process in functional magnetic resonance imag ..."
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Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We investigate the impact of decisions in the secondlevel (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the dataanalytical stability, both proxies for the reproducibility of results. Secondlevel analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account firstlevel (withinsubjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutationbased inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a twostep procedure with minimal cluster size. Based on a simulation study and real data we find that the twostep procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutationbased inference and parametrical inference. Modeling the subjectspecific variability yields a better balance between false positives and false negatives when using parametric inference. 1.
A BOOTSTRAP TEST TO INVESTIGATE CHANGES IN BRAIN CONNECTIVITY FOR FUNCTIONAL MRI
"... Abstract: Functional magnetic resonance imaging (fMRI) allows for the indirect measurement of whole brain neuronal activity using local blood oxygenation level. Functional connectivity, i.e., the correlation between the temporal activity of remote regions, may be used to track brain reorganization ..."
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Abstract: Functional magnetic resonance imaging (fMRI) allows for the indirect measurement of whole brain neuronal activity using local blood oxygenation level. Functional connectivity, i.e., the correlation between the temporal activity of remote regions, may be used to track brain reorganization while, for example, a subject learns a new skill. However, testing the significance of changes in functional connectivity is challenging for individual data, because fMRI time series exhibit dependencies in both space and time that may not be properly captured by classical parametric models. To address this issue, we propose a new statistical procedure in a bootstrap hypothesis testing framework after various strategies were implemented to take temporal dependencies into account. These alternatives were evaluated on Gaussian and nonGaussian MonteCarlo simulations of spacetime processes, as well as on a longitudinal study of motor skill learning. The results demonstrated that neglecting the temporal dependencies or modeling them as an autoregressive process of order 1 may lead to poor control of the false positive rate, i.e. to liberal tests. The version of the procedure based on a circular block bootstrap achieved robust, satisfactory performances in all settings. Key words and phrases: Block bootstrap, correlation, datadriven block length selection, double bootstrap, fMRI, functional connectivity, hypothesis testing. 1.