Results 1  10
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53
A Complex Way to Compute fMRI Activation
 NeuroImage
, 2004
"... In functional magnetic resonance imaging, voxel time courses after Fourier or nonFourier "image reconstruction" are complex valued as a result of phase imperfections due to magnetic field inhomogeneities. Nearly all fMRI studies derive functional "activation " based on magnitude ..."
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Cited by 33 (14 self)
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In functional magnetic resonance imaging, voxel time courses after Fourier or nonFourier "image reconstruction" are complex valued as a result of phase imperfections due to magnetic field inhomogeneities. Nearly all fMRI studies derive functional "activation " based on magnitudeonly voxel time courses [2, 6]. Here we propose to directly model the entire complex or bivariate data rather than just the magnitudeonly data. A nonlinear multiple regression model is used to model activation of the complex signal, and a likelihood ratio test is derived to determine activation in each voxel. We investigate the performance of the model on a real dataset, then compare the magnitudeonly and complex time course models under varying signaltonoise ratios in a simulation study with varying activation contrast e#ects. 1
Parameter estimation in the magnitudeonly and complexvalued fMRI data models. Neuroimage
"... In functional magnetic resonance imaging, voxel time courses are complexvalued data but are traditionally converted to real magnitudeonly data ones. At a large signaltonoise ratio (SNR), the magnitudeonly data Ricean distribution is approximated by a normal distribution that has been suggested ..."
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Cited by 20 (9 self)
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In functional magnetic resonance imaging, voxel time courses are complexvalued data but are traditionally converted to real magnitudeonly data ones. At a large signaltonoise ratio (SNR), the magnitudeonly data Ricean distribution is approximated by a normal distribution that has been suggested as reasonable in magnitudeonly data magnetic resonance images for an SNR of 5 and potentially as low as 3. A complex activation model has been recently introduced by
A neural predictor of cultural popularity
, 2011
"... We use neuroimaging to predict cultural popularity — something that is popular in the broadest sense and appeals to a large number of individuals. Neuroeconomic research suggests that activity in rewardrelated regions of the brain, notably the orbitofrontal cortex and ventral striatum, is predictiv ..."
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Cited by 19 (1 self)
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We use neuroimaging to predict cultural popularity — something that is popular in the broadest sense and appeals to a large number of individuals. Neuroeconomic research suggests that activity in rewardrelated regions of the brain, notably the orbitofrontal cortex and ventral striatum, is predictive of future purchasing decisions, but it is unknown whether the neural signals of a small group of individuals are predictive of the purchasing decisions of the population at large. For neuroimaging to be useful as a measure of widespread popularity, these neural responses would have to generalize to a much larger population that is not the direct subject of the brain imaging itself. Here, we test the possibility of using functional magnetic resonance imaging (fMRI) to predict the relative popularity of a common good: music. We used fMRI to measure the brain responses of a relatively small group of adolescents while listening to songs of largely unknown artists. As a measure of popularity, the sales of these songs were totaled for the three years following scanning, and brain responses were then correlated with these “future ” earnings. Although subjective likability of the songs was not predictive of sales, activity within the ventral striatum was significantly correlated with the number of units sold. These results suggest that the neural responses to goods are not only predictive of purchase decisions for those individuals actually scanned, but such responses generalize to the population at large and may be used to predict cultural popularity.
A statistical analysis of brain morphology using wild bootstrapping
 Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53705, U.S.A. Email: mkchung@wisc.edu Department of Systems Engineering, Australian National University
, 2007
"... Methods for the analysis of brain morphology, including voxelbased morphology and surfacebased morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphom ..."
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Cited by 9 (4 self)
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Methods for the analysis of brain morphology, including voxelbased morphology and surfacebased morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphometric measures usually involves two statistical procedures: 1) invoking a statistical model at each voxel (or point) on the surface of the brain or brain subregion, followed by mapping test statistics (e.g., t test) or their associated p values at each of those voxels; 2) correction for the multiple statistical tests conducted across all voxels on the surface of the brain region under investigation. We propose the use of new statistical methods for each of these procedures. We first use a heteroscedastic linear model to test the associations between the morphological measures at each voxel on the surface of the specified subregion (e.g., cortical or subcortical surfaces) and the covariates of interest. Moreover, we develop a robust test procedure that is based on a resampling method, called wild
Characterizing phaseonly fMRI data with an angular regression model
 Journal of Neuroscience Methods
"... It is well known that fMRI voxel time series are complexvalued with real and imaginary parts. The complexvalued voxel time series are transformed from realimaginary rectangular coordinates to equivalent information magnitudephase polar coordinates. Magnitudeonly data models that discard the p ..."
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Cited by 7 (2 self)
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It is well known that fMRI voxel time series are complexvalued with real and imaginary parts. The complexvalued voxel time series are transformed from realimaginary rectangular coordinates to equivalent information magnitudephase polar coordinates. Magnitudeonly data models that discard the phase portion of the data have dominated fMRI analysis. However these analyzes discard half of the data which may contain valuable biologic information about the vasculature. When phaseonly time series defined within plus and minus pi that discard the magnitude portion of the data have been analyzed, ordinary least squares regression has been the technique of choice. We have explored an angular regression alternative to the OLS model which will account for the angular response of the phase. We found an improvement in parameter estimation along with modeling for the angular regression method in experimentally acquired data. Finally, we look at a map of statistics relating association of the observed voxel phase time courses with a reference function in our acquired data and show the possible detection of biological information in the generally discarded phase. 1.
Striatal topography of probability and magnitude information for decisions under uncertainty
 NeuroImage
, 2012
"... Most decisions involve some element of uncertainty. When the outcomes of these decisions have different likelihoods of occurrence, the decisionmaker must consider both the magnitude of each outcome and the probability of its occurrence, but how do individual decision makers combine the two dimensi ..."
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Cited by 5 (0 self)
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Most decisions involve some element of uncertainty. When the outcomes of these decisions have different likelihoods of occurrence, the decisionmaker must consider both the magnitude of each outcome and the probability of its occurrence, but how do individual decision makers combine the two dimensions of magnitude and probability? Here, we approach the problem by separating in time the presentation of magnitude and probability information, and focus the analysis of fMRI activations on the first piece of information only. Thus, we are able to identify distinct neural circuits for the two dimensions without the confounding effect of divided attention or the cognitive operation of combining them. We find that magnitude information correlates with the size of the response of the ventral striatum while probability information correlates with the response in the dorsal striatum. The relative responsiveness of these two striatal regions correlates with the behavioral tendency to weight one more than the other. The results are consistent with a secondorder process of information aggregation in which individuals make separate judgments for magnitude and probability and then integrate those judgments.
False Discovery Rate for WaveletBased Statistical Parametric Mapping
, 2008
"... Modelbased statistical analysis of functional magnetic resonance imaging (fMRI) data relies on the general linear model and statistical hypothesis testing. Due to the large number of intracranial voxels, it is important to deal with the multiple comparisons problem. Many fMRI analysis tools utiliz ..."
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Cited by 3 (0 self)
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Modelbased statistical analysis of functional magnetic resonance imaging (fMRI) data relies on the general linear model and statistical hypothesis testing. Due to the large number of intracranial voxels, it is important to deal with the multiple comparisons problem. Many fMRI analysis tools utilize Gaussian random field theory to obtain a more sensitive thresholding; this typically involves Gaussian smoothing as a preprocessing step. Waveletbased statistical parametric mapping (WSPM) is an alternative method to obtain parametric maps from nonsmoothed data. It relies on adaptive thresholding of the parametric maps in the wavelet domain, followed by voxelwise statistical testing. The procedure is conservative; it uses Bonferroni correction for strong type I error control. Yet, its sensitivity is close to SPM’s due to the excellent denoising properties of the wavelet transform. Here, we adapt the false discovery rate (FDR) principle to the WSPM framework. Although explicitvalues cannot be obtained, we show that it is possible to retrieve the FDR threshold by a simple iterative scheme. We then validate the approach with an eventrelated visual stimulation task. Our results show better sensitivity with preservation of spatial resolution; i.e., activation clusters align well with the gray matter structures in the visual cortex. The spatial resolution of the activation maps is even high enough to easily identify a voxel that is very likely to be caused by the drainingvein effect.
Component Analysis Approach to Estimation of Tissue Intensity Distributions of 3D images
"... Many segmentation problems in medical imaging rely on accurate modeling and estimation of tissue intensity probability density functions. Gaussian mixture modeling, currently the most common approach, has several drawbacks, such as reliance on a specific model and iterative optimization. It also doe ..."
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Cited by 2 (1 self)
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Many segmentation problems in medical imaging rely on accurate modeling and estimation of tissue intensity probability density functions. Gaussian mixture modeling, currently the most common approach, has several drawbacks, such as reliance on a specific model and iterative optimization. It also does not take advantage of substantially larger amount of data provided by 3D acquisitions, which are becoming standard in clinical environment. We propose a novel completely nonparametric algorithm to estimate the tissue intensity probabilities in 3D images. Instead of relying on traditional framework of iterating between classification and estimation, we pose the problem as an instance of a blind source separation problem, where the unknown distributions are treated as
Multilevel statistical inference from functional near infrared spectroscopy data during Stroop interference
 IEEE Transactions on Biomedical Engineering
, 2008
"... Abstract—Functional nearinfrared spectroscopy (fNIRS) is an emerging technique for monitoring the concentration changes of oxy and deoxyhemoglobin (oxyHb and deoxyHb) in the brain. An important consideration in fNIRSbased neuroimaging modality is to conduct grouplevel analysis from a set of t ..."
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Cited by 2 (0 self)
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Abstract—Functional nearinfrared spectroscopy (fNIRS) is an emerging technique for monitoring the concentration changes of oxy and deoxyhemoglobin (oxyHb and deoxyHb) in the brain. An important consideration in fNIRSbased neuroimaging modality is to conduct grouplevel analysis from a set of time series measured from a group of subjects. We investigate the feasibility of multilevel statistical inference for fNIRS. As a case study, we search for hemodynamic activations in the prefrontal cortex during Stroop interference. Hierarchical general linear model (GLM) is used for making this multilevel analysis. Activation patterns both at the subject and group level are investigated on a comparative basis using various classical and Bayesian inference methods. All methods showed consistent left lateral prefrontal cortex activation for oxyHb during interference condition, while the effects were much less pronounced for deoxyHb. Our analysis showed that mixed effects or Bayesian models are more convenient for faithful analysis of fNIRS data. We arrived at two important conclusions. First, fNIRS has the capability to identify activations at the group level, and second, the mixed effects or Bayesian model is the appropriate mechanism to pass from subject to grouplevel inference. Index Terms—General linear model (GLM), nearinfrared spectroscopy, statistical inference, Stroop task. I.
H.: Twostage empirical likelihood for longitudinal neuroimaging data
 Ann. Appl. Stat
, 2011
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