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34
The Statistical Analysis of fMRI Data
, 2008
"... In recent years there has been explosive growth in the number of neuroimaging studies performed using functional Magnetic Resonance Imaging (fMRI). The field that has grown around the acquisition and analysis of fMRI data is intrinsically interdisciplinary in nature and involves contributions from ..."
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Cited by 35 (0 self)
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In recent years there has been explosive growth in the number of neuroimaging studies performed using functional Magnetic Resonance Imaging (fMRI). The field that has grown around the acquisition and analysis of fMRI data is intrinsically interdisciplinary in nature and involves contributions from researchers in neuroscience, psychology, physics and statistics, among others. A standard fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. Statistics plays a crucial role in understanding the nature of the data and obtaining relevant results that can be used and interpreted by neuroscientists. In this paper we discuss the analysis of fMRI data, from the initial acquisition of the raw data to its use in locating brain activity, making inference about brain connectivity and predictions about psychological or disease states. Along the way, we illustrate interesting and important issues where statistics already plays a crucial role. We also seek to illustrate areas where statistics has perhaps been underutilized and will have an increased role in the future.
Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data
, 2011
"... One of the major objectives of fMRI (functional magnetic resonance imaging) studies is to determine subject-specific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer regional neuronal activity. We posit and investigate ..."
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Cited by 7 (2 self)
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One of the major objectives of fMRI (functional magnetic resonance imaging) studies is to determine subject-specific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer regional neuronal activity. We posit and investigate a Bayesian approach that incorporates spatial dependence in the image and allows for the task-related change in the BOLD signal to change dynamically over the scanning session. In this way, our model accounts for potential learning effects, in addition to other mechanisms of temporal drift in task-related signals. However, using the posterior for inference requires Markov chain Monte Carlo (MCMC) methods. We study the properties of the model and the MCMC algorithms through their performance on simulated and real data sets. 1
The NIRS Analysis Package: Noise Reduction and Statistical Inference
, 2011
"... Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences bet ..."
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Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain MATLAB toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.
Supplement to “Multivariate varying coefficient model for functional responses.” DOI:10.1214/12-AOS1045SUPP
, 2012
"... Motivated by recent work studying massive imaging data in the neu-roimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and ..."
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Cited by 3 (1 self)
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Motivated by recent work studying massive imaging data in the neu-roimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first estab-lish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment.
MULTISCALE ADAPTIVE SMOOTHING MODELS FOR THE HEMODYNAMIC RESPONSE FUNCTION IN FMRI
"... In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are d ..."
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In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are de-veloped in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial infor-mation. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and fre-quency information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm that MASM outperforms several other state-of-the-art methods, such as the smooth finite impulse response (sFIR) model. 1. Introduction. Since the early 1990s
Massive modulation of brain areas after mechanical pain stimulation: a time-resolved fMRI study. Cerebral Cortex (New
, 2013
"... To date, relatively little is known about the spatiotemporal aspects of whole-brain blood oxygenation level-dependent (BOLD) responses to brief nociceptive stimuli. It is known that the majority of brain areas show a stimulus-locked response, whereas only some are character-ized by a canonical hemod ..."
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To date, relatively little is known about the spatiotemporal aspects of whole-brain blood oxygenation level-dependent (BOLD) responses to brief nociceptive stimuli. It is known that the majority of brain areas show a stimulus-locked response, whereas only some are character-ized by a canonical hemodynamic response function. Here, we inves-tigated the time course of brain activations in response to mechanical pain stimulation applied to participants ’ hands while they were undergoing functional magnetic resonance imaging (fMRI) scanning. To avoid any assumption about the shape of BOLD response, we used an unsupervised data-driven method to group voxels sharing a time course similar to the BOLD response to the stimulus and found that whole-brain BOLD responses to painful mechanical stimuli elicit massive activation of stimulus-locked brain areas. This pattern of activations can be segregated into 5 clusters, each with a typical temporal profile. In conclusion, we show that an extensive activity of multiple networks is engaged at different time latencies after presentation of a noxious stimulus. These findings aim to motivate research on a controversial topic, such as the tem-poral profile of BOLD responses, the variability of these response profiles, and the interaction between the stimulus-related BOLD response and ongoing fluctuations in large-scale brain networks.
unknown title
, 2014
"... c, ltern Sponsorships or competing interests that may be relevan a r t i c l e i n f o Article history: ..."
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c, ltern Sponsorships or competing interests that may be relevan a r t i c l e i n f o Article history:
