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Enhanced identification of BOLD-like components with multi-echo simultaneous multi-slice (MESMS) fMRI and multi-echo ICA

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NEUROimage 2015年 112卷 43-51页

作者： Olafsson, Valur Kundu, Prantik Wong, Eric C. Bandettini, Peter A. Liu, Thomas T. Univ Pittsburgh Neurosci Imaging Ctr Pittsburgh PA 15203 USA Icahn Inst Med Mt Sinai Brain Imaging Ctr New York NY 10029 USA Icahn Inst Med Mt Sinai Translat & Mol Imaging Inst New York NY 10029 USA Univ Calif San Diego Ctr Funct Magnet Resonance Imaging La Jolla CA 92093 USA Univ Calif San Diego Dept Radiol La Jolla CA 92093 USA Univ Calif San Diego Dept Psychiat La Jolla CA 92093 USA NIMH Sect Funct Imaging Methods Bethesda MD 20892 USA NIMH Funct MRI Core Facil Bethesda MD 20892 USA Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA

The recent introduction of simultaneous multi-slice (SMS) acquisitions has enabled the acquisition of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data with significantly higher temporal sampling rates. In a parallel development, the use of multi-echo fMRI acquisitions in conjunction with a multi-echo independent component analysis (ME-ICA) approach has been introduced as a means to automatically distinguish functionally-related BOLD signal components from signal artifacts, with significant gains in sensitivity, statistical power, and specificity. In this work, we examine the gains that can be achieved with a combined approach in which data obtained with a multi-echo simultaneous multi-slice (MESMS) acquisition are analyzed with ME-ICA. We find that ME-ICA identifies significantly more BOLD-like components in the MESMS data as compared to data acquired with a conventional multi-echo single-slice acquisition. We demonstrate that the improved performance of MESMS derives from both an increase in the number of temporal samples and the enhanced ability to filter out high-frequency artifacts. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Adult Artifacts Echo-Planar Imaging/methods Echo-Planar Imaging/statistics & numerical data Female Humans image processing, computer-assisted/methods image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging/methods Magnetic Resonance Imaging/statistics & numerical data Male Oxygen/blood Principal Component Analysis

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Gradient-free MCMC methods for dynamic causal modelling

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NEUROimage 2015年 112卷 375-381页

作者： Sengupta, Biswa Friston, Karl J. Penny, Will D. UCL Wellcome Trust Ctr Neuroimaging Inst Neurol London WC1N 3BG England

In this technical note we compare the performance of four gradient-free MCMC samplers (random walk Metropolis sampling, slice-sampling, adaptive MCMC sampling and population-based MCMC sampling with tempering) in terms of the number of independent samples they can produce per unit computational time. For the Bayesian inversion of a single-node neural mass model, both adaptive and population-based samplers are more efficient compared with random walk Metropolis sampler or slice-sampling;yet adaptive MCMC sampling is more promising in terms of compute time. Slice-sampling yields the highest number of independent samples from the target density-albeit at almost 1000% increase in computational time, in comparison to the most efficient algorithm (i.e., the adaptive MCMC sampler). (C) 2015 The Authors. Published by Elsevier Inc.

关键词： Algorithms Bayes Theorem Humans image processing, computer-assisted/methods image processing, computer-assisted/statistics & numerical data Markov Chains Models, Neurological Monte Carlo Method Software Walking/physiology Walking/physiology

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Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators

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NEUROimage 2015年 112卷 14-29页

作者： Mejia, Amanda F. Nebel, Mary Beth Shou, Haochang Crainiceanu, Ciprian M. Pekar, James J. Mostofsky, Stewart Caffo, Brian Lindquist, Martin A. Johns Hopkins Univ Dept Biostat Baltimore MD 21205 USA Kennedy Krieger Inst Ctr Neurodev & Imaging Res Baltimore MD 21205 USA Johns Hopkins Sch Med Dept Radiol Baltimore MD USA Kennedy Krieger Inst FM Kirby Res Ctr Funct Brain Imaging Baltimore MD USA Johns Hopkins Sch Med Dept Neurol Baltimore MD USA Johns Hopkins Sch Med Dept Psychiat & Behav Sci Baltimore MD USA Univ Penn Dept Biostat & Epidemiol Philadelphia PA 19104 USA

A recent interest in resting state functional magnetic resonance imaging (rsfMRI) lies in subdividing the human brain into anatomically and functionally distinct regions of interest. For example, brain parcellation is often a necessary step for defining the network nodes used in connectivity studies. While inference has traditionally been performed on group-level data, there is a growing interest in parcellating single subject data. However, this is difficult due to the inherent low signal-to-noise ratio of rsfMRI data, combined with typically short scan lengths. A large number of brain parcellation approaches employ clustering, which begins with a measure of similarity or distance between voxels. The goal of this work is to improve the reproducibility of single-subject parcellation using shrinkage-based estimators of such measures, allowing the noisy subject-specific estimator to "borrow strength" in a principled manner from a larger population of subjects. We present several empirical Bayes shrinkage estimators and outline methods for shrinkage when multiple scans are not available for each subject. We perform shrinkage on raw inter-voxel correlation estimates and use both raw and shrinkage estimates to produce parcellations by performing clustering on the voxels. While we employ a standard spectral clustering approach, our proposed method is agnostic to the choice of clustering method and can be used as a pre-processing step for any clustering algorithm. Using two datasets - a simulated dataset where the true parcellation is known and is subject-specific and a test-retest dataset consisting of two 7-minute resting-state fMRI scans from 20 subjects - we show that parcellations produced from shrinkage correlation estimates have higher reliability and validity than those produced from raw correlation estimates. Application to test-retest data shows that using shrinkage estimators increases the reproducibility of subject-specific parcellations of the motor cortex by

关键词： Algorithms Bayes Theorem Brain/anatomy & histology Brain/physiology Brain Mapping Brain Mapping Cluster Analysis Humans image processing, computer-assisted/methods image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging/methods Models, Statistical Motor Cortex/anatomy & histology Motor Cortex/physiology Neural Pathways/physiology Reproducibility of Results Rest/physiology Signal-To-Noise Ratio

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Development of a Noninvasive Method to Diagnose Bladder Outlet Obstruction Based on Decorrelation of Sequential Ultrasound images

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UROLOGY 2015年第3期85卷 648-652页

作者： Arif, Muhammad Idzenga, Tim de Korte, Chris L. van Mastrigt, Ron Erasmus MC Sect Furore Dept Urol NL-3015 GE Rotterdam Netherlands Radboud Univ Nijmegen Med Ctr Med UltraSound Imaging Ctr MUSIC Dept Radiol NL-6525 ED Nijmegen Netherlands

OBJECTIVE To develop an ultrasound (US)-based method for noninvasive diagnosing of bladder outlet obstruction, and the relationship between decorrelation of sequential US images and the degree of obstruction in a urethra model was studied. MATERIALS AND METHODS A flexible model of a male urethra was constructed from 15% aqueous solution of polyvinyl alcohol cryogel. To create 4 different degrees of obstruction, polyvinyl alcohol rings representing the prostate were placed around the model. Each model was perfused at different flow rates (1-15 mL/s) with an aqueous solution containing scattering particles mimicking urine. At each flow rate, 10 sequential US images were acquired. The average correlation coefficients between the images were calculated and plotted as a function of the degree of obstruction and the flow rate. RESULTS The average correlation decreased approximately linearly with an increase in the degree of obstruction. This decrease in correlation (decorrelation) might be due to turbulence caused by the obstruction. A higher degree of obstruction creates more turbulence and results in a higher decorrelation between sequential US images. CONCLUSION Quantification of the decorrelation between sequential US images may provide us with a new approach to noninvasively diagnose bladder outlet obstruction. (C) 2015 Elsevier Inc.

关键词： Diagnostic Techniques, Urological Humans image processing, computer-assisted/methods image processing, computer-assisted/statistics & numerical data Male Models, Anatomic Urinary Bladder Neck Obstruction/ultrasonography

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Adjusting the effect of nonstationarity in cluster-based and TFCE inference

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NEUROimage 2011年第3期54卷 2006-2019页

作者： Salimi-Khorshidi, Gholamreza Smith, Stephen M. Nichols, Thomas E. Univ Oxford Ctr Funct MRI Brain FMRIB Oxford England Univ Warwick Dept Stat Coventry CV4 7AL W Midlands England

In nonstationary images, cluster inference depends on the local image smoothness, as clusters tend to be larger in smoother regions by chance alone. In order to correct the inference for such nonstationary, cluster sizes can be adjusted according to a local smoothness estimate. In this study, adjusted cluster sizes are used in a permutation-testing framework for both cluster-based and threshold-free cluster enhancement (TFCE) inference and tested on both simulated and real data. We find that TFCE inference is already fairly robust to nonstationarity in the data, while cluster-based inference requires an adjustment to ensure homogeneity. A group of possible multi-level adjustments are introduced and their results on simulated and real data are assessed using a new performance index. We also find that adjusting for local smoothness via a separate resampling procedure is more effective at removing nonstationarity than an adjustment via a random field theory based smoothness estimator. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Algorithms Alzheimer Disease/pathology Brain/anatomy & histology Brain/pathology Brain Mapping/statistics & numerical data Brain Mapping/statistics & numerical data Cluster Analysis Cognition Disorders/pathology computer Simulation data Interpretation, Statistical False Positive Reactions Humans image processing, computer-assisted/statistics & numerical data Linear Models Magnetic Resonance Imaging/statistics & numerical data Normal Distribution Reproducibility of Results ROC Curve

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Voxel-wise information theoretic EEG-fMRI feature integration

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NEUROimage 2011年第3期55卷 1270-1286页

作者： Ostwald, Dirk Porcaro, Camillo Bagshaw, Andrew P. Univ Birmingham Sch Psychol Birmingham B15 2TT W Midlands England Univ Birmingham Birmingham Univ Imaging Ctr Birmingham B15 2TT W Midlands England Charite Dept Neurol Berlin Germany Charite Bernstein Ctr Computat Neurosci Berlin Germany Newcastle Univ Inst Neurosci Newcastle Upon Tyne NE1 7RU Tyne & Wear England

We have recently proposed the evaluation of a set of information theoretic quantities (ITQs) for the integration of simultaneously acquired EEG-fMRI data (Ostwald, D., Porcaro, C., Bagshaw, A.P., 2010. An information theoretic approach to EEG-fMRI integration of visually evoked responses. Neuroimage. 49, 498-516). In our previous experimental evaluation of the information theoretic framework, we defined the data subsets from which to calculate the ITQs using a priori constraints. In the case of EEG, this meant that data were extracted from a single electrode, while for fMRI the analysed data came from voxels contained within a sphere surrounding the most responsive voxel of visual cortex. While this approach was a natural starting point for the evaluation of the framework in the application to combined EEG-fMRI data sets, a more principled approach to data selection is desirable. Here, we propose to combine standard fMRI data preprocessing and low-resolution electromagnetic tomography (LORETA) for the evaluation of ITQs across the entire three-dimensional brain space. We apply the proposed method to a simultaneous EEG-fMRI data set acquired during checkerboard stimulation and assess the topographical informativeness of EEG (time and frequency domain) and fMRI features with respect to the stimulus and each other. The resulting information theoretic effect size maps are supplemented with a statistical evaluation based on Gaussian null model simulations using a false-discovery rate procedure. Given the contamination of EEG recordings by artefacts induced by the MR scanning environment we further assessed the influence of different advanced EEG preprocessing methods (independent component analysis and functional source separation) on the information topography. The results of this analysis provide evidence for the topographically focussed informativeness of both EEG and fMRI features with respect to the stimulus, but for the current feature selection do not detect EEG-f

关键词： Algorithms Alpha Rhythm Alpha Rhythm Brain/anatomy & histology Brain/physiology Brain Mapping Brain Mapping data Interpretation, Statistical Electrodes Electroencephalography/methods Electroencephalography/statistics & numerical data Electrophysiological Phenomena Eye Movements/physiology Humans image processing, computer-assisted/statistics & numerical data Imaging, Three-Dimensional Information Theory Magnetic Resonance Imaging/methods Magnetic Resonance Imaging/statistics & numerical data Photic Stimulation Principal Component Analysis Software

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The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution

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NEUROimage 2011年第2期58卷 296-302页

作者： Roebroeck, Alard Formisano, Elia Goebel, Rainer Maastricht Univ Fac Psychol & Neurosci Dept Cognit Neurosci NL-6200 MD Maastricht Netherlands

Functional magnetic resonance imaging (fMRI) is increasingly used to study functional connectivity in large-scale brain networks that support cognitive and perceptual processes. We face serious conceptual, statistical and data analysis challenges when addressing the combinatorial explosion of possible interactions within high-dimensional fMRI data. Moreover, we need to know, and account for, the physiological mechanisms underlying our signals. We argue here that (i) model selection procedures for connectivity should include consideration of more than just a few brain structures. (ii) temporal precedence - and causality concepts based on it - are essential in dynamic models of connectivity and (iii) undoing the effect of hemodynamics on fMRI data (by deconvolution) can be an important tool. However, it is crucially dependent upon assumptions that need to be verified. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Brain/physiology Causality Cerebrovascular Circulation/physiology Hemodynamics/physiology Humans image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging/methods Models, Neurological Models, Statistical Nerve Net/physiology Neural Pathways/physiology Stochastic Processes

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Voxelwise Bayesian lesion-deficit analysis

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NEUROimage 2008年第4期40卷 1633-1642页

作者： Chen, Rong Hillis, Argye E. Pawlak, Mikolaj Herskovits, Edward H. Univ Penn Dept Radiol Philadelphia PA 19104 USA Dept Neurol Baltimore MD USA

Relating cognitive deficits to the presence of lesions has been an important means of delineating structure-function associations in the human brain. We propose a voxel-based Bayesian method for lesion-deficit analysis, which identifies complex linear or nonlinear associations among brain-lesion locations, and neurological status. We validated this method using a simulated data set, and we applied this algorithm to data obtained from an acute-stroke study to identify associations among voxels with infarct or hypoperfusion, and impaired word reading. We found that a distributed region involving Brodmann areas ( BA) 22, 37, 39, and 40 was implicated in word reading. (c) 2008 Elsevier Inc. All rights reserved.

关键词： Adult Aged Aged, 80 and over Algorithms Bayes Theorem Brain/pathology Brain Ischemia/pathology Brain Ischemia/psychology Cerebral Infarction/pathology Cerebral Infarction/psychology Cerebrovascular Circulation/physiology Cognition Disorders/pathology computer Simulation Diffusion Diffusion Magnetic Resonance Imaging Dyslexia/pathology Female Humans image processing, computer-assisted/statistics & numerical data Linear Models Male Middle Aged Nonlinear Dynamics Reproducibility of Results Stroke/pathology Stroke/psychology

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Labeling of ambiguous subvoxel fibre bundle configurations in high angular resolution diffusion MRI

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NEUROimage 2008年第1期41卷 58-68页

作者： Savadjiev, Peter Campbell, Jennifer S. W. Descoteaux, Maxime Deriche, Rachid Pike, G. Bruce Siddiqi, Kaleem McGill Univ Ctr Intelligent Machines Montreal PQ Canada McGill Univ Sch Comp Sci Montreal PQ Canada McGill Univ McConnell Brain Imaging Ctr Montreal Neurol Inst Montreal PQ Canada INRIA Sophia Antipolis Project Team Odyssee Sophia Antipolis France

Whereas high angular resolution reconstruction methods for diffusion MRI can estimate multiple dominant fibre orientations within a single imaging voxel, they are fundamentally limited in certain cases of complex subvoxel fibre structures, resulting in ambiguous local orientation distribution functions. In this article we address the important problem of disambiguating such complex subvoxel fibre tract configurations, with the purpose of improving the performance of fibre tractography. We do so by extending a curve inference method to distinguish between the cases of curving and fanning fibre bundles using differential geometric estimates in a local neighbourhood. The key benefit of this method is the inference of curves, instead of only fibre orientations, to model the underlying fibre bundles. This in turn allows distinct fibre geometries that contain nearly identical sets of fibre orientations at a voxel, to be distinguished from one another. Experimental results demonstrate the ability of the method to successfully label voxels into one of the above categories and improve the performance of a fibre-tracking algorithm. (c) 2008 Elsevier Inc. All rights reserved.

关键词： Adult Algorithms Brain/anatomy & histology Brain/cytology Diffusion Magnetic Resonance Imaging/methods Diffusion Magnetic Resonance Imaging/statistics & numerical data Humans image processing, computer-assisted/methods image processing, computer-assisted/statistics & numerical data Motor Cortex/cytology Motor Cortex/physiology Nerve Fibers/physiology Neural Pathways/anatomy & histology Neural Pathways/cytology Neural Pathways/physiology

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SENSE factors for reliable cortical thickness measurement

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NEUROimage 2008年第1期40卷 187-196页

作者： Park, Hae-Jeong Youn, Tak Jeong, Seok-Oh Oh, Maeng-Keun Kim, Sei-Young Kim, Eung-Yeop Yonsei Univ Coll Med Res Inst Radiol Sci Dept Radiol Seoul 120749 South Korea Yonsei Univ Coll Med Brain Korea 21 Proj Med Sci Seoul South Korea Yonsei Univ Coll Med Dept Nucl Med Seoul South Korea Chonnam Natl Univ Sch Med Dept Psychiat Kwangju South Korea Hankuk Univ Foreign Studies Dept Stat Yongin South Korea

The purposes of this study were to examine the effect of sensitivity encoding (SENSE) factors on cortical thickness measurements and to determine which SENSE factor to use to reliably measure cortical thickness in 3.0 T and 1.5 T T1-weighted MRI images. The 3D T1-TFE images were acquired from 11 healthy volunteers with 6 different SENSE acceleration factors from 1.0 (without SENSE acceleration) to 4.0 on a 1.5 T scanner, and 9 different SENSE factors from 1.0 to 6.0, plus a second-day 1.0 acquisition on a 3.0 T scanner. Cortical thickness was calculated for the entire cortical surface that was further subdivided into 33 regions. Repeated measures multivariate analysis of variance revealed that the main effect of SENSE factors (F= 12.485, df=7, p=0.006) was a significant underestimation of cortical thickness at SENSE 5.0 (p=0.022) and 6.0 (p=0.011) at 3.0 T and at SENSE 4.0 (p < 0.000) at 1.5 T. Repeated measures ANOVA showed that thickness measurements at the insula, superior temporal sulcus, the medial part of the superior frontal lobe, and cingulate cortex are highly affected by SENSE factors. SENSE factors affect thickness estimation more significantly at 1.5 T and thus 1.5 T imaging provides less reliable estimates using SENSE techniques. Faster imaging can be done without too much loss of reliability using a high SENSE factor, such as 3.0, at 3.0 T with acquisition time being inversely proportional to the SENSE factor. (c) 2007 Elsevier Inc. All rights reserved.

关键词： Adult Algorithms Analysis of Variance Brain Mapping Brain Mapping Cerebral Cortex/anatomy & histology Cerebral Cortex/physiology Female Humans image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging Male Reproducibility of Results

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