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Quantifying inter-subject agreement in brain-imaging analyses

NEUROimage 2008年第3期39卷 1051-1063页

作者： Wong, Dik Kin Grosenick, Logan Uy, E. Timothy Guimaraes, Marcos Perreau Carvalhaes, Claudio G. Desain, Peter Suppes, Patrick Stanford Univ Ctr Study Language & Informat Stanford CA 94305 USA Univ Estado Rio de Janeiro Inst Matemat & Estatist Rio De Janeiro Brazil Univ Nijmegen Nijmegen Inst Cognit & Informat Nijmegen Netherlands

In brain-imaging research, we are often interested in making quantitative claims about effects across subjects. Given that most imaging data consist of tens to thousands of spatially correlated time series, inter-subject comparisons are typically accomplished with simple combinations of inter-subject data, for example methods relying on group means. Further, these data are frequently taken from reduced channel subsets defined either a priori using anatomical considerations, or functionally using p-value thresholding to choose cluster boundaries. While such methods are effective for data reduction, means are sensitive to outliers, and current methods for subset selection can be somewhat arbitrary. Here, we introduce a novel "partial-ranking" approach to test for inter-subject agreement at the channel level. This non-parametric method effectively tests whether channel concordance is present across subjects, how many channels are necessary for maximum concordance, and which channels are responsible for this agreement. We validate the method on two previously published and two simulated EEG data sets. (c) 2007 Elsevier Inc. All rights reserved.

关键词： Algorithms Analysis of Variance Brain/anatomy & histology Brain/physiology Brain Mapping Brain Mapping computer Simulation Electroencephalography/statistics & numerical data Humans image processing, computer-assisted/statistics & numerical data Models, Anatomic

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Discriminating brain activity from task-related artifacts in functional MRI: Fractal scaling analysis simulation and application

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NEUROimage 2008年第1期40卷 197-212页

作者： Lee, Jae-Min Hu, Jing Gao, Jianbo Crosson, Bruce Peck, Kyung K. Wierenga, Christina E. McGregor, Keith Zhao, Qun White, Keith D. Univ Florida Dept Psychol Gainesville FL 32611 USA Brain Rehabil Res Ctr Malcolm Randall VAMC Gainesville FL 32608 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Univ Florida Dept Clin & Hlth Psychol Gainesville FL 32611 USA Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10021 USA Univ Georgia Dept Phys & Astron Athens GA 30602 USA

Functional magnetic resonance imaging (fMRI) signal changes can be separated from background noise by various processing algorithms, including the well-known deconvolution method. However, discriminating signal changes due to task-related brain activities from those due to task-related head motion or other artifacts correlated in time to the task has been little addressed. We examine whether three exploratory fractal scaling analyses correctly classify these possibilities by capturing temporal self-similarity;namely, fluctuation analysis, wavelet multi-resolution analysis, and detrended fluctuation analysis (DFA). We specifically evaluate whether these fractal analytic methods can be effective and reliable in discriminating activations from artifacts. DFA is indeed robust for such classification. Brain activation maps derived by DFA are similar, but not identical, to maps derived by deconvolution. Deconvolution explicitly utilizes task timing to extract the signals whereas DFA does not, so these methods reveal somewhat different information from the data. DFA is better than deconvolution for distinguishing fMRI activations from task-related artifacts, although a combination of these approaches is superior to either one taken alone. We also present a method for estimating noise levels in fMRI data, validated with numerical simulations suggesting that Birn's model is effective for simulating fMRI signals. Simulations further corroborate that DFA is excellent at discriminating signal changes due to task-related brain activities from those due to task-related artifacts, under a range of conditions. (c) 2007 Elsevier Inc. All rights reserved.

关键词： Adult Aged Algorithms Artifacts Brain/physiology Cerebrovascular Circulation/physiology computer Simulation Female Fingers/physiology Fractals Humans image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging/methods Magnetic Resonance Imaging/statistics & numerical data Male Movement/physiology Psychomotor Performance/physiology Pyramidal Tracts/anatomy & histology Pyramidal Tracts/physiology ROC Curve Software

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Composite voxel-based analysis of volume and T2 relaxometry in temporal lobe epilepsy

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NEUROimage 2008年第3期39卷 1151-1161页

作者： Pell, Gaby S. Briellmann, Regula S. Pardoe, Heath Abbott, David F. Jackson, Graeme D. Brain Res Inst Heidelberg Vic 3081 Australia Univ Melbourne Dept Med Neurol Parkville Vic 3052 Australia

Voxel-based analyses of tissue characteristics such as volume and T2 are usually carried out in isolation. However, as the images are analysed in a common voxel-based framework, it is possible to directly assess the spatial relationships of abnormalities detected by each technique. We utilize this approach in well-characterized patients with unilateral temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS). TLE is associated with potentially widespread volume and T2 signal abnormalities in MRI images but the relationship between these two aspects of tissue abnormality is not well understood. Here we use a novel approach of combined univariate and multivariate voxel-wise analysis to investigate the spatial relationship of these abnormalities. We studied 19 TLE patients and compared them to 115 control subjects. Grey matter (GM) and white matter (WM) volume changes were assessed with voxel-based morphometry (VBM), and changes in T2 relaxation times were evaluated with voxel-based relaxometry (VBR). The volume and T2 changes obtained using the combined univariate approach were found in an extensive area, prominently in the ipsilateral hippocampus and amygdala (overlap of GM-VBM and VBR), and in the remaining temporal lobe (overlap of WM-VBR and VBR). Other cortical and subcortical areas showed isolated volume or T2 changes. The multivariate analysis based on the Hotelling T-2 statistic, indicated a similar pattern of distributed changes across the brain but with a greater degree of statistical significance in certain areas. The composite analyses appear to identify a network of affected areas not as easily appreciated by the individual analysis of volume or T2 changes. (c) 2007 Elsevier Inc. All rights reserved.

关键词： Adult Algorithms Analysis of Variance Epilepsy, Temporal Lobe/pathology Female Hippocampus/pathology Humans image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging/statistics & numerical data Male Sclerosis

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Accurate prediction of V1 location from cortical folds in a surface coordinate system

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NEUROimage 2008年第4期39卷 1585-1599页

作者： Hinds, Oliver P. Rajendran, Niranjini Polimeni, Jonathan R. Augustinack, Jean C. Wiggins, Graham Wald, Lawrence. L. Rosas, H. Diana Potthast, Andreas Schwartz, Eric L. Fischl, Bruce Boston Univ Dept Cognit & Neural Syst Boston MA 02215 USA Harvard Univ Sch Med MGH Dept RadiolAthinoula A Martinos Ctr Cambridge MA 02138 USA Harvard Univ Sch Med MGH Dept NeurolAthinoula A Martinos Ctr Cambridge MA 02138 USA Siemens Med Solut Inc Malvern PA USA Boston Univ Dept Elect & Comp Engn Boston MA 02215 USA Boston Univ Sch Med Dept Anat & Neurobiol Boston MA 02215 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

Previous studies demonstrated substantial variability of the location of primary visual cortex (V1) in stereotaxic coordinates when linear volume-based registration is used to match volumetric image intensities [Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T., and Zilles, K. (2000). Brodmann's areas 17 and 18 brought into stereotaxic space-where and how variable? Neurohnage, 11(1):66-84]. However, other qualitative reports of V1 location [Smith, G. (1904). The morphoiogy of the occipital region of the cerebral hemisphere in man and the apes. Anatomischer Anzeiger, 24:436-451;Stensaas, S.S., Eddington, D.K., and Dobelle, W.H. (1974). The topography and variability of the primary visual cortex in man. J Neurosurg, 40(6):747-755;Rademacher, J., Caviness, V.S., Steinmetz, H., and Galaburda, A.M. (1993). Topographical variation of the human primary cortices: implications for neuroimaging, brain mapping, and neurobiology. Cereb Cortex, 3 (4):313-329] suggested a consistent relationship between V1 and the surrounding cortical folds. Here, the relationship between folds and the location of V1 is quantified using surface-based analysis to generate a probabilistic atlas of human V1. High-resolution (about 200 pm) magnetic resonance imaging (MRI) at 7 T of ex vivo human cerebral hemispheres allowed identification of the full area via the stria of Gennari: a myeloarchitectonic feature specific to V1. Separate, whole-brain scans were acquired using MRI at 1.5 T to allow segmentation and mesh reconstruction of the cortical gray matter. For each individual, V1 was manually identified in the high-resolution volume and projected onto the cortical surface. Surface-based intersubject registration [Fischl, B., Screno, M.I., Tootell, R.B., and Dale, A.M. (1999b). High-resolution intersubject averaging and a coordinate system for the cortical surface. Haiti Brain Mapp, 8(4):272-84] was performed to align the primary cortical folds of individual hemispheres to those of a reference

关键词： Aged Algorithms Autopsy Autopsy Cerebral Cortex/anatomy & histology Female Functional Laterality/physiology Humans image processing, computer-assisted/methods image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging Male Middle Aged Models, Statistical Predictive Value of Tests Stereotaxic Techniques Visual Cortex/anatomy & histology

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Estimating neurotransmitter kinetics with ntPET: A simulation study of temporal precision and effects of biased data

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NEUROimage 2008年第3期39卷 1162-1179页

作者： Normandin, Marc D. Morris, Evan D. Indiana Univ Sch Med Dept Radiol Res Inst 2 Indianapolis IN 46202 USA Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47907 USA Indiana Univ Purdue Univ Dept Biomed Engn Indianapolis IN 46202 USA

We recently introduced neurotransmitter PET (ntPET), an analysis technique that estimates the kinetics of stimulus-induced neurotransmitter (NT) release. Here, we evaluate two formulations of ntPET. The arterial (ART) approach measures the tracer input function (TIF) directly. The reference (REF) approach derives the TIF from reference region data. Arterial sampling is considered the gold standard in PET modeling but reference region approaches are preferred for reduced cost and complexity. If simulated PET data with unbiased TIFs were analyzed using ART or REF, temporal precision was better than 3 min provided NT concentration peaked less than 30 min into the scanning session. The consequences of biased TlFs or stimulus-induced changes in tracer delivery were also evaluated. ART TIFs were biased by the presence of uncorrected radiometabolites in the plasma whereas REF TIFs were biased by specific binding in the reference region. Simulated changes in tracer delivery emulated ethanol-induced blood flow alterations observed previously with PET. ART performance deteriorated significantly if metabolites amounted to 50% of plasma radioactivity by 60 min. The accuracy and precision of REF were preserved even if the reference region contained 40% of the receptor density of the target region. Both methods were insensitive to blood flow alterations (proportional changes in K-1 and K-2). Our results suggest that PET data contain information - heretofore not extracted - about the timing of NT release. The REF formulation of ntPET proved to be robust to many plausible model violations and under most circumstances is an appropriate alternative to ART. (c) 2007 Elsevier Inc. All rights reserved.

关键词： Algorithms Bias (Epidemiology) Brain Chemistry Central Nervous System Depressants/pharmacology Cerebral Arteries/anatomy & histology Cerebral Arteries/physiology Cerebrovascular Circulation/drug effects Cerebrovascular Circulation/physiology computer Simulation Ethanol/pharmacology Humans image processing, computer-assisted/statistics & numerical data Kinetics Microdialysis Models, Statistical Neurotransmitter Agents/metabolism Positron-Emission Tomography/methods Reproducibility of Results

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Assessing multiple-group diagnostic problems with multi-dimensional receiver operating characteristic surfaces: Application to proton MR Spectroscopy (MRS) in HIV-related neurological injury

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NEUROimage 2008年第1期40卷 248-255页

作者： Yiannoutsos, Constantin T. Nakas, Christos T. Navia, Bradford A. Indiana Univ Sch Med Indianapolis IN 46202 USA Univ Thessaly Sch Agr Sci Lab Biometry Magnisia Greece Tufts Med Sch Boston Boston MA USA

We present the multi-dimensional Receiver Operating Characteristic (ROC) surface, a plot of the true classification rates of tests based on levels of biological markers, for multi-group discrimination, as an extension of the ROC curve, commonly used in two-group diagnostic testing. The volume under this surface (VUS) is a global accuracy measure of a test to classify subjects in multiple groups and useful to detect trends in marker measurements. We used three-dimensional ROC surfaces, and associated VUS, to discriminate between HIV-negative (NEG), HIV-positive neurologically asymptomatic (NAS) subjects and patients with AIDS demential complex (ADC), using brain metabolites measured by proton MRS. These were ratios of markers of inflammation, Choline (Cho) and myoinositol (MI), and brain injury, N-acetyl aspartate (NAA), divided by Creatine (Cr), measured in the basal ganglia and the frontal white matter. Statistically significant trends were observed in the three groups with respect to MI/ Cr (VUS=0.43;95% confidence interval (CI) 0.33-0.53), Cho/Cr (0.36;0.27-0.45) in the basal ganglia and NAA/Cr in the frontal white matter (FWM) (0.29;0.20-0.38), suggesting a continuum of injury during the neurologically asymptomatic stage of HIV infection, particularly with respect to brain inflammation. Adjusting for age increased the combined classification accuracy of age and NAA/Cr (p=0.053). Pairwise comparisons suggested that neuronal damage associated with NAA/Cr decreases was mainly observed in individuals with ADC, raising issues of synergism between HIV infection and age and possible acceleration of neurological deterioration in an aging HIV-positive population. The three-dimensional ROC surface and its associated VUS are useful for assessing marker accuracy, detecting data trends and offering insight in disease processes affecting multiple groups. (c) 2007 Elsevier Inc. All rights reserved.

关键词： Adult Aging/physiology AIDS Dementia Complex/diagnosis AIDS Dementia Complex/metabolism AIDS Dementia Complex/pathology Algorithms Aspartic Acid/analogs & derivatives Aspartic Acid/metabolism Basal Ganglia/pathology Brain/pathology Brain Chemistry Choline/metabolism Creatinine/metabolism Female HIV Infections/pathology Humans image processing, computer-assisted/statistics & numerical data Inositol/metabolism Magnetic Resonance Imaging/statistics & numerical data Male Middle Aged Predictive Value of Tests ROC Curve

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Time-frequency discriminant analysis of MEG signals

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NEUROimage 2008年第1期40卷 174-186页

作者： Ho, Moon-Ho Ringo Ombao, Hernando Edgar, J. Christopher Canive, Jose M. Miller, Gregory A. Nanyang Technol Univ Sch Human & Social Sci Div Psychol Singapore 639798 Singapore Brown Univ Ctr Stat Sci Providence RI 02912 USA Univ Illinois Beckman Inst Dept Psychol Urbana IL 61801 USA New Mexico VA Hlth Care Syst Psychiat Serv Albuquerque NM USA Univ New Mexico Dept Psychiat Albuquerque NM 87131 USA

This paper introduces a novel statistical method that can identify relevant time-frequency features in brain signals to distinguish between groups. The feature of interest is the spectrum which characterizes the distribution of a given signal's variance (or power) across frequency oscillations. Brain signals are generally nonstationary in that the distribution of the signals' power across frequency changes over time. The classical Fourier analysis is not formally suitable for time series signals with time-varying spectra. This paper utilizes the SLEX (Smooth Localized Complex EXponentials) basis function to capture the transient features of brain signals. The SLEX basis consists of a set of localized orthogonal Fourier-like waveforms with a built-in mechanism for representing localized spectral features. The best basis is first chosen that maximizes group dissimilarity in the time-varying spectra. However, not all spectral features extracted from the best basis may be useful for discrimination and classification purpose. A thresholding scheme is further developed to remove irrelevant features from the best basis to improve accuracy for classification. In simulations the proposed SLEX-thresholding discriminant method was able to consistently identify the most discriminant time-frequency features and was able to correctly classify signals at a high rate. The method was then applied to magnetoencephalographic data from a standard paired-click paradigm. Discrimination between individuals with schizophrenia and a healthy comparison group confirmed the utility of the method. (c) 2007 Elsevier Inc. All rights reserved.

关键词： Adult Algorithms Chronic Disease computer Simulation Discriminant Analysis Electroencephalography/statistics & numerical data Female Fourier Analysis Fourier Analysis Humans image processing, computer-assisted/statistics & numerical data Magnetoencephalography/statistics & numerical data Magnetoencephalography/statistics & numerical data Male Neural Networks (computer) Schizophrenia/pathology

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Registration-based method for determining relative PET/MR left-right image orientation

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NEUROimage 2008年第1期40卷 213-216页

作者： Laymon, C. M. Ziolko, S. K. Becker, C. R. Price, J. C. Mountz, J. M. UPMC Pittsburgh PA 15213 USA Univ Pittsburgh Dept Radiol Pittsburgh PA 15260 USA

A quality control method is described for verifying that the coordinate systems of a PET and MR image from the same subject have the same relative left-right orientation. Such ambiguities can arise, for example, at the coordinating center receiving data from many institutions in large multisite studies. In this study, image registration was performed on the PET/MR image pair. The MR image was then reflected (left/right reversed) and the image registration was performed again. A comparison of the values of the cost function describing the accuracy of the registration was used to identify the correct relative orientation. In 122 studies using data with known orientations, 100% accuracy of the method was observed. (c) 2007 Elsevier Inc. All rights reserved.

关键词： Adult Aged Aged, 80 and over Algorithms Aniline Compounds/diagnostic use data Interpretation, Statistical Female Fluorodeoxyglucose F18/diagnostic use Functional Laterality/physiology Humans image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging/statistics & numerical data Male Middle Aged Positron-Emission Tomography/statistics & numerical data Quality Control Radiopharmaceuticals/diagnostic use Radiopharmaceuticals/diagnostic use Thiazoles/diagnostic use

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Investigating signal integration with canonical correlation analysis of fMRI brain activation data

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NEUROimage 2008年第1期41卷 35-44页

作者： Bruguier, Antoine Preuschoff, Kerstin Quartz, Steven Bossaerts, Peter Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

How the brain integrates signals from specific areas has been a longstanding critical question for neurobiologists. Two recent observations suggest a new approach to fMRI data analysis of this question. First, in many instances, the brain analyzes inputs by decomposing the information along several salient dimensions. For example, earlier work demonstrated that the brain splits a monetary gamble in terms of expected reward (ER) and variance of the reward (risk) [Preuschoff, K., Bossaerts, P., Quartz, S., 2006. Neural differentiation of expected reward and risk in human subcortical structures. Neuron 51, 381-390]. However, since ER and risk activate separate brain regions, the brain needs to integrate these activations to obtain an overall evaluation of the gamble. Second, recent evidence suggests that the correlation of the activity between neurons may serve a specific organizational purpose [Romo, R., Hernandez, A., Zainos, A., Salinas, E., 2003. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38, 649-657;Salinas, E., Sejnowski, T.J., 2001. Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2, 539]. Specifically, it is hypothesized that correlations allow brain regions to integrate several signals in a way that minimizes noise. Under this hypothesis, we show here that canonical correlation analysis of fMRI data identifies how the signals from several regions are combined. A general linear model then verifies whether the identified combination indeed activates a projection area in the brain. We illustrate the proposed procedure on data recorded while human subjects played a simple card game. We show that the brain adds the signals of ER and risk to form a measure that activates the medial prefrontal cortex, consistent with the role of this brain structure in the evaluation of monetary gambles. (c) 2008 Elsevier Inc. All rights reserved.

关键词： Algorithms Brain/physiology Gambling/psychology Humans image processing, computer-assisted/statistics & numerical data Linear Models Magnetic Resonance Imaging/statistics & numerical data Neurons/physiology Oxygen/blood Prefrontal Cortex/physiology Reproducibility of Results Reward Risk-Taking

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Rank-order versus mean based statistics for neuroimaging

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NEUROimage 2007年第4期35卷 1531-1537页

作者： Rorden, Chris Bonilha, Leonardo Nichols, Thomas E. Univ S Carolina Dept Commun Sci & Disorders Columbia SC 29208 USA Univ S Carolina Dept Neuropsychiat Columbia SC 29208 USA Univ Michigan Dept Biostat Ann Arbor MI 48109 USA

Traditional analysis of neuroimaging data uses parametric statistics, such as the t-test. These tests are designed to detect mean differences. In fact, even nonparametric techniques such as Statistical non-parametric Mapping (SnPM) use the mean-based t statistic to measure effect size. We note that these measures may not be particularly sensitive for detecting differences when the mean is not an accurate measure of central tendency - for example if one of the groups is experiencing a ceiling or floor effect (causing a skewed data distribution). Here we introduce a nonparametric approach for neuroimaging data analysis that is based on the rank-order of data (and is therefore less influenced by outliers than the t-test). We suggest that this approach may offer a small benefit for datasets where the assumptions of the t-test have been violated, for example datasets where data from one of the groups exhibits a skewed distribution due to floor or ceiling effects. (c) 2007 Elsevier Inc. All rights reserved.

关键词： Algorithms Brain/pathology computer Simulation data Interpretation, Statistical Epilepsy, Temporal Lobe/pathology False Positive Reactions Hippocampus/pathology Humans image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging/statistics & numerical data Sample Size statistics, Nonparametric

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