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FLOPPING POLYPEPTIDE-CHAINS AND SULEIKAS SUBTLE IMPERFECTIONS - ANALYSIS OF VARIATIONS IN THE ELECTRON MICROGRAPH OF A PURPLE MEMBRANE CRYSTAL

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ULTRAMICROSCOPY 1993年第1-4期49卷 387-396页

作者： FRANK, J CHIU, W HENDERSON, R SUNY ALBANY DEPT BIOMED SCIALBANYNY 12222 BAYLOR COLL MED VERNA & MARRS MCLEAN DEPT BIOCHEMHOUSTONTX 77030 MRC MOLEC BIOL LABCAMBRIDGEENGLAND

Patch averaging, a method of data analysis combining correlation averaging with multivariate statistical analysis, was applied to a high-resolution image of a purple membrane thin crystal. The observed variational patterns fall in two categories: non-crystallographic, related to small electron-optical distortions of the microscope, and crystallographic. conceivably related to variations in the locations of flexible segments of the polypeptide chain on the surface of the membrane.

关键词： Bacteriorhodopsins/chemistry Crystallography image processing, computer-assisted/methods image processing, computer-assisted/statistics & numerical data Microscopy, Electron Multivariate Analysis

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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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A model-based method for retrospective correction of geometric distortions in diffusion-weighted EPI

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NEUROimage 2002年第1期16卷 177-199页

作者： Andersson, JLR Skare, S Karolinska MR Res Ctr S-17176 Stockholm Sweden

The self-diffusion tensor may be calculated from several echo-planar image acquisitions preceded by different diffusion gradients. Unfortunately, these diffusion gradients cause geometric distortion that must be corrected before estimation of the tensor. In the present paper we suggest and implement a method for retrospective correction of these distortions firmly based on a physical model for the diffusion-weighted images. This method simultaneously estimates subject movement and distortion parameters by finding the set of parameters that minimizes residual error when fitting data to the diffusion tensor model. We show how this notion can be formalized as a quadratic form thereby facilitating the implementation of a rapid algorithm. In addition, we suggest models for how distortions vary with slice position and gradient direction that allow us to substantially reduce the dimensionality of the parameter space. Our results indicate that we are able to estimate both eddy current-induced distortion and subject movement directly from the data without need of any additional measurements. (C) 2002 Elsevier Science (USA).

关键词： Algorithms Artifacts Echo-Planar Imaging/statistics & numerical data Head Movements Humans image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging Models, Statistical

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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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Automatic anatomical brain MRI segmentation combining label propagation and decision fusion

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NEUROimage 2006年第1期33卷 115-126页

作者： Heckemann, Rolf A. Hajnal, Joseph V. Aljabar, Paul Rueckert, Daniel Hammers, Alexander Univ London Imperial Coll Sci Technol & Med MRC Clin Sci Ctr Imaging Sci Dept London W12 0HS England Univ London Imperial Coll Sci Technol & Med Dept Comp London W12 0HS England Univ London Imperial Coll Sci Technol & Med MRC Clin Sci Ctr Div Neurosci & Mental Hlth London W12 0HS England

Regions in three-dimensional magnetic resonance (MR) brain images can be classified using protocols for manually segmenting and labeling structures. For large cohorts, time and expertise requirements make this approach impractical. To achieve automation, an individual segmentation can be propagated to another individual using an anatomical correspondence estimate relating the atlas image to the target image. The accuracy of the resulting target labeling has been limited but can potentially be improved by combining multiple segmentations using decision fusion. We studied segmentation propagation and decision fusion on 30 normal brain MR images, which had been manually segmented into 67 structures. Correspondence estimates were established by nonrigid registration using free-form deformations. Both direct label propagation and an indirect approach were tested. Individual propagations showed an average similarity index (SI) of 0.754 +/- 0.016 against manual segmentations. Decision fusion using 29 input segmentations increased SI to 0.836 +/- 0.009. For indirect propagation of a single source via 27 intermediate images, SI was 0.779 +/- 0.013. We also studied the effect of the decision fusion procedure using a numerical simulation with synthetic input data. The results helped to formulate a model that predicts the quality improvement of fused brain segmentations based on the number of individual propagated segmentations combined. We demonstrate a practicable procedure that exceeds the accuracy of previous automatic methods and can compete with manual delineations. (c) 2006 Elsevier Inc. All rights reserved.

关键词： Adult Algorithms Automatic data processing Brain/anatomy & histology Decision Support Techniques Female Humans image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging/statistics & numerical data Male Middle Aged Models, Statistical Quality Control Reference Values

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Segmentation of subcortical brain structures using fuzzy templates

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NEUROimage 2005年第4期28卷 915-924页

作者： Zhou, J Rajapakse, JC Nanyang Technol Univ Sch Comp Engn Bioinformat Res Ctr Singapore 639798 Singapore

We propose a novel method to automatically segment subcortical structures of human brain in magnetic resonance images by using fuzzy templates. A set of fuzzy templates of the structures based on features such as intensity, spatial location, and relative spatial relationship among structures are first created from a set of training images by defining the fuzzy membership functions and by fusing the information of features. Segmentation is performed by registering the fuzzy templates of the structures on the test image and then by fusing them with the tissue maps of the test image. The final decision is taken in order to optimize the certainty in the intensity, location, relative position, and tissue content of the structure. Our method does not require specific expert definition of each structure or manual interactions during segmentation process. The technique is demonstrated with the segmentation of five structures: thalamus, putamen, caudate, hippocampus, and amygdala;the performance of the present method is comparable with previous techniques. (c) 2005 Elsevier Inc. All rights reserved.

关键词： Algorithms Artifacts Artificial Intelligence Brain/anatomy & histology Brain/physiology Brain Mapping Brain Mapping Functional Laterality/physiology Fuzzy Logic Humans image processing, computer-assisted/statistics & numerical data Magnetic Resonance Imaging/statistics & numerical data Reproducibility of Results

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Statistical tests for fMRI based on experimental randomization

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NEUROimage 2003年第2期19卷 226-232页

作者： Raz, J Zheng, H Ombao, H Turetsky, B Harvard Univ Sch Med Dept Hlth Care Policy Boston MA 02115 USA Univ Penn Div Biostat Philadelphia PA 19104 USA Univ Illinois Dept Stat Urbana IL USA Univ Penn Brain Behav Lab Philadelphia PA 19104 USA

Statistical parametric mapping (SPM) analysis of fMRI data requires specifying correctly the temporal and spatial noise covariance structure. This is a difficult if not impossible task. When these assumptions are not satisfied, statistical inference can be invalid or inefficient. Permutation tests are free of strong assumptions on the distribution of signal noise. We propose permutation tests of fMRI data based on experimental randomization of the stimulus sequences. Smooth hemodynamic response curves are estimated using quadratic B-splines. We study fMRI data obtained from event-related potential (ERP) oddball paradigm. Tests of two-tone and three-tone stimulus sequences are conducted to illustrate the application of the proposed method. Comparisons of the SPM and proposed method show that a permutation test is more specific and less susceptible to artifacts. (C) 2003 Elsevier Science (USA). All rights reserved.

关键词： Arousal/physiology Artifacts Brain Mapping Brain Mapping Cerebral Cortex/physiology Event-Related Potentials, P300/physiology Humans image processing, computer-assisted/statistics & numerical data Imaging, Three-Dimensional/statistics & numerical data Linear Models Magnetic Resonance Imaging/statistics & numerical data Mathematical Computing Pitch Perception/physiology Psychomotor Performance/physiology Random Allocation

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Parallel networks operating across attentional deployment and motion processing: A multi-seed partial least squares fMRI study

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NEUROimage 2006年第4期29卷 1192-1202页

作者： Caplan, JB Luks, TL Simpson, GV Glaholt, M McIntosh, AR Baycrest Ctr Geriatr Care Rotman Res Inst Toronto ON M6A 2E1 Canada Univ Calif San Francisco Dynam Neuroimaging Lab San Francisco CA 94143 USA

Anticipatory deployment of attention may operate through networks of brain areas that modulate the representations of to-be-attended items in advance of their occurrence through top-down control. Luks and Simpson (2004) (Luks, T.L., Simpson, G.V., 2004. Preparatory deployment of attention to motion activates higher order motion-processing brain regions. Neuroimage 22, 1515-1522) found activations in both control areas and sensory areas during anticipatory deployment of attention to visual motion in the absence of stimuli. In the present follow-up analysis, we tested which network activity during anticipatory deployment of attention is functionally connected with task-related network activity during subsequent selective processing of motion stimuli. Following a cue (anticipatory phase), participants monitored a sequence of complex motion stimuli for a target motion pattern (task phase). We analyzed fMR signal using a partial least squares analysis with previously identified cue- and motion-related voxels as seed regions. The method identified two networks that covaried with the activity of seed regions during the cue and motion-stimulus-processing phases of the task. We suggest that the first network, involving ventral intraparietal sulcus, superior parietal lobule and motor areas, is related to anticipatory and sustained visuomotor attention. Operating in parallel to this visuomotor attention network, there is a second network, involving visual occipital areas, frontal areas as well as angular and supramarginal gyri, that may underlie anticipatory and sustained visual attention processes. (c) 2005 Elsevier Inc. All rights reserved.

关键词： Adult Attention/physiology Brain Mapping Brain Mapping Cerebral Cortex/physiology Cues Female Frontal Lobe/physiology Humans image processing, computer-assisted/statistics & numerical data Least-Squares Analysis Magnetic Resonance Imaging/statistics & numerical data Male Mathematical Computing Motion Perception/physiology Motor Cortex/physiology Nerve Net/physiology Occipital Lobe/physiology Orientation/physiology Parietal Lobe/physiology Pattern Recognition, Visual/physiology Psychomotor Performance/physiology Size Perception/physiology

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Automatic analysis of agarose gel images

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BIOINFORMATICS 2001年第11期17卷 1084-1090页

作者： Adiga, PSU Bhomra, A Turri, MG Nicod, A Datta, SR Jeavons, P Mott, R Flint, J Univ Oxford Wellcome Trust Ctr Human Genet Psychiat Genet Grp Oxford OX3 7BN England Univ Oxford Comp Lab Oxford OX1 3QD England

Motivation: Automatic tools to speed up routine biological processes are very much sought after in bio-medical research. Much repetitive work in molecular biology, such as allele calling in genetic analysis, can be made semiautomatic or task specific automatic by using existing techniques from computer science and signal processing. computerized analysis is reproducible and avoids various forms of human error. Semi-automatic techniques with an interactive check on the results speed up the analysis and reduce the error. Results: We have successfully implemented an image processing software package to automatically analyze agarose gel images of polymorphic DNA markers. We have obtained up to 90% accuracy for the classification of alleles in good quality images and up to 70% accuracy in average quality images. These results are obtained within a few seconds. Even after subsequent interactive checking to increase the accuracy of allele classification to 100%, the overall speed with which the data can be processed is greatly increased, compared to manual allele classification.

关键词： Animals Computational Biology DNA/genetics DNA/isolation & purification Electrophoresis, Agar Gel/statistics & numerical data Genetic Markers image processing, computer-assisted/statistics & numerical data Polymorphism, Genetic Sequence Analysis, DNA/statistics & numerical data Software Software Design

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Diffusion tensor imaging segmentation of white matter structures using a Reproducible Objective Quantification Scheme (ROQS)

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NEUROimage 2007年第1期35卷 166-174页

作者： Niogi, Sumit N. Mukherjee, Pratik McCandliss, Brace D. Cornell Univ Weill Med Coll Sackler Inst Dev Psychobiol New York NY 10021 USA Cornell Univ Weill Med Coll Dept Physiol New York NY 10021 USA Univ Calif San Francisco Dept Radiol San Francisco CA 94143 USA

Reproducible Objective Quantification Scheme (ROQS) is a novel method for regional white matter measurements of diffusion tensor imaging (DTI) parameters that overcomes the limitations of previous approaches for analyzing large cohorts of subjects reliably. ROQS is a semi-automated technique that exploits the fiber orientation information from the diffusion tensor in conjunction with a binary masking and chain-linking algorithm to segment anatomically distinct white matter tracts for subsequent quantitative analysis of DTI parameters such as fractional anisotropy and apparent diffusion coefficient. When applied to 3-T whole-brain DTI of normal adult volunteers, ROQS is shown to segment the corpus callosum much faster than manual region of interest (ROI) delineation, and with better reproducibility and accuracy. (c) 2006 Elsevier Inc. All rights reserved.

关键词： Adult Algorithms Anisotropy Brain/anatomy & histology Corpus Callosum/anatomy & histology Corpus Callosum/physiology databases, Factual Diffusion Magnetic Resonance Imaging/statistics & numerical data Humans image processing, computer-assisted/statistics & numerical data Observer Variation Reference Values Reproducibility of Results

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