The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform
Sharmila Majumdar - One of the best experts on this subject based on the ideXlab platform.
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Longitudinal analysis of MRI T2 knee cartilage Laminar Organization in a subset of patients from the osteoarthritis initiative: A texture approach
Magnetic resonance in medicine, 2010Co-Authors: Julio Carballido-gamio, John A. Lynch, Thomas M. Link, Gabby B. Joseph, Sharmila MajumdarAbstract:Cartilage magnetic resonance imaging T(2) relaxation time is sensitive to hydration, collagen content, and tissue anisotropy, and a potential imaging-based biomarker for knee osteoarthritis. This longitudinal pilot study presents an improved cartilage flattening technique that facilitates texture analysis using gray-level co-occurrence matrices parallel and perpendicular to the cartilage layers, and the application of this technique to the knee cartilage of 13 subjects of the osteoarthritis initiative at baseline, 1-year follow-up, and 2-year follow-up. Cartilage flattening showed minimum distortion (~ 0.5 ms) of mean T(2) values between nonflattened and flattened T(2) maps. Gray-level co-occurrence matrices texture analysis of flattened T(2) maps detected a cartilage Laminar Organization at baseline, 1-year follow-up, and 2-year follow-up by yielding significant (P< 0.05) differences between texture parameters perpendicular and parallel to the cartilage layers. Tendencies showed higher contrast, dissimilarity, angular second moment, and energy perpendicular to the cartilage layers; and higher homogeneity, entropy, variance, and correlation parallel to them. Significant (P< 0.05) longitudinal texture changes were also detected reflecting subtle signs of a Laminar disruption. Tendencies showed decreasing contrast, dissimilarity, and entropy; and increasing homogeneity, energy, and correlation. Results of this study warrant further investigation to complete the assessment of the usefulness of the presented methodology in the study of knee osteoarthritis. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc.
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Longitudinal analysis of MRI T2 knee cartilage Laminar Organization in a subset of patients from the osteoarthritis initiative
Magnetic resonance in medicine, 2009Co-Authors: Julio Carballido-gamio, Gabrielle Blumenkrantz, John A. Lynch, Thomas M. Link, Sharmila MajumdarAbstract:The purpose of this pilot study was to longitudinally quantify the T2 Laminar integrity of knee cartilage in a subset of subjects with osteoarthritis from the Osteoarthritis Initiative at baseline, 1-year follow-up, and 2-year follow-up. Cartilage from 13 subjects was divided into six compartments and subdivided into deep and superficial layers. At each time point, mean T2 values in superficial and deep layers were compared. Longitudinal analysis included full-thickness mean T2, mean deep T2, mean superficial T2, mean T2 Laminar difference, mean percentage T2 Laminar difference, and two-dimensional measures of cartilage thickness. More compartments showed significantly higher superficial T2 than deep T2 values at baseline and 1-year follow-up compared to 2-year follow-up. No significant longitudinal changes of full-thickness mean T2 and superficial T2 values were observed. Significant longitudinal changes were observed in the deep T2 values, T2 Laminar difference, and percentage T2 Laminar difference. Cartilage thickness had no influence on T2 analysis. Results of this study suggest that Laminar analysis may improve the sensitivity to detect longitudinal T2 changes and that disruption of the T2 Laminar Organization of knee cartilage may be present in knee osteoarthritis progressors. Further investigation is warranted to evaluate the potential of the presented methodology to better characterize evolution and pathophysiology of osteoarthritis. Magn Reson Med, 2010. © 2009 Wiley-Liss, Inc.
Julio Carballido-gamio - One of the best experts on this subject based on the ideXlab platform.
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Longitudinal analysis of MRI T2 knee cartilage Laminar Organization in a subset of patients from the osteoarthritis initiative: A texture approach
Magnetic resonance in medicine, 2010Co-Authors: Julio Carballido-gamio, John A. Lynch, Thomas M. Link, Gabby B. Joseph, Sharmila MajumdarAbstract:Cartilage magnetic resonance imaging T(2) relaxation time is sensitive to hydration, collagen content, and tissue anisotropy, and a potential imaging-based biomarker for knee osteoarthritis. This longitudinal pilot study presents an improved cartilage flattening technique that facilitates texture analysis using gray-level co-occurrence matrices parallel and perpendicular to the cartilage layers, and the application of this technique to the knee cartilage of 13 subjects of the osteoarthritis initiative at baseline, 1-year follow-up, and 2-year follow-up. Cartilage flattening showed minimum distortion (~ 0.5 ms) of mean T(2) values between nonflattened and flattened T(2) maps. Gray-level co-occurrence matrices texture analysis of flattened T(2) maps detected a cartilage Laminar Organization at baseline, 1-year follow-up, and 2-year follow-up by yielding significant (P< 0.05) differences between texture parameters perpendicular and parallel to the cartilage layers. Tendencies showed higher contrast, dissimilarity, angular second moment, and energy perpendicular to the cartilage layers; and higher homogeneity, entropy, variance, and correlation parallel to them. Significant (P< 0.05) longitudinal texture changes were also detected reflecting subtle signs of a Laminar disruption. Tendencies showed decreasing contrast, dissimilarity, and entropy; and increasing homogeneity, energy, and correlation. Results of this study warrant further investigation to complete the assessment of the usefulness of the presented methodology in the study of knee osteoarthritis. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc.
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Longitudinal analysis of MRI T2 knee cartilage Laminar Organization in a subset of patients from the osteoarthritis initiative
Magnetic resonance in medicine, 2009Co-Authors: Julio Carballido-gamio, Gabrielle Blumenkrantz, John A. Lynch, Thomas M. Link, Sharmila MajumdarAbstract:The purpose of this pilot study was to longitudinally quantify the T2 Laminar integrity of knee cartilage in a subset of subjects with osteoarthritis from the Osteoarthritis Initiative at baseline, 1-year follow-up, and 2-year follow-up. Cartilage from 13 subjects was divided into six compartments and subdivided into deep and superficial layers. At each time point, mean T2 values in superficial and deep layers were compared. Longitudinal analysis included full-thickness mean T2, mean deep T2, mean superficial T2, mean T2 Laminar difference, mean percentage T2 Laminar difference, and two-dimensional measures of cartilage thickness. More compartments showed significantly higher superficial T2 than deep T2 values at baseline and 1-year follow-up compared to 2-year follow-up. No significant longitudinal changes of full-thickness mean T2 and superficial T2 values were observed. Significant longitudinal changes were observed in the deep T2 values, T2 Laminar difference, and percentage T2 Laminar difference. Cartilage thickness had no influence on T2 analysis. Results of this study suggest that Laminar analysis may improve the sensitivity to detect longitudinal T2 changes and that disruption of the T2 Laminar Organization of knee cartilage may be present in knee osteoarthritis progressors. Further investigation is warranted to evaluate the potential of the presented methodology to better characterize evolution and pathophysiology of osteoarthritis. Magn Reson Med, 2010. © 2009 Wiley-Liss, Inc.
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Early Laminar Organization of the human cerebrum demonstrated by automatic segmentation of diffusion tensor MR images in extremely premature infants
2004Co-Authors: Julio Carballido-gamio, Luis C Maas, Pratik Mukherjee, Srivathsa Veeraraghavan, Steven P Miller, Savannah C Partridge, Roland G Henry, A. J. Barkovich, Daniel B VigneronAbstract:J. Carballido-Gamio, P. Mukherjee, L. C. Maas, S. Veeraraghavan, S. P. Miller, S. C. Partridge, R. G. Henry, A. J. Barkovich, D. B. Vigneron University of California, San Francisco, San Francisco, California, United States Introduction Diffusion tensor imaging (DTI) is a non-invasive technique that probes diffusion of water molecules at a microstructural level. DTI has been used to reveal the transient early Laminar architecture of the developing fetal mouse brain ex vivo [1], many features of which are not apparent on conventional MR imaging. The purpose of this work is to present an automatic segmentation technique to delineate the early cerebral Laminar Organization of premature human newborns in vivo, taking advantage of the multi-channel information yielded by DTI. The segmentation technique is based on a Mamdani-type fuzzy inference system (FIS) [2]. Methods Two extremely premature infants born at estimated gestational ages (EGAs) of 24 and 25 weeks were imaged at 1.5 T (Signa EchoSpeed scanner; GE Medical Systems, Milwaukee, WI) with a high sensitivity neonatal head coil incorporated into an MR-compatible incubator [3]. The infants were imaged at EGAs of 25 and 27 weeks (Subjects 1 and 2, respectively). DT images were acquired with a multi-repetition, single-shot echoplanar technique (TR/TE 7000/99.5 ms, slice thickness 3 mm, no gap, 3 NEX, 36 x 18 cm FOV, 256 x 128 acquisition matrix, in-plane resolution 1.4 mm × 1.4 mm, b=600 s/mm), transferred to a workstation and registered to the image with b=0 s/mm prior to tensor calculation. Maps of the rotationally-invariant apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were calculated, and manual segmentation was performed taking as a reference the ex-vivo work presented in [4]. Based on the pattern of ADC and FA values observed at the different cerebral lamina, an automatic segmentation technique based on fuzzy logic was developed in MATLAB (The Mathworks, Inc. Natick, MA). First, ADC values were clustered into low, medium, and high ADC groups using fuzzy c-means to get an initial estimate of the ADC value distribution: CSF (high ADC), subplate (medium ADC), deep-to-subplate and cortical layers (low ADC). CSF pixels were removed from subsequent segmentation, and remaining pixels were clustered according to their FA values into subplate (low FA), deep-to-subplate and cortical layers (medium FA), and noise (high FA). Then a Mamdani-type FIS with two inputs (FA and ADC pixels different from CSF) and 1 output was built to segment the subplate. Each input was fuzzified by two Gaussian membership functions which were created based on the means and standard deviations of the low and medium ADC and FA groups, and the segmentation was accomplished with the following two rules based on observations of the manual segmentation: 1. If the ADC is medium and the FA is low, then the pixel is subplate. 2. If the ADC is low and the FA is medium, then the pixel is non-subplate. Because the output was continuous, an automatic threshold equal to the point of intersection of the 2 output membership functions (trapezoidals) was applied to get a binary image representing subplate pixels in its majority. Based on a-priori knowledge about the size of the subplate, a connectivity
Melissa Zavaglia - One of the best experts on this subject based on the ideXlab platform.
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discrimination of the hierarchical structure of cortical layers in 2 photon microscopy data by combined unsupervised and supervised machine learning
Scientific Reports, 2019Co-Authors: Melissa Zavaglia, Guangyu Wang, Hong Xie, Rene Werner, Jisong Guan, Claus C HilgetagAbstract:The Laminar Organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the Laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical Laminar Organization in high-resolution 2-photon microscopic neural image data of mouse brain without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the Laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical Organization. Thus, our work paves a new way for studying the anatomical Organization of the cerebral cortex, and potentially its functional Organization.
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discrimination of the hierarchical structure of cortical layers in 2 photon microscopy data by combined unsupervised and supervised machine learning
bioRxiv, 2018Co-Authors: Melissa Zavaglia, Guangyu Wang, Hong Xie, Rene Werner, Jisong Guan, Claus C HilgetagAbstract:The Laminar Organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the Laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical Laminar Organization in high-resolution 2-photon microscopic neural image data without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the Laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical Organization. Thus, our work paves a new way for studying the anatomical Organization of the cerebral cortex, and potentially its functional Organization.
Noboru Mizuno - One of the best experts on this subject based on the ideXlab platform.
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Laminar Organization of the substantia nigra pars reticulata in the macaque monkey, with special reference to the caudato-nigro-tectal link
Experimental brain research, 1993Co-Authors: H. Tokuno, Masahiko Takada, Yasuko Kondo, Noboru MizunoAbstract:A Laminar Organization of the substantia nigra pars reticulata (SNr) was revealed in the macaque monkey by using an semihorizontal section plane parallel to the long axis of the SNr in the frontal plane. After injecting horseradish peroxidase conjugated to wheat germ agglutinin into the caudate nucleus (Cd) or putamen, anterogradely labelled fibres and axon terminals in the SNr were observed to form parallel bands in the anteromedial-posterolateral direction. After injecting Fast Blue into the superior colliculus, a cluster of retrogradely labelled neuronal cell bodies also formed a single band arranged in the similar anteromedial-posterolateral direction. The cluster was observed along the anterolateral margin of the SNr. This SNr region containing nigrotectal neurons appeared to overlap with one of the bands containing terminals of striatonigral fibres arising from the body of the Cd. The caudato-nigro-tectal link via the anterolateral marginal area might be involved in the control of saccadic eye movements.
Claus C Hilgetag - One of the best experts on this subject based on the ideXlab platform.
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discrimination of the hierarchical structure of cortical layers in 2 photon microscopy data by combined unsupervised and supervised machine learning
Scientific Reports, 2019Co-Authors: Melissa Zavaglia, Guangyu Wang, Hong Xie, Rene Werner, Jisong Guan, Claus C HilgetagAbstract:The Laminar Organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the Laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical Laminar Organization in high-resolution 2-photon microscopic neural image data of mouse brain without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the Laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical Organization. Thus, our work paves a new way for studying the anatomical Organization of the cerebral cortex, and potentially its functional Organization.
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discrimination of the hierarchical structure of cortical layers in 2 photon microscopy data by combined unsupervised and supervised machine learning
bioRxiv, 2018Co-Authors: Melissa Zavaglia, Guangyu Wang, Hong Xie, Rene Werner, Jisong Guan, Claus C HilgetagAbstract:The Laminar Organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the Laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical Laminar Organization in high-resolution 2-photon microscopic neural image data without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the Laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical Organization. Thus, our work paves a new way for studying the anatomical Organization of the cerebral cortex, and potentially its functional Organization.
