Recently we proposed a feature selection method based on stability theory (SCoRS - Survival Count on Random Subspaces) and showed that the proposed approach was able to improve classification accuracy using different datasets. In the present work we propose: (i) an extension of SCoRS using reproducibility instead of model accuracy as the parameter optimization criterion and (ii) a procedure to estimate the rate of false positive selection associated with the set of features obtained. Our results using the proposed framework showed that, as expected, the optimal parameter was more stable across the cross-validation folds, the spatial map displaying the features selected was less noisy and there was no decrease in classification accuracy. In addition, our results suggest that the estimated false positive rate for the features selected by SCoRS is under 0.05 for both optimization approaches, nevertheless lower when optimizing reproducibility in comparison with the standard optimization approach.
最近，我们提出了一种基于稳定性理论的特征选择方法(SCoRS - Survival Count on Random Subspaces)，并表明该方法能够在不同的数据集上提高分类精度。在目前的工作中，我们提出:(i)使用可重复性而不是模型精度作为参数优化标准的SCoRS扩展;(ii)估计与所获得的特征集相关的假阳性选择率的程序。我们使用该框架的结果表明，正如预期的那样，最优参数在交叉验证折叠中更加稳定，显示所选特征的空间地图噪声更小，分类精度没有下降。此外，我们的结果表明，两种优化方法对SCoRS选择的特征的估计假阳性率都在0.05以下，但在优化再现性时，与标准优化方法相比，假阳性率较低。
{"title":"Stability-Based Multivariate Mapping Using SCoRS","authors":"J. Rondina, J. Shawe-Taylor, J. Miranda","doi":"10.1109/PRNI.2013.58","DOIUrl":"https://doi.org/10.1109/PRNI.2013.58","url":null,"abstract":"Recently we proposed a feature selection method based on stability theory (SCoRS - Survival Count on Random Subspaces) and showed that the proposed approach was able to improve classification accuracy using different datasets. In the present work we propose: (i) an extension of SCoRS using reproducibility instead of model accuracy as the parameter optimization criterion and (ii) a procedure to estimate the rate of false positive selection associated with the set of features obtained. Our results using the proposed framework showed that, as expected, the optimal parameter was more stable across the cross-validation folds, the spatial map displaying the features selected was less noisy and there was no decrease in classification accuracy. In addition, our results suggest that the estimated false positive rate for the features selected by SCoRS is under 0.05 for both optimization approaches, nevertheless lower when optimizing reproducibility in comparison with the standard optimization approach.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130909141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Permutation tests are widely used for significance testing in classification-based fMRI analyses, but the precise manner of relabeling varies, and is generally non-trivial for MVPA because of the complex data structure. Here, we describe two common means of carrying out permutation tests. In the first, which we call the "dataset-wise" scheme, the examples are relabeled prior to conducting the cross-validation, while in the second, the "fold-wise" scheme, each fold of the cross-validation is relabeled independently. While the dataset-wise scheme maintains more of the true dataset's structure, additional work is needed to determine which method should be preferred in practice, since the two methods often result in different null distributions (and so p-values).
{"title":"MVPA Permutation Schemes: Permutation Testing in the Land of Cross-Validation","authors":"J. Etzel, T. Braver","doi":"10.1109/PRNI.2013.44","DOIUrl":"https://doi.org/10.1109/PRNI.2013.44","url":null,"abstract":"Permutation tests are widely used for significance testing in classification-based fMRI analyses, but the precise manner of relabeling varies, and is generally non-trivial for MVPA because of the complex data structure. Here, we describe two common means of carrying out permutation tests. In the first, which we call the \"dataset-wise\" scheme, the examples are relabeled prior to conducting the cross-validation, while in the second, the \"fold-wise\" scheme, each fold of the cross-validation is relabeled independently. While the dataset-wise scheme maintains more of the true dataset's structure, additional work is needed to determine which method should be preferred in practice, since the two methods often result in different null distributions (and so p-values).","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114271016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Decoding, i.e. predicting stimulus related quantities from functional brain images, is a powerful tool to demonstrate differences between brain activity across conditions. However, unlike standard brain mapping, it offers no guaranties on the localization of this information. Here, we consider decoding as a statistical estimation problem and show that injecting a spatial segmentation prior leads to unmatched performance in recovering predictive regions. Specifically, we use ℓ1-penalization to set voxels to zero and Total-Variation (TV) penalization to segment regions. Our contribution is two-fold. On the one hand, we show via extensive experiments that, amongst a large selection of decoding and brain-mapping strategies, TV+ℓ1 leads to best region recovery. On the other hand, we consider implementation issues related to this estimator. To tackle efficiently this joint prediction-segmentation problem we introduce a fast optimization algorithm based on a primal-dual approach. We also tackle automatic setting of hyper-parameters and fast computation of image operation on the irregular masks that arise in brain imaging.
{"title":"Identifying Predictive Regions from fMRI with TV-L1 Prior","authors":"Alexandre Gramfort, B. Thirion, G. Varoquaux","doi":"10.1109/PRNI.2013.14","DOIUrl":"https://doi.org/10.1109/PRNI.2013.14","url":null,"abstract":"Decoding, i.e. predicting stimulus related quantities from functional brain images, is a powerful tool to demonstrate differences between brain activity across conditions. However, unlike standard brain mapping, it offers no guaranties on the localization of this information. Here, we consider decoding as a statistical estimation problem and show that injecting a spatial segmentation prior leads to unmatched performance in recovering predictive regions. Specifically, we use ℓ1-penalization to set voxels to zero and Total-Variation (TV) penalization to segment regions. Our contribution is two-fold. On the one hand, we show via extensive experiments that, amongst a large selection of decoding and brain-mapping strategies, TV+ℓ1 leads to best region recovery. On the other hand, we consider implementation issues related to this estimator. To tackle efficiently this joint prediction-segmentation problem we introduce a fast optimization algorithm based on a primal-dual approach. We also tackle automatic setting of hyper-parameters and fast computation of image operation on the irregular masks that arise in brain imaging.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125086487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes Simitar, a toolbox for studying the similarity structure of patterns of brain activation in different experimental conditions. We focus on supporting two types of analysis, namely, the calculation of local similarity matrices for all locations in the brain and the identification of locations where similarity has a desired structure, via an intuitive interface.
{"title":"Simitar: Simplified Searching of Statistically Significant Similarity Structure","authors":"Francisco Pereira, M. Botvinick","doi":"10.1109/PRNI.2013.10","DOIUrl":"https://doi.org/10.1109/PRNI.2013.10","url":null,"abstract":"This paper describes Simitar, a toolbox for studying the similarity structure of patterns of brain activation in different experimental conditions. We focus on supporting two types of analysis, namely, the calculation of local similarity matrices for all locations in the brain and the identification of locations where similarity has a desired structure, via an intuitive interface.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121315932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a method for dimensionality reduction of high-dimensional input data in a binary classification problem. The method is based on selecting a few latent variables that maximize the Kullback-Leibler (KL) distance between the two class distributions, under the assumption that these distributions are multivariate Gaussian. Numerical performance is demonstrated by solving the challenging problem of classifying patients with major depressive disorder (MDD) into responders vs. non-responders to an anti-depressant treatment using pre-treatment resting electroencephalography (EEG) data. The extracted feature set measures consistent connectivity and includes the magnitude coherence features among all electrode pairs in a 3Hz to 30Hz bandwidth with 1Hz resolution. An overall 86% prediction performance indicates the effectiveness of the KLDR method in this application. This performance level was found to exceed that of other dimensionality reduction methods, namely the unsupervised principal component (PCA) and the supervised Fisher discriminant analysis (FDA) methods.
{"title":"Latent Variable Dimensionality Reduction Using a Kullback-Leibler Criterion and Its Application to Predict Antidepressant Treatment Response","authors":"A. Khodayari-Rostamabad, J. Reilly, G. Hasey","doi":"10.1109/PRNI.2013.46","DOIUrl":"https://doi.org/10.1109/PRNI.2013.46","url":null,"abstract":"In this paper, we propose a method for dimensionality reduction of high-dimensional input data in a binary classification problem. The method is based on selecting a few latent variables that maximize the Kullback-Leibler (KL) distance between the two class distributions, under the assumption that these distributions are multivariate Gaussian. Numerical performance is demonstrated by solving the challenging problem of classifying patients with major depressive disorder (MDD) into responders vs. non-responders to an anti-depressant treatment using pre-treatment resting electroencephalography (EEG) data. The extracted feature set measures consistent connectivity and includes the magnitude coherence features among all electrode pairs in a 3Hz to 30Hz bandwidth with 1Hz resolution. An overall 86% prediction performance indicates the effectiveness of the KLDR method in this application. This performance level was found to exceed that of other dimensionality reduction methods, namely the unsupervised principal component (PCA) and the supervised Fisher discriminant analysis (FDA) methods.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131013265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seonjoo Lee, V. Zipunnikov, N. Shiee, C. Crainiceanu, B. Caffo, D. Pham
In the study of brain disease processes and aging, longitudinal imaging studies are becoming increasingly commonplace. Indeed, there are hundreds of studies collecting multi-sequence multi-modality brain images at multiple time points on hundreds of subjects over many years. A fundamental problem in this context is how to classify subjects according to their baseline and longitudinal changes in the presence of strong spatio-temporal biological and technological measurement error. We propose a fast and scalable clustering approach by defining a metric between latent trajectories of brain images. Methods were motivated by and applied to a longitudinal voxel-based morphometry study of multiple sclerosis. Results indicate that there are two distinct patterns of ventricular change that are associated with clinical outcomes.
{"title":"Clustering of High Dimensional Longitudinal Imaging Data","authors":"Seonjoo Lee, V. Zipunnikov, N. Shiee, C. Crainiceanu, B. Caffo, D. Pham","doi":"10.1109/PRNI.2013.18","DOIUrl":"https://doi.org/10.1109/PRNI.2013.18","url":null,"abstract":"In the study of brain disease processes and aging, longitudinal imaging studies are becoming increasingly commonplace. Indeed, there are hundreds of studies collecting multi-sequence multi-modality brain images at multiple time points on hundreds of subjects over many years. A fundamental problem in this context is how to classify subjects according to their baseline and longitudinal changes in the presence of strong spatio-temporal biological and technological measurement error. We propose a fast and scalable clustering approach by defining a metric between latent trajectories of brain images. Methods were motivated by and applied to a longitudinal voxel-based morphometry study of multiple sclerosis. Results indicate that there are two distinct patterns of ventricular change that are associated with clinical outcomes.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124771024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditionally clinicians and medical researchers have been using either totally data driven approaches like PCA/CCA/ICA or ROI based analysis for exploratory analysis of brain images. However, PCA/CCA/ICA based approaches suffer from lack of interpretability of results and on the other hand ROI based approaches are too rigid and wrongly assume that the signal lies totally within a predefined region. In this paper, we propose a novel approach which stands in stark contrast with both these approaches as it borrows strength from both these paradigms and leads to statistically refined definitions of ROIs based on information from data. Our approach, called Anatomically Constrained PCA (AC-PCA) provides a principled way of incorporating prior information in the form of probabilistic or binary ROIs while still allowing the data to softly modify the original ROI definitions. Experimental results on cortical thickness images show the superiority of AC-PCA for MCI classification compared to ROI and unconstrained PCA (a totally data based approach).
{"title":"Anatomically-Constrained PCA for Image Parcellation","authors":"Paramveer S. Dhillon, J. Gee, L. Ungar, B. Avants","doi":"10.1109/PRNI.2013.16","DOIUrl":"https://doi.org/10.1109/PRNI.2013.16","url":null,"abstract":"Traditionally clinicians and medical researchers have been using either totally data driven approaches like PCA/CCA/ICA or ROI based analysis for exploratory analysis of brain images. However, PCA/CCA/ICA based approaches suffer from lack of interpretability of results and on the other hand ROI based approaches are too rigid and wrongly assume that the signal lies totally within a predefined region. In this paper, we propose a novel approach which stands in stark contrast with both these approaches as it borrows strength from both these paradigms and leads to statistically refined definitions of ROIs based on information from data. Our approach, called Anatomically Constrained PCA (AC-PCA) provides a principled way of incorporating prior information in the form of probabilistic or binary ROIs while still allowing the data to softly modify the original ROI definitions. Experimental results on cortical thickness images show the superiority of AC-PCA for MCI classification compared to ROI and unconstrained PCA (a totally data based approach).","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125029860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modern cognitive experiments in functional Magnetic Resonance Imaging (fMRI) often aim at understanding the temporal dynamics of the brain response in regions activated by a given stimulus. The study of the variability of the hemodynamic response function (HRF) and its characteristics can provide some answers. In this context, we aim at improving the accuracy of the HRF estimation. To do so, we relied on a Joint-Detection-Estimation (JDE) framework that enables robust detection of brain activity as well as HRF estimation, in a Bayesian setting [2]. So far, the hemodynamic results provided by the JDE formalism have depended on a prior parcellation of the data performed before JDE inference. In this study, we propose a new approach to relax this prior knowledge: using consensus clustering techniques based on random parcellations of the data, we combine hemodynamics results provided by different parcellations, so as to robustify the HRF estimation.
{"title":"Hemodynamic Estimation Based on Consensus Clustering","authors":"S. Badillo, G. Varoquaux, P. Ciuciu","doi":"10.1109/PRNI.2013.61","DOIUrl":"https://doi.org/10.1109/PRNI.2013.61","url":null,"abstract":"Modern cognitive experiments in functional Magnetic Resonance Imaging (fMRI) often aim at understanding the temporal dynamics of the brain response in regions activated by a given stimulus. The study of the variability of the hemodynamic response function (HRF) and its characteristics can provide some answers. In this context, we aim at improving the accuracy of the HRF estimation. To do so, we relied on a Joint-Detection-Estimation (JDE) framework that enables robust detection of brain activity as well as HRF estimation, in a Bayesian setting [2]. So far, the hemodynamic results provided by the JDE formalism have depended on a prior parcellation of the data performed before JDE inference. In this study, we propose a new approach to relax this prior knowledge: using consensus clustering techniques based on random parcellations of the data, we combine hemodynamics results provided by different parcellations, so as to robustify the HRF estimation.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131466815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Complex pathological brain patterns generally are found in neurodegenerative diseases which can be correlated with different clinical onsets of a particular pathology. Currently, an objective method that aids to determine such signs, in terms of global and local changes, is not available in clinical practice and the whole interpretation is dependent on the radiologist's skills. In this paper, we propose a fully automatic method that analyzes the brain structure under a multidimensional frame and highlights relevant brain patterns. An association of such patterns with the disease is herein evaluated in three classification tasks, involving probable Alzheimer's disease (AD) patients, Mild Cognitive Impairment (MCI) patients and normal subjects (NC). A set of 75 brain MR images from NC subjects (25), MCI (25) and probable AD (25) patients, split into training (15 subjects) and testing (60 subjects) sets, was used to evaluate the performance of the proposed approach. Preliminary results show that the proposed method reaches a maximum classification accuracy of 80% when discriminating AD patients from NC, of 75% for classification of MCI patients from NC.
{"title":"Discovering Regional Pathological Patterns in Brain MRI","authors":"Andrea Pulido, A. Rueda, E. Romero, N. Malpica","doi":"10.1109/PRNI.2013.47","DOIUrl":"https://doi.org/10.1109/PRNI.2013.47","url":null,"abstract":"Complex pathological brain patterns generally are found in neurodegenerative diseases which can be correlated with different clinical onsets of a particular pathology. Currently, an objective method that aids to determine such signs, in terms of global and local changes, is not available in clinical practice and the whole interpretation is dependent on the radiologist's skills. In this paper, we propose a fully automatic method that analyzes the brain structure under a multidimensional frame and highlights relevant brain patterns. An association of such patterns with the disease is herein evaluated in three classification tasks, involving probable Alzheimer's disease (AD) patients, Mild Cognitive Impairment (MCI) patients and normal subjects (NC). A set of 75 brain MR images from NC subjects (25), MCI (25) and probable AD (25) patients, split into training (15 subjects) and testing (60 subjects) sets, was used to evaluate the performance of the proposed approach. Preliminary results show that the proposed method reaches a maximum classification accuracy of 80% when discriminating AD patients from NC, of 75% for classification of MCI patients from NC.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115181542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Resting state fMRI reveals intrinsic network characteristics present in the brain. They are correlated with behavioral measures, and have made surprising insights in the brains' connectivity structure possible. At the core of many of those studies is the correlation of behavioral measures, and the characteristics of networks among a set of brain regions. In this paper we evaluate methods that identify functional networks in resting state fMRI in light of predicting treatment response of patients suffering from social anxiety disorder. Results illustrate differences in prediction when obtaining network labelings by population-wide-clustering, subject-specific parcellation, transferring anatomical region labels, or mapping networks from a previous large scale resting state study.
{"title":"Predicting Treatment Response from Resting State fMRI Data: Comparison of Parcellation Approaches","authors":"Satrajit S. Ghosh, A. Keshavan, G. Langs","doi":"10.1109/PRNI.2013.64","DOIUrl":"https://doi.org/10.1109/PRNI.2013.64","url":null,"abstract":"Resting state fMRI reveals intrinsic network characteristics present in the brain. They are correlated with behavioral measures, and have made surprising insights in the brains' connectivity structure possible. At the core of many of those studies is the correlation of behavioral measures, and the characteristics of networks among a set of brain regions. In this paper we evaluate methods that identify functional networks in resting state fMRI in light of predicting treatment response of patients suffering from social anxiety disorder. Results illustrate differences in prediction when obtaining network labelings by population-wide-clustering, subject-specific parcellation, transferring anatomical region labels, or mapping networks from a previous large scale resting state study.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125874725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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