Pub Date : 2014-06-04DOI: 10.1109/PRNI.2014.6858552
E. Zarogianni, T. Moorhead, A. Starkey, S. Lawrie
To date, there are no reliable markers for making an early diagnosis of schizophrenia before clinical diagnostic criteria are fully met. Neuroimaging and pattern classification techniques are promising tools towards predicting transition to schizophrenia. Here, we investigated the diagnostic performance of a combination of neuroanatomical and clinical data in predicting transition to schizophrenia in subjects at high familial risk (HR) for the disorder. Baseline structural magnetic resonance imaging (MRI) and clinical data from 17 HR subjects, who subsequently developed schizophrenia and an age and sex-matched group of 17 HR subjects who did not make a transition to the disease, yet had psychotic symptoms, were included in the analysis. We employed Support Vector Machine, along with a recursive feature selection technique to classify subjects at an individual level. Combination of both structural MRI and clinical data achieved an accuracy of 94% in predicting at baseline disease conversion in subjects at genetic HR. Overall, this paper presents a promising step in combining neuroanatomical and clinical information to improve early prediction of schizophrenia.
{"title":"Combining neuroanatomical and clinical data to improve individualized early diagnosis of schizophrenia in subjects at high familial risk","authors":"E. Zarogianni, T. Moorhead, A. Starkey, S. Lawrie","doi":"10.1109/PRNI.2014.6858552","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858552","url":null,"abstract":"To date, there are no reliable markers for making an early diagnosis of schizophrenia before clinical diagnostic criteria are fully met. Neuroimaging and pattern classification techniques are promising tools towards predicting transition to schizophrenia. Here, we investigated the diagnostic performance of a combination of neuroanatomical and clinical data in predicting transition to schizophrenia in subjects at high familial risk (HR) for the disorder. Baseline structural magnetic resonance imaging (MRI) and clinical data from 17 HR subjects, who subsequently developed schizophrenia and an age and sex-matched group of 17 HR subjects who did not make a transition to the disease, yet had psychotic symptoms, were included in the analysis. We employed Support Vector Machine, along with a recursive feature selection technique to classify subjects at an individual level. Combination of both structural MRI and clinical data achieved an accuracy of 94% in predicting at baseline disease conversion in subjects at genetic HR. Overall, this paper presents a promising step in combining neuroanatomical and clinical information to improve early prediction of schizophrenia.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114642800","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}
Pub Date : 2014-06-04DOI: 10.1109/PRNI.2014.6858505
A. Abdulkadir, O. Ronneberger, S. Tabrizi, S. Klöppel
We propose to use Gaussian process regression to remove confounds from gray matter (GM) density maps in order to improve performance in automated detection of neurodegenrative diseases. Age, total intracranial volume, sex, and acquisition site were included as design variables. Based on data from the control populations, a Gaussian process regression model was learned for each voxel. This model was used to compute maps of expected GM densities based on the subject's characteristics. For classification, the maps of expected GM densities were subtracted from the observed GM densities, thereby reducing confounding effects. The performance with and without subtraction of confounding effects were evaluated in four classification tasks: (1) patients with mild cognitive impairment (MCI) that did convert to Alzheimer's disease (AD) versus stable MCI patients, (2) patients with AD versus age-matched controls, (3) pre-manifest patients with Huntington's disease (HD) versus controls, and (4) manifest HD patients versus age-matched controls. The proposed method improved the classification performance in most cases, and never caused a significant decrease. The performance was similar to that obtained after reduction of confounding effects with kernel linear regression.
{"title":"Reduction of confounding effects with voxel-wise Gaussian process regression in structural MRI","authors":"A. Abdulkadir, O. Ronneberger, S. Tabrizi, S. Klöppel","doi":"10.1109/PRNI.2014.6858505","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858505","url":null,"abstract":"We propose to use Gaussian process regression to remove confounds from gray matter (GM) density maps in order to improve performance in automated detection of neurodegenrative diseases. Age, total intracranial volume, sex, and acquisition site were included as design variables. Based on data from the control populations, a Gaussian process regression model was learned for each voxel. This model was used to compute maps of expected GM densities based on the subject's characteristics. For classification, the maps of expected GM densities were subtracted from the observed GM densities, thereby reducing confounding effects. The performance with and without subtraction of confounding effects were evaluated in four classification tasks: (1) patients with mild cognitive impairment (MCI) that did convert to Alzheimer's disease (AD) versus stable MCI patients, (2) patients with AD versus age-matched controls, (3) pre-manifest patients with Huntington's disease (HD) versus controls, and (4) manifest HD patients versus age-matched controls. The proposed method improved the classification performance in most cases, and never caused a significant decrease. The performance was similar to that obtained after reduction of confounding effects with kernel linear regression.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124231860","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}
Pub Date : 2014-06-04DOI: 10.1109/PRNI.2014.6858512
V. Borghesani, Fabian Pedregosa, E. Eger, M. Buiatti, M. Piazza
The application of multivariate approaches to neuroimaging data analysis is providing cognitive neuroscientists with a new perspective on the neural substrate of conceptual knowledge. In this paper we show how the combined use of decoding models and of representational similarity analysis (RSA) can enhance our ability to investigate the inter-categorical distinctions as well as the intra-categorical similarities of neural semantic representations. By means of a linear decoding model, we have been able to predict the category of the words subjects were seeing while undergoing a functional magnetic resonance images (fMRI) acquisition. Moreover, RSA in anatomically defined region of interest (ROIs) revealed a significant correlation with length of words and real item size in primary and secondary visual areas (V1 and V2), while a semantic distance effect was significant in inferotemporal areas (BA37 and BA20). Together, these findings illustrate the possibility to decode the distinctive neural patterns of semantic categories and to investigate the peculiar aspects of the neural representations of each single category. We have in fact been able to show a significant correlation between cognitive and neural semantic distance and to describe the gradient of information coding that characterizes the ventral path: from purely perceptual to purely conceptual. These results would not have been possible without a double exploration of the same dataset by means of decoding models and RSA.
{"title":"A perceptual-to-conceptual gradient of word coding along the ventral path","authors":"V. Borghesani, Fabian Pedregosa, E. Eger, M. Buiatti, M. Piazza","doi":"10.1109/PRNI.2014.6858512","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858512","url":null,"abstract":"The application of multivariate approaches to neuroimaging data analysis is providing cognitive neuroscientists with a new perspective on the neural substrate of conceptual knowledge. In this paper we show how the combined use of decoding models and of representational similarity analysis (RSA) can enhance our ability to investigate the inter-categorical distinctions as well as the intra-categorical similarities of neural semantic representations. By means of a linear decoding model, we have been able to predict the category of the words subjects were seeing while undergoing a functional magnetic resonance images (fMRI) acquisition. Moreover, RSA in anatomically defined region of interest (ROIs) revealed a significant correlation with length of words and real item size in primary and secondary visual areas (V1 and V2), while a semantic distance effect was significant in inferotemporal areas (BA37 and BA20). Together, these findings illustrate the possibility to decode the distinctive neural patterns of semantic categories and to investigate the peculiar aspects of the neural representations of each single category. We have in fact been able to show a significant correlation between cognitive and neural semantic distance and to describe the gradient of information coding that characterizes the ventral path: from purely perceptual to purely conceptual. These results would not have been possible without a double exploration of the same dataset by means of decoding models and RSA.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"318 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116422322","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}
Pub Date : 2014-06-04DOI: 10.1109/PRNI.2014.6858507
Karen Sandø Ambrosen, K. J. Albers, T. Dyrby, Mikkel N. Schmidt, Morten Mørup
Diffusion magnetic resonance imaging enables measuring the structural connectivity of the human brain at a high spatial resolution. Local noisy connectivity estimates can be derived using tractography approaches and statistical models are necessary to quantify the brain's salient structural organization. However, statistically modeling these massive structural connectivity datasets is a computational challenging task. We develop a high-performance inference procedure for the infinite relational model (a prominent non-parametric Bayesian model for clustering networks into structurally similar groups) that defines structural units at the resolution of statistical support. We apply the model to a network of structural brain connectivity in full image resolution with more than one hundred thousand regions (voxels in the gray-white matter boundary) and around one hundred million connections. The derived clustering identifies in the order of one thousand salient structural units and we find that the identified units provide better predictive performance than predicting using the full graph or two commonly used atlases. Extracting structural units of brain connectivity at the full image resolution can aid in understanding the underlying connectivity patterns, and the proposed method for large scale data driven generation of structural units provides a promising framework that can exploit the increasing spatial resolution of neuro-imaging technologies.
{"title":"Nonparametric Bayesian clustering of structural whole brain connectivity in full image resolution","authors":"Karen Sandø Ambrosen, K. J. Albers, T. Dyrby, Mikkel N. Schmidt, Morten Mørup","doi":"10.1109/PRNI.2014.6858507","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858507","url":null,"abstract":"Diffusion magnetic resonance imaging enables measuring the structural connectivity of the human brain at a high spatial resolution. Local noisy connectivity estimates can be derived using tractography approaches and statistical models are necessary to quantify the brain's salient structural organization. However, statistically modeling these massive structural connectivity datasets is a computational challenging task. We develop a high-performance inference procedure for the infinite relational model (a prominent non-parametric Bayesian model for clustering networks into structurally similar groups) that defines structural units at the resolution of statistical support. We apply the model to a network of structural brain connectivity in full image resolution with more than one hundred thousand regions (voxels in the gray-white matter boundary) and around one hundred million connections. The derived clustering identifies in the order of one thousand salient structural units and we find that the identified units provide better predictive performance than predicting using the full graph or two commonly used atlases. Extracting structural units of brain connectivity at the full image resolution can aid in understanding the underlying connectivity patterns, and the proposed method for large scale data driven generation of structural units provides a promising framework that can exploit the increasing spatial resolution of neuro-imaging technologies.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127128432","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}
Pub Date : 2014-06-04DOI: 10.1109/PRNI.2014.6858532
Loizos Markides, D. Gillies
The non-invasive nature of Functional Magnetic Resonance Imaging (fMRI) has encouraged a large number of exploratory research studies that aim to identify regions of the brain that are involved in the workings of specific tasks. Conventionally, this kind of studies make use of supervised encoding methodologies, such as the General Linear Model (GLM), in which the contribution of different brain regions to a given task is studied as a function of the linear regression or correlation of the BOLD signal and the task regressors. Recently, decoding methodologies are taking the lead, as they allow for the use of unsupervised non-parametric approaches for the analysis of group fMRI datasets, such as Independent Component Analysis (ICA). A long standing problem with ICA techniques is the evaluation of the significance of the resulting spatial components that are involved in the underlying tasks that the subjects were performing in the scanner. In this paper, we describe the use of two different statistical association metrics for identifying significant components that result from a group ICA of event-related fMRI data. The suggested metrics have been evaluated against a real fMRI dataset in order to illustrate further their merits and drawbacks.
{"title":"Unsupervised metrics of brain region significance for event-related fMRI intersession experiments","authors":"Loizos Markides, D. Gillies","doi":"10.1109/PRNI.2014.6858532","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858532","url":null,"abstract":"The non-invasive nature of Functional Magnetic Resonance Imaging (fMRI) has encouraged a large number of exploratory research studies that aim to identify regions of the brain that are involved in the workings of specific tasks. Conventionally, this kind of studies make use of supervised encoding methodologies, such as the General Linear Model (GLM), in which the contribution of different brain regions to a given task is studied as a function of the linear regression or correlation of the BOLD signal and the task regressors. Recently, decoding methodologies are taking the lead, as they allow for the use of unsupervised non-parametric approaches for the analysis of group fMRI datasets, such as Independent Component Analysis (ICA). A long standing problem with ICA techniques is the evaluation of the significance of the resulting spatial components that are involved in the underlying tasks that the subjects were performing in the scanner. In this paper, we describe the use of two different statistical association metrics for identifying significant components that result from a group ICA of event-related fMRI data. The suggested metrics have been evaluated against a real fMRI dataset in order to illustrate further their merits and drawbacks.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"375 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124685416","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}
Pub Date : 2014-06-04DOI: 10.1109/PRNI.2014.6858517
Mathieu Dubois, F. Hadj-Selem, Tommy Löfstedt, M. Perrot, C. Fischer, V. Frouin, E. Duchesnay
The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (ℓ2 penalty) or scattered (ℓ1 penalty) predictive pattern with a very limited relevance. A penalty like Total Variation (TV) that exploits the natural 3D structure of the images can increase the spatial coherence of the weight map. However, TV penalization leads to non-smooth optimization problems that are hard to minimize. We propose an optimization framework that minimizes any combination of ℓ1, ℓ2, and TV penalties while preserving the exact ℓ1 penalty. This algorithm uses Nesterov's smoothing technique to approximate the TV penalty with a smooth function such that the loss and the penalties are minimized with an exact accelerated proximal gradient algorithm. We propose an original continuation algorithm that uses successively smaller values of the smoothing parameter to reach a prescribed precision while achieving the best possible convergence rate. This algorithm can be used with other losses or penalties. The algorithm is applied on a classification problem on the ADNI dataset. We observe that the TV penalty does not necessarily improve the prediction but provides a major breakthrough in terms of support recovery of the predictive brain regions.
{"title":"Predictive support recovery with TV-Elastic Net penalty and logistic regression: An application to structural MRI","authors":"Mathieu Dubois, F. Hadj-Selem, Tommy Löfstedt, M. Perrot, C. Fischer, V. Frouin, E. Duchesnay","doi":"10.1109/PRNI.2014.6858517","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858517","url":null,"abstract":"The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (ℓ2 penalty) or scattered (ℓ1 penalty) predictive pattern with a very limited relevance. A penalty like Total Variation (TV) that exploits the natural 3D structure of the images can increase the spatial coherence of the weight map. However, TV penalization leads to non-smooth optimization problems that are hard to minimize. We propose an optimization framework that minimizes any combination of ℓ1, ℓ2, and TV penalties while preserving the exact ℓ1 penalty. This algorithm uses Nesterov's smoothing technique to approximate the TV penalty with a smooth function such that the loss and the penalties are minimized with an exact accelerated proximal gradient algorithm. We propose an original continuation algorithm that uses successively smaller values of the smoothing parameter to reach a prescribed precision while achieving the best possible convergence rate. This algorithm can be used with other losses or penalties. The algorithm is applied on a classification problem on the ADNI dataset. We observe that the TV penalty does not necessarily improve the prediction but provides a major breakthrough in terms of support recovery of the predictive brain regions.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127019544","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}
Pub Date : 2014-04-16DOI: 10.1109/PRNI.2014.6858538
E. Olivetti, S. M. Kia, P. Avesani
Brain decoding is a data analysis paradigm for neuroimaging experiments that is based on predicting the stimulus presented to the subject from the concurrent brain activity. In order to make inference at the group level, a straightforward but sometimes unsuccessful approach is to train a classifier on the trials of a group of subjects and then to test it on unseen trials from new subjects. The extreme difficulty is related to the structural and functional variability across the subjects. We call this approach decoding across subjects. In this work, we address the problem of decoding across subjects for magnetoen-cephalographic (MEG) experiments and we provide the following contributions: first, we formally describe the problem and show that it belongs to a machine learning sub-field called transductive transfer learning (TTL). Second, we propose to use a simple TTL technique that accounts for the differences between train data and test data. Third, we propose the use of ensemble learning, and specifically of stacked generalization, to address the variability across subjects within train data, with the aim of producing more stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we compare the standard approach of not modelling the differences across subjects, to the proposed one of combining TTL and ensemble learning. We show that the proposed approach is consistently more accurate than the standard one.
{"title":"MEG decoding across subjects","authors":"E. Olivetti, S. M. Kia, P. Avesani","doi":"10.1109/PRNI.2014.6858538","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858538","url":null,"abstract":"Brain decoding is a data analysis paradigm for neuroimaging experiments that is based on predicting the stimulus presented to the subject from the concurrent brain activity. In order to make inference at the group level, a straightforward but sometimes unsuccessful approach is to train a classifier on the trials of a group of subjects and then to test it on unseen trials from new subjects. The extreme difficulty is related to the structural and functional variability across the subjects. We call this approach decoding across subjects. In this work, we address the problem of decoding across subjects for magnetoen-cephalographic (MEG) experiments and we provide the following contributions: first, we formally describe the problem and show that it belongs to a machine learning sub-field called transductive transfer learning (TTL). Second, we propose to use a simple TTL technique that accounts for the differences between train data and test data. Third, we propose the use of ensemble learning, and specifically of stacked generalization, to address the variability across subjects within train data, with the aim of producing more stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we compare the standard approach of not modelling the differences across subjects, to the proposed one of combining TTL and ensemble learning. We show that the proposed approach is consistently more accurate than the standard one.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124308820","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}
Pub Date : 1900-01-01DOI: 10.1109/PRNI.2014.6858536
K. Niehaus, I. A. Clark, C. Bourne, C. Mackay, E. Holmes, Stephen M. Smith, M. Woolrich, E. Duff
Many cognitive processes are challenging to study due to their scarce occurrence. Here we demonstrate how pattern recognition and brain imaging can enhance the study of such processes by providing fast, sensitive, and non-intrusive detection of these events. This can enable efficient experimental and clinical intervention. We focus on the study of traumatic events producing flashbacks associated with post-traumatic stress disorder (PTSD), using an experimental analogue of trauma (a traumatic film). These events are rare and challenging to reliably elicit in experimental settings. We show that a classifier can be built to predict, based upon brain response, which stimuli are likely to induce these rare flashbacks at the point of exposure. An ability to predict these stimuli makes possible the trialing of context-specific preventative clinical interventions. We present results from two independent datasets, outlining key analytic challenges.
{"title":"MVPA to enhance the study of rare cognitive events: An investigation of experimental PTSD","authors":"K. Niehaus, I. A. Clark, C. Bourne, C. Mackay, E. Holmes, Stephen M. Smith, M. Woolrich, E. Duff","doi":"10.1109/PRNI.2014.6858536","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858536","url":null,"abstract":"Many cognitive processes are challenging to study due to their scarce occurrence. Here we demonstrate how pattern recognition and brain imaging can enhance the study of such processes by providing fast, sensitive, and non-intrusive detection of these events. This can enable efficient experimental and clinical intervention. We focus on the study of traumatic events producing flashbacks associated with post-traumatic stress disorder (PTSD), using an experimental analogue of trauma (a traumatic film). These events are rare and challenging to reliably elicit in experimental settings. We show that a classifier can be built to predict, based upon brain response, which stimuli are likely to induce these rare flashbacks at the point of exposure. An ability to predict these stimuli makes possible the trialing of context-specific preventative clinical interventions. We present results from two independent datasets, outlining key analytic challenges.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"10 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113975638","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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