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2013 International Workshop on Pattern Recognition in Neuroimaging最新文献

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Stability-Based Multivariate Mapping Using SCoRS 基于稳定性的多变量SCoRS映射
2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.58
J. Rondina, J. Shawe-Taylor, J. Miranda
{"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":null,"pages":null},"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}
引用次数: 4
MVPA Permutation Schemes: Permutation Testing in the Land of Cross-Validation MVPA排列方案:交叉验证领域的排列测试
2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.44
J. Etzel, T. Braver
{"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":null,"pages":null},"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}
引用次数: 40
Identifying Predictive Regions from fMRI with TV-L1 Prior 利用TV-L1先验识别fMRI预测区域
2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.14
Alexandre Gramfort, B. Thirion, G. Varoquaux
{"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":null,"pages":null},"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}
引用次数: 88
Simitar: Simplified Searching of Statistically Significant Similarity Structure 相似:统计显著相似结构的简化搜索
2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.10
Francisco Pereira, M. Botvinick
{"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":null,"pages":null},"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}
引用次数: 3
Latent Variable Dimensionality Reduction Using a Kullback-Leibler Criterion and Its Application to Predict Antidepressant Treatment Response 使用Kullback-Leibler标准的潜变量降维及其在预测抗抑郁药物治疗反应中的应用
2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.46
A. Khodayari-Rostamabad, J. Reilly, G. Hasey
{"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":null,"pages":null},"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}
引用次数: 0
Clustering of High Dimensional Longitudinal Imaging Data 高维纵向成像数据的聚类
2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.18
Seonjoo Lee, V. Zipunnikov, N. Shiee, C. Crainiceanu, B. Caffo, D. Pham
{"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":null,"pages":null},"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}
引用次数: 1
Anatomically-Constrained PCA for Image Parcellation 解剖约束的PCA图像分割
2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.16
Paramveer S. Dhillon, J. Gee, L. Ungar, B. Avants
{"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":null,"pages":null},"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}
引用次数: 4
Hemodynamic Estimation Based on Consensus Clustering 基于一致聚类的血流动力学估计
2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.61
S. Badillo, G. Varoquaux, P. Ciuciu
{"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":null,"pages":null},"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}
引用次数: 12
Discovering Regional Pathological Patterns in Brain MRI 发现脑MRI的区域病理模式
2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.47
Andrea Pulido, A. Rueda, E. Romero, N. Malpica
{"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":null,"pages":null},"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}
引用次数: 3
Predicting Treatment Response from Resting State fMRI Data: Comparison of Parcellation Approaches 从静息状态fMRI数据预测治疗反应:分割方法的比较
2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.64
Satrajit S. Ghosh, A. Keshavan, G. Langs
{"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":null,"pages":null},"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}
引用次数: 3
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