2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)最新文献_第2页

Permutation tests for classification: revisited 分类的排列试验:重述

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981495

M. Ganz, E. Konukoglu

{"title":"Permutation tests for classification: revisited","authors":"M. Ganz, E. Konukoglu","doi":"10.1109/PRNI.2017.7981495","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981495","url":null,"abstract":"In recent years, the focus on validating the statistical methods used in the field of neuroimaging has increased. While several papers have already highlighted the importance of non-parametric methods and especially permutation testing for general linear models (GLMs), it seems like the importance of validating classification results other than through cross-validation has taken a back seat. But classification, especially binary classification, is one of the most common tools in neuroimaging. Often permutations are not performed using the argument that they are too computationally expensive, especially for trainingintensive classifier as e.g. neural networks. In the following we want to re-visit the use of permutation tests for validating cross-validation results statistically and employ recent approximate permutation methods that reduce the number of permutations that need to be performed. We evaluate the feasibility of using full as well as approximate permutation methods in the extreme cases of small and unbalanced data sets. Our results indicate the applicability of a tail and Gamma approximation to perform permutation testing for binary classification tasks.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"22 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130653522","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: 2

Comparison of distances for supervised segmentation of white matter tractography 白质束状图监督分割距离的比较

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981502

E. Olivetti, Giulia Bertò, P. Gori, N. Sharmin, P. Avesani

{"title":"Comparison of distances for supervised segmentation of white matter tractography","authors":"E. Olivetti, Giulia Bertò, P. Gori, N. Sharmin, P. Avesani","doi":"10.1109/PRNI.2017.7981502","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981502","url":null,"abstract":"Tractograms are mathematical representations of the main paths of axons within the white matter of the brain, from diffusion MRI data. Such representations are in the form of polylines, called streamlines, and one streamline approximates the common path of tens of thousands of axons. The analysis of tractograms is a task of interest in multiple fields, like neurosurgery and neurology. A basic building block of many pipelines of analysis is the definition of a distance function between streamlines. Multiple distance functions have been proposed in the literature, and different authors use different distances, usually without a specific reason other than invoking the “common practice”. To this end, in this work we want to test such common practices, in order to obtain factual reasons for choosing one distance over another. For these reason, in this work we compare many streamline distance functions available in the literature. We focus on the common task of automatic bundle segmentation and we adopt the recent approach of supervised segmentation from expert-based examples. Using the HCP dataset, we compare several distances obtaining guidelines on the choice of which distance function one should use for supervised bundle segmentation.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125324762","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: 13

Automatic 1D convolutional neural network-based detection of artifacts in MEG acquired without electrooculography or electrocardiography 基于自动一维卷积神经网络的无眼电或心电图的脑磁图伪影检测

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981506

Prabhat Garg, E. Davenport, G. Murugesan, B. Wagner, C. Whitlow, J. Maldjian, A. Montillo

{"title":"Automatic 1D convolutional neural network-based detection of artifacts in MEG acquired without electrooculography or electrocardiography","authors":"Prabhat Garg, E. Davenport, G. Murugesan, B. Wagner, C. Whitlow, J. Maldjian, A. Montillo","doi":"10.1109/PRNI.2017.7981506","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981506","url":null,"abstract":"Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by electrical neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-Blinks (EB) and Cardiac Activity (CA) are two of the most common types of non-neuronal artifacts. They can be measured by affixing eye proximal electrodes, as in electrooculography (EOG) and chest electrodes, as in electrocardiography (EKG), however this complicates imaging setup, decreases patient comfort, and often induces further artifacts from facial twitching and postural muscle movement. We propose an EOG- and EKG-free approach to identify eye-blink, cardiac, or neuronal signals for automated artifact suppression. Our contributions are two-fold. First, we combine a data driven, multivariate decomposition approach based on Independent Component Analysis (ICA) and a highly accurate classifier constructed as a deep 1-D Convolutional Neural Network. Second, we visualize the features learned to reveal what features the model uses and to bolster user confidence in our model’s training and potential for generalization. We train and test three variants of our method on resting state MEG data from 49 subjects. Our cardiac model achieves a 96% sensitivity and 99% specificity on the set-aside test-set. Our eye-blink model achieves a sensitivity of 85% and specificity of 97%. This work facilitates automated MEG processing for both, clinical and research use, and can obviate the need for EOG or EKG electrodes.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127410795","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: 13

Tree ensemble methods and parcelling to identify brain areas related to Alzheimer’s disease 树集成方法和包裹识别与阿尔茨海默病相关的大脑区域

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981513

M. Wehenkel, C. Bastin, C. Phillips, P. Geurts

引用次数: 8

Pattern classification reveals developmental differences in how memories influence new learning 模式分类揭示了记忆如何影响新学习的发展差异

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981512

M. Schlichting, Katharine F. Guarino, Hannah E. Roome, A. Preston

引用次数: 0

Large brain effective network from EEG/MEG data and dMR information 基于脑电/脑磁图数据和dMR信息的大型脑有效网络

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981511

Brahim Belaoucha, T. Papadopoulo

引用次数: 1

Modeling dynamic functional connectivity using a wishart mixture model 使用wishart混合模型建模动态功能连接

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981505

S. F. V. Nielsen, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup

{"title":"Modeling dynamic functional connectivity using a wishart mixture model","authors":"S. F. V. Nielsen, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup","doi":"10.1109/PRNI.2017.7981505","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981505","url":null,"abstract":"Dynamic functional connectivity (dFC) has recently become a popular way of tracking the temporal evolution of the brains functional integration. However, there does not seem to be a consensus on how to choose the complexity, i.e. number of brain states, and the time-scale of the dynamics, i.e. the window length. In this work we use the Wishart Mixture Model (WMM) as a probabilistic model for dFC based on variational inference. The framework admits arbitrary window lengths and number of dynamic components and includes the static one-component model as a special case. We exploit that the WMM framework provides model selection by quantifying models generalization to new data. We use this to quantify the number of states within a prespecified window length. We further propose a heuristic procedure for choosing the window length based on contrasting for each window length the predictive performance of dFC models to their static counterparts and choosing the window length having largest difference as most favorable for characterizing dFC. On synthetic data we find that generalizability is influenced by window length and signal-tonoise ratio. Too long windows cause dynamic states to be mixed together whereas short windows are more unstable and influenced by noise and we find that our heuristic correctly identifies an adequate level of complexity. On single subject resting state fMRI data we find that dynamic models generally outperform static models and using the proposed heuristic points to a windowlength of around 30 seconds provides largest difference between the predictive likelihood of static and dynamic FC.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125978163","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

Real-time filtering of gradient artifacts from simultaneous EEG-fMRI data 从同时的EEG-fMRI数据梯度伪影的实时滤波

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981510

S. Shaw

引用次数: 4