2015 International Workshop on Pattern Recognition in NeuroImaging最新文献

Hidden Markov Models for Reading Words from the Human Brain 阅读人脑单词的隐马尔可夫模型

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.31

S. Schoenmakers, T. Heskes, M. Gerven

引用次数: 0

Tractography Mapping for Dissimilarity Space across Subjects 不同学科间不同空间的轨迹图映射

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.24

P. Avesani, Thien Bao Nguyen, Nivedita Agarwal, M. Bromberg, L. Shah, E. Olivetti

{"title":"Tractography Mapping for Dissimilarity Space across Subjects","authors":"P. Avesani, Thien Bao Nguyen, Nivedita Agarwal, M. Bromberg, L. Shah, E. Olivetti","doi":"10.1109/PRNI.2015.24","DOIUrl":"https://doi.org/10.1109/PRNI.2015.24","url":null,"abstract":"Structural brain connectivity can be studied with the help of diffusion magnetic resonance imaging (dMRI), through which the pathways of the neuronal axons of the white matter can be reconstructed at the millimeter scale. Such connectivity structure, called deterministic tractography, is represented as a set of polylines in 3D space, called streamlines. Streamlines have a non-homogeneous number of points and, for this reason, the dissimilarity representation (DR) has been proposed as accurate Euclidean embedding. By providing a vectorial representation of the streamlines, DR enables the use of most machine learning and pattern recognition algorithms for connectivity analysis. However, the DR is subject-specific and thus applies only to intra-subject analysis, while neuroscientific studies often address inter-subject comparisons. For this reason, in this work, we propose an algorithmic solution to build a common vectorial representation for streamlines across subjects. The core idea is based on finding a small set of corresponding streamlines, a problem known as streamline mapping. With experiments on a task of segmentation, we show that the quality of alignment of tractographies, through the common vectorial representation, is even superior to that of the traditional linear registration.","PeriodicalId":380902,"journal":{"name":"2015 International Workshop on Pattern Recognition in NeuroImaging","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125561424","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

Joint Feature Extraction from Functional Connectivity Graphs with Multi-task Feature Learning 基于多任务特征学习的功能连接图联合特征提取

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.17

A. Altmann, B. Ng

引用次数: 1

Mind the Noise Covariance When Localizing Brain Sources with M/EEG M/EEG定位脑源时注意噪声协方差

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.25

D. Engemann, D. Strohmeier, E. Larson, Alexandre Gramfort

引用次数: 8

A Comparison of Strategies for Incorporating Nuisance Variables into Predictive Neuroimaging Models 将有害变量纳入预测神经影像学模型的策略比较

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.28

A. Rao, J. Monteiro, J. Ashburner, L. Portugal, Orlando Fernandes Junior, L. Oliveira, M. Pereira, J. Miranda

引用次数: 11

Predicting Pure Amnestic Mild Cognitive Impairment Conversion to Alzheimer's Disease Using Joint Modeling of Imaging and Clinical Data 利用影像学和临床数据联合建模预测纯粹的遗忘性轻度认知障碍转化为阿尔茨海默病

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.23

V. Kebets, J. Richiardi, Mitsouko van Assche, Rachel Goldstein, M. Meulen, P. Vuilleumier, D. Ville, F. Assal

引用次数: 5

Testing Multimodal Integration Hypotheses with Application to Schizophrenia Data 应用精神分裂症数据检验多模态整合假说

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.20

M. C. Axelsen, Nikolaj Bak, Lars Kai Hansen

{"title":"Testing Multimodal Integration Hypotheses with Application to Schizophrenia Data","authors":"M. C. Axelsen, Nikolaj Bak, Lars Kai Hansen","doi":"10.1109/PRNI.2015.20","DOIUrl":"https://doi.org/10.1109/PRNI.2015.20","url":null,"abstract":"Multimodal data sets are getting more and more common. Integrating these data sets, the information from each modality can be combined to improve performance in classification problems. Fusion/integration of modalities can be done at several levels. The most appropriate fusion level is related to the conditional dependency between modalities. A varying degree of inter-modality dependency can be present across the modalities. A method for assessing the conditional dependency structure of the modalities and their relationship to intra-modality dependencies in each modality is therefore needed. The aim of the present paper is to propose a method for assessing these inter-modality dependencies. The approach is based on two permutations of an analyzed data set, each exploring different dependencies between and within modalities. The method was tested on the Kaggle MLSP 2014 Schizophrenia Classification Challenge data set which is composed of features from functional magnetic resonance imaging (MRI) and structural MRI. The results support the use of a permutation strategy for testing conditional dependencies between modalities in a multimodal classification problem.","PeriodicalId":380902,"journal":{"name":"2015 International Workshop on Pattern Recognition in NeuroImaging","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121303230","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

MVPA Permutation Schemes: Permutation Testing for the Group Level MVPA排列方案:组级排列测试

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.29

J. Etzel

引用次数: 26

Graph Embeddings of Dynamic Functional Connectivity Reveal Discriminative Patterns of Task Engagement in HCP Data 动态功能连接的图嵌入揭示了HCP数据中任务参与的判别模式

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.21

R. Monti, R. Lorenz, Peter J Hellyer, R. Leech, C. Anagnostopoulos, G. Montana

{"title":"Graph Embeddings of Dynamic Functional Connectivity Reveal Discriminative Patterns of Task Engagement in HCP Data","authors":"R. Monti, R. Lorenz, Peter J Hellyer, R. Leech, C. Anagnostopoulos, G. Montana","doi":"10.1109/PRNI.2015.21","DOIUrl":"https://doi.org/10.1109/PRNI.2015.21","url":null,"abstract":"There is increasing evidence to suggest functional connectivity networks are non-stationary. This has lead to the development of novel methodologies with which to accurately estimate time-varying functional connectivity networks. Many of these methods provide unprecedented temporal granularity by estimating a functional connectivity network at each point in time, resulting in high-dimensional output which can be studied in a variety of ways. One possible method is to employ graph embedding algorithms. Such algorithms effectively map estimated networks from high-dimensional spaces down to a low dimensional vector space, thus facilitating visualization, interpretation and classification. In this work, the dynamic properties of functional connectivity are studied using working memory task data from the Human Connectome Project. A recently proposed method is employed to estimate dynamic functional connectivity networks. The results are subsequently analyzed using two graph embedding methods based on linear projections. These methods are shown to provide informative embeddings that can be directly interpreted as functional connectivity networks.","PeriodicalId":380902,"journal":{"name":"2015 International Workshop on Pattern Recognition in NeuroImaging","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127343530","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: 10

MEG/EEG Source Imaging with a Non-Convex Penalty in the Time-Frequency Domain 时频域非凸惩罚的MEG/EEG源成像

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.14

D. Strohmeier, Alexandre Gramfort, J. Haueisen

{"title":"MEG/EEG Source Imaging with a Non-Convex Penalty in the Time-Frequency Domain","authors":"D. Strohmeier, Alexandre Gramfort, J. Haueisen","doi":"10.1109/PRNI.2015.14","DOIUrl":"https://doi.org/10.1109/PRNI.2015.14","url":null,"abstract":"Due to the excellent temporal resolution, MEG/EEG source imaging is an important measurement modality to study dynamic processes in the brain. As the bio electromagnetic inverse problem is ill-posed, constraints have to be imposed on the source estimates to find a unique solution. These constraints can be applied either in the standard or a transformed domain. The Time-Frequency Mixed Norm Estimate applies a composite convex regularization functional promoting structured sparsity in the time-frequency domain by combining an l2,1-mixed-norm and an l1-norm penalty on the coefficients of the Gabor TF decomposition of the source signals, to improve the reconstruction of spatially sparse neural activations with non-stationary and transient signals. Due to the l1-norm based constraints, the resulting source estimates are however biased in amplitude and often suboptimal in terms of source selection. In this work, we present the iterative reweighted Time-Frequency Mixed Norm Estimate, which employs a composite non-convex penalty formed by the sum of an l2,0.5-quasinorm and an l0.5-quasinorm penalty. The resulting non-convex problem is solved with a reweighted convex optimization scheme, in which each iteration is equivalent to a weighted Time-Frequency Mixed-Norm Estimate solved efficiently using a block coordinate descent scheme and an active set strategy. We compare our approach to alternative solvers using simulations and analysis of MEG data and demonstrate the benefit of the iterative reweighted Time-Frequency Mixed Norm Estimate with regard to active source identification, amplitude bias correction, and temporal unmixing of activations.","PeriodicalId":380902,"journal":{"name":"2015 International Workshop on Pattern Recognition in NeuroImaging","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133668679","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: 7