2013 International Workshop on Pattern Recognition in Neuroimaging最新文献_第5页

Utility of Partial Correlation for Characterising Brain Dynamics: MVPA-based Assessment of Regularisation and Network Selection 脑动力学特征的部分相关的效用:基于mvpa的正则化和网络选择评估

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.24

E. Duff, T. Makin, Sasidhar S. Madugula, Stephen M. Smith, M. Woolrich

引用次数: 8

Localizing and Comparing Weight Maps Generated from Linear Kernel Machine Learning Models 由线性核机器学习模型生成的权重图的定位和比较

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.40

J. Schrouff, J. Crémers, G. Garraux, Luca Baldassarre, J. Miranda, C. Phillips

引用次数: 37

Prediction of Second Neurological Attack in Patients with Clinically Isolated Syndrome Using Support Vector Machines 应用支持向量机预测临床孤立综合征患者神经系统二次发作

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.30

Viktor Wottschel, O. Ciccarelli, D. Chard, David H. Miller, D. Alexander

{"title":"Prediction of Second Neurological Attack in Patients with Clinically Isolated Syndrome Using Support Vector Machines","authors":"Viktor Wottschel, O. Ciccarelli, D. Chard, David H. Miller, D. Alexander","doi":"10.1109/PRNI.2013.30","DOIUrl":"https://doi.org/10.1109/PRNI.2013.30","url":null,"abstract":"The aim of this study is to predict the conversion from clinically isolated syndrome to clinically definite multiple sclerosis using support vector machines. The two groups of converters and non-converters are classified using features that were calculated from baseline data of 73 patients. The data consists of standard magnetic resonance images, binary lesion masks, and clinical and demographic information. 15 features were calculated and all combinations of them were iteratively tested for their predictive capacity using polynomial kernels and radial basis functions with leave-one-out cross-validation. The accuracy of this prediction is up to 86.4% with a sensitivity and specificity in the same range indicating that this is a feasible approach for the prediction of a second clinical attack in patients with clinically isolated syndromes, and that the chosen features are appropriate. The two features gender and location of onset lesions have been used in all feature combinations leading to a high accuracy suggesting that they are highly predictive. However, it is necessary to add supporting features to maximise the accuracy.","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":"133807250","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

Antagonistic Activation Patterns Underlie Multi-functionality of the Right Temporo-Parietal Junction 拮抗激活模式是右侧颞顶交界处多功能的基础

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.25

D. Bzdok, R. Langner, S. Eickhoff, A. Laird, P. Fox

引用次数: 0

Brain Decoding via Graph Kernels 通过图核进行大脑解码

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.43

Sandro Vega-Pons, P. Avesani

{"title":"Brain Decoding via Graph Kernels","authors":"Sandro Vega-Pons, P. Avesani","doi":"10.1109/PRNI.2013.43","DOIUrl":"https://doi.org/10.1109/PRNI.2013.43","url":null,"abstract":"An emergent trend in data analysis of functional brain recordings is based on multivariate pattern recognition. Unlike univariate approaches, it is designed as a prediction task by decoding the brain state. fMRI brain decoding is a challenging classification problem due to the noisy, redundant and spatio-temporal correlated data, where there are generally much more features than samples. The use of a classifier requires that raw data is mapped into n-dimensional real vectors where the structural information of the data is not taken into account. Alternative methods propose a different data representation based on a graph encoding. While graphs provide a more powerful representation, machine learning algorithms for this type of encoding become computationally intensive. The contribution of this paper is the introduction of a graph kernel with a lower computational complexity that allows taking advantage from both the representative power of graphs and the discrimination power of kernel-based classifiers such as Support Vector Machines. We provide experimental results for a discrimination task between faces and houses on a fMRI dataset. We also investigate on synthetic data, how the brain decoding task differs according to the different encodings: vectorial and graph-based. A remarkable feature of the graph approach is its capability to handle data from different subjects, without the need of any intersubject alignment. An intersubject decoding experiment is also performed for the faces versus houses problem.","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":"116145735","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: 14

Creating Group-Level Functionally-Defined Atlases for Diagnostic Classification 为诊断分类创建组级功能定义地图集

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.17

Francisco Pereira, J. M. Walz, H. E. Çetingül, S. Sudarsky, M. Nadar, R. Prakash

引用次数: 2

Descending Variance Graphs for Segmenting Neurological Structures 神经结构分割的下降方差图

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.52

G. Stetten, Cindy Wong, Vikas Shivaprabhu, Ada Zhang, S. Horvath, Jihang Wang, J. Galeotti, V. Gorantla, H. Aizenstein

{"title":"Descending Variance Graphs for Segmenting Neurological Structures","authors":"G. Stetten, Cindy Wong, Vikas Shivaprabhu, Ada Zhang, S. Horvath, Jihang Wang, J. Galeotti, V. Gorantla, H. Aizenstein","doi":"10.1109/PRNI.2013.52","DOIUrl":"https://doi.org/10.1109/PRNI.2013.52","url":null,"abstract":"We present a novel and relatively simple method for clustering pixels into homogeneous patches using a directed graph of edges between neighboring pixels. For a 2D image, the mean and variance of image intensity is computed within a circular region centered at each pixel. Each pixel stores its circle's mean and variance, and forms the node in a graph, with possible edges to its 4 immediate neighbors. If at least one of those neighbors has a lower variance than itself, a directed edge is formed, pointing to the neighbor with the lowest variance. Local minima in variance thus form the roots of disjoint trees, representing patches of relative homogeneity. The method works in n-dimensions and requires only a single parameter: the radius of the circular (spherical, or hyper spherical) regions used to compute variance around each pixel. Setting the intensity of all pixels within a given patch to the mean at its root pixel significantly reduces image noise while preserving anatomical structure, including location of boundaries. The patches may themselves be clustered using techniques that would be computationally too expensive if applied to the raw pixels. We demonstrate such clustering to identify fascicles in the median nerve in high-resolution 2D ultrasound images, as well as white matter hyper intensities in 3D magnetic resonance images.","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":"132532742","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

Two Test Statistics for Cross-Modal Graph Community Significance 跨模态图群体显著性的两个检验统计量

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.27

J. Richiardi, A. Altmann, M. Greicius

引用次数: 1

Second Order Scattering Descriptors Predict fMRI Activity Due to Visual Textures 二阶散射描述子预测由于视觉纹理的fMRI活动

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-01 DOI: 10.1109/PRNI.2013.11

Michael Eickenberg, Fabian Pedregosa, M. Senoussi, Alexandre Gramfort, B. Thirion

引用次数: 4

HRF Estimation Improves Sensitivity of fMRI Encoding and Decoding Models HRF估计提高了fMRI编码和解码模型的灵敏度

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-05-13 DOI: 10.1109/PRNI.2013.50

Fabian Pedregosa, Michael Eickenberg, B. Thirion, Alexandre Gramfort

引用次数: 9