2013 International Workshop on Pattern Recognition in Neuroimaging最新文献

Brain PET Attenuation Correction without CT: An Investigation 无需CT的脑PET衰减校正:一种探讨

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.37

M. Dewan, Y. Zhan, G. Hermosillo, B. Jian, X. Zhou

引用次数: 2

Importance Sampling Spherical Harmonics to Improve Probabilistic Tractography 采样球面谐波对改进概率轨迹成像的重要性

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.21

H. E. Çetingül, Laura Dumont, M. Nadar, P. Thompson, G. Sapiro, C. Lenglet

引用次数: 0

How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data 如何在独立分量分析(ICA)中检测重构源的质量

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.35

M. Grosse-Wentrup, S. Harmeling, T. Zander, N. Hill, B. Scholkopf

引用次数: 6

Detection of Cognitive Impairment in MS Based on an EEG P300 Paradigm 基于脑电P300范式的多发性硬化症认知功能障碍检测

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.38

J. V. Schependom, M. D'hooge, Krista Cleynhens, M. D'hooghe, J. Keyser, G. Nagels

{"title":"Detection of Cognitive Impairment in MS Based on an EEG P300 Paradigm","authors":"J. V. Schependom, M. D'hooge, Krista Cleynhens, M. D'hooghe, J. Keyser, G. Nagels","doi":"10.1109/PRNI.2013.38","DOIUrl":"https://doi.org/10.1109/PRNI.2013.38","url":null,"abstract":"Cognitive impairment affects half of the multiple sclerosis (MS) patient population and is an important factor of quality of life. Cognitive impairment is, however, difficult to detect. Apart from the traditional features used in P300 experiments (e.g. amplitude and latency at different electrodes), we want to investigate the value of network-features on the classification of MS patients as cognitively intact or impaired. We included 305 MS patients, recruited at the National MS Center Melsbroek (Belgium). About half of them was denoted cognitively impaired (143). We divided this patient group in a training set (on which we used 10-fold cross validation) and an independent test set. Results are reported on this last group to increase the generalizability. We found the correlations linking electrodes from one hemisphere with the other significantly different between the two groups MS patients. Especially in the parietal region this difference was very significant (1.5E-12). Using a simple cutoff on this variable, lead to a Percentage Correctly Classified (PCC) of 0.70 and an Area Under Curve (AUC) of the Receiver Operator Curve (ROC) of 0.76. The network parameters that were calculated showed a comparable result for the total number of edges included in the network. Combining these features in a logistic regression model, artificial neural networks or Naive Bayes resulted in a PCC's of 0.68-0.70. These results support the recent suggestion that cognitive dysfunction in MS is caused by a disconnection mechanism in the cerebellum. We have obtained these results applying graph theoretical analyses on EEG data instead of the more common fMRI-analyses. The classification accuracy obtained is, however, not yet sufficient for application in clinical practice.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"378 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115906287","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

Local-Aggregate Modeling for Multi-subject Neuroimage Data via Distributed Optimization 基于分布式优化的多主体神经图像数据局部聚合建模

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.60

Yue Hu, Genevera I. Allen

引用次数: 0

A Graph-Based Brain Parcellation Method Extracting Sparse Networks 一种基于图的稀疏网络脑分割方法

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.48

N. Honnorat, H. Eavani, T. Satterthwaite, C. Davatzikos

引用次数: 3

Randomized Approach to Differential Inference in Multi-subject Functional Connectivity 多主体功能连接的随机化差分推理方法

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.29

Manjari Narayan, Genevera I. Allen

引用次数: 8

Mining the Hierarchy of Resting-State Brain Networks: Selection of Representative Clusters in a Multiscale Structure 静息状态脑网络的层次挖掘:多尺度结构中代表性簇的选择

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.23

Pierre Bellec

引用次数: 26

Learning Predictive Cognitive Structure from fMRI Using Supervised Topic Models 使用监督主题模型从fMRI学习预测性认知结构

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.12

Oluwasanmi Koyejo, Priyank Patel, Joydeep Ghosh, R. Poldrack

引用次数: 2

Identifying Network Correlates of Brain States Using Tensor Decompositions of Whole-Brain Dynamic Functional Connectivity 利用全脑动态功能连接的张量分解识别脑状态的网络关联

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.28

Nora Leonardi, D. Ville

{"title":"Identifying Network Correlates of Brain States Using Tensor Decompositions of Whole-Brain Dynamic Functional Connectivity","authors":"Nora Leonardi, D. Ville","doi":"10.1109/PRNI.2013.28","DOIUrl":"https://doi.org/10.1109/PRNI.2013.28","url":null,"abstract":"Network organization is fundamental to the human brain and alterations of this organization by brain states and neurological diseases is an active field of research. Many studies investigate functional networks by considering temporal correlations between the fMRI signal of distinct brain regions over long periods of time. Here, we propose to use the higher-order singular value decomposition (HOSVD), a tensor decomposition, to extract whole-brain network signatures from group-level dynamic functional connectivity data. HOSVD is a data-driven multivariate method that fits the data to a 3-way model, i.e., connectivity x time x subjects. We apply the proposed method to fMRI data with alternating epochs of resting and watching of movie excerpts, where we captured dynamic functional connectivity by sliding window correlations. By regressing the connectivity maps' time courses with the experimental paradigm, we find a characteristic connectivity pattern for the difference between the brain states. Using leave-one-subject-out cross-validation, we then show that the combination of connectivity patterns generalizes to unseen subjects as it predicts the paradigm. The proposed technique can be used as feature extraction for connectivity-based decoding and holds promise for the study of dynamic brain networks.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133748254","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: 26