2014 International Workshop on Pattern Recognition in Neuroimaging最新文献_第2页

Discriminative subnetwork mining for multiple thresholded connectivity-networks-based classification of mild cognitive impairment 基于多阈值连接网络的轻度认知障碍分类的判别子网络挖掘

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858518

Fei Fei, Biao Jie, Lipeng Wang, Daoqiang Zhang

{"title":"Discriminative subnetwork mining for multiple thresholded connectivity-networks-based classification of mild cognitive impairment","authors":"Fei Fei, Biao Jie, Lipeng Wang, Daoqiang Zhang","doi":"10.1109/PRNI.2014.6858518","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858518","url":null,"abstract":"Recent studies on brain connectivity networks have suggested that many brain diseases, such as, Alzheimer's disease (AD) and mild cognitive impairment (MCI), are related with large-scale connectivity networks, rather than individual brain regions. However, it is challenging to find those networks from the whole connectivity network due to the complexity of brain networks. In this paper, we propose a novel method to mine the discriminative subnetworks for classifying MCI patients from healthy controls (HC). Specifically, we first apply multiple thresholds to generate multiple thresholded connectivity networks, and extract a set of frequent subnetworks from each of the two groups (i.e., MCI and HC), respectively. Then, we measure the discriminative ability of those frequent subnetworks using graph-kernel-based classification method and select the most discriminative subnetworks for subsequent classification. The results on the functional connectivity networks of 12 MCI and 25 HC show that our method can obtain a competitive results compared with state-of-the-art methods on MCI classification.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116102490","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: 5

Joint laplacian diagonalization for multi-modal brain community detection 联合拉普拉斯对角化用于多模态脑社区检测

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858515

Luca Dodero, Vittorio Murino, Diego Sona

引用次数: 4

Permutation distributions of fMRI classification do not behave in accord with central limit theorem fMRI分类的排列分布不符合中心极限定理

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858506

Mohammed Al-Rawi, A. Freitas, J. Duarte, M. Castelo‐Branco

{"title":"Permutation distributions of fMRI classification do not behave in accord with central limit theorem","authors":"Mohammed Al-Rawi, A. Freitas, J. Duarte, M. Castelo‐Branco","doi":"10.1109/PRNI.2014.6858506","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858506","url":null,"abstract":"Applying Monte Carlo method on Fisher's exact test is a prominent choice in estimating an existent statistical effect in large data. When used to analyze classification results, the method, which is widely known as permutation testing, works by testing the null hypothesis after generating a permutation distribution (PD) of classification accuracies/errors that is centered around chance-level. In principle, these PDs should behave in accord with the central limit theorem (CLT) if the independence condition in the cross-validation classification error (test statistic) is fulfilled. Permutation testing has been widely used in pattern classification applied to neuroimaging studies to eradicate chance performance. In this work, we used Anderson-Darling test to evaluate the accordance level of PDs of classification accuracies to normality expected under CLT. An exhaustive simulation study was carried out using functional magnetic resonance imaging data that were collected while human subjects responded to visual stimulation paradigms and the following classifiers were considered: support vector machines, logistic regression, ridge logistic regression, Gaussian Naive Bayes, sparse multinomial logistic regression, and artificial neural networks. Our results showed that while the standard normal distribution does not adequately fit to PDs, it tends to fit well when the mean classification accuracy averaged over a set of independent classifiers is considered. We also found that across-run lk motion correction of the fMRI data weakens the accordance of PDs with CLT and this phenomenon could be due to the across runs dependence resulting from motion correction.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115968018","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

Decoding perceptual thresholds from MEG/EEG 从MEG/EEG中解码感知阈值

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858510

Y. Bekhti, N. Zilber, Fabian Pedregosa, P. Ciuciu, V. V. Wassenhove, Alexandre Gramfort

{"title":"Decoding perceptual thresholds from MEG/EEG","authors":"Y. Bekhti, N. Zilber, Fabian Pedregosa, P. Ciuciu, V. V. Wassenhove, Alexandre Gramfort","doi":"10.1109/PRNI.2014.6858510","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858510","url":null,"abstract":"Magnetoencephalography (MEG) can map brain activity by recording the electromagnetic fields generated by the electrical currents in the brain during a perceptual or cognitive task. This technique offers a very high temporal resolution that allows noninvasive brain exploration at a millisecond (ms) time scale. Decoding, a.k.a. brain reading, consists in predicting from neuroimaging data the subject's behavior and/or the parameters of the perceived stimuli. This is facilitated by the use of supervised learning techniques. In this work we consider the problem of decoding a target variable with ordered values. This target reflects the use of a parametric experimental design in which a parameter of the stimulus is continuously modulated during the experiment. The decoding step is performed by a Ridge regression. The evaluation metric, given the ordinal nature of the target is performed by a ranking metric. On a visual paradigm consisting of random dot kinematograms with 7 coherence levels recorded on 36 subjects we show that one can predict the perceptual thresholds of the subjects from the MEG data. Results are obtained in sensor space and for source estimates in relevant regions of interests (MT, pSTS, mSTS, VLPFC).","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"74 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125704823","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

A study of spatial variation in fMRI brain networks via independent vector analysis: Application to schizophrenia 通过独立向量分析研究fMRI脑网络的空间变化:在精神分裂症中的应用

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858520

Shruti Gopal, Robyn L. Miller, A. Michael, T. Adalı, S. Baum, V. Calhoun

{"title":"A study of spatial variation in fMRI brain networks via independent vector analysis: Application to schizophrenia","authors":"Shruti Gopal, Robyn L. Miller, A. Michael, T. Adalı, S. Baum, V. Calhoun","doi":"10.1109/PRNI.2014.6858520","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858520","url":null,"abstract":"Spatial variability in intrinsic brain networks has not been well studied in fMRI. Independent vector analysis (IVA), is a blind source separation approach that can be used for segregating fMRI data into temporally coherent, maximally spatially independent networks enabling comparison among subjects similar to group independent component analysis (GICA). Using simulated and small sample real data, it has been shown that spatial independence in IVA is achieved while jointly maximizing the dependence across subjects. This study was motivated by the fact that IVA has not yet been applied to a large sample size or to analyze multi-group data for spatial differences. We introduce several new ways to quantify differences in variability of IVA-derived connectivity networks between schizophrenia patients (SZ = 82) from healthy controls (HC = 89) in a large (N=171) data set. Results show that IVA identified significant group differences in the auditory cortex, the basal ganglia, the sensorimotor network and medial visual cortex. Variance maps of the spatial networks showed that there is greater variability in the patients primarily in sensory networks whereas the default mode network showed more variability in the controls. In summary, IVA enables the study of spatial variation in intrinsic brain networks, an area that has not been in focus.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121491682","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

Higher dimensional fMRI connectivity dynamics show reduced dynamism in schizophrenia patients 高维fMRI连接动态显示精神分裂症患者活力降低

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858534

Robyn L. Miller, Maziar Yaesoubi, V. Calhoun, Shruti Gopal

引用次数: 6

Improved MEG/EEG source localization with reweighted mixed-norms 基于加权混合规范的改进MEG/EEG源定位方法

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858545

D. Strohmeier, J. Haueisen, Alexandre Gramfort

{"title":"Improved MEG/EEG source localization with reweighted mixed-norms","authors":"D. Strohmeier, J. Haueisen, Alexandre Gramfort","doi":"10.1109/PRNI.2014.6858545","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858545","url":null,"abstract":"MEG/EEG source imaging allows for the noninvasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, a priori information is required to find a unique source estimate. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation can be assumed. Due to the convexity, ℓ-norm based constraints are often used for this, which however lead to source estimates biased in amplitude and often suboptimal in terms of source selection. As an alternative, non-convex regularization functionals such as ℓ p-quasinorms with 0 <; p <; 1 can be used. In this work, we present a MEG/EEG inverse solver based on a ℓ 2,0.5-quasinorm penalty promoting spatial sparsity as well as temporal stationarity of the brain activity. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate, which is based on reweighted convex optimization and combines a block coordinate descent scheme and an active set strategy to solve each surrogate problem efficiently. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method outperforms the standard Mixed Norm Estimate in terms of active source identification and amplitude bias.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116809372","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: 15

Improved method for automatic cerebrovascular labelling using stochastic tunnelling 改进的随机隧道自动脑血管标记方法

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858519

Sahar Ghanavati, J. Lerch, J. Sled

引用次数: 1

Semi-supervised learning in MCI-to-ad conversion prediction — When is unlabeled data useful? 半监督学习在mci到广告转换预测中的应用——未标记数据何时有用?

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858535

E. Moradi, Jussi Tohka, Christian Gaser

引用次数: 12

Functional hyperalignment of resting state FMRI sessions driven by autonomic activity 由自主神经活动驱动的静息状态FMRI会话的功能性超对齐

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858525

V. Iacovella, Andrea Bertana, P. Avesani

{"title":"Functional hyperalignment of resting state FMRI sessions driven by autonomic activity","authors":"V. Iacovella, Andrea Bertana, P. Avesani","doi":"10.1109/PRNI.2014.6858525","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858525","url":null,"abstract":"Extraction of between-participants common patterns of brain activity is currently one of the most debated challenges in cognitive neuroscience. So far, by applying functional hyperalignment method [1] to functional magnetic resonance imaging (FMRI) data, relevant results have been obtained on datasets where different participants underwent the same stimulations. This challenge is however less explored when participants undergo resting state sessions: there, it is hard to characterize whether different participants experienced similar brain states. Here we propose to combine autonomic activity information with FMRI acquisitions of resting state to characterize between-participants synchrony levels and drive the extraction of functional hyperalignment transformations. We show that between participants common patterns are modulated by functionally aligning using time-segments characterized by different synchrony. We study this effect on different brain areas, showing that modulations are not homogeneous and suggesting that different brain areas carry different contributions to between-participants common patterns.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131126903","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