2014 International Workshop on Pattern Recognition in Neuroimaging最新文献

Causal and anti-causal learning in pattern recognition for neuroimaging 神经影像模式识别中的因果与反因果学习

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2015-12-15 DOI: 10.1109/PRNI.2014.6858551

S. Weichwald, B. Scholkopf, T. Ball, M. Grosse-Wentrup

引用次数: 14

Single-trials ERPs predict correct answers to intelligence test questions 单次试验erp预测智力测试问题的正确答案

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858528

Achim Leydecker, F. Biessmann, S. Fazli

引用次数: 3

A MAP approach for convex non-negative matrix factorization in the diagnosis of brain tumors 凸非负矩阵分解在脑肿瘤诊断中的MAP方法

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858550

A. Vilamala, L. B. Muñoz, A. Vellido

引用次数: 2

Data-driven multisubject neuroimaging analyses for naturalistic stimuli 自然刺激的数据驱动多主体神经成像分析

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858511

F. Biessmann, Michael Gaebler, Jan-Peter Lamke, Uijong Ju, S. Hetzer, C. Wallraven, K. Müller

引用次数: 3

Multiple subject learning for inter-subject prediction 多学科学习的学科间预测

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858548

S. Takerkart, L. Ralaivola

引用次数: 7

A validation of a multi-spatialscale method for multivariate pattern analysis 多空间尺度多变量模式分析方法的验证

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858513

J. Bulthé, J. V. D. Hurk, Nicky Daniels, B. Smedt, H. O. D. Beeck

引用次数: 6

Spatial discriminant ICA for RS-fMRI characterisation RS-fMRI表征的空间判别ICA

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858546

Alejandro Tabas-Diaz, E. Balaguer-Ballester, L. Igual

引用次数: 15

Full Bayesian multi-task learning for multi-output brain decoding and accommodating missing data 多输出脑解码和适应缺失数据的全贝叶斯多任务学习

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858533

A. Marquand, Steven C. R. Williams, O. Doyle, M. J. Rosa

{"title":"Full Bayesian multi-task learning for multi-output brain decoding and accommodating missing data","authors":"A. Marquand, Steven C. R. Williams, O. Doyle, M. J. Rosa","doi":"10.1109/PRNI.2014.6858533","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858533","url":null,"abstract":"Multi-task learning (MTL) has recently been demonstrated to be highly promising for decoding multiple target variables from neuroimaging data. Its primary advantage is that it makes more efficient use of the data than existing decoding models, leading to improved accuracy. In this work, we propose a novel Bayesian MTL approach, motivated by problems such as clinical applications where accurate quantification of uncertainty is crucial. We present a Markov chain Monte Carlo approach to perform inference in the model and demonstrate the approach using a publicly available neuroimaging dataset. We study the conditions where MTL is likely to improve performance: we first evaluate MTL as an approach for accommodating missing data, which is an important problem that has received little attention from the neuroimaging community. We then examine whether it is beneficial to include classification and regression tasks in the same model. We relate our conclusions to results from geostatistics, where MTL methods were pioneered, and make recommendations for neuroimaging practitioners using MTL.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"13 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131437291","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

Intensity normalisation for large-scale fMRI brain decoding 大规模fMRI脑解码的强度归一化

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858531

Loizos Markides, D. Gillies

{"title":"Intensity normalisation for large-scale fMRI brain decoding","authors":"Loizos Markides, D. Gillies","doi":"10.1109/PRNI.2014.6858531","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858531","url":null,"abstract":"Among the long-term goals of the fairly new area of brain decoding is the exploitation of the results for the creation of advanced brain-computer interfaces, which can potentially establish a solid communication channel with people in vegetative state. Recent attempts for large-scale brain decoding form a both powerful and promising foundation towards that goal, since they aim to extract accurate representations of certain stimuli within the human brain, given a large number of different studies. An inherent problem with across-study brain decoding is that the classification algorithms end up discriminating among studies instead among stimuli. This is due to study-specific nuisance effects, which cannot be removed by standard preprocessing methodologies, and which may cause two volumes representing different stimuli within a single study to be closer to one another than two volumes representing similar stimuli across different studies. Considering that a large number of previous studies suggest that across-subject and across-session decoding works, we have come to believe that the problem of degraded across-study accuracy is introduced by differing stimuli activation values across studies, originating from study-specific and not subject-specific idiosyncrasies. Therefore, the problem of correct stimuli classification across studies is reduced to the one of consistent intensity normalisation across studies, in order to provide persistent representations of stimuli in the brain. In this work, we provide a thorough discussion on the performance of four different intensity normalisation techniques, in order to evaluate their applicability as a pre-processing step for large-scale brain decoding.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"51 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":"128019021","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

Multi-subject Bayesian Joint Detection and Estimation in fMRI 功能磁共振多主体贝叶斯联合检测与估计

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI: 10.1109/PRNI.2014.6858508

S. Badillo, S. Desmidt, Chantal Ginisty, P. Ciuciu

{"title":"Multi-subject Bayesian Joint Detection and Estimation in fMRI","authors":"S. Badillo, S. Desmidt, Chantal Ginisty, P. Ciuciu","doi":"10.1109/PRNI.2014.6858508","DOIUrl":"https://doi.org/10.1109/PRNI.2014.6858508","url":null,"abstract":"Modern cognitive experiments in functional Magnetic Resonance Imaging (fMRI) rely on a cohort of subjects sampled from a population of interest to study characteristics of the healthy brain or to identify biomarkers on a specific pathology (e.g., Alzheimer's disease) or disorder (e.g., ageing). Group-level studies usually proceed in two steps by making random-effect analysis on top of intra-subject analyses, to localize activated regions in response to stimulations or to estimate brain dynamics. Here, we focus on improving the accuracy of group-level inference of the hemodynamic response function (HRF). We rest on a existing Joint Detection-Estimation (JDE) framework which aims at detecting evoked activity and estimating HRF shapes jointly. So far, region-specific group-level HRFs have been captured by averaging intra-subject HRF profiles. Here, our approach extends the JDE formalism to the multi-subject context by proposing a hierarchical Bayesian modeling that includes an additional layer for describing the link between subject-specific and group-level HRFs. This extension outperforms the original approach both on artificial and real multi-subject datasets.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"12 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":"117165589","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