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ORCIDPRNI 2015: Stanford, CA, USA
- 2015 International Workshop on Pattern Recognition in NeuroImaging, Stanford, CA, USA, June 10-12, 2015. IEEE Computer Society 2015, ISBN 978-1-4673-7145-2
Oral Session I: Graphical Methods and Connectivity
- Ricardo Pio Monti
, Romy Lorenz, Peter Hellyer, Robert Leech, Christoforos Anagnostopoulos, Giovanni Montana:
Graph Embeddings of Dynamic Functional Connectivity Reveal Discriminative Patterns of Task Engagement in HCP Data. 1-4 - Itir Önal, Mete Ozay, Fatos T. Yarman-Vural:
Modeling Voxel Connectivity for Brain Decoding. 5-8 - Denis A. Engemann, Daniel Strohmeier, Eric Larson, Alexandre Gramfort:
Mind the Noise Covariance When Localizing Brain Sources with M/EEG. 9-12 - Paolo Avesani, Bao Nguyen Thien, Nivedita Agarwal, Mark Bromberg, Lubdha Shah, Emanuele Olivetti:
Tractography Mapping for Dissimilarity Space across Subjects. 13-16
Oral Session II: Sparse Techniques
- Elvis Dohmatob, Michael Eickenberg, Bertrand Thirion, Gaël Varoquaux:
Speeding-Up Model-Selection in Graphnet via Early-Stopping and Univariate Feature-Screening. 17-20 - Daniel Strohmeier, Alexandre Gramfort, Jens Haueisen:
MEG/EEG Source Imaging with a Non-Convex Penalty in the Time-Frequency Domain. 21-24 - Joao M. Monteiro, Anil Rao, John Ashburner, John Shawe-Taylor, Janaina Mourão Miranda:
Multivariate Effect Ranking via Adaptive Sparse PLS. 25-28 - André Altmann, Bernard Ng:
Joint Feature Extraction from Functional Connectivity Graphs with Multi-task Feature Learning. 29-32
Oral Session III: Multimodal Integration
- Sofie Therese Hansen, Irene Winkler, Lars Kai Hansen, Klaus-Robert Müller, Sven Dähne:
Fusing Simultaneous EEG and fMRI Using Functional and Anatomical Information. 33-36 - Martin C. Axelsen, Nikolaj Bak, Lars Kai Hansen:
Testing Multimodal Integration Hypotheses with Application to Schizophrenia Data. 37-40 - Yuhong Li, Qi Dou, Jinze Yu, Fucang Jia, Jing Qin, Pheng-Ann Heng:
Automatic Brain Tumor Segmentation from MR Images via a Multimodal Sparse Coding Based Probabilistic Model. 41-44 - Tingting Ye, Chen Zu, Biao Jie, Dinggang Shen, Daoqiang Zhang:
Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification. 45-48
Oral Session IV: Statistical Estimation and Modeling
- Ali Faisal, Anni Nora, Jaeho Seol, Hanna Renvall, Riitta Salmelin:
Kernel Convolution Model for Decoding Sounds from Time-Varying Neural Responses. 49-52 - Manjari Narayan, Genevera I. Allen:
Population Inference for Node Level Differences in Multi-subject Functional Connectivity. 53-56 - Aina Frau-Pascual, Florence Forbes, Philippe Ciuciu:
Variational Physiologically Informed Solution to Hemodynamic and Perfusion Response Estimation from ASL fMRI Data. 57-60 - Anil Rao, Joao M. Monteiro, John Ashburner, Liana Catarina Lima Portugal, Orlando Fernandes Junior, Leticia de Oliveira, Mirtes Pereira, Janaina Mourão Miranda:
A Comparison of Strategies for Incorporating Nuisance Variables into Predictive Neuroimaging Models. 61-64
Oral Session V: Statistical Inference and Best Practices
- Joset A. Etzel:
MVPA Permutation Schemes: Permutation Testing for the Group Level. 65-68 - Emanuele Olivetti, Dirk B. Walther:
A Bayesian Test for Comparing Classifier Errors. 69-72 - Andrés Hoyos Idrobo, Yannick Schwartz, Gaël Varoquaux, Bertrand Thirion:
Improving Sparse Recovery on Structured Images with Bagged Clustering. 73-76 - Alex F. Mendelson, Maria A. Zuluaga, Brian F. Hutton, Sébastien Ourselin:
Bolstering Heuristics for Statistical Validation of Prediction Algorithms. 77-80
Oral Session VI: Novel Applications
- Jessica Schrouff, Christophe Phillips, Josef Parvizi, Janaina Mourão Miranda:
Predicting Numerical Processing in Naturalistic Settings from Controlled Experimental Conditions. 81-84 - Valeria Kebets, Jonas Richiardi, Mitsouko van Assche, Rachel Goldstein, M. van der Meulen, Patrik Vuilleumier, Dimitri Van De Ville, Frédéric Assal:
Predicting Pure Amnestic Mild Cognitive Impairment Conversion to Alzheimer's Disease Using Joint Modeling of Imaging and Clinical Data. 85-88 - Sanne Schoenmakers, Tom Heskes, Marcel van Gerven:
Hidden Markov Models for Reading Words from the Human Brain. 89-92 - Aki Nikolaidis, Drew Goatz, Paris Smaragdis, Arthur F. Kramer:
Predicting Skill-Based Task Performance and Learning with fMRI Motor and Subcortical Network Connectivity. 93-96
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