Machine Learning
Improved Detection Sensitivity in Functional MRI Data Using a Brain Parcelling Technique
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Learning to Decode Cognitive States from Brain Images
Machine Learning
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Cognitive States from fMRI Images by Machine Learning and Multivariate Classification
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Classification in Very High Dimensional Problems with Handfuls of Examples
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Probabilistic Anatomo-Functional Parcellation of the Cortex: How Many Regions?
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Detection of spatial activation patterns as unsupervised segmentation of fMRI data
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Total Variation Regularization Enhances Regression-Based Brain Activity Prediction
WBD '10 Proceedings of the 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
Editorial: Brain decoding: Opportunities and challenges for pattern recognition
Pattern Recognition
A novel sparse graphical approach for multimodal brain connectivity inference
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Fiber connectivity integrated brain activation detection
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
A novel sparse group Gaussian graphical model for functional connectivity estimation
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal. Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches. Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task.