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There is increasing evidence to suggest functional connectivity networks are non-stationary. This has lead to the development of novel methodologies with which to accurately estimate time-varying functional connectivity networks. Many of these methods provide unprecedented temporal granularity by estimating a functional connectivity network at each point in time, resulting in high-dimensional output...
The massively dynamic nature of human brain cannot be represented by considering only a collection of voxel intensity values obtained from fMRI measurements. It has been observed that the degree of connectivity among voxels provide important information for modeling cognitive activities. Moreover, spatially close voxels act together to generate similar BOLD responses to the same stimuli. In this study,...
Magneto encephalography (MEG) and electroen-cephalography (EEG) are imaging methods that measure neuronal dynamics non invasively with high temporal precision. It is often desired in MEG and EEG analysis to estimate the neural sources of the signals. Strategies used for this purpose often take into account the covariance between sensors to yield more precise estimates of the sources. Here we investigate...
Structural brain connectivity can be studied with the help of diffusion magnetic resonance imaging (dMRI), through which the pathways of the neuronal axons of the white matter can be reconstructed at the millimeter scale. Such connectivity structure, called deterministic tractography, is represented as a set of polylines in 3D space, called streamlines. Streamlines have a non-homogeneous number of...
The Graph Net (aka S-Lasso), as well as other "spar-sity + structure" priors like TV-L1, are not easily applicable to brain data because of technical problems concerning the selection of the regularization parameters. Also, in their own right, such models lead to challenging high-dimensional optimization problems. In this manuscript, we present some heuristics for speeding up the overall...
Due to the excellent temporal resolution, MEG/EEG source imaging is an important measurement modality to study dynamic processes in the brain. As the bio electromagnetic inverse problem is ill-posed, constraints have to be imposed on the source estimates to find a unique solution. These constraints can be applied either in the standard or a transformed domain. The Time-Frequency Mixed Norm Estimate...
Unsupervised learning approaches, such as Sparse Partial Least Squares (SPLS), may provide useful insights into the brain mechanisms by finding relationships between two sets of variables (i.e. Views) from the same subjects. The algorithm outputs two sets of paired weight vectors, where each pair expresses an "effect" between both views. However, each effect can be described by a different...
Using sparse regularization in classifier learning is an appealing strategy to locate relevant brain regions and connections between regions within high-dimensional brain imaging data. A major drawback of sparse classifier learning is the lack of stability to data perturbations, which leads to different sets of features being selected. Here, we propose to use multi-task feature learning (MFL) to generate...
Simultaneously measuring electro physical and hemodynamic signals has become more accessible in the last years and the need for modeling techniques that can fuse the modalities is growing. In this work we augment a specific fusion method, the multimodal Source Power Co-modulation (mSPoC), to not only use functional but also anatomical information. The goal is to extract correlated source components...
Multimodal data sets are getting more and more common. Integrating these data sets, the information from each modality can be combined to improve performance in classification problems. Fusion/integration of modalities can be done at several levels. The most appropriate fusion level is related to the conditional dependency between modalities. A varying degree of inter-modality dependency can be present...
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