<![CDATA[ IEEE Journal of Selected Topics in Signal Processing - new TOC ]]>
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TOC Alert for Publication# 4200690 2016September26<![CDATA[Table of Contents]]>107977978162<![CDATA[Introduction to the Issue on Advanced Signal Processing for Brain Networks]]>10711311133241<![CDATA[Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling]]>10711341149851<![CDATA[Bayesian Inference of Task-Based Functional Brain Connectivity Using Markov Chain Monte Carlo Methods]]>10711501159873<![CDATA[Archetypal Analysis for Modeling Multisubject fMRI Data]]>10711601171857<![CDATA[A Combined Static and Dynamic Model for Resting-State Brain Connectivity Networks]]>10711721181673<![CDATA[Network-Based Statistic Show Aberrant Functional Connectivity in Alzheimer's Disease]]>10711821188466<![CDATA[Graph Frequency Analysis of Brain Signals]]>107118912031166<![CDATA[Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction]]>transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is learned from an independent FDG-PET dataset to improve functional connectivity-based prediction of AD. The prior acts as a regularization of connectivity learning and improves the estimation of discriminative patterns from distinct rs-fMRI datasets. Our approach is a two-stage classification strategy that combines several seed-based connectivity maps to cover a large number of functional networks that identify AD physiopathology. Experimental results show that our transmodal approach increases classification accuracy compared to pure rs-fMRI approaches, without resorting to additional invasive acquisitions. The method successfully recovers brain regions known to be impacted by the disease.]]>10712041213978<![CDATA[Localizing Sources of Brain Disease Progression with Network Diffusion Model]]>107121412251148<![CDATA[Effective Connectivity Analysis in Brain Networks: A GPU-Accelerated Implementation of the Cox Method]]>107122612371601<![CDATA[Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery]]>107123812535811<![CDATA[MERLiN: Mixture Effect Recovery in Linear Networks]]>causal variables, however, and linear combinations need to be considered. In electroencephalographic studies, for example, one is not interested in establishing cause-effect relationships between electrode signals (the observed variables), but rather between cortical signals (the causal variables) which can be recovered as linear combinations of electrode signals. We introduce Mixture Effect Recovery in Linear Networks (MERLiN), a family of causal inference algorithms that implement a novel means of constructing causal variables from non-causal variables. We demonstrate through application to EEG data how the basic MERLiN algorithm can be extended for application to different (neuroimaging) data modalities. Given an observed linear mixture, the algorithms can recover a causal variable that is a linear effect of another given variable. That is, MERLiN allows us to recover a cortical signal that is affected by activity in a certain brain region, while not being a direct effect of the stimulus. The Python/Matlab implementation for all presented algorithms is available on https://github.com/sweichwald/MERLiN.]]>107125412662859<![CDATA[Identifying Seizure Onset Zone From the Causal Connectivity Inferred Using Directed Information]]>107126712832939<![CDATA[Event-Related Functional Network Identification: Application to EEG Classification]]>107128412941564<![CDATA[Multilinear Discriminant Analysis With Subspace Constraints for Single-Trial Classification of Event-Related Potentials]]>107129513052394<![CDATA[An Informed Multitask Diffusion Adaptation Approach to Study Tremor in Parkinson's Disease]]>10713061314891<![CDATA[Modeling Effective Connectivity in High-Dimensional Cortical Source Signals]]>107131513251051