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Deficits in working memory (WM) are a consistent neurocognitive marker for schizophrenia. Previous studies have suggested that WM is the product of coordinated activity in distributed functionally connected brain regions. Independent component analysis (ICA) is a data-driven approach that can identify temporally coherent networks that underlie fMRI(More)
Children and adolescents who develop schizophrenia tend to have greater symptom severity than adults who develop the illness. Since the brain continues to mature into early adulthood, developmental differences in brain structure and function may provide clues to the underlying neurobiology of schizophrenia. With an emerging body of evidence supporting(More)
We introduce the nonparametric meta-data dependent relational (NMDR) model, a Bayesian nonparametric stochastic block model for network data. The NMDR allows the entities associated with each node to have mixed membership in an unbounded collection of latent communities. Learned regression models allow these memberships to depend on, and be predicted from,(More)
Deficits in the connectivity between brain regions have been suggested to play a major role in the pathophysiology of schizophrenia. A functional magnetic resonance imaging (fMRI) analysis of schizophrenia was implemented using independent component analysis (ICA) to identify multiple temporally cohesive, spatially distributed regions of brain activity that(More)
A number of recent studies have combined multiple experimental paradigms and modalities to find relevant biological markers for schizophrenia. In this study, we extracted fMRI features maps from the analysis of three experimental paradigms (auditory oddball, Sternberg item recognition, sensorimotor) for a large number (n=154) of patients with schizophrenia(More)
Stochastic block models characterize observed network relationships via latent community memberships. In large social networks, we expect entities to participate in multiple communities, and the number of communities to grow with the network size. We introduce a new model for these phenomena, the hierarchical Dirichlet process relational model, which allows(More)
We introduce a new variational inference objective for hierarchical Dirichlet process ad-mixture models. Our approach provides novel and scalable algorithms for learning nonparametric topic models of text documents and Gaussian admixture models of image patches. Improving on the point estimates of topic probabilities used in previous work, we define full(More)
OBJECTIVE Previous studies have shown that patients with schizophrenia have less modulation of the task-positive and default mode neural networks during novelty detection. The diminished modulation may be interpreted as less functional activation of the task-positive network and less functional deactivation of the default mode network. The relationship(More)
Discussion Papers are a series of manuscripts in their draft form. They are not intended for circulation or distribution except as indicated by the author. For that reason Discussion Papers may not be reproduced or distributed without the written consent of the author. Abstract: Using micro-level household data in the 2001 Comprehensive Survey of the Living(More)