Paper accepted in PLoS ONE

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May 062015
 

Our paper with the tile “Connecting Anxiety and Genomic Copy Number Variation: a Genome-Wide Analysis in Mice” was accepted for publication at PLoS ONE.

Abstract:

Genomic copy number variants (CNVs) have been implicated in multiple psychiatric disorders, but not much is known about their influence on anxiety disorders specifically. Using next-generation sequencing (NGS) and two additional array-based genotyping approaches, we detected CNVs in a mouse model consisting of two inbred mouse lines showing high (HAB) and low (LAB) anxiety-related behavior, respectively. An influence of CNVs on gene expression in the central (CeA) and basolateral (BLA) amygdala, paraventricular nucleus (PVN), and cingulate cortex (Cg) was shown by a two-proportion Z-test (p = 1.6 x 10-31), with a positive correlation in the CeA (p = 0.0062), PVN (p = 0.0046) and Cg (p = 0.0114), indicating a contribution of CNVs to the genetic predisposition to trait anxiety in the specific context of HAB/LAB mice. In order to confirm anxiety-relevant CNVs and corresponding genes in a second mouse model, we further examined CD-1 outbred mice. We revealed the distribution of CNVs by genotyping 64 CD 1 individuals using a high-density genotyping array (Jackson Laboratory). 78 genes within those CNVs were identified to show nominally significant association (48 genes), or a statistical trend in their association (30 genes) with the time animals spent on the open arms of the elevated plus-maze (EPM). Fifteen of them were considered promising candidate genes of anxiety-related behavior as we could show a significant overlap (permutation test, p = 0.0051) with genes within HAB/LAB CNVs. Thus, here we provide what is to our knowledge the first extensive catalogue of CNVs in CD-1 mice and potential corresponding candidate genes linked to anxiety-related behavior in mice.

 Posted by at 06:27

Paper accepted for Oral Presentation at PRNI 2015

 Research  Comments Off on Paper accepted for Oral Presentation at PRNI 2015
May 062015
 

Our paper with the title “Joint Feature Extraction from Functional Connectivity Graphs with Multi-task Feature Learning” was selected for oral presentation at PRNI 2015 (Stanford, USA in June 2015).

Abstract:

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 sparse and stable classifiers. In classification experiments on functional connectivity estimated from resting state functional magnetic resonance imaging (fMRI), we show that MFL more consistently selects the same connections across bootstrap samples and provides more interpretable models in multiclass settings than standard sparse classifiers, while achieving similar classification performance.

 Posted by at 06:24