12,754 research outputs found
Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia
Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of
brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in
brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest
quantitative differences in brain texture that, alongside discrete volumetric changes, may
serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and
voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27
patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy)
were also used as covariates in VBM analyses to test for correspondence with regional brain
volume. Linear discriminant analysis tested if texture and volumetric data predicted
diagnostic group membership (schizophrenia or control). We found that uniformity and
entropy of grey matter differed significantly between individuals with schizophrenia and
controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group,
these texture parameters correlated with volumes of the left hippocampus, right amygdala
and cerebellum. The best predictor of diagnostic group membership was the combination of
fine texture heterogeneity and left hippocampal size. This study highlights the presence of
distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural
abnormality of the hippocampus. The conjunction of these features has potential as a
neuroimaging endophenotype of schizophrenia
Estimation of extended mixed models using latent classes and latent processes: the R package lcmm
The R package lcmm provides a series of functions to estimate statistical
models based on linear mixed model theory. It includes the estimation of mixed
models and latent class mixed models for Gaussian longitudinal outcomes (hlme),
curvilinear and ordinal univariate longitudinal outcomes (lcmm) and curvilinear
multivariate outcomes (multlcmm), as well as joint latent class mixed models
(Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome and a
time-to-event that can be possibly left-truncated right-censored and defined in
a competing setting. Maximum likelihood esimators are obtained using a modified
Marquardt algorithm with strict convergence criteria based on the parameters
and likelihood stability, and on the negativity of the second derivatives. The
package also provides various post-fit functions including goodness-of-fit
analyses, classification, plots, predicted trajectories, individual dynamic
prediction of the event and predictive accuracy assessment. This paper
constitutes a companion paper to the package by introducing each family of
models, the estimation technique, some implementation details and giving
examples through a dataset on cognitive aging
Ideological and Temporal Components of Network Polarization in Online Political Participatory Media
Political polarization is traditionally analyzed through the ideological
stances of groups and parties, but it also has a behavioral component that
manifests in the interactions between individuals. We present an empirical
analysis of the digital traces of politicians in politnetz.ch, a Swiss online
platform focused on political activity, in which politicians interact by
creating support links, comments, and likes. We analyze network polarization as
the level of intra- party cohesion with respect to inter-party connectivity,
finding that supports show a very strongly polarized structure with respect to
party alignment. The analysis of this multiplex network shows that each layer
of interaction contains relevant information, where comment groups follow
topics related to Swiss politics. Our analysis reveals that polarization in the
layer of likes evolves in time, increasing close to the federal elections of
2011. Furthermore, we analyze the internal social network of each party through
metrics related to hierarchical structures, information efficiency, and social
resilience. Our results suggest that the online social structure of a party is
related to its ideology, and reveal that the degree of connectivity across two
parties increases when they are close in the ideological space of a multi-party
system.Comment: 35 pages, 11 figures, Internet, Policy & Politics Conference,
University of Oxford, Oxford, UK, 25-26 September 201
EEG analytics for early detection of autism spectrum disorder: a data-driven approach
Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.This research was supported by National Institute of Mental Health (NIMH) grant R21 MH 093753 (to WJB), National Institute on Deafness and Other Communication Disorders (NIDCD) grant R21 DC08647 (to HTF), NIDCD grant R01 DC 10290 (to HTF and CAN) and a grant from the Simons Foundation (to CAN, HTF, and WJB). We are especially grateful to the staff and students who worked on the study and to the families who participated. (R21 MH 093753 - National Institute of Mental Health (NIMH); R21 DC08647 - National Institute on Deafness and Other Communication Disorders (NIDCD); R01 DC 10290 - NIDCD; Simons Foundation)Published versio
Nod2 Deficiency in mice is Associated with Microbiota Variation Favouring the Expansion of mucosal CD4+ LAP+ Regulatory Cells
Nucleotide-binding Oligomerization Domain-2 (NOD2) mutations are associated with an increased risk to develop Crohn's Disease. In previous studies, we have shown that Nod2-/- mice manifest increased proportion of Lamina Propria (LP) CD4+ LAP+ Foxp3- regulatory cells, when compared with Nod2+/+ mice, while CD4+ Foxp3 + regulatory cells were not affected. Here, we investigated the Nod2 gut microbiota, by 16S rRNA pyrosequencing, at steady state and after TNBS-colitis induction in mice reared separately or in cohousing, correlating the microbial profiles with LP regulatory T cells proportion and tissue cytokines content. We found that enrichment of Rikenella and Alistipes (Rikenellaceae) in Nod2-/- mice at 8 weeks of age reared separately was associated with increased proportion of CD4+ LAP+ Foxp3- cells and less severe TNBS-colitis. In co-housed mice the acquisition of Rickenellaceae by Nod2+/+ mice was associated with increased CD4+ LAP+ Foxp3- proportion and less severe colitis. Severe colitis was associated with enrichment of gram-negative pathobionts (Escherichia and Enterococcus), while less severe colitis with protective bacteria (Barnesiella, Odoribacter and Clostridium IV). Environmental factors acting on genetic background with different outcomes according to their impact on microbiota, predispose in different ways to inflammation. These results open a new scenario for therapeutic attempt to re-establish eubiosis in Inflammatory Bowel Disease patients with NOD2 polymorphisms
Techniques for clustering gene expression data
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered
Influence of Statistical Estimators of Mutual Information and Data Heterogeneity on the Inference of Gene Regulatory Networks
The inference of gene regulatory networks from gene expression data is a difficult problem because the performance of the inference algorithms depends on a multitude of different factors. In this paper we study two of these. First, we investigate the influence of discrete mutual information (MI) estimators on the global and local network inference performance of the C3NET algorithm. More precisely, we study different MI estimators (Empirical, Miller-Madow, Shrink and Schürmann-Grassberger) in combination with discretization methods (equal frequency, equal width and global equal width discretization). We observe the best global and local inference performance of C3NET for the Miller-Madow estimator with an equal width discretization. Second, our numerical analysis can be considered as a systems approach because we simulate gene expression data from an underlying gene regulatory network, instead of making a distributional assumption to sample thereof. We demonstrate that despite the popularity of the latter approach, which is the traditional way of studying MI estimators, this is in fact not supported by simulated and biological expression data because of their heterogeneity. Hence, our study provides guidance for an efficient design of a simulation study in the context of network inference, supporting a systems approach
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