Effect of image analysis software on neurofunctional activation during processing of emotional human faces

Functional brain imaging techniques such as functional MRI (fMRI) that allow the in vivo investigation of the human brain have been exponentially employed to address the neurophysiological substrates of emotional processing. Despite the growing number of fMRI studies in the field, when taken separately these individual imaging studies demonstrate contrasting findings and variable pictures, and are unable to definitively characterize the neural networks underlying each specific emotional condition. Different imaging packages, as well as the statistical approaches for image processing and analysis, probably have a detrimental role by increasing the heterogeneity of findings. In particular, it is unclear to what extent the observed neurofunctional response of the brain cortex during emotional processing depends on the fMRI package used in the analysis. In this pilot study, we performed a double analysis of an fMRI dataset using emotional faces. The Statistical Parametric Mapping (SPM) version 2.6 (Wellcome Department of Cognitive Neurology, London, UK) and the XBAM 3.4 (Brain Imaging Analysis Unit, Institute of Psychiatry, Kings College London, UK) programs, which use parametric and non-parametric analysis, respectively, were used to assess our results. Both packages revealed that processing of emotional faces was associated with an increased activation in the brain`s visual areas (occipital, fusiform and lingual gyri), in the cerebellum, in the parietal cortex, in the cingulate cortex (anterior and posterior cingulate), and in the dorsolateral and ventrolateral prefrontal cortex. However, blood oxygenation level-dependent (BOLD) response in the temporal regions, insula and putamen was evident in the XBAM analysis but not in the SPM analysis. Overall, SPM and XBAM analyses revealed comparable whole-group brain responses. Further Studies are needed to explore the between-group compatibility of the different imaging packages in other cognitive and emotional processing domains. (C) 2009 Elsevier Ltd. All rights reserved.

Analysis of body posture in children with mild to moderate asthma

The mechanical alterations related to the excessive use of accessory respiratory muscles and the mouth breathing observed in children with asthma may lead to the development of alterations in head posture, shoulders, thoracic region and, consequently, in alterations of body posture. The purpose of this study was to assess body posture changes of children with asthma compared to a non-asthmatic control group matched for gender, age, weight, and height. Thirty children with asthma and 30 non-asthmatic children aged 7 to 12 years were enrolled in this study. Digital photographic records were obtained for analysis of the body posture of the children by computed photogrammetry. The intraclass correlation coefficient and Student`s t test (p < 0.05) were used for statistical analysis. There were no significant differences between groups for the angles analyzed, except for the knee flexor angle. These results demonstrate that children with asthma did not present postural alterations compared to non-asthmatic controls since the only angle for which there was a significant difference between groups showed weak reproducibility. The findings of this study do not support the notion that children with asthma present alterations in body posture.

Asymmetric page split generalized index search trees for formal concept analysis

Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.

Proteomic analysis of low- to high-grade astrocytomas reveals an alteration of the expression level of raf kinase inhibitor protein and nucleophosmin

Proteomic approaches have been useful for the identification of aberrantly expressed proteins in complex diseases such as cancer. These proteins are not only potential disease biomarkers, but also targets for therapy. The aim of this study was to identify differentially expressed proteins in diffuse astrocytoma grade II, anaplastic astrocytoma grade III and glioblastoma multiforme grade IV in human tumor samples and in non-neoplastic brain tissue as control using 2-DE and MS. Tumor and control brain tissue dissection was guided by histological hematoxylin/eosin tissue sections to provide more than 90% of tumor cells and astrocytes. Six proteins were detected as up-regulated in higher grade astrocytomas and the most important finding was nucleophosmin (NPM) (p < 0.05), whereas four proteins were down-regulated, among them raf kinase inhibitor protein (RKIP) (p < 0.05). We report here for the first time the alteration of NPM and RKIP expression in brain cancer. Our focus on these proteins was due to the fact that they are involved in the PI3K/AKT/mTOR and RAS/RAF/MAPK pathways, known for their contribution to the development and progression of gliomas. The proteomic data for NPM and RKIP were confirmed by Western blot, quantitative real-time PCR and immunohistochemistry. Due to the participation of NPM and RKIP in uncontrolled proliferation and evasion of apoptosis, these proteins are likely targets for drug development.