Biomedical subjects

Montserrat Robles

Publications and source records attributed to Montserrat Robles.

4 recordsLinked to original sources

PubMed2006

Emotional words induce enhanced brain activity in schizophrenic patients with auditory hallucinations.

Neuroimaging studies of emotional response in schizophrenia have mainly used visual (faces) paradigms and shown globally reduced brain activity. None of these studies have used an auditory paradigm. Our principal aim is to evaluate the emotional response of patients with schizophrenia to neutral and emotional words. An auditory emotional paradigm based on the most frequent words heard by psychotic patients with auditory hallucinations was designed. This paradigm was applied to evaluate cerebral activation with functional magnetic resonance imaging (fMRI) in 11 patients with schizophrenia with persistent hallucinations and 10 healthy subjects. We found a clear enhanced activity of the frontal lobe, temporal cortex, insula, cingulate, and amygdala (mainly right side) in patients when hearing emotional words in comparison with controls. Our findings are consistent with other studies suggesting a relevant role for emotional response in the pathogenesis and treatment of auditory hallucinations.

Adult↗

PubMed2005

Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research.

SUMMARY: We present here Blast2GO (B2G), a research tool designed with the main purpose of enabling Gene Ontology (GO) based data mining on sequence data for which no GO annotation is yet available. B2G joints in one application GO annotation based on similarity searches with statistical analysis and highlighted visualization on directed acyclic graphs. This tool offers a suitable platform for functional genomics research in non-model species. B2G is an intuitive and interactive desktop application that allows monitoring and comprehension of the whole annotation and analysis process. AVAILABILITY: Blast2GO is freely available via Java Web Start at http://www.blast2go.de. SUPPLEMENTARY MATERIAL: http://www.blast2go.de -> Evaluation.

Algorithms↗

PubMed2005

Clinical Decision Support Systems (CDSS) in GRID Environments.

This paper presents an architecture defined for searching and executing Clinical Decision Support Systems (CDSS) in a LCG2/GT2 Grid environment, using web-based protocols. A CDSS is a system that provides a classification of the patient illness according to the knowledge extracted from clinical practice and using the patient's information in a structured format. The CDSS classification engines can be installed in any site and can be used by different medical users from a Virtual Organization (VO). All users in a VO can consult and execute different classification engines that have been installed in the Grid independently of the platform, architecture or site where the engines are installed or the users are located. The present paper present a solution to requirements such as short-job execution, reducing the response delay on LCG2 environments and providing grid-enabled authenticated access through web portals. Resource discovering and job submission is performed through web services, which are also described in the article.

Classification↗

PubMed2004

Benign/malignant classifier of soft tissue tumors using MR imaging.

UNLABELLED: This article presents a pattern-recognition approach to the soft tissue tumors (STT) benign/malignant character diagnosis using magnetic resonance (MR) imaging applied to a large multicenter database. OBJECTIVE: To develop and test an automatic classifier of STT into benign or malignant by using classical MR imaging findings and epidemiological information. MATERIALS AND METHODS: A database of 430 patients (62% benign and 38% malignant) from several European multicenter registers. There were 61 different histologies (36 with benign and 25 with malignant nature). Three pattern-recognition methods (artificial neural networks, support vector machine, k-nearest neighbor) were applied to learn the discrimination between benignity and malignancy based on a defined MR imaging findings protocol. After the systems had learned by using training samples (with 302 cases), the clinical decision support system was tested in the diagnosis of 128 new STT cases. RESULTS: An 88-92% efficacy was obtained in a not-viewed set of tumors using the pattern-recognition techniques. The best results were obtained with a back-propagation artificial neural network. CONCLUSION: Benign vs. malignant STT discrimination is accurate by using pattern-recognition methods based on classical MR image findings. This objective tool will assist radiologists in STT grading.

Algorithms↗