Classifier combination for in vivo magnetic resonance spectra of brain tumours

In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in different classes of tumours and trying to reproducepart of the WHO classification hierarchy. Each stage uses a particular set of classification features, which are selected using a combination of classical statistical analysis, splitting performance and previous knowledge.Classifiers with different behaviour are combined using a simple voting scheme in order to extract different error patterns: LDA, decision trees and the k-NN classifier. A special label named "unknown¿ is used when the outcomes of the different classifiers disagree. Cascading is alsoused to incorporate class distances computed using LDA into decision trees. Both cascading and voting are effective tools to improve classification accuracy. Experiments also show that it is possible to extract useful information from the classification process itself in order to helpusers (clinicians and radiologists) to make more accurate predictions and reduce the number of possible classification mistakes.

DWI and complex brain network analysis predicts vascular cognitive impairment in spontaneous hypertensive rats undergoing executive function tests

The identification of biomarkers of vascular cognitive impairment is urgent for its early diagnosis. The aim of this study was to detect and monitor changes in brain structure and connectivity, and to correlate them with the decline in executive function. We examined the feasibility of early diagnostic magnetic resonance imaging (MRI) to predict cognitive impairment before onset in an animal model of chronic hypertension: Spontaneously Hypertensive Rats. Cognitive performance was tested in an operant conditioning paradigm that evaluated learning, memory, and behavioral flexibility skills. Behavioral tests were coupled with longitudinal diffusion weighted imaging acquired with 126 diffusion gradient directions and 0.3 mm(3) isometric resolution at 10, 14, 18, 22, 26, and 40 weeks after birth. Diffusion weighted imaging was analyzed in two different ways, by regional characterization of diffusion tensor imaging (DTI) indices, and by assessing changes in structural brain network organization based on Q-Ball tractography. Already at the first evaluated times, DTI scalar maps revealed significant differences in many regions, suggesting loss of integrity in white and gray matter of spontaneously hypertensive rats when compared to normotensive control rats. In addition, graph theory analysis of the structural brain network demonstrated a significant decrease of hierarchical modularity, global and local efficacy, with predictive value as shown by regional three-fold cross validation study. Moreover, these decreases were significantly correlated with the behavioral performance deficits observed at subsequent time points, suggesting that the diffusion weighted imaging and connectivity studies can unravel neuroimaging alterations even overt signs of cognitive impairment become apparent.

Imaging bilinguals: When the neurosciences meet the languange sciences

The starting point of our investigation was the longstanding notion that bilingual individuals need effective mechanisms to prevent interference from one language while processing material in the other (e.g. Penfield and Roberts, 1959). To demonstrate how the prevention of interference is implemented in the brain we employed event-related brain potentials (ERPs; see Munte, Urbach, ¨ Duzel and Kutas, 2000, for an introductory review) ¨ and functional magnetic resonance imaging (fMRI) techniques, thus pursuing a combined temporal and spatial imaging approach. In contrast to previous investigations using neuroimaging techniques in bilinguals, which had been mainly concerned with the localization of the primary and secondary languages (e.g. Perani, Paulesu, Galles, Dupoux, Dehaene, Bettinardi, Cappa, Fazio and Mehler, 1998; Chee, Caplan, Soon, Sriram, Tan, Thiel and Weekes, 1999), our study addressed the dynamic aspects of bilingual language processing.

Morphological and Magnetic Properties of Superparamagnetic Carbon-Coated Fe Nanoparticles Produced by Arc Discharge.

Spherical carbon coated iron particles of nanometric diameter in the 5-10 nm range have been produced by arc discharge at near-atmospheric pressure conditions (using 5-8·10 4 Pa of He). The particles exhibit a crystalline dense iron core with an average diameter 7.4 ± 2.0 nm surrounded by a sealed carbon shell, shown by transmission electron microscopy (TEM), selected-area diffrac- tion (SAED), energy-dispersive X-ray analysis (STEM-EDX) and electron energy loss spectroscopy (EELS). The SAED, EDX and EELS results indicate a lack of traces of core oxidized phases showing an efficient protection role of the carbon shell. The magnetic properties of the nanoparticles have been investigated in the 5-300 K temperature range using a superconducting quantum interference device (SQUID). The results reveal a superparamagnetic behaviour with an average monodomain diameter of 7.6 nm of the nanoparticles. The zero field cooled and field cooled (ZFC-FC)magnetization curves show a blocking temperature (TB)at room temperature very suitable for biomedical applications (drug delivery, magnetic resonance imaging-MRI-, hyperthermia).