CMB signal in WMAP 3yr data with FastICA

We present an application of the fast Independent Component Analysis (FastICA) to the WMAP 3yr data with the goal of extracting the CMB signal. We evaluate the confidence of our results by means of Monte Carlo simulations including CMB, foreground contaminations and instrumental noise specific of each WMAP frequency band. We perform a complete analysis involving all or a subset of the WMAP channels in order to select the optimal combination for CMB extraction, using the frequency scaling of the reconstructed component as a figure of merit. We found that the combination KQVW provides the best CMB frequency scaling, indicating that the low frequency foreground contamination in Q, V and W bands is better traced by the emission in the K band. The CMB angular power spectrum is recovered up to the degree scale, it is consistent within errors for all WMAP channel combination considered, and in close agreement with the WMAP 3yr results. We perform a statistical analysis of the recovered CMB pattern, and confirm the sky asymmetry reported in several previous works with independent techniques.Comment: 10 pages, 7 figures, submitted to MNRA

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Material flow during the extrusion of simple and complex cross-sections using FEM

This paper deals with the extrusion of rod and shape sections and uses a 3D finite element model analysis (FEM) to predict the effect of die geometry on maximum extrusion load. A description of material flow in the container is considered in more detail for rod and shape sections in order to fully comprehend the transient conditions occurring during the process cycle. A comparison with experiments is made to assess the relative importance of some extrusion parameters in the extrusion process and to ensure that the numerical discretisation yields a realistic simulation of the process. The usefulness and the limitation of FEM are discussed when modelling complex shapes. Results are presented for velocity contours and shear stress distribution during the extrusion process. It is shown that for most of the shapes investigated, the material making up the extrudate cross-sections originates from differing regions of virgin material within the billet. The outside surface of the extrudate originates from the material moving along the dead metal zone (DMZ) and the core of the extrudate from the central deformation zone. The FE program appears to predict all the major characteristics of the flow observed macroscopically

Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression

Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging approach for studies and novel treatments of major depressive disorder (MDD). EEG performed simultaneously with an rtfMRI-nf procedure allows an independent evaluation of rtfMRI-nf brain modulation effects. Frontal EEG asymmetry in the alpha band is a widely used measure of emotion and motivation that shows profound changes in depression. However, it has never been directly related to simultaneously acquired fMRI data. We report the first study investigating electrophysiological correlates of the rtfMRI-nf procedure, by combining rtfMRI-nf with simultaneous and passive EEG recordings. In this pilot study, MDD patients in the experimental group (n=13) learned to upregulate BOLD activity of the left amygdala using an rtfMRI-nf during a happy emotion induction task. MDD patients in the control group (n=11) were provided with a sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the upper alpha band and BOLD activity across the brain were examined. Average individual changes in frontal EEG asymmetry during the rtfMRI-nf task for the experimental group showed a significant positive correlation with the MDD patients' depression severity ratings, consistent with an inverse correlation between the depression severity and frontal EEG asymmetry at rest. Temporal correlations between frontal EEG asymmetry and BOLD activity were significantly enhanced, during the rtfMRI-nf task, for the amygdala and many regions associated with emotion regulation. Our findings demonstrate an important link between amygdala BOLD activity and frontal EEG asymmetry. Our EEG asymmetry results suggest that the rtfMRI-nf training targeting the amygdala is beneficial to MDD patients, and that alpha-asymmetry EEG-nf would be compatible with the amygdala rtfMRI-nf. Combination of the two could enhance emotion regulation training and benefit MDD patients.Comment: 28 pages, 16 figures, to appear in NeuroImage: Clinica

Material flow during the extrusion of simple and complex cross-sections using FEM

This paper deals with the extrusion of rod and shape sections and uses a 3D finite element model analysis (FEM) to predict the effect of die geometry on maximum extrusion load. A description of material flow in the container is considered in more detail for rod and shape sections in order to fully comprehend the transient conditions occurring during the process cycle. A comparison with experiments is made to assess the relative importance of some extrusion parameters in the extrusion process and to ensure that the numerical discretisation yields a realistic simulation of the process. The usefulness and the limitation of FEM are discussed when modelling complex shapes. Results are presented for velocity contours and shear stress distribution during the extrusion process. It is shown that for most of the shapes investigated, the material making up the extrudate cross-sections originates from differing regions of virgin material within the billet. The outside surface of the extrudate originates from the material moving along the dead metal zone (DMZ) and the core of the extrudate from the central deformation zone. The FE program appears to predict all the major characteristics of the flow observed macroscopically

Cerebral perfusion MR imaging using FAIR-HASTE in chronic carotid occlusive disease: comparison with dynamic susceptibility contrast-perfusion MR imaging.

To determine the efficacy of flow-sensitive alternating inversion recovery using half-Fourier single-shot turbo spin-echo (FAIR-HASTE) in detecting cerebral hypoperfusion in chronic carotid occlusive disease, we subjected 12 patients with various degrees of cervical internal carotid artery stenoses and/or occlusion (Stenosis group) and 24 volunteers (Normal group) to FAIR-HASTE. In addition, 10 out of 12 patients in the Stenosis group underwent dynamic susceptibility contrast-perfusion magnetic resonance imaging (DSC-pMRI) before and after revascularization in the dominantly affected side. The absolute asymmetry indexes (AIs) of both cerebral hemispheres in the Normal and Stenosis groups were compared in FAIR-HASTE. In addition, the AIs were compared with those in the Stenosis group before and after revascularization in both FAIR-HASTE and regional cerebral blood flow (rCBF), calculated with DSC-pMRI. A statistically significant difference was recognized between the AIs in the Normal and Stenosis groups (AI = 2.25 +- 1.92, 8.09 +- 4.60, respectively ; p < 0.0001). Furthermore, in the Stenosis group the AIs on both FAIR-HASTE (8.88 +- 4.93, 2.22 +- 1.79, respectively ; p = 0.0003) and rCBF (7.13 +- 3.57, 1.25 +- 1.33, respectively ; p = 0.0003) significantly decreased after revascularization. In the Stenosis group, before revascularization, signal intensity on both FAIR-HASTE and rCBF had a tendency to be lower in the dominantly affected side. FAIR-HASTE imaging was useful in the detection and evaluation of cerebral hypoperfusion in chronic occlusive carotid disease

Personality cannot be predicted from the power of resting state EEG

In the present study we asked whether it is possible to decode personality traits from resting state EEG data. EEG was recorded from a large sample of subjects (N = 309) who had answered questionnaires measuring personality trait scores of the 5 dimensions as well as the 10 subordinate aspects of the Big Five. Machine learning algorithms were used to build a classifier to predict each personality trait from power spectra of the resting state EEG data. The results indicate that the five dimensions as well as their subordinate aspects could not be predicted from the resting state EEG data. Finally, to demonstrate that this result is not due to systematic algorithmic or implementation mistakes the same methods were used to successfully classify whether the subject had eyes open or eyes closed and whether the subject was male or female. These results indicate that the extraction of personality traits from the power spectra of resting state EEG is extremely noisy, if possible at all.Comment: 14 pages, 4 figure