Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

EEG microstates associated with salience and frontoparietal networks in frontotemporal dementia, schizophrenia and Alzheimer's disease

OBJECTIVE: There are relevant links between resting-state fMRI networks, EEG microstate classes and psychopathological alterations in mental disorders associated with frontal lobe dysfunction. We hypothesized that a certain microstate class, labeled C and correlated with the salience network, was impaired early in frontotemporal dementia (FTD), and that microstate class D, correlated with the frontoparietal network, was impaired in schizophrenia. METHODS: We measured resting EEG microstate parameters in patients with mild FTD (n = 18), schizophrenia (n = 20), mild Alzheimer's disease (AD; n = 19) and age-matched controls (old n = 19, young n = 18) to investigate neuronal dynamics at the whole-brain level. RESULTS: The duration of class C was significantly shorter in FTD than in controls and AD, and the duration of class D was significantly shorter in schizophrenia than in controls, FTD and AD. Transition analysis showed a reversed sequence of activation of classes C and D in FTD and schizophrenia patients compared with that in controls, with controls preferring transitions from C to D, and patients preferring D to C. CONCLUSION: The duration and sequence of EEG microstates reflect specific aberrations of frontal lobe functions in FTD and schizophrenia. SIGNIFICANCE: This study highlights the importance of subsecond brain dynamics for understanding of psychiatric disorders.

Opportunistic Routing for Multi-flow Video Dissemination over Flying Ad-Hoc Networks

A reliable and robust routing service for Flying Ad-Hoc Networks (FANETs) must be able to adapt to topology changes. User experience on watching live video sequences must also be satisfactory even in scenarios with buffer overflow and high packet loss ratio. In this paper, we introduce a Cross-layer Link quality and Geographical-aware beaconless opportunistic routing protocol (XLinGO). It enhances the transmission of simultaneous multiple video flows over FANETs by creating and keeping reliable persistent multi-hop routes. XLinGO considers a set of cross-layer and human-related information for routing decisions, as performance metrics and Quality of Experience (QoE). Performance evaluation shows that XLinGO achieves multimedia dissemination with QoE support and robustness in a multi-hop, multi-flow, and mobile network environments.

The role of controlled attention and selective encoding for kindergarteners' learning

Selectivity in encoding, aspects of attentional control and their contribution to learning performance were explored in a sample of preschoolers. While the children are performing a learning task, their encoding of relevant and attention towards irrelevant information was recorded through an eye-tracking device. Recognition of target items was used as measure of learning outcome, and individual differences in resistance to interference and inhibition of attention to task-irrelevant stimuli (i.e. distractibility) were used as measures of executive control of attention. Results indicated well-developed selectivity during encoding in young children. Recognition performance was related to selective encoding and aspects of attentional control, explaining individual differences in learning. Copyright © 2011 John Wiley & Sons, Ltd.