Late migration of percutaneous bio-absorbable devices--a word of caution.

BACKGROUND: Closures of atrial septal defects or a patent foramen ovale (PFO) are increasingly performed percutaneously. The experience of late migration of a new bio-absorbable device is presented here, followed by conceptual discussion. METHODS: Six months post PFO closure with a BioSTAR® device a patient presented with chest pain. Echocardiography showed a hyperechogenic structure perforating the aortic wall. RESULTS: Surgical exploration showed a perforation of the ascending aorta by one metallic, non absorbable arm. This is the second case of late (>6 months) dislocation of the residual framework of the occluder. CONCLUSIONS: The overall incidence of perforation of cardiac structures due to secondary dislocation is low. However this complication exists and should kept in mind in symptomatic patients with new onset of chest pain, after percutaneous procedures. The concept of biodegradation, with residual, non absorbable metal braiding, should be reviewed, analyzing in particular long term results and incidence of secondary dislocation.

The automatic selection of ventilation parameters during the initial phase of mechanical ventilation.

OBJECTIVE: To test a method that allows automatic set-up of the ventilator controls at the onset of ventilation. DESIGN: Prospective randomized crossover study. SETTING: ICUs in one adult and one children's hospital in Switzerland. PATIENTS: Thirty intubated stable, critically ill patients (20 adults and 10 children). INTERVENTIONS: The patients were ventilated during two 20-min periods using a modified Hamilton AMADEUS ventilator. During the control period the ventilator settings were chosen immediately prior to the study. During the other period individual settings were automatically determined by the ventilatior (AutoInit). MEASUREMENTS AND RESULTS: Pressure, flow, and instantaneous CO2 concentration were measured at the airway opening. From these measurements, series dead space (V(DS)), expiratory time constant (RC), tidal volume (VT, total respiratory frequency (f(tot), minute ventilation (MV), and maximal and mean airway pressure (Paw, max and Paw, mean) were calculated. Arterial blood gases were analyzed at the end of each period. Paw, max was significantly less with the AutoInit ventilator settings while f(tot) was significantly greater (P < 0.05). The other values were not statistically significant. CONCLUSIONS: The AutoInit ventilator settings, which were automatically derived, were acceptable for all patients for a period of 20 min and were not found to be inferior to the control ventilator settings. This makes the AutoInit method potentially useful as an automatic start-up procedure for mechanical ventilation.

Auditory motion affects visual biological motion processing.

The processing of biological motion is a critical, everyday task performed with remarkable efficiency by human sensory systems. Interest in this ability has focused to a large extent on biological motion processing in the visual modality (see, for example, Cutting, J. E., Moore, C., & Morrison, R. (1988). Masking the motions of human gait. Perception and Psychophysics, 44(4), 339-347). In naturalistic settings, however, it is often the case that biological motion is defined by input to more than one sensory modality. For this reason, here in a series of experiments we investigate behavioural correlates of multisensory, in particular audiovisual, integration in the processing of biological motion cues. More specifically, using a new psychophysical paradigm we investigate the effect of suprathreshold auditory motion on perceptions of visually defined biological motion. Unlike data from previous studies investigating audiovisual integration in linear motion processing [Meyer, G. F. & Wuerger, S. M. (2001). Cross-modal integration of auditory and visual motion signals. Neuroreport, 12(11), 2557-2560; Wuerger, S. M., Hofbauer, M., & Meyer, G. F. (2003). The integration of auditory and motion signals at threshold. Perception and Psychophysics, 65(8), 1188-1196; Alais, D. & Burr, D. (2004). No direction-specific bimodal facilitation for audiovisual motion detection. Cognitive Brain Research, 19, 185-194], we report the existence of direction-selective effects: relative to control (stationary) auditory conditions, auditory motion in the same direction as the visually defined biological motion target increased its detectability, whereas auditory motion in the opposite direction had the inverse effect. Our data suggest these effects do not arise through general shifts in visuo-spatial attention, but instead are a consequence of motion-sensitive, direction-tuned integration mechanisms that are, if not unique to biological visual motion, at least not common to all types of visual motion. Based on these data and evidence from neurophysiological and neuroimaging studies we discuss the neural mechanisms likely to underlie this effect.

Aging effects on selective attention-related electroencephalographic patterns during face encoding.

Previous electrophysiological studies revealed that human faces elicit an early visual event-related potential (ERP) within the occipito-temporal cortex, the N170 component. Although face perception has been proposed to rely on automatic processing, the impact of selective attention on N170 remains controversial both in young and elderly individuals. Using early visual ERP and alpha power analysis, we assessed the influence of aging on selective attention to faces during delayed-recognition tasks for face and letter stimuli, examining 36 elderly and 20 young adults with preserved cognition. Face recognition performance worsened with age. Aging induced a latency delay of the N1 component for faces and letters, as well as of the face N170 component. Contrasting with letters, ignored faces elicited larger N1 and N170 components than attended faces in both age groups. This counterintuitive attention effect on face processing persisted when scenes replaced letters. In contrast with young, elderly subjects failed to suppress irrelevant letters when attending faces. Whereas attended stimuli induced a parietal alpha band desynchronization within 300-1000 ms post-stimulus with bilateral-to-right distribution for faces and left lateralization for letters, ignored and passively viewed stimuli elicited a central alpha synchronization larger on the right hemisphere. Aging delayed the latency of this alpha synchronization for both face and letter stimuli, and reduced its amplitude for ignored letters. These results suggest that due to their social relevance, human faces may cause paradoxical attention effects on early visual ERP components, but they still undergo classical top-down control as a function of endogenous selective attention. Aging does not affect the face bottom-up alerting mechanism but reduces the top-down suppression of distracting letters, possibly impinging upon face recognition, and more generally delays the top-down suppression of task-irrelevant information.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Preperceptual and stimulus-selective enhancement of low-level human visual cortex excitability by sounds.

Evidence of multisensory interactions within low-level cortices and at early post-stimulus latencies has prompted a paradigm shift in conceptualizations of sensory organization. However, the mechanisms of these interactions and their link to behavior remain largely unknown. One behaviorally salient stimulus is a rapidly approaching (looming) object, which can indicate potential threats. Based on findings from humans and nonhuman primates suggesting there to be selective multisensory (auditory-visual) integration of looming signals, we tested whether looming sounds would selectively modulate the excitability of visual cortex. We combined transcranial magnetic stimulation (TMS) over the occipital pole and psychophysics for "neurometric" and psychometric assays of changes in low-level visual cortex excitability (i.e., phosphene induction) and perception, respectively. Across three experiments we show that structured looming sounds considerably enhance visual cortex excitability relative to other sound categories and white-noise controls. The time course of this effect showed that modulation of visual cortex excitability started to differ between looming and stationary sounds for sound portions of very short duration (80 ms) that were significantly below (by 35 ms) perceptual discrimination threshold. Visual perceptions are thus rapidly and efficiently boosted by sounds through early, preperceptual and stimulus-selective modulation of neuronal excitability within low-level visual cortex.

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both modalities. In this paper, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentations. Then, we present two approaches for the fusion of 3D CT and US images: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.

Automated analysis of background EEG and reactivity during therapeutic hypothermia in comatose patients after cardiac arrest.

Visual analysis of electroencephalography (EEG) background and reactivity during therapeutic hypothermia provides important outcome information, but is time-consuming and not always consistent between reviewers. Automated EEG analysis may help quantify the brain damage. Forty-six comatose patients in therapeutic hypothermia, after cardiac arrest, were included in the study. EEG background was quantified with burst-suppression ratio (BSR) and approximate entropy, both used to monitor anesthesia. Reactivity was detected through change in the power spectrum of signal before and after stimulation. Automatic results obtained almost perfect agreement (discontinuity) to substantial agreement (background reactivity) with a visual score from EEG-certified neurologists. Burst-suppression ratio was more suited to distinguish continuous EEG background from burst-suppression than approximate entropy in this specific population. Automatic EEG background and reactivity measures were significantly related to good and poor outcome. We conclude that quantitative EEG measurements can provide promising information regarding current state of the patient and clinical outcome, but further work is needed before routine application in a clinical setting.