Multi-detector CT angiographic imaging in the follow-up of patients after endovascular abdominal aortic aneurysm repair (EVAR)

BACKGROUND: Multidetector computed tomography (MDCT) angiography represents the standard of reference in the follow-up of patients after endovascular abdominal aortic aneurysm repair (EVAR), being effective in the detection of the full spectrum of possible complications on both axial and 3D images. METHODS: The purpose of this article is to review the normal CT angiography findings of the different types of stent-grafts and to describe the radiological findings of early and late complications after EVAR on axial and reconstructed images. A selection of cases of post-EVAR MDCT angiography is presented to learn the techniques most commonly used for endovascular treatment, the correct CT scanning technique to acquire the data, the full gamut of possible procedure-related complications and how these complications usually appear on CT images. CONCLUSION: MDCT angiography is an effective and specific technique in both the pre- and postoperative settings of EVAR procedures. A better understanding of the procedure, the devices, the normal postoperative imaging features and the possible procedure-related complications ensures optimal planning and follow-up of patients undergoing an EVAR procedure

Associations of event-related brain potentials and Alzheimer’s disease severity: a longitudinal study

Background: So far, no cost-efficient, widely-used biomarkers have been established to facilitate the objectivization of Alzheimer’s disease (AD) diagnosis and monitoring. Research suggests that event-related potentials (ERPs) reflect neurodegenerative processes in AD and might qualify as neurophysiological AD markers. Objectives: First, to examine which ERP component correlates the most with AD severity, as measured by the Mini-Mental State Examination (MMSE). Then, to analyze the temporal change of this component as AD progresses. Methods: Sixty-three subjects (31 with possible, 32 with probable AD diagnosis) were recruited as part of the cohort study Prospective Dementia Registry Austria (PRODEM). For a maximum of 18 months patients revisited every 6 months for follow-up assessments. ERPs were elicited using an auditory oddball paradigm. P300 and N200 latency was determined with regard to target as well as difference wave ERPs, whereas P50 amplitude was measured from standard stimuli waveforms. Results: P300 latency exhibited the strongest association with AD severity (e.g., r = –0.512, p Conclusions: P300 and N200 latency significantly correlated with disease severity in probable AD, whereas P50 amplitude did not. P300 latency, which showed the highest correlation coefficients with MMSE, significantly increased over the course of the 18 months study period in probable AD patients. The magnitude of the observed prolongation is in line with other longitudinal AD studies and substantially higher than in normal ageing, as reported in previous trials (no healthy controls were included in our study).</br

Bayesian Gaussian process classification from event-related brain potentials in Alzheimer’s disease

Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer’s disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPAGPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study (PRODEM), we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks. GPC outperforms the probabilistic reference method in both tasks, with the highest AUC overall (0.802) being achieved using the new spatiotemporal method in the prediction of rapid cognitive decline

Bayesian Gaussian process classification from event-related brain potentials in Alzheimer’s disease

Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer’s disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPAGPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study (PRODEM), we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks. GPC outperforms the probabilistic reference method in both tasks, with the highest AUC overall (0.802) being achieved using the new spatiotemporal method in the prediction of rapid cognitive decline