Prevalence, imaging patterns and risk factors of interstitial lung disease in connective tissue disease: a systematic review and meta-analysis.

INTRODUCTION: Interstitial lung disease (ILD) is a frequent manifestation of connective tissue disease (CTD) with substantial variability in prevalence and outcomes reported across CTD subtypes. This systematic review summarises the prevalence, risk factors and ILD patterns on chest computed tomography of CTD-ILD. METHODS: A comprehensive search was performed in Medline and Embase to identify eligible studies. Meta-analyses were completed using a random effects model to determine the pooled prevalence of CTD-ILD and ILD patterns. RESULTS: 11 582 unique citations were identified with 237 articles included. Pooled prevalence of ILD was 11% in rheumatoid arthritis (95% CI 7-15%), 47% in systemic sclerosis (44-50%), 41% in idiopathic inflammatory myositis (33-50%), 17% in primary Sjögren's syndrome (12-21%), 56% in mixed connective tissue disease (39-72%) and 6% in systemic lupus erythematosus (3-10%). Usual interstitial pneumonia was the most prevalent ILD pattern in rheumatoid arthritis (pooled prevalence of 46%), while nonspecific interstitial pneumonia was the most common ILD pattern in all other CTD subtypes (pooled prevalence range 27-76%). Across all CTDs with available data, positive serology and higher inflammatory markers were risk factors for development of ILD. DISCUSSION: We identified substantial variability in ILD across CTD subtypes suggesting that CTD-ILD is too heterogenous to be considered a single entity.

Visualization for understanding of neurodynamical systems.

Complex neurodynamical systems are quite difficult to analyze and understand. New type of plots are introduced to help in visualization of high-dimensional trajectories and show global picture of the phase space, including relations between basins of attractors. Color recurrence plots (RPs) display distances from each point on the trajectory to all other points in a two-dimensional matrix. Fuzzy Symbolic Dynamics (FSD) plots enhance this information mapping the whole trajectory to two or three dimensions. Each coordinate is defined by the value of a fuzzy localized membership function, optimized to visualize interesting features of the dynamics, showing to which degree a point on the trajectory belongs to some neighborhood. The variance of the trajectory within the attraction basin plotted against the variance of the synaptic noise provides information about sizes and shapes of these basins. Plots that use color to show the distance between each trajectory point and a larger number of selected reference points (for example centers of attractor basins) are also introduced. Activity of 140 neurons in the semantic layer of dyslexia model implemented in the Emergent neural simulator is analyzed in details showing different aspects of neurodynamics that may be understood in this way. Influence of connectivity and various neural properties on network dynamics is illustrated using visualization techniques. A number of interesting conclusions about cognitive neurodynamics of lexical concept activations are drawn. Changing neural accommodation parameters has very strong influence on the dwell time of the trajectories. This may be linked to attention deficits disorders observed in autism in case of strong enslavement, and to ADHD-like behavior in case of weak enslavement.

Understanding neurodynamical systems via Fuzzy Symbolic Dynamics.

Neurodynamical systems are characterized by a large number of signal streams, measuring activity of individual neurons, local field potentials, aggregated electrical (EEG) or magnetic potentials (MEG), oxygen use (fMRI) or activity of simulated neurons. Various basis set decomposition techniques are used to analyze such signals, trying to discover components that carry meaningful information, but these techniques tell us little about the global activity of the whole system. A novel technique called Fuzzy Symbolic Dynamics (FSD) is introduced to help in understanding of the multidimensional dynamical system's behavior. It is based on a fuzzy partitioning of the signal space that defines a non-linear mapping of the system's trajectory to the low-dimensional space of membership function activations. This allows for visualization of the trajectory showing various aspects of observed signals that may be difficult to discover looking at individual components, or to notice otherwise. FSD mapping can be applied to raw signals, transformed signals (for example, ICA components), or to signals defined in the time-frequency domain. To illustrate the method two FSD visualizations are presented: a model system with artificial radial oscillatory sources, and the output layer (50 neurons) of Respiratory Rhythm Generator (RRG) composed of 300 spiking neurons.