Discriminating Acute Respiratory Distress Syndrome from other forms of respiratory failure via iterative machine learning.

Acute Respiratory Distress Syndrome (ARDS) is associated with high morbidity and mortality. Identification of ARDS enables lung protective strategies, quality improvement interventions, and clinical trial enrolment, but remains challenging particularly in the first 24 hours of mechanical ventilation. To address this we built an algorithm capable of discriminating ARDS from other similarly presenting disorders immediately following mechanical ventilation.

The effect of community-based programs on diabetes prevention in low- and middle-income countries: a systematic review and meta-analysis.

Background: The increasing prevalence of type 2 diabetes mellitus (T2DM) can have a substantial impact in low- and middle-income countries (LMICs). Community-based programs addressing diet, physical activity, and health behaviors have shown significant benefits on the prevention and management of T2DM, mainly in high-income countries. However, their effects on preventing T2DM in the at-risk population of LMICs have not been thoroughly evaluated.

An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI.

BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the "pipeline") significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions.

Automated iterative reclustering framework for determining hierarchical functional networks in resting state fMRI.

To spatially cluster resting state-functional magnetic resonance imaging (rs-fMRI) data into potential networks, there are only a few general approaches that determine the number of networks/clusters, despite a wide variety of techniques proposed for clustering. For individual subjects, extraction of a large number of spatially disjoint clusters results in multiple small networks that are spatio-temporally homogeneous but irreproducible across subjects. Alternatively, extraction of a small number of clusters creates spatially large networks that are temporally heterogeneous but spatially reproducible across subjects.

A Hybrid LDA+gCCA Model for fMRI Data Classification and Visualization.

Linear predictive models are applied to functional MRI (fMRI) data to estimate boundaries that predict experimental task states for scans. These boundaries are visualized as statistical parametric maps (SPMs) and range from low to high spatial reproducibility across subjects (e.g.

Evaluation of spatio-temporal decomposition techniques for group analysis of fMRI resting state data sets.

The existing functional connectivity assessment techniques rely on different mathematical and neuro-physiological models. They may consequently provide different sets of spatial connectivity maps and associated temporal responses within their significant spatiotemporal sets of components. Note that the word component is used to generically refer to spatio-temporal pairs of maps and associated time courses.

Enhancing reproducibility of fMRI statistical maps using generalized canonical correlation analysis in NPAIRS framework.

Common fMRI data processing techniques usually minimize a temporal cost function or fit a temporal model to extract an activity map. Here, we focus on extracting a highly, spatially reproducible statistical parametric map (SPM) from fMRI data using a cost function that does not depend on a model of the subjects' temporal response. Based on a generalized version of canonical correlation analysis (gCCA), we propose a method to extract a highly reproducible map by maximizing the sum of pair-wise correlations between some maps.

A mutual information-based metric for evaluation of fMRI data-processing approaches.

We propose a novel approach for evaluating the performance of activation detection in real (experimental) datasets using a new mutual information (MI)-based metric and compare its sensitivity to several existing performance metrics in both simulated and real datasets. The proposed approach is based on measuring the approximate MI between the fMRI time-series of a validation dataset and a calculated activation map (thresholded label map or continuous map) from an independent training dataset. The MI metric is used to measure the amount of information preserved during the extraction of an activation map from experimentally related fMRI time-series.

A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains.

We explored the effects of aging on 2 large-scale brain networks, the default mode network (DMN) and the task-positive network (TPN). During functional magnetic resonance imaging scanning, young and older participants carried out 4 visual tasks: detection, perceptual matching, attentional cueing, and working memory. Accuracy of performance was roughly matched at 80% across tasks and groups.