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. METHODS: The Cochrane Library (CENTRAL), MEDLINE, EMBASE and two clinical trial registries were searched to identify eligible studies. We applied a 10 years limit (from 01 Jan 2008 to 06 Mar 2018) on English language literature. We included randomized controlled trials (RCTs) with programs focused on lifestyle changes such as weight loss and/or physical activity increase, without pharmacological treatments, which aimed to alter incidence of diabetes or one of the T2DM risk factors, of at least 6 months duration based on follow-up, conducted in LMICs. RESULTS: Six RCTs randomizing 2574 people were included. The risk of developing diabetes in the intervention groups reduced more than 40%, RR (0.57 [0.30, 1.06]), for 1921 participants (moderate quality evidence), though it was not statistically significant. Significant differences were observed in weight, body mass index, and waist circumference change in favor of community-based programs from baseline, (MD [95% CI]; - 2.30 [- 3.40, - 1.19], p < 0.01, I2 = 87%), (MD [95% CI]; - 1.27 [- 2.10, - 0.44], p < 0.01, I2 = 96%), and (MD [95% CI]; - 1.66 [- 3.17, - 0.15], p = 0.03, I2 = 95%), respectively. The pooled effect showed a significant reduction in fasting blood glucose and HbA1C measurements in favor of the intervention (MD [95% CI]; - 4.94 [- 8.33, - 1.55], p < 0.01, I2 = 62%), (MD [95% CI]; - 1.17 [- 1.51, - 0.82], p < 0.01, I2 = 46%), respectively. No significant difference was observed in 2-h blood glucose values, systolic or diastolic blood pressure change between the two groups. CONCLUSION: Based on available literature, evidence suggests that community-based interventions may reduce the incidence rate of T2DM and may positively affect anthropometric indices and HbA1C. Due to the heterogeneity observed between trials we recommend more well-designed RCTs with longer follow-up durations be executed, to confirm whether community-based interventions lead to reduced T2DM events in the at-risk population of LMIC settings.

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. We propose a fully automatic, iterative reclustering framework in which a small number of spatially large, heterogeneous networks are initially extracted to maximize spatial reproducibility. Subsequently, the large networks are iteratively subdivided to create spatially reproducible subnetworks until the overall within-network homogeneity does not increase substantially. The proposed approach discovers a rich network hierarchy in the brain while simultaneously optimizing spatial reproducibility of networks across subjects and individual network homogeneity. We also propose a novel metric to measure the connectivity of brain regions, and in a simulation study show that our connectivity metric and framework perform well in the face of low signal to noise and initial segmentation errors. Experimental results generated using real fMRI data show that the proposed metric improves stability of network clusters across subjects, and generates a meaningful pattern for spatially hierarchical structure of the brain.

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. Such differences may confound the application of functional connectivity measurements in neuroscientific and clinical applications. Using several performance metrics we evaluated six fMRI resting-state connectivity measurement techniques including three fully exploratory techniques: 1) Melodic-Independent Component Analysis (ICA), 2) agnostic Canonical Variates Analysis (aCVA), and 3) generalized Canonical Correlation Analysis (gCCA); and three seed-based techniques: 1) seed gCCA (sgCCA) and 2, 3) seed Partial Least Squares (sPLS) with a posterior cingulate seed and two different time-series normalizations. We separately assessed the temporal and spatial domains for: 1) technique stability as a function of sample size using RV coefficients, and 2) subspace component similarity between pairs of techniques using CCA. Overall gCCA was the only technique that displayed high temporal and spatial stabilities, together with high spatial and temporal subspace similarities with multiple other techniques. ICA, aCVA and sgCCA tended to be the most stable spatially and produced similar spatial subspaces. All techniques produced relatively unstable and dissimilar temporal subspaces, except sPLS that produced relatively high temporal and lower spatial subspace stabilities, but with unique power-spectral Hurst coefficients ≪ 1. Our results indicate that spatial maps from resting state data sets are much less dependent on the analysis technique used than are the associated time series. Such temporal variability is coupled with individual spatial component maps, which may be quite dissimilar across techniques even with similar spatial subspaces. Therefore, we suggest that consensus estimation approaches, i.e. a 2nd-level gCCA, would have great utility to produce and aid interpretation of stable results from BOLD fMRI resting state data analysis.

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. Specifically, a clinical team examined medical records from 1263 ICU-admitted, mechanically ventilated patients, retrospectively assigning each patient a diagnosis of "ARDS" or "non-ARDS" (e.g., pulmonary edema). Exploiting data readily available in the clinical setting, including patient demographics, laboratory test results from before the initiation of mechanical ventilation, and features extracted by natural language processing of radiology reports, we applied an iterative pre-processing and machine learning framework. The resulting model successfully discriminated ARDS from non-ARDS causes of respiratory failure (AUC = 0.85) among patients meeting Berlin criteria for severe hypoxia. This analysis also highlighted novel patient variables that were informative for identifying ARDS in ICU settings.

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. In a group analysis, each map is calculated from a linear combination of fMRI scans of a subset of subjects under study. The proposed method is applied to BOLD fMRI datasets without any spatial smoothing from 10 subjects performing a simple reaction time (RT) task. Using the NPAIRS split-half resampling framework with a reproducibility measure based on SPM correlations, we compare the proposed approach with canonical variate analysis (CVA) and a simple general linear model (GLM). gCCA provides statistical parametric maps with higher reproducibility than CVA and GLM with correlation reproducibilities across independent split-half SPMs of 0.78, 0.46, and 0.41, respectively. Our results show that gCCA is an efficient approach for extracting the default mode network, assessing brain connectivity, and processing event-related and resting-state datasets in which the temporal BOLD signal varies from subject to subject.

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. The processing method that preserves maximal information between the maps and related time-series is proposed to be superior. The results on simulation datasets for multiple analysis models are consistent with the results of ROC curves, but are shown to have lower information content than for real datasets, limiting their generalizability. In real datasets for group analyses using the general linear model (GLM; FSL4 and SPM5), we show that MI values are (1) larger for groups of 15 versus 10 subjects and (2) more sensitive measures than reproducibility (for continuous maps) or Jaccard overlap metrics (for thresholded maps). We also show that (1) for an increasing fraction of nominally active voxels, both MI and false discovery rate (FDR) increase, and (2) at a fixed FDR, GLM using FSL4 tends to extract more voxels and more information than SPM5 using the default processing techniques in each package.

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. Modulations of activity across conditions were assessed, as well as functional connectivity of both networks. Younger adults showed a broader engagement of the DMN and older adults a more extensive engagement of the TPN. Functional connectivity in the DMN was reduced in older adults, whereas the main pattern of TPN connectivity was equivalent in the 2 groups. Age-specific connectivity also was seen in TPN regions. Increased activity in TPN areas predicted worse accuracy on the tasks, but greater expression of a connectivity pattern associated with a right dorsolateral prefrontal TPN region, seen only in older adults, predicted better performance. These results provide further evidence for age-related differences in the DMN and new evidence of age differences in the TPN. Increased use of the TPN may reflect greater demand on cognitive control processes in older individuals that may be partially offset by alterations in prefrontal functional connectivity.

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., Strother , 2004; LaConte , 2003). Such inter-subject pattern reproducibility is an essential characteristic of interpretable SPMs that generalize across subjects. Therefore, we introduce a flexible hybrid model that optimizes reproducibility by simultaneously enhancing the prediction power and reproducibility. This hybrid model is formed by a weighted summation of the optimization functions of a linear discriminate analysis (LDA) model and a generalized canonical correlation (gCCA) model (Afshin-Pour , 2012). LDA preserves the model's ability to discriminate the fMRI scans of multiple brain states while gCCA finds a linear combination for each subject's scans such that the estimated boundary map is reproducible. The hybrid model is implemented in a split-half resampling framework (Strother , 2010) which provides reproducibility (r) and prediction (p) quality metrics. Then the model was compared with LDA, and Gaussian Naive Bayes (GNB). For simulated fMRI data, the hybrid model outperforms the other two techniques in terms of receiver operating characteristic (ROC) curves, particularly for detecting less predictable but spatially reproducible networks. These techniques were applied to real fMRI data to estimate the maps for two task contrasts. Our results indicate that compared to LDA and GNB, the hybrid model can provide maps with large increases in reproducibility for small reductions in prediction, which are jointly closer to the ideal performance point of (p=1, r=1).

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. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard "fixed" preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets.