Detecting signals in FMRI data using powerful FDR procedures

Functional magnetic resonance imaging (FMRI) has revolutionized the study of linking physical stimuli with localized brain activity. Among the challenges of working with FMRI data, they are noisy, they exhibit spatial correlation, and they are usually large containing tens of thousands of voxels of information. The notion of False Discovery Rate (FDR) has made a great impact on how to perform powerful multiple hypothesis tests to detect signals in such large multivariate data. The spatial dependence in FMRI data requires special care since, if ignored, it can lead to a loss of control of size as well as a deterioration in power of FDR procedures. This article advocates transforming the voxel-wise test statistics to wavelet space, where the coefficients are approximately uncorrelated. We demonstrate, through a series of experiments, that an FDR procedure in wavelet space enhanced by P-value adaptive thresholding (EPAT), maintains control of the size of the multiple-testing procedure and offers substantially increased power over an FDR procedure that is applied directly to the map of (spatially dependent) test statistics. The EPAT methodology, developed here for FMRI data, is generic and can be applied in other dependent data settings

Using enhanced FDR to find activation in FMRI images

The False Discovery Rate (FDR) criterion has been proposed for use in multiple-comparisons testing problems (Benjamini and Hochberg, 1995). A multiple testing procedure uses the FDR criterion if the procedure controls the expected proportion of false positives that are identified as active. Functional Magnetic Resonance Imaging (FMRI) are used to create images of a subject\u27s brain which show changes in blood oxygenation that can occur because of regional brain activation. The aim of many FMRI experiments is to locate regions of the brain that are activated by a specific task

Aerobic Exercise Training and Inducible Inflammation: Results of a Randomized Controlled Trial in Healthy, Young Adults.

Background Consensus panels regularly recommend aerobic exercise for its health-promoting properties, due in part to presumed anti-inflammatory effects, but many studies show no such effect, possibly related to study differences in participants, interventions, inflammatory markers, and statistical approaches. This variability makes an unequivocal determination of the anti-inflammatory effects of aerobic training elusive. Methods and Results We conducted a randomized controlled trial of 12 weeks of aerobic exercise training or a wait list control condition followed by 4 weeks of sedentary deconditioning on lipopolysaccharide (0, 0.1, and 1.0 ng/mL)-inducible tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), and on toll-like receptor 4 in 119 healthy, sedentary young adults. Aerobic capacity by cardiopulmonary exercise testing was measured at study entry (T1) and after training (T2) and deconditioning (T3). Despite a 15% increase in maximal oxygen consumption, there were no changes in inflammatory markers. Additional analyses revealed a differential longitudinal aerobic exercise training effect by lipopolysaccharide level in inducible TNF -α ( P=0.08) and IL-6 ( P=0.011), showing T1 to T2 increases rather than decreases in inducible (lipopolysaccharide 0.1, 1.0 versus 0.0 ng/mL) TNF- α (51% increase, P=0.041) and IL-6 (42% increase, P=0.11), and significant T2 to T3 decreases in inducible TNF- α (54% decrease, P=0.007) and IL-6 (55% decrease,

Supervised Machine Learning Approach to Identify Early Predictors of Poor Outcome in Patients with COVID-19 Presenting to a Large Quaternary Care Hospital in New York City

Background: The progression of clinical manifestations in patients with coronavirus disease 2019 (COVID-19) highlights the need to account for symptom duration at the time of hospital presentation in decision-making algorithms. Methods: We performed a nested case–control analysis of 4103 adult patients with COVID-19 and at least 28 days of follow-up who presented to a New York City medical center. Multivariable logistic regression and classification and regression tree (CART) analysis were used to identify predictors of poor outcome. Results: Patients presenting to the hospital earlier in their disease course were older, had more comorbidities, and a greater proportion decompensated (<4 days, 41%; 4–8 days, 31%; >8 days, 26%). The first recorded oxygen delivery method was the most important predictor of decompensation overall in CART analysis. In patients with symptoms for <4, 4–8, and >8 days, requiring at least non-rebreather, age ≥ 63 years, and neutrophil/lymphocyte ratio ≥ 5.1; requiring at least non-rebreather, IL-6 ≥ 24.7 pg/mL, and D-dimer ≥ 2.4 µg/mL; and IL-6 ≥ 64.3 pg/mL, requiring non-rebreather, and CRP ≥ 152.5 mg/mL in predictive models were independently associated with poor outcome, respectively. Conclusion: Symptom duration in tandem with initial clinical and laboratory markers can be used to identify patients with COVID-19 at increased risk for poor outcomes