Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects.

Brain imaging methods have long held promise as diagnostic aids for neuropsychiatric conditions with complex behavioral phenotypes such as Attention-Deficit/Hyperactivity Disorder. This promise has largely been unrealized, at least partly due to the heterogeneity of clinical populations and the small sample size of many studies. A large, multi-center dataset provided by the ADHD-200 Consortium affords new opportunities to test methods for individual diagnosis based on MRI-observable structural brain attributes and functional interactions observable from resting-state fMRI. In this study, we systematically calculated a large set of standard and new quantitative markers from individual subject datasets. These features (>12,000 per subject) consisted of local anatomical attributes such as cortical thickness and structure volumes, and both local and global resting-state network measures. Three methods were used to compute graphs representing interdependencies between activations in different brain areas, and a full set of network features was derived from each. Of these, features derived from the inverse of the time series covariance matrix, under an L1-norm regularization penalty, proved most powerful. Anatomical and network feature sets were used individually, and combined with non-imaging phenotypic features from each subject. Machine learning algorithms were used to rank attributes, and performance was assessed under cross-validation and on a separate test set of 168 subjects for a variety of feature set combinations. While non-imaging features gave highest performance in cross-validation, the addition of imaging features in sufficient numbers led to improved generalization to new data. Stratification by gender also proved to be a fruitful strategy to improve classifier performance. We describe the overall approach used, compare the predictive power of different classes of features, and describe the most impactful features in relation to the current literature.

The effects of interleukin-1beta in tumor necrosis factor-alpha-induced acute pulmonary inflammation in mice.

We determined the role of interleukin-1beta (IL-1beta) signaling on tumor necrosis factor alpha-induced (TNF-alpha) lung neutrophil influx as well as neutrophil chemoattractant macrophage inflammatory protein (MIP-2) and KC and soluble TNF-alpha receptor (TNFR) levels utilizing wildtype (WT), TNF receptor double knockout (TNFR1/TNFR2 KO), and IL-1beta KO mice after oropharyngeal instillation with TNF-alpha. A significant increase in neutrophil accumulation in bronchoalveolar lavage fluid (BALF) and lung interstitium was detected in the WT mice six hours after TNF-alpha exposure. This correlated with an increase in BALF MIP-2. In contrast, BALF neutrophil numbers were not increased by TNF-alpha treatment of IL-1beta KOs, correlating with a failure to induce BALF MIP-2 and a trend toward increased BALF soluble TNFR1. TNF-alpha-instillation increased lavage and serum KC and soluble TNFR2 irrespective of IL-1beta expression. These results suggest IL-1beta contributes, in part, to TNF-alpha-mediated, chemokine release, and neutrophil recruitment to the lung, potentially associated with altered soluble TNFR1 release into the BALF.

IL-1beta augments TNF-alpha-mediated inflammatory responses from lung epithelial cells.

Interleukin-1beta (IL-1beta) and tumor necrosis factor-alpha (TNF-alpha) mediate the development of numerous inflammatory lung diseases. Since IL-1beta is typically activated in situations where TNF-alpha is produced, it was hypothesized that IL-1beta alters TNF-alpha-induced proinflammatory epithelial cell function by altering TNF receptor shedding and surface abundance. In this study, the impact of IL-1beta on TNF-alpha-mediated chemokine production as well as TNF receptor surface expression and shedding were investigated from mouse pulmonary epithelial cells (MLE-15). Interleukin-1beta rapidly and persistently enhanced soluble and surface TNFR2. These effects were dependent on TNFR1 expression. TNFR2 small-interfering RNA (siRNA) shifted IL-1beta responses, significantly increasing surface and shed TNFR1 implying IL-1beta selectively modifies TNF receptors depending on cellular receptor composition. mRNA expression of both receptors was unaltered by IL-1beta up to 24 h or in combination with TNF-alpha indicating effects were post-transcriptional. Interleukin-1beta pretreatment enhanced TNF-alpha-induced macrophage inflammatory protein (MIP)-2 and KC mRNA expression as well as MIP-2 and KC protein levels at the same time point analyzed. Experiments utilizing siRNA against the TNF receptors and a TNFR1 neutralizing antibody demonstrated TNF-alpha induced MIP-2 through TNFR1, whereas both receptors may have contributed to KC production. These data suggest IL-1beta modulates TNF-alpha-mediated inflammatory lung diseases by enhancing epithelial cell TNF receptor surface expression.