Faculty Bibliography

To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80-0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples.

Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods' performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost.

Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach-the Complex Systems-Causal Network (CS-CN) method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry.

OBJECTIVE: Inflammatory mediators, such as PGE2 and IL-1beta, are produced by osteoarthritic joint tissues, where they may contribute to disease pathogenesis. We examined whether inflammation, reflected in plasma and peripheral blood leukocytes (PBLs) reflected presence of osteoarthritis (OA), progression or symptoms in patients with symptomatic knee osteoarthritis (SKOA). METHODS: SKOA patients were enrolled in a 24-month prospective study of radiographic progression. Standardized knee radiographs were obtained at baseline and 24 months. Biomarkers assessed at baseline included plasma lipids PGE2 and 15-HETE, and transcriptome analysis of PBLs by microarray and qPCR. RESULTS: Baseline PGE synthases (PGES) by PBL microarray gene expression, and plasma PGE2 distinguished SKOA patients from non-OA controls (AUCs 0.87 and 0.89 respectively, p<0.0001). Baseline plasma 15-HETE was significantly elevated in SKOA versus non-OA controls (p<0.019). In the 146 patients who completed the 24-month study, elevated baseline expression of IL-1beta, TNFalpha and COX-2 mRNA in PBLs predicted higher risk for radiographic progression by joint space narrowing (JSN). In a multivariate model, AUC point estimates of models containing COX-2 in combination with demographic traits overlap the confidence interval of the base model in two out of the three JSN outcome measures (JSN >0.0mm, >0.2mm and >0.5mm, AUC=0.62-0.67). CONCLUSION: Inflammatory plasma lipid biomarkers PGE2 and 15-HETE identify patients with SKOA. PBL inflammatory transcriptome identifies a subset of SKOA patients at higher risk for radiographic progression. These findings may reflect low-grade inflammation in OA and may be useful as diagnostic and prognostic biomarkers in clinical development of disease-modifying OA drugs

OBJECTIVE: Pro- and anti-inflammatory mediators, such as IL-1beta and IL1Ra, are produced by joint tissues in osteoarthritis (OA), where they may contribute to pathogenesis. We examined whether inflammatory events occurring within joints are reflected in plasma of patients with symptomatic knee osteoarthritis (SKOA). DESIGN: 111 SKOA subjects with medial disease completed a 24-month prospective study of clinical and radiographic progression, with clinical assessment and specimen collection at 6-month intervals. The plasma biochemical marker IL1Ra was assessed at baseline and 18 months; other plasma biochemical markers were assessed only at 18 months, including IL-1beta, TNFalpha, VEGF, IL-6, IL-6Ralpha, IL-17A, IL-17A/F, IL-17F, CRP, sTNF-RII, and MMP-2. RESULTS: In cross-sectional studies, WOMAC (total, pain, function) and plasma IL1Ra were modestly associated with radiographic severity after adjustment for age, gender and body mass index (BMI). In addition, elevation of plasma IL1Ra predicted joint space narrowing (JSN) at 24 months. BMI did associate with progression in some but not all analyses. Causal graph analysis indicated a positive association of IL1Ra with JSN; an interaction between IL1Ra and BMI suggested either that BMI influences IL1Ra or that a hidden confounder influences both BMI and IL1Ra. Other protein biomarkers examined in this study did not associate with radiographic progression or severity. CONCLUSIONS: Plasma levels of IL1Ra were modestly associated with the severity and progression of SKOA in a causal fashion, independent of other risk factors. The findings may be useful in the search for prognostic biomarkers and development of disease-modifying OA drugs.

BACKGROUND: Pre-deployment identification of soldiers at risk for long-term posttraumatic stress psychopathology after home coming is important to guide decisions about deployment. Early post-deployment identification can direct early interventions to those in need and thereby prevents the development of chronic psychopathology. Both hold significant public health benefits given large numbers of deployed soldiers, but has so far not been achieved. Here, we aim to assess the potential for pre- and early post-deployment prediction of resilience or posttraumatic stress development in soldiers by application of machine learning (ML) methods. METHODS: ML feature selection and prediction algorithms were applied to a prospective cohort of 561 Danish soldiers deployed to Afghanistan in 2009 to identify unique risk indicators and forecast long-term posttraumatic stress responses. RESULTS: Robust pre- and early postdeployment risk indicators were identified, and included individual PTSD symptoms as well as total level of PTSD symptoms, previous trauma and treatment, negative emotions, and thought suppression. The predictive performance of these risk indicators combined was assessed by cross-validation. Together, these indicators forecasted long term posttraumatic stress responses with high accuracy (pre-deployment: AUC=0.84 (95% CI=0.81-0.87), post-deployment: AUC=0.88 (95% CI=0.85-0.91)). LIMITATIONS: This study utilized a previously collected data set and was therefore not designed to exhaust the potential of ML methods. Further, the study relied solely on self-reported measures. CONCLUSIONS: Pre-deployment and early post-deployment identification of risk for long-term posttraumatic psychopathology are feasible and could greatly reduce the public health costs of war.

Purpose: We and others have demonstrated low grade inflammation exists in OA joint tissues, where it may contribute to disease pathogenesis. In the current studies we assessed whether inflammatory events occurring within joint tissues were reported in the peripheral blood leukocytes (PBLs) of patients with symptomatic knee OA (SKOA). Methods: PBL inflammatory gene expression (IL-1, TNFalpha, COX-2) was assessed in two independent cohorts of patients with SKOA, and a cohort of healthy control subjects: 1) 111 patients with tibiofemoral medial OA and 21 healthy volunteers from the NYUHJD Cohort, and 2) 200 patients from the OAI progression cohort who had "high quality radiographs", at both baseline and 24 months, and had KL2 or 3 in the signal knee at baseline. Radiographic progression was defined as narrowing of medial joint space width (JSW) in the signal knee between baseline and 24-months in each cohort. Radiographic progressors were defined as subjects who had JSN >0.0, 0.2 and 0.5mm over 24 months. For measuring predictive performance, we used the area under the curve (AUC) of a receiver operating characteristics (ROC). OAI SKOA subjects were dichotomized as radiographic non-progressors (JSN <0.0 mm) and progressors (JSN>0.0mm) for association studies. Results: Elevated PBL expression of IL-1, TNFalpha or COX-2 identified SKOA patients who were "fast progressors" (mean JSN 0= 0.71, 0.75 and 0.71 mm / 24 months, respectively) compared to patients with levels below the median. In a multivariable model, anthropometric traits alone (BMI, gender, age) did not predict progression, whereas addition of PBL gene expressions improved prediction of fast progressors (JSN>0.5mm). We next examined inflammatory gene expression in PBLs of radiographic progressors in the OAI cohort. Similar to the NYUHJD cohort, elevated expression of IL-1beta, TNFalpha and COX-2 mRNA distinguished radiographic progressors from non-progressors (Table 1). PBL IL-1beta expression found to be strongest predictor of all three radiographic progressors. In multivariate models that combine all three markers did not improve upon IL-1beta predictivity. We thus conclude that either the signal in TNFalpha and Cox-2 is already subsumed by IL-1beta and/or that it is not easy to capture the non-overlapping signals without increasing the sample size (i.e., fitting a stronger multivariate predictor will require more sample size). Conclusions: We identified, and confirmed in two cohorts, increased inflammatory gene expression (IL-1, TNFalpha or COX-2) by PBLs that predict radiographic progression in patients with SKOA. The data indicate that inflammatory events within joint tissues of patients with SKOA are reported in the peripheral blood. These PBL transcriptome signals of local joint inflammation merit further study as potential biomarkers for OA disease progression. (Table Presented)

BACKGROUND: Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivors. Identifying interchangeable sets of risk indicators may increase the efficiency of early risk assessment. The study goal is to use supervised machine learning (ML) to uncover interchangeable, maximally predictive combinations of early risk indicators. METHODS: Data variables (features) reflecting event characteristics, emergency department (ED) records and early symptoms were collected in 957 trauma survivors within ten days of ED admission, and used to predict PTSD symptom trajectories during the following fifteen months. A Target Information Equivalence Algorithm (TIE*) identified all minimal sets of features (Markov Boundaries; MBs) that maximized the prediction of a non-remitting PTSD symptom trajectory when integrated in a support vector machine (SVM). The predictive accuracy of each set of predictors was evaluated in a repeated 10-fold cross-validation and expressed as average area under the Receiver Operating Characteristics curve (AUC) for all validation trials. RESULTS: The average number of MBs per cross validation was 800. MBs' mean AUC was 0.75 (95% range: 0.67-0.80). The average number of features per MB was 18 (range: 12-32) with 13 features present in over 75% of the sets. CONCLUSIONS: Our findings support the hypothesized existence of multiple and interchangeable sets of risk indicators that equally and exhaustively predict non-remitting PTSD. ML's ability to increase prediction versatility is a promising step towards developing algorithmic, knowledge-based, personalized prediction of post-traumatic psychopathology.

Field of cancerization in the airway epithelium has been increasingly examined to understand early pathogenesis of non-small cell lung cancer. However, the extent of field of cancerization throughout the lung airways is unclear. Here we sought to determine the differential gene and microRNA expressions associated with field of cancerization in the peripheral airway epithelial cells of patients with lung adenocarcinoma. We obtained peripheral airway brushings from smoker controls (n=13) and from the lung contralateral to the tumor in cancer patients (n=17). We performed gene and microRNA expression profiling on these peripheral airway epithelial cells using Affymetrix GeneChip and TaqMan Array. Integrated gene and microRNA analysis was performed to identify significant molecular pathways. We identified 26 mRNAs and 5 miRNAs that were significantly (FDR <0.1) up-regulated and 38 mRNAs and 12 miRNAs that were significantly down-regulated in the cancer patients when compared to smoker controls. Functional analysis identified differential transcriptomic expressions related to tumorigenesis. Integration of miRNA-mRNA data into interaction network analysis showed modulation of the extracellular signal-regulated kinase/mitogen-activated protein kinase (ERK/MAPK) pathway in the contralateral lung field of cancerization. In conclusion, patients with lung adenocarcinoma have tumor related molecules and pathways in histologically normal appearing peripheral airway epithelial cells, a substantial distance from the tumor itself. This finding can potentially provide new biomarkers for early detection of lung cancer and novel therapeutic targets.

Discovery of causal relations from data is a fundamental objective of several scientific disciplines. Most causal discovery algorithms that use observational data can infer causality only up to a statistical equivalency class, thus leaving many causal relations undetermined. In general, complete identification of causal relations requires experimentation to augment discoveries from observational data. This has led to the recent development of several methods for active learning of causal networks that utilize both observational and experimental data in order to discover causal networks. In this work, we focus on the problem of discovering local causal pathways that contain only direct causes and direct effects of the target variable of interest and propose new discovery methods that aim to minimize the number of required experiments, relax common sufficient discovery assumptions in order to increase discovery accuracy, and scale to high-dimensional data with thousands of variables. We conduct a comprehensive evaluation of new and existing methods with data of dimensionality up to 1,000,000 variables. We use both artificially simulated networks and in-silico gene transcriptional networks that model the characteristics of real gene expression data.