Babak Shahbaba

Maternal cortisol over the course of pregnancy and subsequent child amygdala and hippocampus volumes and affective problems

Stress-related variation in the intrauterine milieu may impact brain development and emergent function, with long-term implications in terms of susceptibility for affective disorders. Studies in animals suggest limbic regions in the developing brain are particularly sensitive to exposure to the stress hormone cortisol. However, the nature, magnitude, and time course of these effects have not yet been adequately characterized in humans. A prospective, longitudinal study was conducted in 65 normal, healthy mother–child dyads to examine the association of maternal cortisol in early, mid-, and late gestation with subsequent measures at approximately 7 y age of child amygdala and hippocampus volume and affective problems. After accounting for the effects of potential confounding pre- and postnatal factors, higher maternal cortisol levels in earlier but not later gestation was associated with a larger right amygdala volume in girls (a 1 SD increase in cortisol was associated with a 6.4% increase in right amygdala volume), but not in boys. Moreover, higher maternal cortisol levels in early gestation was associated with more affective problems in girls, and this association was mediated, in part, by amygdala volume. No association between maternal cortisol in pregnancy and child hippocampus volume was observed in either sex. The current findings represent, to the best of our knowledge, the first report linking maternal stress hormone levels in human pregnancy with subsequent child amygdala volume and affect. The results underscore the importance of the intrauterine environment and suggest the origins of neuropsychiatric disorders may have their foundations early in life.

Neural function, injury, and stroke subtype predict treatment gains after stroke

This study was undertaken to better understand the high variability in response seen when treating human subjects with restorative therapies poststroke. Preclinical studies suggest that neural function, neural injury, and clinical status each influence treatment gains; therefore, the current study hypothesized that a multivariate approach incorporating these 3 measures would have the greatest predictive value.

Genetic Variation in the Human Brain Dopamine System Influences Motor Learning and Its Modulation by L-Dopa

Dopamine is important to learning and plasticity. Dopaminergic drugs are the focus of many therapies targeting the motor system, where high inter-individual differences in response are common. The current study examined the hypothesis that genetic variation in the dopamine system is associated with significant differences in motor learning, brain plasticity, and the effects of the dopamine precursor L-Dopa. Skilled motor learning and motor cortex plasticity were assessed using a randomized, double-blind, placebo-controlled, crossover design in 50 healthy adults during two study weeks, one with placebo and one with L-Dopa. The influence of five polymorphisms with established effects on dopamine neurotransmission was summed using a gene score, with higher scores corresponding to higher dopaminergic neurotransmission. Secondary hypotheses examined each polymorphism individually. While training on placebo, higher gene scores were associated with greater motor learning (p = .03). The effect of L-Dopa on learning varied with the gene score (gene score*drug interaction, p = .008): participants with lower gene scores, and thus lower endogenous dopaminergic neurotransmission, showed the largest learning improvement with L-Dopa relative to placebo (p<.0001), while L-Dopa had a detrimental effect in participants with higher gene scores (p = .01). Motor cortex plasticity, assessed via transcranial magnetic stimulation (TMS), also showed a gene score*drug interaction (p = .02). Individually, DRD2/ANKK1 genotype was significantly associated with motor learning (p = .02) and its modulation by L-Dopa (p<.0001), but not with any TMS measures. However, none of the individual polymorphisms explained the full constellation of findings associated with the gene score. These results suggest that genetic variation in the dopamine system influences learning and its modulation by L-Dopa. A polygene score explains differences in L-Dopa effects on learning and plasticity most robustly, thus identifying distinct biological phenotypes with respect to L-Dopa effects on learning and plasticity. These findings may have clinical applications in post-stroke rehabilitation or the treatment of Parkinsons disease.

A pluripotency signature predicts histologic transformation and influences survival in follicular lymphoma patients

Histologic transformation (HT) of follicular lymphoma to diffuse large B-cell lymphoma (DLBCL-t) is associated with accelerated disease course and drastically worse outcome, yet the underlying mechanisms are poorly understood. We show that a network of gene transcriptional modules underlies HT. Central to the network hierarchy is a signature strikingly enriched for pluripotency-related genes. These genes are typically expressed in embryonic stem cells (ESCs), including MYC and its direct targets. This core ESC-like program was independent of proliferation/cell-cycle and overlapped but was distinct from normal B-cell transcriptional programs. Furthermore, we show that the ESC program is correlated with transcriptional programs maintaining tumor phenotype in transgenic MYC-driven mouse models of lymphoma. Although our approach was to identify HT mechanisms rather than to derive an optimal survival predictor, a model based on ESC/differentiation programs stratified patient outcomes in 2 independent patient cohorts and was predictive of propensity of follicular lymphoma tumors to transform. Transformation was associated with an expression signature combining high expression of ESC transcriptional programs with reduced expression of stromal programs. Together, these findings suggest a central role for an ESC-like signature in the mechanism of HT and provide new clues for potential therapeutic targets.

Split Hamiltonian Monte Carlo

We show how the Hamiltonian Monte Carlo algorithm can sometimes be speeded up by “splitting” the Hamiltonian in a way that allows much of the movement around the state space to be done at low computational cost. One context where this is possible is when the log density of the distribution of interest (the potential energy function) can be written as the log of a Gaussian density, which is a quadratic function, plus a slowly-varying function. Hamiltonian dynamics for quadratic energy functions can be analytically solved. With the splitting technique, only the slowly-varying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics. Another context where splitting helps is when the most important terms of the potential energy function and its gradient can be evaluated quickly, with only a slowly-varying part requiring costly computations. With splitting, the quick portion can be handled with a small stepsize, while the costly portion uses a larger stepsize. We show that both of these splitting approaches can reduce the computational cost of sampling from the posterior distribution for a logistic regression model, using either a Gaussian approximation centered on the posterior mode, or a Hamiltonian split into a term that depends on only a small number of critical cases, and another term that involves the larger number of cases whose influence on the posterior distribution is small.

Improving classification when a class hierarchy is available using a hierarchy-based prior

We introduce a new method for building classification models when we have prior knowledge of how the classes can be arranged in a hierarchy, based on how easily they can be distinguished. The new method uses a Bayesian form of the multinomial logit (MNL, a.k.a. “softmax”) model, with a prior that introduces correlations between the parameters for classes that are nearby in the tree. We compare the performance on simulated data of the new method, the ordinary MNL model, and a model that uses the hierarchy in different way. We also test the new method on a document labelling problem, and find that it performs better than the other methods, particularly when the amount of training data is small.

Gene function classification using Bayesian models with hierarchy-based priors

BackgroundWe investigate whether annotation of gene function can be improved using a classification scheme that is aware that functional classes are organized in a hierarchy. The classifiers look at phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and an MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome.ResultsThe results from all three models show substantial improvement over previous methods, which were based on the C5 decision tree algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining the three sources of information in this dataset, our new approach to combining data sources produces a higher accuracy rate than applying our models to each data source alone.ConclusionTogether, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information.

Markov Chain Monte Carlo from Lagrangian Dynamics.

Hamiltonian Monte Carlo (HMC) improves the computational efficiency of the Metropolis–Hastings algorithm by reducing its random walk behavior. Riemannian HMC (RHMC) further improves the performance of HMC by exploiting the geometric properties of the parameter space. However, the geometric integrator used for RHMC involves implicit equations that require fixed-point iterations. In some cases, the computational overhead for solving implicit equations undermines RHMC’s benefits. In an attempt to circumvent this problem, we propose an explicit integrator that replaces the momentum variable in RHMC by velocity. We show that the resulting transformation is equivalent to transforming Riemannian Hamiltonian dynamics to Lagrangian dynamics. Experimental results suggest that our method improves RHMC’s overall computational efficiency in the cases considered. All computer programs and datasets are available online (http://www.ics.uci.edu/babaks/Site/Codes.html) to allow replication of the results reported in this article.Hamiltonian Monte Carlo (HMC) improves the computational efficiency of the Metropolis algorithm by reducing its random walk behavior. Riemannian Manifold HMC (RMHMC) further improves HMCs performance by exploiting the geometric properties of the parameter space. However, the geometric integrator used for RMHMC involves implicit equations that require costly numerical analysis (e.g., fixed-point iteration). In some cases, the computational overhead for solving implicit equations undermines RMHMCs benefits. To avoid this problem, we propose an explicit geometric integrator that replaces the momentum variable in RMHMC by velocity. We show that the resulting transformation is equivalent to transforming Riemannian Hamilton dynamics to Lagrangian dynamics. Experimental results show that our method improves RMHMCs overall computational efficiency. All computer programs and data sets are available online (http://www.ics.uci.edu/~babaks/Site/Codes.html) in order to allow replications of the results reported in this paper.

Maternal Exposure to Childhood Trauma Is Associated During Pregnancy With Placental-Fetal Stress Physiology.

BACKGROUND The effects of exposure to childhood trauma (CT) may be transmitted across generations; however, the time period(s) and mechanism(s) have yet to be clarified. We address the hypothesis that intergenerational transmission may begin during intrauterine life via the effect of maternal CT exposure on placental-fetal stress physiology, specifically placental corticotropin-releasing hormone (pCRH). METHODS The study was conducted in a sociodemographically diverse cohort of 295 pregnant women. CT exposure was assessed using the Childhood Trauma Questionnaire. Placental CRH concentrations were quantified in maternal blood collected serially over the course of gestation. Linear mixed effects and Bayesian piece-wise linear models were employed to test hypothesized relationships. RESULTS Maternal CT exposure (CT+) was significantly associated with pCRH production. Compared with nonexposed women, CT+ was associated with an almost 25% increase in pCRH toward the end of gestation, and the pCRH trajectory of CT+ women exhibited an approximately twofold steeper increase after the pCRH inflection point at 19 weeks gestation. CONCLUSIONS To the best of our knowledge, this finding represents the first report linking maternal CT exposure with placental-fetal stress physiology, thus identifying a potential novel biological pathway of intergenerational transmission that may operate as early as during intrauterine life.

What time is it? Deep learning approaches for circadian rhythms

Motivation: Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken. Results: We first curate several large synthetic and biological time series datasets containing labels for both periodic and aperiodic signals. We then use deep learning methods to develop and train BIO_CYCLE, a system to robustly estimate which signals are periodic in high-throughput circadian experiments, producing estimates of amplitudes, periods, phases, as well as several statistical significance measures. Using the curated data, BIO_CYCLE is compared to other approaches and shown to achieve state-of-the-art performance across multiple metrics. We then use deep learning methods to develop and train BIO_CLOCK to robustly estimate the time at which a particular single-time-point transcriptomic experiment was carried. In most cases, BIO_CLOCK can reliably predict time, within approximately 1 h, using the expression levels of only a small number of core clock genes. BIO_CLOCK is shown to work reasonably well across tissue types, and often with only small degradation across conditions. BIO_CLOCK is used to annotate most mouse experiments found in the GEO database with an inferred time stamp. Availability and Implementation: All data and software are publicly available on the CircadiOmics web portal: circadiomics.igb.uci.edu/. Contacts: fagostin@uci.edu or pfbaldi@uci.edu Supplementary information: Supplementary data are available at Bioinformatics online.