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.developmental programming | fetal origins | hypothalamus-pituitaryadrenal axis | depression | emotion regulation

Objective To better understand the high variability in response seen when treating human subjects with restorative therapies post-stroke. 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 three measures would have the greatest predictive value. Methods Patients 3-6 months post-stroke underwent a battery of assessments before receiving 3-weeks of standardized upper extremity robotic therapy. Candidate predictors included measures of brain injury (including to gray and white matter), neural function (cortical function and cortical connectivity), and clinical status (demographics/medical history, cognitive/mood, and impairment). Results Among all 29 patients, predictors of treatment gains identified measures of brain injury (smaller corticospinal tract (CST) injury), cortical function (greater ipsilesional motor cortex (M1) activation), and cortical connectivity (greater inter-hemispheric M1-M1 connectivity). Multivariate modeling found that best prediction was achieved using both CST injury and M1-M1 connectivity (r2=0.44, p=0.002), a result confirmed using Lasso regression. A threshold was defined whereby no subject with >63% CST injury achieved clinically significant gains. Results differed according to stroke subtype: gains in patients with lacunar stroke were exclusively predicted by a measure of intra-hemispheric connectivity. Interpretation Response to a restorative therapy after stroke is best predicted by a model that includes measures of both neural injury and function. Neuroimaging measures were the best predictors and may have an ascendant role in clinical decision-making for post-stroke rehabilitation, which remains largely reliant on behavioral assessments. Results differed across stroke subtypes, suggesting utility of lesion-specific strategies.

Objective In adults, one of the major determinants of leukocyte telomere length (LTL), a predictor of age-related diseases and mortality, is cumulative psychosocial stress exposure. More recently we reported that exposure to maternal psychosocial stress during intrauterine life is associated with LTL in young adulthood. The objective of the present study was to determine how early in life this effect of stress on LTL is apparent by quantifying the association of maternal psychosocial stress during pregnancy with newborn telomere length. Study Design In a prospective study of N = 27 mother-newborn dyads maternal pregnancy-specific stress was assessed in early gestation and cord blood peripheral blood mononuclear cells were subsequently collected and analyzed for LTL measurement. Results After accounting for the effects of potential determinants of newborn LTL (gestational age at birth, weight, sex, and exposure to antepartum obstetric complications), there was a significant, independent, linear effect of pregnancy-specific stress on newborn LTL that accounted for 25% of the variance in adjusted LTL (β = −0.099; P = .04). Conclusion Our finding provides the first preliminary evidence in human beings that maternal psychological stress during pregnancy may exert a “programming” effect on the developing telomere biology system that is already apparent at birth, as reflected by the setting of newborn LTL.

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 Parkinson's disease.

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. Supplemental materials for this paper are available online.

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 corticotrophin-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 piecewise linear models were employed to test hypothesized relationships. Results Maternal CT exposure (CT+) was significantly associated with pCRH production. Compared to non-exposed women, CT+ was associated with an almost 25% increase in pCRH towards the end of gestation, and the pCRH trajectory of CT+ women exhibited an approximately two-fold steeper increase after the pCRH inflection point at 19 wks 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.

Hamiltonian Monte Carlo (HMC) improves the computational e ciency 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 suggests that our method improves RHMC's overall computational e ciency in the cases considered. All computer programs and data sets are available online (http://www.ics.uci.edu/~babaks/Site/Codes.html) in order to allow replication of the results reported in this paper.

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.eduSupplementary information: Supplementary data are available at Bioinformatics online.