Biostatistics, Computation and Data Management Core


Hal Stern
Chancellor’s Professor Department of Statistics

Michael Phelan
Assistant Director, UC Irvine Center Center for Statistical Consulting

Graduate Student, Department of Statistics


Core Aims

The projects of the proposed Conte Center integrate human and experimental animal studies to investigate the overarching hypothesis that fragmentation and unpredictability of early life maternal and environmental signals (FRAG) contribute to adolescent vulnerabilities and subsequent mental illness via mechanisms involving aberrant development and maturation of emotional brain circuits. The Biostatistics, Computation and Data Management (BCDM) Core works closely with Projects 1-4 and the Imaging Core to plan and support their statistical analyses, to develop innovative models that predict risk for mental illness,  and to provide data management support.  This work is achieved through addressing the following aims:

Aim 1: Provide computational and biostatistics support for studies of the relationship of FRAG with age- and sex-specific vulnerabilities and mental illness.  Address the contributions of FRAG in the context of other risk factors and optimize approaches to the analysis of complex datasets that include cognitive, emotional, and imaging data.

Aim 2: Use data from multiple human cohorts to develop and test measures that easily, reliably, and accurately assess fragmented and unpredictable maternal and environmental signals.

Aim 3: Develop statistical / computational models that integrate measures of early-life experiences with cognitive, emotional and imaging data to predict anhedonia and risk for mental illness.

Aim 4: Develop a Center information resource, provide data management support to all projects and cores, and facilitate integration across projects. This includes accommodating human demographic, cognitive, emotional, and imaging data and experimental animal data.


Project Publications

Baram, T. Z., Davis, E. P., Obenaus, A., Sandman, C. A., Small, S. L., Solodkin, A., Stern, H. (2012), “Fragmentation and Unpredictability of Early-Life Experience in Mental Disorders,” American Journal of Psychiatry, Vol. 169(9), pp. 907-915.

Heins, KA and Stern, HS (2014) “A Statistical Model for Event Sequence Data,” in Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS 2014), pp. 338-346.

Molet, J., Heins, K., Zhuo, X., Mei, Y.T., Regev, L., Baram, T.Z., and Stern, H. (2016) “Fragmentation and High Entropy of Neonatal Experience Predict Adolescent Emotional Outcome,” Translational Psychiatry (2016) 6, e702; doi:10.1038/tp.2015.200 (published online 5 January 2016)

Stern, H. S. (2016) “A Test by Any Other Name: P values, Bayes Factors and Statistical Inference,” Multivariate Behavioral Research, 51:1, 23-29.

Davis, E.P., Stout, S.A., Molet, J., Vegetabile, B., Glynn, L.M., Sandman, C.A., Heins, K., Stern, H., Baram, T.Z. (2017) “Exposure to unpredictable maternal sensory signals influences cognitive development across species,” Proceedings of the National Academy of Sciences, 114(39):10390-10395.

Keator, D.B., Chan, J., Nichols, N., Fana, F., Stern, H., Baram, T.Z., Small, S.L. (2017) “A Semantic Cross-Species Derived Data Management Application,” Data Science Journal, 16:45, 1-10.

Glynn, L.M., Howland, M.A., Sandman, C.A., Davis, E.P., Phelan, M., Baram, T.Z., Stern, H.S. (2018) “Prenatal Maternal Mood Patterns Predict Child Temperament and Adolescent Mental Health,” Journal of Affective Disorders, 228, 83-90.

Vegetabile, B., Stout-Oswald, S.A., Davis, E.P., Baram, T.Z., Stern, H. (submitted). Estimating the Entropy Rate of Finite Markov Chains with Application to Behavior Studies

Vegetabile, B., Gillen, D.L., Stern, H. (submitted).  Optimally Balanced Gaussian Process Propensity Scores for Estimating Treatment Effects

Horizontal axis: measurement occasions (1=age 1; 2=age 5; 3=age 9.5).

Vertical axis: age-appropriate measure of child self-control (standardized).

Plotting information:
O=female, X=male;  red=low entropy rate, green=medium entropy rate, blue=high entropy rat