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Data Science in Drug Addiction Lab

About

The recently established Data Science in Drug Addiction Lab led by Dr. Yihong Zhao aims to use Big Data analytical approaches to reveal novel insights into biological, brain, and behavioral processes associated with problematic substance uses (e.g., early substance use initiation, binge drinking, substance use disorder and its co-occurring mental health illness) across different developmental stages. Advances in technologies allow us to generate an enormous amount of data in an inexpensive way, making it possible to discover the dynamic interactions of diverse biological, brain, and behavioral mechanism underlying addiction problems. However, extracting hidden information from the vast amount of the data presents new computational challenges. In addition, limitations on analytical capacity may delay important discoveries in addiction research. To accelerate discovery science in substance use disorder research, we need to integrate and analyze these data in new ways to turn our vast stores of data into knowledge.

Research Aim(s)

  • Identification of developmental risk and protective factors for early initiation of substance use at brain, mind, and behavioral levels
  • Examination of sex difference in human brain structural and functional features linked to problematic substance use
  • Integrative analysis of data from multiple sources (e.g., genetics, imaging, and behavior measures)

Active Research Projects

  • Applications of the deep learning approaches to substance use studies
  • Linking human genetic variations with atypical development of brain structure and function in longitudinal adolescent samples
  • Developing tools for visualization of high-dimensional data

Recent Publications

  • Zhao Y, Klein A, Castellanos F. X., Milham M. P. (2019) Brain Age Prediction: Cortical and Subcortical Shape Covariation in the Developing Human Brain. Neuroimage 202: 116149. DOI: 10.1016/j.neuroimage.2019.116149
  • Zhao Y, Ge Y, Zheng ZL (2019). Brain Imaging-Guided Analysis Reveals DNA Methylation Profiles Correlated with Insular Surface Area and Alcohol Use Disorder. Alcohol Clin Exp Res. 43: 628 – 639
  • Zhao Y, Zheng Z-L, Castellanos FX (2017) Analysis of alcohol use disorders from the Nathan Kline Institute-Rockland Sample: Correlation of brain cortical thickness with neuroticism. Drug and Alcohol Dependence 170: 66-73
  • Zhao Y, Castellanos FX (2016) Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders: promises and limitations. Journal of Child Psychology & Psychiatry 57: 421-439
  • Reiss P, Huo L, Zhao Y, Kelly C, Ogden RT (2015) Wavelet-domain regression and predictive inference in psychiatric neuroimaging. Annals of Applied Statistics 9: 1076-1101


Training Offered

Undergraduate and graduate students in computer science, statistics, biostatistics, bioinformatics, psychology, and related disciplines are invited to apply for volunteer positions in the Data Science in Drug Addiction lab.  Duties may include preprocessing brain imaging and genetics data, performing exploratory data analysis, using machine learning approach for brain-behavior analysis, and running simulation studies. Training and supervision will be provided. Students are required to commit approximately 10 hours/week in the research lab. Opportunities for conference poster and co-authored publication are available for selected students. If interested in joining the Data Science in Drug Addiction lab, please send your resume/cv to Dr. Yihong Zhao at yihong.zhao@smithers.rutgers.edu.

Personnel 

Core Faculty:
Yihong Zhao, Ph.D., Director

Affiliated Faculty:
Marsha Bates, Ph.D. (Rutgers)
Denise Hien, Ph.D. (Rutgers)
Matthew Lee, Ph.D. (Rutgers)
Lesia Ruglass, Ph.D. (City College of New York)
Lei Yu, Ph.D. (Rutgers)

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