Career Profile

I obtained my Bachelor's degrees (Bachelor of Management and Bachelor of Science) in 2012 and Master's degrees in Psychology in 2015 from Southwest University in China. I was awarded National First-class Graduate Scholarship and National Scholarship for Postgraduate during my graduate study. I obtained my Ph.D. degree from University College Dublin in 2019. My Ph.D. projects focused on the reward processing endophenotypes of alcohol misuse, in which computational modeling and neural modulation analysis were employed to interrogate tasked-related fMRI and EEG data.

Research Experiences

Post-doctoral associate

2019-Present
University of Vermont

I am currently working as post-doctoral researcher in ENIGMA addiction working group, superived by Hugh Garavan and Scott Mackey. My interest is to identify neuroimaging and genetic biomarkers for substance dependence

Doctor of Philosophy

2015 - 2019
University College Dublin

My main research works during my Ph.D. were based on IMAGEN project. IMAGEN is a European research project examining how biological, psychological, and environmental factors during adolescence may influence brain development and mental health. I characterized the adolescent reward system with activation and connectivity maps during reward anticipation, receipt, omission and prediction error. I have gained a deep understanding of fMRI data processing (e.g., PPI analysis, model-based neuromodulation analysis) and much experience in dealing with a large sample of fMRI data. I also helped the team to pre-process EEG data, process behavioral log files from Presentation. I have participated in two data competitions that used 1) resting-state data to predict autism (https://paris-saclay-cds.github.io/autism_challenge/); 2) structural data to predict brain age (https://www.photon-ai.com/pac2019), in which I gained a lot of understanding of machine learning.

Master

2012-2015
Southwest University

My main studies were using EEG. I have gained essential research experiences including experiment design, running EEG experiment, analyzing EEG data during this period. To make nice figures, I learned how to use Adobe illustrator and photoshop. I also attended several workshops on fMRI data which gave tutorials on fMRI data analysis. I had 2 first-author papers and was awarded National Scholarship for Postgraduate once and National First-class Graduate Scholarship for three times.

Publication

2014-present

Whelan, R., Cao, Z., O’Halloran, L. & Pennie, B. (in press). Genetics, imaging and cognition: Big Data approaches to addiction research. In Cognition And Addiction: From Mechanisms to Interventions, Ed. Verdejo-Garcia, A. Elsevier, Amsterdam.

Zeng, J., Wang, Y., Zeng, J., Cao, Z., Chen, H., Liu, Y., ... & Su, L. (2019). Predicting the behavioural tendency of loss aversion. Scientific reports, 9(1), 5024.

O'Halloran, L., Rueda‐Delgado, L. M., Jollans, L., Cao, Z., Boyle, R., Vaughan, C., ... & Whelan, R. (2019). Inhibitory‐control event‐related potentials correlate with individual differences in alcohol use. Addiction biology.

Orr, C., Spechler, P., Cao, Z., Albaugh, M., Chaarani, B., Mackey, S., ... & Garavan, H. (2019). Grey Matter Volume Differences Associated with Extremely Low Levels of Cannabis Use in Adolescence. Journal of Neuroscience, 39(10), 1817-1827.

Cao, Z., Bennett, M., Orr, C., Icke, I., Banaschewski, T., Barker, G. J., ... & Whelan, R. (2019). Mapping adolescent reward anticipation, receipt, and prediction error during the monetary incentive delay task. Human brain mapping.

O'halloran, L., Cao,Z., Ruddy, K., Jollans, L., Albaugh, M. D., Aleni, A., ... & Whelan, R. (2018). Neural circuitry underlying sustained attention in healthy adolescents and in ADHD symptomatology. NeuroImage, 169, 395-406.

Padilla, M. M., O’Halloran, L., Bennett, M., Cao, Z., & Whelan, R. (2017). Impulsivity and reward processing endophenotypes in youth alcohol misuse. Current Addiction Reports, 4(4), 350-363.

Jollans, L., Cao, Z.., Icke, I., Greene, C., Kelly, C., Banaschewski, T., ... & Whelan, R. (2016). Ventral striatum connectivity during reward anticipation in adolescent smokers. Developmental neuropsychology, 41(1-2), 6-21.

Cao, Z., Li, Y., Hitchman, G., Qiu, J., & Zhang, Q. (2015). Neural correlates underlying insight problem solving: Evidence from EEG alpha oscillations. Experimental brain research, 233(9), 2497-2506.

Zeng, J., Cao, Z., Huang, J., Hitchman, G., & Zhang, Q. (2014). Predictability influences whether outcomes are processed in terms of original or relative values. Brain and cognition, 90, 1-7.

Projects

Maturation of brain reward system associated with developmental changes in sensation seeking and alcohol use -The main goals are 1). Quantify developmental changes in the brain reward system from adolescence and adulthood. 2) Examine how behavioural changes (i.e., sensation seeking and alcohol misuse) coincide with the maturation of brain reward system.
Altered reward prediciton error processing in binge drinkers -Rescorla–Wagner model is used to estimate trial-by-trial RPE. P3 and FRN are modualted by RPE and have different amplitude between binge drinkers and non-binge drinkers
Bayesian analysis with EEG Stop Signal Task -When people see a sequence of go and stop trials in SST, their belief of next trial being a stop trial will change according to types of previous trials in a Bayesian fashion. The belief updating can be modelled using dynamic belief model (DBM). The electrophysiological correlates underlying this Bayesian update of belief will be examined using multiple linear regression.
Mapping adolescents reward system -Adolescence is a neurodevelopmental period associated with an altered sensitivity to rewarding outcomes and situations. These alterations in reward sensitivity often contribute to the emergence of impulsive decisions and risk-taking behaviours and reward system dysfunction is involved in the pathogenesis of numerous psychiatric disorders during adolescence. Therefore, an understanding of the neurobiology underlying reward processing in adolescents is important. The functional neuroanatomy and connectivity of reward processing have been extensively described in adults, with relatively less research on adolescents. There is a limited number of empirical investigations into adolescent reward processing and extant studies often employ small sample sizes (Bjork, et al., 2004; Bjork, et al., 2010). Consequently, much of the current information is derived from meta-analyses that amalgamate data from a range of different reward-related tasks with widely different methodological parameters (Silverman, et al., 2015).
A handy GUI for EEG quality control -Currently, more algorithms have been developed to automatically detect and remove EEG artefacts, but manual inventions are still needed. When I started running QC on EEG data two years ago, I had to click the menu multiple times to load dataset, remove bad epoches, interpolate bad channels, remove ICs and save dataset. I couldn’t find an efficient way to do that from eeglab, so I incorporated QC related functions from eeglab and assembled them into one GUI. The features are: all GUI are built by code, eeglab functions are customized to meet the needs.
Allen Brain Tool -A tiny tool to fetch gene expression values from Allen Brain Atlas and test against stats maps
Python Elastic Net -Sklearn elastic net is supper slow, so I converted the glmnet-python into sklearn fashion, which makes powerful tools from sklearn and other python module available to be incorporated into analysis.
Python-CPM -Rough implementatoin of the CPM that is described in Nature Protocol.
Python-fMRI course -This is an excellent course with mathematical details and tutorials on how to implement analysis with python. Though it took me about 1-2 month to do this course by myself, I gained a much deeper understanding of the fMRI data analysis by doing this.

Skills & Proficiency

Matlab

Python/Sklearn

R

SPM

EEGLAB

PS/AI

HTML5/CSS