Neuroinformatics

Research Activities:

Specifically, I am dedicated to developing new statistical and computational methodologies for analyzing large-scale, complex-structured medical imaging data. See the website for more details. This involves the examination of various neuroimaging datasets, including spatial-temporal functional Magnetic Resonance Imaging (fMRI), Diffusion Tensor Imaging (DTI) data, multiple neuron spike trains (sequences of action potentials generated by neurons), multi-channel EEG (electroencephalogram) recordings, and data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). As a statistician, I actively engage in interdisciplinary research, collaborating with faculty members, research scientists, and students across diverse fields such as neuroscience, psychology, computer science, engineering, physics, and radiology.

Selected Publications:

Spatial-temporal functional Magnetic Resonance Imaging (fMRI):

Zhang, C.M., Guo, X.^(s), Chen, M., and Du, X.Z.^(s) (2023). "Semi-parametric inference for large-scale data with temporally dependent noise,"
Electronic Journal of Statistics, 17(2):2962-3007.
Zhang, C.M., Han, Y., and Jia, S.^(s) (2016). "Accounting for time series errors in partially linear model with single- or multiple-runs,"
Journal of Computational and Graphical Statistics, 25(1):123-143. [MATLAB codes available on GitHub]
Guo, X.^(s) and Zhang, C.M. (2015). "Estimation of the error auto-correlation matrix in semiparametric model for fMRI data,"
Statistica Sinica, 25(2):475-498.
Zhang, C.M., Fan, J.^(a), and Yu, T.^(s) (2011). "Multiple testing via FDR_L for large-scale imaging data,"
Annals of Statistics, 39(1):613-642.
Zhang, C.M. and Zhang, Z.J. (2010). "Regularized estimation of hemodynamic response function for fMRI data,"
Statistics and Its Interface, 3(1):15-31.
Zhang, C.M. and Yu, T.^(s) (2008). "Semiparametric detection of significant activation for brain fMRI,"
Annals of Statistics, 36(4):1693-1725. [typo]
Zhang, C.M., Lu, Y.^(s), Johnstone, T., Oaks, T., and Davidson, R.J. (2008). "Efficient modeling and inference for event-related functional MRI data,"
Computational Statistics and Data Analysis, 52(10):4859-4871.
Zhang, C.M., Jiang, Y.^(s), and Yu, T.^(s) (2007). "A comparative study of one-level and two-level semiparametric estimation of hemodynamic response function for fMRI data,"
Statistics in Medicine, 26(21):3845-3861. (Special Issue on statistical analysis of neuronal data.)

Diffusion Tensor Imaging (DTI) data:

Yu, T.^(s), Zhang, C.M., Alexander, A.L., and Davidson, R.J. (2013). "Local tests for identifying anisotropic diffusion areas in human brain with DTI,"
Annals of Applied Statistics, 7(1):201-225.

Multiple neuron spike trains (sequences of action potentials generated by neurons):

Zhang, C.M., Gao, M.^(s), and Jia, S.J.^(s) (2024). "DAG-informed structure learning from multi-dimensional point processes,"
Journal of Machine Learning Research, 25(352):1-56. (This paper focuses on neuron spike train data.)
Gao, M.^(s), Zhang, C.M., and Zhou, J. (2024). "Learning network-structured dependence from non-stationary multivariate point process data,"
IEEE Transactions on Information Theory, 70(8):5935-5968. (This paper focuses on neuron spike train data.)
Zhang, C.M., Chai, Y.^(s), Guo, X.^(s), Gao, M.^(s), Devilbiss, D.M., and Zhang, Z. (2016). "Statistical learning of neuronal functional connectivity,"
Technometrics, 58(3):350-359. (Special Issue on Big Data.) [MATLAB codes available on GitHub]

Multi-channel EEG (electroencephalogram) recordings:

Guo, R.S.^(s), Zhang, C.M., and Zhang, Z.J. (2020). "Maximum Independent Component Analysis with application to EEG data,"
Statistical Science, 35(1):145-157. (Special Issue on Statistics and Science, with the Guest Editor David Siegmund).

Alzheimer's Disease Neuroimaging Initiative (ADNI) study:

Shen, Y.B.^(s), Park, Y.H., Chakraborty, S., and Zhang, C.M. (2023). "Bayesian simultaneous partial envelope model with application to an imaging genetics analysis,"
The New England Journal of Statistics in Data Science, 1(2):237-269. [R package available on GitHub]

Detection of spatial signal from general imaging data:

Zhang, C.M. (2014). "Assessing mean and median filters in multiple testing for large-scale imaging data,"
TEST, 23(1):51-71.

Funding:

The research is supported by the U.S. National Science Foundation (NSF), the Wisconsin Alumni Research Foundation (WARF), and the Association for Women in Mathematics (AWM).

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