Mr. Wenxing Hu Profile

Wenxing Hu

PhD Student at Tulane Univ

SPIE Involvement:

Author

Publications (3)

Proceedings Article | 28 February 2020 Presentation + Paper

A graph deep learning model for the classification of groups with different IQ using resting state fMRI

Gang Qu, Wenxing Hu, Li Xiao, Yu-Ping Wang

Proceedings Volume 11317, 113170A (2020) https://doi.org/10.1117/12.2549274

KEYWORDS: Brain, Functional magnetic resonance imaging, Data modeling, Convolution, Neuroimaging, Convolutional neural networks, Medical imaging, Performance modeling, Visualization, Statistical analysis

Read Abstract +

SPIE Journal Paper | 11 April 2019 Open Access

Distance canonical correlation analysis with application to an imaging-genetic study

Wenxing Hu, Aiying Zhang, Biao Cai, Vince Calhoun, Yu-Ping Wang

JMI, Vol. 6, Issue 02, 026501, (April 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.026501

KEYWORDS: Brain, Simulation of CCA and DLA aggregates, Neuroimaging, Canonical correlation analysis, Genetics, Functional magnetic resonance imaging, Brain imaging, Visualization, Alzheimer's disease, Neurons

Read Abstract +

DOWNLOAD PAPER

Proceedings Article | 6 March 2018 Presentation + Paper

A hybrid correlation analysis with application to imaging genetics

Wenxing Hu, Jian Fang, Vince Calhoun, Yu-Ping Wang

Proceedings Volume 10579, 1057905 (2018) https://doi.org/10.1117/12.2293556

KEYWORDS: Brain, Functional magnetic resonance imaging, Genetics, Canonical correlation analysis, Statistical analysis, Neuroimaging, Genomics, Biological research

Read Abstract +

Investigating the association between brain regions and genes continues to be a challenging topic in imaging genetics. Current brain region of interest (ROI)-gene association studies normally reduce data dimension by averaging the value of voxels in each ROI. This averaging may lead to a loss of information due to the existence of functional sub-regions. Pearson correlation is widely used for association analysis. However, it only detects linear correlation whereas nonlinear correlation may exist among ROIs. In this work, we introduced distance correlation to ROI-gene association analysis, which can detect both linear and nonlinear correlations and overcome the limitation of averaging operations by taking advantage of the information at each voxel. Nevertheless, distance correlation usually has a much lower value than Pearson correlation. To address this problem, we proposed a hybrid correlation analysis approach, by applying canonical correlation analysis (CCA) to the distance covariance matrix instead of directly computing distance correlation. Incorporating CCA into distance correlation approach may be more suitable for complex disease study because it can detect highly associated pairs of ROI and gene groups, and may improve the distance correlation level and statistical power. In addition, we developed a novel nonlinear CCA, called distance kernel CCA, which seeks the optimal combination of features with the most significant dependence. This approach was applied to imaging genetic data from the Philadelphia Neurodevelopmental Cohort (PNC). Experiments showed that our hybrid approach produced more consistent results than conventional CCA across resampling and both the correlation and statistical significance were increased compared to distance correlation analysis. Further gene enrichment analysis and region of interest (ROI) analysis confirmed the associations of the identified genes with brain ROIs. Therefore, our approach provides a powerful tool for finding the correlation between brain imaging and genomic data.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years