• Welcome to my GitHub profile!
• 🤔 I am a master student in Biomedical Engineering, working with Prof. Fan zhaoyang at the Fan Magnetic Resonance (MR) Imaging Research Lab at the University of Southern California, Los Angeles, America.
• "Attendre et espérer.”
• 📫 Reach me at [email protected]

Computational Neuroscience MRI technology CV in medical imaging Optimization theory

1. In progress (expected graduation 06/2025)

   University of Southern California (USC)

   • Degree: Master of Science in Biomedical Engineering
   • Location: Los Angeles, California, USA
   • GPA: 4.0/4.0

2. 09/2019 - 06/2023

   Southern University of Science and Technology (SUSTech)

   • Degree: Bachelor of Science in Biomedical Engineering
   • Location: Shenzhen, Guangdong, China
   • GPA: 3.5/4.0
   • Thesis: Network control theory to identify the critical nodes and input signals from task fMRI

1. Neural Computing and Control Laboratory. Supervisor: Prof. Quanying Liu
2. Fan Magnetic Resonance (MR) Imaging Research Lab. Supervisor: Prof. Fan zhaoyang

1. Use network control theory to identify the critical nodes and input signals from task fMRI.

   • About: The human brain is an orderly dynamic system and it coordinates task-related regions hierarchically to perform a complex cognitive task. However, the underlying regulation mechanisms of how the brain organizes these neural circuits remain elusive from a computational perspective. Brain network control theory provides a basic theoretical architecture for linking brain structure and functional dynamics. In our study, we utilize the network control theory to reveal the relationships between the brain's anatomical structure and the observed coactivation pattern of cognitive function.
   • Method: We constructed a linear model to model the brain network and then used the pinning control strategy to input energy to the model, forcing the model output to track the real fMRI signals and optimize the node selectionsand input energy. We proposed to use the Half-Quadratic Splitting algorithm to solve the optimized model and analyze the controllability of the brain structure to determine how the brain balances energy consumption and neural circuit integration. We have already tested this framework in working memory task and identify the critical brain control regions and corresponding input energy. Now we are trying to expand the this framework to non-linear model to see if we can get similar results.

2. Use LWI to detect the prostate cancer in MRI iamges.

   • About: In the MRI images, the T2 relaxation process of tissues can be described by a multiexponential model.Several studies have shown that quantitative analyses of multiexponential T2 relaxation can provide tissuespecific information for diagnostic applications. Luminal water imaging (LWI) is a new application of multiexponential T2 mapping to detect the prostate cancer.
   • Method: We construct a multiexponential model and use the prostate MRI data from healthy volunteers to optimize the model. Then we would use the model in the prostate cancer patients volunteer to validate if the model works.

3. Use radiomics and deep learning to predict the pituitary adenoma consistency.

   • About: Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. In this research, we want to combine radiomics, deep learning, and transfer learning to solve this problem with MRI images.
   • Method: For radiomics part, We collect the MRI images of pituitary adenoma of patients and preprocess the images. Then we use Pyradiomics to extract the features from the images and then use the Extra Tree Classifier to determine adenoma consistency. For the deep learning part, we use the U-net to extract the image features, then we freeze the parameters of the down-sampling and use the output of the U-net down sampling as the input of a classifier model to predict the adenoma consistency. We can get the classification result by combining results from two paths

Here are some of my research papers and articles published in scientific journals and conferences:

1. Reverse engineering the brain input: Network control theory to identify cognitive task-related control nodes
   • Authors: Zhichao Liang, Yinuo Zhang, Jushen Wu.
   • Published In: IEEE Engineering in Medicine and Biology Society, 2024

• Network control theory to identify the critical nodes and input signals from task fMRI
  • Submitted to: Neuroimage
  • Co-authors: Zhichao Liang, Jushen Wu, Yinuo Zhang
  • Status: Under review

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