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Ze Wang, PhD

Wang Ze

Ze Wang, PhD

Professor
Department of Diagnostic Radiology and Nuclear Medicine
Center for Advanced Imaging Research (CAIR)

Phone: 410-706-2797
Email: ze.wang@som.umaryland.edu

Biosketch

Dr. Wang received his PhD from Shanghai Jiao Tong University. His major research interests are in MR imaging, neuroimaging signal processing, and imaging-based translational research in Alzheimer's disease and addiction. He has published over 112 journal papers in these research topics. Regarding MRI, he focuses on arterial spin labeling (ASL) perfusion MRI and image reconstruction. His ASL work includes a 3D background suppressed spiral readout ASL MRI sequence, a series of ASL MRI data processing methods, as well as the first open-source software package for processing ASL data: ASLtbx.

In MR reconstruction, he developed a multi-dimensional k-space based parallel imaging reconstruction algorithm: MCMLI for 2D and SNAPPI for 3D imaging and an optimized and super-fast dictionary searching algorithm: MRF-ZOOM for magnetic resonance fingerprinting, which can work without a pre-defined full dictionary. In neuroimaging, he developed an fMRI-based brain entropy mapping (BEN) tool and a multivariate lesion-symptom mapping algorithm (SVR-LSM). His recent interest in these areas includes incorporating deep machine learning in ASL data processing and image reconstruction. He has disseminated and is still maintaining three open source software packages: ASLtbx, SVR-LSM, and BENtbx.

Free Research Tools for Downloading >

Note that software updates are now released through https://github.com/zewangnew.

Complete Faculty Profile >


Research Projects and Highlights

Please select a title to learn more:

ASL MRI in Alzheimer's Disease Research

The goal of this project is to deep clean ASL MRI and use it for detecting early AD and predicting AD progression. Existing data from Alzheimer's Disease Neuroimaging Initiative (ADNI, http://adni.loni.usc.edu/) are used.

Visit our PMC Project page for:

- project members
- publications

Learn more about this project

Deep learning in Arterial Spin Labeling Perfusion MRI

Improving ASL MRI using deep machine learning

This aim of this project is to use deep machine learning to improve spatial and temporal resolution and signal-to-noise-ratio (SNR) of ASL perfusion MRI.

Learn more about this project

Brain entropy mapping using resting-state fMRI

Entropy measures irregularity of a dynamic system, which also indicates the information capacity. The human brain is a complex functional system with ongoing information generation and processing. Higher entropy generally bestows a better functional flexibility, which is beneficial in many situations but might be detrimental  for a certain range of brain functions such as mood and emotions. Nevertheless, measuring brain entropy provides a tool to assess such information processing balance and the changes due to disorders. The aim of this project is to further develop brain entropy techniques and use them in neurodegenerative disease and drug addiction studies.

 

Deep learning based CEST MRI

This project is in collaboration with Dr. Ravinder Reddy from University of Pennsylvania for correcting B0, B1 inhomogeneity induced confounds using deep learning.  The main thrust is to learn the smooth Z-spectrum using deep learning. Based on the learned Z-spectrum representations, much fewer than regular data acquisitions can be used to reconstruct the Z-spectrum and subsequently correct the B0 or B1 offset induced quantification inaccuracy. 

An inverse-U shaped brain entropy progression in the AD continuum

In a recently published paper (https://www.frontiersin.org/articles/10.3389/fnagi.2020.596122/full), we characterized resting brain entropy in normal aging and patients with different level of Alzheimer's Disease (AD) (from early Mild Cognitive Impairment to AD) using resting state fMRI. We found abnormal brain entropy (BEN) changes in the default mode network, medial temporal lobe, and prefrontal cortex which are associated with cognitive impairment and AD pathology. Collectively, the results showed that: (1) BEN increased with age and pathological deposition in normal aging but decreased with age and pathological deposition in the AD continuum; (2) AD showed catastrophic BEN reduction, which was related to more severe cognitive impairment and daily function disability; and (3) BEN decreased with education years in normal aging, but not in the AD continuum. BEN evolution follows an inverse-U trajectory when AD progresses from normal aging to AD dementia. Education is beneficial for suppressing the entropy increase potency in normal aging.

Visit our PMC Project page for:

- project members
- publications

 

Sleep effects on brain connections and mental health

Sleep and development have been a recent research focus in my laboratory. Years ago, researchers have shown the immediate amyloid protein deposition in the brain after a short-term sleep deprivation. Amyloid is the neurotoxic waste in the brain and needs to be transported out by CSF. But CSF is basically static most of the time. The best time to have more CSF and increased flow rate is at night when you lay down and fall asleep. It is this time that our cerebral blood flow reduces. Because our brain has a fixed volume, the reduce of cerebral blood flow creates space for CSF and the inhomogeneous change of blood flow creates power for CSF to flow and then transport the neural waste out. This is why our brain generates two times CSF at night than daytime.  

Based on these facts, we have been looking for opportunity to investigate the sleep effects on the brain for a while. Now this is made possible by using the large data (brain images and neuropsychological measures, biological measures, and many other measures) from the Adolescent Brain and Cognitive Development study and we just published the first paper in Human Brain Mapping (link to the paper). In this paper, we found that: 1) lack of sleep in teens is associated with altered connections between and within two important brain networks: one is the dorsal attention network that is mainly responsible for attention, memory, and inhibition control; the other is the default mode network which has been shown to have an important role for facilitating general brain function; 2) sleep deficits are associated with more mental problems and this correlation is bidirectional and is mediated through the brain connections within and through the two networks; 3) the associations and mediation effects can last long at least for a year as our data shown.

This is part of our strong interest in brain vs behavior relationship study. While our initial plan was to examine sleep effects on brain activity in terms of the dynamic change of the acquired brain signal and on the inter-regional connection as well as the mediation effects by other contributing factors such as puberty and socio-economic status. Our postdoc research fellow, Dr. Nils Yang, who is the first author of this paper and contributed the most, chose to investigate the scientific question from another point of view: the brain effects on the sleep vs mental health associations which is important too.
 
For an average person, our paper can have the following take home message: nowadays, teenagers are getting less and less sleep because of all kinds of excitations. Unfortunately,  this is with consequences. One possible consequence is the harm to mental health, which may reciprocally impact sleep quality and start a worse-to-worse cycle. Another possible consequence is the change of brain connections. These consequences may last for a long time.  Because the adolescent brain is still under rapid development, sustained sleep deficits may lead to permanent impairment to the brain and to the cognitive functions. Getting good sleep back is crucial to teens' brain and mental health.  In extreme cases where sleep quality is difficult to be improved soon, an alternative potential approach can be some intervention that can specifically improve brain function connectivity.
 
We should always be cautious about the potential caveats. The findings are still correlational. We can not tell whether sleep deficits causes mental health problems or vice versa. We can not tell that the observed functional connectivity changes are caused by sleep deficits or mental problems.  Because the ABCD project will continuously acquire data from the same teens for 10 years, the precious longitudinal data can answer whether these effects are causal or not.
 
 
In a paper in press by Lancet Child and Adolescent Health (The full paper freely available through: https://authors.elsevier.com/a/1fVA38Mut2J9hQ

we step forward to investigate the effects of insufficient sleep on the developmental brain structure and function and cognition. We used large data to achieve high statistical power to identify the potential effects and used a statistically well-defined method to delineate the effects of insufficient sleep from many other covariates that are known to affect brain and cognition. Moreover, we aimed to see whether the potential effects can sustain for years.

We defined insufficient sleep as less than 9 h according to the recommendation given by American Academy of Sleep Medicine.  At the baseline (the first time of experiment), kids (9-10 years old) had insufficient sleep showed more mental/behavioral problems such as impulsivity, stress, depression, anxiety, aggressive behavior, psychosis etc, and impaired cognitive functions such as decision making, conflict solving, working memory, crystal intelligence, verbal intelligence etc. Kids with insufficient sleep showed detrimental effects in brain structure and functions. For example, insufficient sleep group had smaller brain volume and cortical area in several important regions such as cingulate cortex, temporal cortex, and visual cortex. 

A striking finding is that these effects were long-lasting as we found very similar effects at the 2-year follow up experiment. We also found that the long-lasting effects of insufficient sleep on behavior and cognition were mediated by brain structure and functional connectivity from regions that are known to be important for sleep.    

Implications of our study include: insufficient sleep may modulate neural development over 2 years, leading to compromised cognitive functions and behavioral problems in early adolescents. Insufficient sleep can have long-lasting effects on brain structure, function, and cognition.  Sleep affects neurobehavior through the brain, suggesting that a healthy brain development may partially counteract the impact of sleep loss. These effects are independent of sex, socioeconomic status, puberty status and several other factors that may affect brain and behaviors and highlight the crucial need for early intervention to facilitate long-term development outcomes in adolescents.

 The message for average person will be: nowadays, teenagers are getting less and less sleep because of all kinds of excitations. Unfortunately,  this is with consequences. One possible consequence is the harm to behavior, mental health, and cognition, which may reciprocally impact sleep quality and start a worse-to-worse cycle. Another possible consequence is the change of brain connections. These consequences may last for a long time.  Because the adolescent brain is still under rapid development, sustained sleep deficits may lead to permanent impairment to the brain and to the cognitive functions. Getting good sleep back is crucial to teens' brain and mental health.  In extreme cases where sleep quality is difficult to be improved soon, an alternative potential approach can be some intervention that can specifically improve brain function connectivity.

 This work was performed by Dr. Fan Nils Yang, who is a postdoctoral fellow in Dr. Wang’s lab. The equally contributed author, Dr. Weizhen Xie has also made substantially contribution to this study. 

 

Lab Members (7-9 new fellows will join the lab in the fall of 2022, 2 will leave)

Lei Zhang, PhD

Lei Zhang, PhD
Dr. Zhang began working in Dr. Wang's lab as a Research Associate in October 2019. 

Aldo Camargo Fernandez-Baca, PhD

Aldo Camargo Fernandez-Baca, PhD
Dr. Fernandez-Baca joined Dr. Wang's lab as a postdoc fellow in July 2019. His major research focus is arterial spin labeling perfusion MRI for studying Alzheimer's disease.

Yiran Li

Yiran Li, PhD candidate
Mr Li is a PhD student in Dr. Wang's lab. His research focuses on deep learning-based medical imaging processing, including image synthesis, denoising, and reconstruction. 

Publications:

  • Yiran Li, Danfeng Xie, Abigail Cember, Ravi Prakash Reddy Nanga, Hanlu Yang, Dushyant Kumar, Hari Hariharan, Li Bai, John A. Detre, Ravinder Reddy, Ze Wang, Accelerating GluCEST imaging using Deep Learning, MRM, 2020 Oct;84(4):1724-1733. doi: 10.1002/mrm.28289.
  • Li, Yiran, Sudipto Dolui, Dan-Feng Xie, Ze Wang, and Alzheimer’s Disease Neuroimaging Initiative. "Priors-guided slice-wise adaptive outlier cleaning for arterial spin labeling perfusion MRI." Journal of neuroscience methods 307 (2018): 248-253.
  • Xie, Danfeng, Yiran Li, HanLu Yang, Donghui Song, Yuanqi Shang, Qiu Ge, Li Bai, and Ze Wang. "BOLD fMRI-Based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks." In International Workshop on Machine Learning in Medical Imaging, pp. 373-381. Springer, Cham, 2019.
  • Li, Zheng, Qingping Liu, Yiran Li, Qiu Ge, Yuanqi Shang, Donghui Song, Ze Wang, and Jun Shi. "A Two-Stage Multi-loss Super-Resolution Network for Arterial Spin Labeling Magnetic Resonance Imaging." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 12-20. Springer, Cham, 2019.
  • Xie, Danfeng, Yiran Li, Hanlu Yang, Li Bai, Tianyao Wang, Fuqing Zhou, Lei Zhang, and Ze Wang. "Denoising arterial spin labeling perfusion MRI with deep machine learning." Magnetic Resonance Imaging (2020).

 

Fan Nils Yang, PhD

Fan Nils Yang, PhD

Fan Nils Yang, PhD, is a postdoctoral fellow broadly interested in the neurobiological basis underlying human cognition and behavior. By applying his skillset in functional brain imaging, arterial spin labeling (ASL) perfusion fMRI, and blood oxygen level-dependent (BOLD) fMRI, in basic and translational research, the ultimate goal of his work is to improve health and aging-related outcomes.

 

Tong Lu

Tong Lu

Research assistant Tong Lu is a PhD student majoring in mathematical statistics with particular research interests in network/graph statistics, neuroimaging statistics, and machine learning-based modeling and optimization. Her current research focuses on developing novel machine learning algorithms and graph theory to model high-throughput biomedical data with complex and organized structures.

 

Lab Alumni (since 2019)

Jue Lu, PhD

Jue Lu, PhD
Dr. Lu is a mathematician. He was a visiting scholar in Dr. Wang's lab working on brain entropy mapping. The following is the publication list in this research topic:

Jue Lu, Ze Wang. The Systematic Bias of Entropy Calculation in the Multi-scale Entropy Algorithm, Entropy, 2021, 23(6), 659; https://doi.org/10.3390/e23060659

Hanlu Yang

Hanlu Yang
Miss Yang was a master's degree student in Dr. Wang's lab from 2018 to 2020. She is now a PhD student in UMBC. Her major focus is deep-learning based MR image reconstruction. She has contributed to several papers:

 

  • POCS Augmented CycleGAN for MR Image Reconstruction (Medical Image Analysis, Under revision)
  • Xie, Danfeng, Yiran Li, HanLu Yang, Donghui Song, Yuanqi Shang, Qiu Ge, Li Bai, and Ze Wang. "BOLD fMRI-Based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks." In International Workshop on Machine Learning in Medical Imaging, pp. 373-381. Springer, Cham, 2019.
  • Xie, Danfeng, Yiran Li, Hanlu Yang, Li Bai, Tianyao Wang, Fuqing Zhou, Lei Zhang, and Ze Wang. "Denoising arterial spin labeling perfusion MRI with deep machine learning." Magnetic Resonance Imaging (2020).

Liandong Lin, PhD

Liandong Lin, PhD

Dr. Lin was a visiting scholar in Dr. Wang's lab from 2019 to 2020. He has published six peer-reviewed journal papers and three international conference papers. He is currently working on fMRI signal processing. His research interests also include MR technique development and deep machine learning.

Danfeng Xie, PhD

Danfeng Xie, PhD
Dr. Xie received his PhD while working in Dr. Wang's lab in 2020. He was a postdoc fellow until Feb 2021. His research focuses on deep learning-based ASL MRI denoising, acceleration, and cross-modality prediction.

Publications:

  • Yiran Li, Danfeng Xie, Abigail Cember, Ravi Prakash Reddy Nanga, Hanlu Yang, Dushyant Kumar, Hari Hariharan, Li Bai, John A. Detre, Ravinder Reddy, Ze Wang, Accelerating GluCEST imaging using Deep Learning, MRM, 2020 Oct;84(4):1724-1733. doi: 10.1002/mrm.28289.
  • Xie, Danfeng,Yiran Li, Hanlu Yang, Li Bai, Tianyao Wang, Fuqing Zhou, Lei Zhang, and Ze Wang. "Denoising arterial spin labeling perfusion MRI with deep machine learning." Magnetic Resonance Imaging (2020).
  • Li, Yiran, Sudipto Dolui, Danfeng Xie, Ze Wang, and Alzheimer’s Disease Neuroimaging Initiative. "Priors-guided slice-wise adaptive outlier cleaning for arterial spin labeling perfusion MRI." Journal of neuroscience methods 307 (2018): 248-253.
  • Xie, Danfeng,Yiran Li, Hanlu Yang, Li Bai, Ze Wang. A Learning-From-Noise Dilated Wide Activation Network for Denoising Arterial Spin Labeling (ASL) Perfusion Images. 28th Annual Meeting ISMRM, 2020.
  • Xie, Danfeng, Yiran Li, Hanlu Yang, Donghui Song, Yuanqi Shang, Qiu ge, Li Bai and Ze Wang. Estimating Cerebral Blood Flow from BOLD Signal Using Deep Dilated Wide Activation Networks. 28th Annual Meeting ISMRM, 2020.
  • Xie, Danfeng, Yiran Li, Hanlu Yang, Donghui Song, Yuanqi Shang, Qiu ge, Li Bai and Ze Wang, “BOLD fMRI-based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks”. 10th International workshop on Machine Learning in Medical Imaging, 2019.
  • Xie, Danfeng, Yiran Li, Li Bai, and Ze Wang.“Super-ASL: Improving SNR & Temporal Resolution of ASL MRI Using Deep Learning.” ISMRM workshop on Machine Learning 2018. 
  • Xie, Danfeng, Yiran Li, Li Bai, and Ze Wang. “Denoising Arterial Spin Labeling Cerebral Blood Flow Images Using Deep Learning-Based Methods.” 26th Joint Annual Meeting ISMRM-ESMRMB. ISMRM-ESMRMB, 2018.
  • Xie, Danfeng, Lei Zhang, and Li Bai. "Deep learning in visual computing and signal processing." Applied Computational Intelligence and Soft Computing 2017 (2017).

 

Donghui Song

Mr. Donghui Song got his Master degree in Cognitive Neuroscience under the supervision of Dr. Wang. He is currently a Research Specialist in Dr. Wang's lab. He has published about 10 peer-reviewed papers including 3 first-authored ones. He is proficient at processing fMRI data using the tools such as SPM, FSL, ASLtbx, BENtbx or packaged pipelines such as fMRIprep. He has been well trained for performing fMRI and TMS experiments.

Grants and Proposals

PIGrant TitleTotal Project PeriodFunding SourceTotal Costs
Ze Wang Assessing ASL CBF as a biomarker for early disease detection and disease progression 05/01/19 – 02/28/23 NIH/NIA $1,371,655.00
Ze Wang Assessing ASL CBF as a biomarker for early disease detection and disease progression (Supplement) 05/01/20 – 04/31/21 NIH/NIA $377,046.00
Ze Wang Brain entropy mapping in Alzheimer’s Disease 05/01/20 – 04/31/21 UMB ICTR $35,000.00
Ze Wang Brain entropy mapping in Alzheimer's Disease 08/15/21-06/30/25 NIH/NIA $2,311,282
John A Detre, Ze Wang, Yulin Chang Academic Industrial Partnership on Advanced Perfusion MRI (pending) 12/01/21-11/30/26 NIH/NIBIB $956,010
Ze Wang sub-contract with Dr. Ravi Reddy's P41 03/15/22-03/14/24  NIH   
Ze Wang Improving Arterial Spin Labeling Perfusion MRI with Advanced Deep Learning 05/01/2022-04/30/2023 UMB ICTR $35,000
Ze Wang Brain entropy based chronic pain study 09/30/2022-08/31/2024 NIA $447000

Publications

Click here to view Dr. Wang's publications on Pubmed

or here on Google Scholar.

Please refer Dr. Wang's faculty profile for highlighted publications


Two postdoc positions open in Dr. Ze Wang’s lab

The first one will be supported by a pending R01 for up to five years. We are looking for self-motivated candidates who will focus on deep learning based MR image reconstruction and image processing. Required skills and experience include: deep learning and MR image reconstruction. C++ programming experience will be preferred but not a necessary condition. The ideal candidate should have background in one or more of the following fields: image reconstruction, machine learning, MR physics, electrical engineering, computer science, or mathematics. This position will have intensive interactions with leading experts and will work with teams from University of Pennsylvania and Siemens. There is much chance to learn and grow.

The second position will be supported by Dr. Wang’s R01 project for 4 or more years. The R01 is to develop and evaluate new resting state fMRI processing strategies in normal aging and Alzheimer’s Disease. In particular, we are focusing on brain entropy and coherence mapping. The ideal candidates should have method development experience in fMRI or neuroimaging signal processing. Background in electrical engineering, computer science, or mathematics is highly preferred. Python, Matlab programming skills are required. Experience on multi-band fMRI data processing is considered a plus.

Candidates for both positions are expected to have good communication skills and have good writing skills.

We expect to have more fundings in the coming years. Please come back for more positions open in the near future. The Wang Lab has 6 members at this moment. Some information can be found in https://www.medschool.umaryland.edu/pi/Ze-Wang-PhD/.

Please send your CV to  ze.wang@som.umaryland.edu.

Abubabar Yamin (incoming postdoc)

Dr. Yamin will join the lab in the fall of 2022.

Sadiq Alishba will join the lab in the fall of 2022.

Ms Sadiq Alishba will obtain her PhD and join the lab in the fall of 2022.