辦公室：上海大學寶山校區東區19号樓108室

電 話： 021-66138351

E-mail： wmwin@shu.edu.cn

學術主頁：https://www.researchgate.net/profile/Wang-Min-3

個人簡介：

副教授，碩士生導師。上海市超級博士後、上海市白玉蘭人才浦江項目。

2017年6月本科畢業于燕山大學，2022年7月博士畢業于上海大學。2021年至2022年在德國慕尼黑工業大學進行國家公派聯合培養博士生留學項目，2022年7月至2024年12月在上海大學從事博士後研究工作。2024年12月任上海大學生命科學學院副教授。王敏在生物醫學工程領域有着10餘年的學習和研究經曆，構建了基于PET腦影像的個體代謝腦網絡新方法,在PET/MRI醫學圖像分析和神經退化性疾病診療方面具有紮實的研究基礎。主持含自然基金青年項目在内的4項項目，已在EJNMMI、Neuroimage、HBM等領域内國際知名影像學期刊上以第一/共一作者署名發表SCI論文數篇。擔任EJNMMI、JNM、ART、ER等多本高水平期刊編委。

研究領域：

生物醫學工程-PET/MRI腦影像分析

代謝腦影像相關的方法論及其臨床應用

阿爾茲海默症及帕金森病等相關神經退化性疾病的分子影像智能分析

在研承擔項目：

[1] 國家自然科學基金青年項目，基于動态PET/MRI 影像的合作協同進化多層腦網絡算法及ICVD 應用研究，2024年-2026年，主持

[2] 中國博士後科學基金面上項目，基于DTI 結構約束的個體代謝連接組學方法及其在AD 早期診斷中的應用，2022年-2024年，主持

[3]國家自然科學基金面上項目，基于腦灰-白質圖卷積模型的SCD影像标記物提取及臨床應用研究，參與，2024年-2027年

[4] 上海市白玉蘭人才項目浦江項目，2024年-2026年，主持

[5] 上海市“超級博士後”激勵資助計劃，2022年-2024年，主持

主要學術論文：

[1] Cui, L., Zhang, Z., Tu, Y. Y., Wang, M., Guan, Y. H., Li, Y. H., Xie, F., & Guo, Q. H. (2024). Association of precuneus Aβ burden with default mode network function. Alzheimer's & dementia: the journal of the Alzheimer's Association.https://doi.org/10.1002/alz.14380

[2] Wang, M., Wei, M., Wang, L., Song, J., Rominger, A., Shi, K., Jiang, J., & Alzheimer’s Disease Neuroimaging Initiative (2024). Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer's Disease Continuum. Brain sciences, 14(6), 575. https://doi.org/10.3390/brainsci14060575

[3] Wang, M., Lu, J., Zhang, Y., Zhang, Q., Wang, L., Wu, P., Brendel, M., Rominger, A., Shi, K., Zhao, Q., Jiang, J., & Zuo, C. (2024). Characterization of tau propagation pattern and cascading hypometabolism from functional connectivity in Alzheimer's disease. Human brain mapping, 45(7), e26689. https://doi.org/10.1002/hbm.26689

[4] Liu, Y., Wang, M., Yu, X., Han, Y., Jiang, J., & Yan, Z. (2024). An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation. Medical physics, 51(6), 4105–4120. https://doi.org/10.1002/mp.16984

[5] Lu, J., Ju, Z., Wang, M., Sun, X., Jia, C., Li, L., Bao, W., Zhang, H., Jiao, F., Lin, H., Yen, T. C., Cui, R., Lan, X., Zhao, Q., Guan, Y., Zuo, C., & Shanghai Memory Study (SMS) (2023). Feasibility of 18F-florzolotau quantification in patients with Alzheimer's disease based on an MRI-free tau PET template. European radiology, 33(7), 4567–4579. https://doi.org/10.1007/s00330-023-09571-7

[6] Wang, M., Schutte, M., Grimmer, T., Lizarraga, A., Schultz, T., Hedderich, D. M., Diehl-Schmid, J., Rominger, A., Ziegler, S., Navab, N., Yan, Z., Jiang, J., Yakushev, I., & Shi, K. (2022). Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach. European journal of nuclear medicine and molecular imaging, 50(1), 80–89. https://doi.org/10.1007/s00259-022-05949-9

[7] Wang, M., Cui, B., Shan, Y., Yang, H., Yan, Z., Sundar, L. K. S., Alberts, I., Rominger, A., Wendler, T., Shi, K., Ma, Y., Jiang, J., & Lu, J. (2022). Non-Invasive Glucose Metabolism Quantification Method Based on Unilateral ICA Image Derived Input Function by Hybrid PET/MR in Ischemic Cerebrovascular Disease. IEEE journal of biomedical and health informatics, 26(10), 5122–5129. https://doi.org/10.1109/JBHI.2022.3193190

[8] Jiang, J., Wang, M., Alberts, I., Sun, X., Li, T., Rominger, A., Zuo, C., Han, Y., Shi, K., & Initiative, F. T. A. D. N. (2022). Using radiomics-based modelling to predict individual progression from mild cognitive impairment to Alzheimer's disease. European journal of nuclear medicine and molecular imaging, 49(7), 2163–2173. https://doi.org/10.1007/s00259-022-05687-y

[9] Wang, M., Yan, Z., Zhang, H., Lu, J., Li, L., Yu, J., Wang, J., Matsuda, H., Zuo, C., Jiang, J., & Alzheimer’s Disease Neuroimaging Initiative (2021). Parametric estimation of reference signal intensity in the quantification of amyloid-beta deposition: an 18F-AV-45 study. Quantitative imaging in medicine and surgery, 11(1), 249–263. https://doi.org/10.21037/qims-20-110

[10] Wang, M., Jiang, J., Yan, Z., Alberts, I., Ge, J., Zhang, H., Zuo, C., Yu, J., Rominger, A., Shi, K., & Alzheimer’s Disease Neuroimaging Initiative (2020). Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia. European journal of nuclear medicine and molecular imaging, 47(12), 2753–2764. https://doi.org/10.1007/s00259-020-04814-x

[11] Wang, M., Yan, Z., Xiao, S. Y., Zuo, C., & Jiang, J. (2020). A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment. Behavioural neurology, 2020, 2825037. https://doi.org/10.1155/2020/2825037