张寒
副教授、研究员、博导
研究方向:智能医学
办公室:生物医学工程学院大楼404房间
电话:
个人简历
研究领域
教学与课程
学术任职
代表性论文
实验室介绍

个人简历:

张寒任生物医学工程学院常任轨副教授 (Tenure-track Associate Professor)、研究员、博导、独立课题组组长 (Principal Investigator, PI)、脑疾病与智能计算实验室 (Disease and Intelligent Analytics Lab, DIAL) 主任。他在浙江大学信息与电子工程学系获工学学士学位,在北京师范大学认知神经科学与学习国家重点实验室获认知神经科学博士学位,历任杭州师范大学认知与脑疾病研究中心研究员、美国北卡大学教堂山分校 (University of North Carolina at Chapel Hill) 放射系 (Department of Radiology) 和生物医学研究成像中心(Biomedical Research Imaging Center, BRIC) 博士后、讲师、助理教授、张江实验室脑与智能科技研究院研究员。

张寒在脑网络、脑影像分析及脑发育、脑老化及其在临床中的应用研究形成了120多篇论文,发表在包括 PNAS, Brain, Human Brain Mapping, NeuroImage, IEEE TMI/Trans Cybernetics 等期刊以及 AAAIMICCAI 等会议上,被引近4000次,H因子33H10因子73。他是国家海外YQ项目获得者、上海市浦江人才、IEEE 高级会员,60余国际期刊/会议/研究基金的评审人和3个国际期刊的副主编,以通讯和合作PI主持美国NIH项目5项。他曾做为PI之一参与了美国婴儿脑连接组 (Baby Connectome Project, BCP) 计划。张寒合作和独立培养硕士生6名、博士生19 (包括访问学生) ,是7名本科生的合作导师,并指导了21名访问学者和17名博士后,指导的学生获得MICCAI会议的美国国立卫生局奖 (NIH Award) 和国际人脑图谱大会最佳摘要奖 (OHBM Merit Abstract Award) 等。回国后作为项目负责人或研究骨干参与了国家自然科学基金委重点项目、科技部国家重点研发计划、上海市基础研究特区计划等研究项目,开展脑发育、脑老化、脑疾病人工智能前沿研究。






  • 脑网络构建

  • 婴幼儿脑发育

  • 正常和异常脑老化

  • 阿尔茨海默症和相关痴呆

  • 脑肿瘤

  • 计算机辅助诊断

  • 医学影像深度学习




教学与课程:

研究生论文写作

科学表达与交流

脑科学与脑疾病


学术任职:

IEEE 资深会员

Brain Informatics 杂志副主编

Frontiers in Oncology 杂志副主编

Frontiers in Oncology 杂志特邀编辑

医学图像计算研讨会在线委员会委员

PLOS ONE 杂志副主编



代表性论文:

脑网络构建方法学:

  • Ghanbari, M., Zhou, Z., Hsu, L.-M., Han, Y., Sun, Y., Yap, P.-T., Zhang, H.*, Shen, D.*, Altered Connectedness of the Brain Chronnectome During the Progression to Alzheimer’s Disease, Neuroinformatics, 2021, Accepted.

  • Li, G., Liu, Y., Zheng, Y., Wu, Y., Li, D., Liang, X., Chen, Y., Cui, Y., Yap, P.-T., Qiu, S.*, Zhang, H.*, Shen, D.*, 2021, Multiscale Neural Modeling of Resting-state fMRI Reveals Executive-Limbic Malfunction as a Core Mechanism in Major Depressive Disorder, NeuroImage: Clinical, 2021, 31: 102758.

  • Zhou, Z., Chen, X., Zhang, Y., Hu, D., Qiao, L., Yu, R., Yap, P.-T., Pan, G.*, Zhang, H.*, Shen, D.*, A Toolbox for Brain Network Construction and Classification (BrainNetClass). Human Brain Mapping, 2020, 41(10): 2808-2826.

  • Li, G., Liu, Y., Zheng, Y., Li, D., Liang, X., Chen, Y., Cui, Y., Yap, P.-T., Qiu, S.*, Zhang, H.*, Shen, D.*, Large-scale Dynamic Causal Modeling of Major Depressive Disorder based on Resting-state fMRI. Human Brain Mapping, 2019.

  • Yu, R.#, Zhang, H.#, An, L., Chen, X., Wei, Z., Shen, D., 2017. Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification. Human Brain Mapping, 38(5): 2370-2383.

 

脑疾病个体诊断:

  • Chen, Y.#, Zhou, Z.#, Liang, Y., Tan X., Li, Y., Qin, C., Feng, Y., Ma, X., Mo, Z., Xia, J., Zhang, H.*, Qiu, S.*, Shen, D.*, Classification of Type 2 Diabetes Mellitus with or without Cognitive Impairment from Healthy Controls Using High-order Functional Connectivity. Human Brain Mapping, 2021, 42(14): 4671-4684.

  • Jiang, W., Zhang, H.*, Zeng, L.-L., Shen, H., Jian, Q., Thung, K.-H., Yap, P.-T., Liu, H., Hu, D., Wang, W.*, Shen, D.*, Dynamic neural circuit disruptions associated with antisocial behaviors. Human Brain Mapping, 2021, 42 (2): 329-344

  • Ding, Z.#, Zhang, H.#, Lv, X.#, Xie, F., Liu L., Li, L., Shen, D., 2018. Radiation-induced Brain Structural and Functional Abnormalities in Pre-symptomatic Phase and Outcome Prediction. Human Brain Mapping, 39(1):407-427.

  • Chen, X.#, Zhang, H.#, Zhang, L., Shen, C., Lee, S.-W., Shen, D., 2017. Extraction of Dynamic Functional Connectivity from Brain Grey Matter and White Matter for MCI Classification, Human Brain Mapping, 38(10):5019-5034.

 

阿尔茨海默症和相关痴呆:

  • Yue, L.#, Hu, D.#, Zhang, H.#, Wen, J., Wu, Y., Li, W., Sun, L., Li, X., Wang, J., Li, G., Wang, T., Shen, D., Xiao, 2021, Prediction of 7-year’s Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment. Human Brain Mapping. DOI: 10.1002/hbm.25216.

  • Kam, T.-E., Zhang, H.*, Jiao, Z., Shen, D., Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection, IEEE Transactions on Medical Imaging, 2020, 39(2): 478–487.

  • Ghanbari, M., Hsu, L.-M., Zhou, Z., Ghanbari, A., Mo, Z., Yap, P.-T., Zhang, H.*, Shen, D.*, A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer's Disease, MICCAI 2020, Lima, Peru, Oct 4-8, 2020 (MICCAI 2020 NIH Award).

  • Jiao, Z., Huang, P., Kam, T.-E., Hsu, L.-M., Wu, Y., Zhang, H.*, Shen, D.*, Dynamic Routing Capsule Networks for Mild Cognitive Impairment Diagnosis, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.

 

婴幼儿脑发育:

  • Wen, X., Wang, R., Yin, W., Lin, W., Zhang, H.*, Shen, D.*, Development of Dynamic Functional Architecture during Early Infancy. Cerebral Cortex,2020, 30(11): 5626-5638.

  • Wen, X., Zhang, H.*, Li, G., Liu, M., Yin, W., Lin, W., Zhang, J., Shen, D.*, First-Year Development of Modules and Hubs in Infant Brain Functional Networks. NeuroImage, 2019, 185:222-235.

  • Zhang, H., Shen, D., Lin, W., 2019. Resting-state Functional MRI Studies on Infant Brains: a Decade of Gap-Filling Efforts, NeuroImage, 185:664-684.

  • Sousia, M., Wen X., Zhou, Z., Jin, B., Kam, T.-E., Hsu, L.-M., Wu, Z., Li, G., Wang, L., Rekik, I., Lin, W., Shen, D., Zhang, H.*, and for UNC/UMN Baby Connectome Project Consortium, A computational framework for dissociating development-related from individually variable flexibility in regional modularity assignment in early infancy, MICCAI 2020, Lima, Peru, Oct 4-8, 2020.

  • Jiang, W., Zhang, H.*, Wu, Y., Hsu, L.-M., Hu, D., Shen, D.*, Early Development of Infant Brain Complex Network, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.

  • Zhou, Z., Zhang, H.*, Hsu, L.-M., Lin, W., Pan, G.*, Shen, D.*, and for the UNC/UMN Baby Connectome Project Consortium, Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.

  • Kam, T.-E., Wen, X., Jin, B., Jiao, Z., Hsu, L.-M., Zhou, Z., Liu, Y., Yamashita, K., Hung, S.-C., Lin, W., Zhang, H.*, Shen, D.*, and for UNC/UMN Baby Connectome Project Consortium, A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.

  • Zhang, H., Stanley, N., Mucha, P.J., Yin, W., Lin, W., Shen, D., Multi-layer large-scale functional connectome reveals infant brain developmental patterns. MICCAI 2018, Granada, Spain, Sep. 16-20, 2018.

 

脑肿瘤:

  • Tang, Z., Xu, Y., Jin, L., Aibaidula, A., Lu, J., Jiao, Z., Wu, J.*, Zhang, H.*, Shen, D.*, Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients. IEEE Transactions on Medical Imaging, 2020, 39(6): 2100-2109.

  • Chen, L.#, Zhang, H.#,Lu, J.#, Thung, K., Aibaidula, A., Liu, L., Chen, S., Jin, L., Wu, J., Wang, Q., Zhou, L., Shen, D., 2018. Multi-label Nonlinear Matrix Completion with Transductive Multi-task Feature Selection for Joint MGMT and IDH1 Status Prediction of Patient with High-Grade Gliomas. IEEE Transactions on Medical Imaging, 37(8):1775-1787.

  • Cao, B., Zhang, H.*, Wang, N.*, Gao, X., Shen, D.*, Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis, AAAI 2020, New York, USA, Feb 7-12, 2020 (Travel award).

  • Huang, P., Li, D., Jiao, Z., Wei, D., Li, G., Zhang, H.*, Shen, D.*, CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.




实验室介绍:

在上科大生物医学工程学院的脑疾病与智能计算实验室 (Disease and Intelligent Analytics Lab, DIAL) 致力于探索以下研究方向:

  • 对脑疾病和精神障碍检测更加敏感的脑网络构建技术:包括但不限于脑高阶网络、动态网络、有向网络等网络构建技术,目标指向更精准敏感的疾病检测,以及与之相关的工具开发。

  • 基于脑功能成像和脑网络的机器学习和深度学习算法:从脑结构磁共振成像、弥散磁共振成像、功能磁共振成像等构建多种脑网络并开发机器学习和深度学习算法进行基于脑网络时空特征的疾病分类、预测。

  • 脑肿瘤多模态影像分析、自动评估、手术计划:基于多模态脑成像 (MRICTPET的个体化、智能化脑肿瘤分割、分型、功能定位、预后评估、图像生成、基因组分析、手术计划等。

  • 婴幼儿脑发育及发育异常:构建婴幼儿发育大队列,基于人工智能和大数据开展人脑早期结构、功能、网络发育研究,构建发育常模、轨迹、图谱,研究基因-环境-影像-行为的关联,为异常发育研究提供理论依据。

  • 脑老化和老化相关疾病的早期诊断:构建人脑正常老化大队列,基于人工智能和大数据开展人脑正常和异常老化相关结构、功能、网络的改变,构建老化常模、轨迹、图谱,研究基因-环境-影像-行为的关联,为异常老化早期检测研究提供理论依据。