Han Zhang’s CV

Han Zhang Ph.D.

Research Assistant Professor, IDEA Lab, Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599

 

EDUCATION BACKGROUND

2006–2011      Ph.D. in Cognitive Neuroscience (Neuroimage Computing), National Key Laboratory for Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

2001–2005     B.S. in Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

 

RESEARCH POSITIONS

2018.11-                    Assistant Professor, Department of Radiology and BRIC, UNC-CH

2017.2 –  2018.10  Instructor, Department of Radiology and BRIC, UNC-CH

2015.7 – 2017.1     Postdoctoral Research Fellow, Department of Radiology and BRIC, UNC-CH

2011.7 – 2016.6     Faculty Investigator, Institutes of Psychological Sciences, Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Zhejiang, China

2014.4 – 2015.5     Part-time P.I. in the Hand surgery department, Huashan Hospital, Fudan University, Shanghai, China

2013.1 – 4                Visiting scholar, Center for NMR Research, College of Medicine, Pennsylvania State University, Hershey, PA, USA

 

OTHER POSITIONS

2015 – now        Youth Committee member, Molecular Imaging Association, Chinese Society of Radiology

2017 – now        Member, International Society for Magnetic Resonance in Medicine (ISMRM)

2017 – now        Member, Organization of Human Brain Mapping (OHBM)

2017 – now        Member, Medical Image Computing and Computer Assisted Intervention Society (MICCAI)

2018 – now        Senior Member, IEEE

 

JOURNAL PAPERS  (# co-first author, * corresponding author)

  1. Zhou, Z., Chen, X., Zhang, Y., Hu, D., Qiao, L., Yu, R., Yap, P.-T., Pan, G.*, Zhang, H.*, Shen, D.*, 2020, A Toolbox for Brain Network Construction and Classification (BrainNetClass). Human Brain Mapping.
  2. Tang, Z., Xu, Y., Jin, L., Aibaidula, A., Lu, J., Jiao, Z., Wu, J.*, Zhang, H.*, Shen, D.*, 2020, Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients. IEEE Transactions on Medical Imaging.
  3. Jia, X.-Z., Ji, G.-J., Liao, W., Lv, Y.-T., Wang, J., Wang, Z., Zhang, H., Liu, D.-Q., Zang, Y.-F., 2020. Percent amplitude of fluctuation: a simple measure for resting-state fMRI signal at single voxel level. PLoS ONE.
  4. 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.
  5. 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, 2019.
  6. Wang, F., Lian, C., Wu, Z., Zhang, H., Li, T., Meng, Y., Wang, L., Lin, W., Shen, D., Li, G., Developmental Topography of Cortical Thickness during Infancy”, PNAS, Accepted.
  7. Jia, X.-Z., Wang, J., Sun, H.-Y., Zhang, H., Liao, W., Wang, Z., Yan, C.-G., Song, X.-W., Zang, Y.-F., 2019. RESTplus: an improved toolkit for resting-state functional magnetic resonance imaging data processing, Science Bulletin, 64:953-954.
  8. Lu, J., Yang, Q.X., Zhang, H., Eslinger, P.J., Zhang, X., Wu, S., Zhang, B., Zhu, B., Karunanayaka, P.R., Disruptions of the olfactory and default mode networks in Alzheimer’s disease, Brain and Behavior, Accepted.
  9. Zheng, Y., Chen, X., Li, D., Liu, Y., Tan, X., Liang, Y., Zhang, H.*, Qiu, S.*, Shen, D.*, Treatment-Naïve First Episode Depression Classification Based on High-order Brain Functional Network, Journal of Affective Disorders, Accepted.
  10. Xu, X., He, P., Yap, P.-T., Zhang, H., Nie, J., Shen, D., Meta-network Analysis of Structural Correlation Networks Provides Insights into Brain Network Development, Frontiers in Human Neuroscience, Accepted.
  11. Liang, Y.#, Zhang, H.#, Tan, X., Liu, J., Qin, C., Zeng, H., Zheng, Y., Liu, Y., Chen, J., Leng, X., Qiu, S., Shen, D., Local diffusion homogeneity provides supplementary information in T2DM-related WM microstructural abnormality detection, Frontiers in Neuroscience, Accepted.
  12. Zhang, H., Giannakopoulos. P., Haller, S., Lee, S.-W., Qiu, S., Shen, D., Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment, Neuroinformatics, Accepted.
  13. Zhang, Y., Zhang, H., Chen, X., Liu, M., Zhu, X., Lee, S.-W., Shen, D., Strength and Similarity Guided Group-level Brain Functional Network Construction for MCI Diagnosis. Pattern Recognition, Accepted.
  14. Nie, D.#, Lu, J. #, Zhang, H.#, Adeli, E., Wang, J., Yu, Z., Liu, L., Wang, Q., Wu, J., Shen, D., Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages, Scientific Report, Accepted.
  15. 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, Accepted.
  16. Liu, L.#, Zhang, H.#, Wu, J., Yu, Z., Chen, X., Rekik, I., Wang, Q., Lu, J., Shen, D., Overall Survival Time Prediction for High-grade Glioma Patients based on Large-scale Brain Functional Networks, Brain Imaging and Behavior, Accepted.
  17. He, P., Xu, X., Zhang, H., Li, G., Nie, J., Yap, P.-T., Shen, D., Spatiotemporal Analysis of Developing Brain Networks, Frontiers in Neuroinformatics, Accepted.
  18. Zhang, H., Shen, D., Lin, W., Resting-state Functional MRI Studies on Infant Brains: a Decade of Gap-Filling Efforts, NeuroImage, Accepted.
  19. Wang, J., Wang, Q., Zhang, H., Chen, J., Wang S., Shen D., Sparse Multi-View Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-related Functional Connectivity Patterns, IEEE Transactions on Cybernetics, 2018, Accepted.
  20. Zhao, F., Zhang, H., Rekik, I., An, Z., Shen, D. Diagnosis of Autism Spectrum Disorders Using Multi-level High-order Functional Networks Derived from Resting-State Functional MRI, Frontiers in Human Neuroscience, 2018, Accepted.
  21. Liu, L., Wang, Q., Adeli, E., Zhang, L. Zhang, H., Shen, D. Exploring Diagnosis and Imaging Biomarkers of Parkinson’s Disease via Iterative Canonical Correlation Analysis Based Feature Selection, Computerized Medical Imaging and Graphics, 2018, Accepted.
  22. Zhou, Y., Zhang, H., Zhang, L., Cao, X., Yang, R., Feng, Q., Yap, P.-T., Shen, D. Functional MRI Registration with Tissue-Specific Patch-Based Functional Correlation Tensors. Human Brain Mapping, 2018, Accepted.
  23. Chen, L.#, Zhang, H.#, Lu, J.#, Thung, K., Aibaidula, A., Liu, L., Chen, S., Jin, L., Wu, J., Wang, Q., Zhou, L., Shen, D. 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, 2018, Accepted.
  24. Huang, H., Lu, J., Wu, J., Ding, Z., Chen, S., Duan, L., Cui, J.-L., Chen, F., Kang, D.-Z., Qi, L., Qiu, W., Shen, D., Zang, Y.-F., Zhang, H*. Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis. Scientific Report, 2017, Accepted.
  25. Lu, J.#, Zhang, H.#, Hameed, N.U.F., Zhang, J., Yuan, S., Qiu, T., Shen, D., Wu, J. An automated method for identifying an independent component analysis based language network in brain tumor subjects using resting-state fMRI. Scientific Report 7, Article number: 13769. 09 Oct, 2017, doi:10.1038/s41598-017-14248-5.
  26. Ding, Z.#, Zhang, H.#, Lv, X.#, Xie, F., Liu L., Li, L., Shen, D. Radiation-induced Brain Structural and Functional Abnormalities in Pre-symptomatic Phase and Outcome Prediction. Human Brain Mapping, 09 Oct, 2017, In press.
  27. Yu, E., Liao, Z., Tan, Y., Qiu, Y., Zhu, J., Zhang, H., Wang, J., Wang, X., Wang, H., Chen, Y., Zhang, Q., Li, Y., Mao, D., Ding, Z. High-sensitivity neuroimaging biomarkers for the identification of amnestic mild cognitive impairment based on resting-state fMRI and a triple network model. Brain Imaging and Behavior. 03 May, 2017, In press.
  28. Zhang, H., Chen, X., Zhang, Y., Shen, D., Test-retest reliability of “high-order” functional connectivity in young healthy adults. Frontiers in Neuroscience, Aug. 2, 2017. DOI: 10.3389/fnins.2017.00439.
  29. Zhang, L., Zhang, H., Chen, X., Wang, Q., Yap, P.-T., Shen, D., 2017. Learning-Based Structurally-Guided Construction of Resting-State Functional Correlation Tensors. Magnetic Resonance Imaging, 43:110-121. DOI: 10.1016/j.mri.2017.07.008
  30. Chen, X.#, Zhang, H.#, Zhang, L., Shen, C., Lee, S.-W., Shen, D. Extraction of Dynamic Functional Connectivity from Brain Grey Matter and White Matter for MCI Classification, Human Brain Mapping, In Press. DOI: 10.1002/hbm.23711.
  31. Zhang, Y., Zhang, H., Chen, X., Lee, S.-W., Shen, D. Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis, Scientific Reports, 2017. In Press. DOI: 10.1038/s41598-017-06509-0.
  32. Chen, X., Zhang, H., Shen, D., 2017. Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification. Neuroinformatics, 15(3):271-284.
  33. Wang, J., Wang, Q., Peng, J., Nie, D., Zhao, F., Kim, M., Zhang, H., Wee, C.-Y., Wang, S., Shen, D., 2017. Multi-Task Diagnosis for Autism Spectrum Disorders Using Multi-Modality Features: A Multi-Center Study. Human Brain Mapping,38(6):3081-3097. DOI: 10.1002/hbm.23575.
  34. Ji, L., Zhang, H., Potter, G., Zang, Y.-F., Steffens, D., Guo, H., Wang, L., 2017. Multiple Neuroimaging Measures for Examining Exercise-Induced Neuroplasticity in Older Adults: a quasi-experimental study. Frontiers in Aging Neuroscience, 9:102.
  35. Meng, Y., Li, G., Rekik, I., Zhang, H., Gao, Y., Lin, W., Shen, D., 2017. Can we predict subject-specific dynamic cortical thickness maps during infancy from birth? Human Brain Mapping, 38(6):2865-2874.
  36. 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.
  37. Lu, Y.-C.#, Zhang, H.#, Zheng, M.-X., Hua, X.-Y., Qiu, Y.-Q., Shen, Y.-D., Jiang, S., Xu, J.-G., Gu, Y.-D., Xu, W.-D. Local and Extensive Neuroplasticity in Carpal Tunnel Syndrome: A Resting-state fMRI Study. Neurorehabilitation & Neural Repair, In Press.
  38. Yuan, L., He, H., Zhang, H., Zhong, J., 2016. Evaluating the Influence of Spatial Resampling for Motion Correction in Resting-State Functional MRI. Frontiers in Neuroscience, 10:591.
  39. Duan, L., Wang, M., Sun, F., Zhao, Z.-J., Xing, M., Zang, Y.-F., Louis S., Cui, J., Zhang, H.*, 2016. Characterizing the Blood Oxygen Level-Dependent Fluctuations in Musculoskeletal Tumours Using Functional Magnetic Resonance Imaging. Scientific Report, 36522. DOI:10.1038/srep36522.
  40. Qiao, L., Zhang, H., Kim, M., Teng, S., Zhang, L., Shen, D., 2016. Estimating functional brain networks by incorporating a modularity prior, NeuroImage, 141:399-407. DOI:10.1016/j.neuroimage.2016.07.058.
  41. Zhang, H., Chen, X., Shi, F., Li, G., Kim, M., Giannakopoulos, P., Haller, S., Shen, D., 2016. Topographic Information based High-Order Functional Connectivity and its Application in Abnormality Detection for Mild Cognitive Impairment,Journal of Alzheimer’s Disease, 54(3): 1095-1112. DOI: 10.3233/JAD-160092.
  42. Zhang, S., Chen, J., Kuang, L., Cao, J., Zhang, H., Ai, M., Wang, W., Zhang, S., Wang, S., Liu, S., Fang, W., 2016. Association between abnormal default mode network activity and suicidality in depressed adolescents. BMC Psychiatry, 16. DOI: 10.1186/s12888-016-1047-7.
  43. Chen, X., Zhang, H., Gao, Y., Wee, C.-Y., Li, G., Shen, D., the Alzheimer’s Disease Neuroimaging Initiative, 2016. High-order resting-state functional connectivity network for MCI classification. Human Brain Mapping, 37(9):3282-96. DOI:10.1002/hbm.23240.
  44. Huang, H., Ding, Z., Mao, D., Yuan, J., Zhu, F., Chen, S., Xu, Y., Lou, L., Feng, X., Qi, L., Qiu, W., Zhang, H.*, Zang, Y.-F., May 24, 2016. PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI. Neuroinformatics. DOI: 1007/s12021-016-9304-y.
  45. Wang, J., Yang, N., Liao, W., Zhang, H., Yan, C.-G., Zang, Y.-F., Zuo, X.-N., 2015. Dorsal anterior cingulate cortex in typically developing children: Laterality analysis. Developmental Cognitive Neuroscience 15, 117–129. DOI: 1016/j.dcn.2015.10.002.
  46. Fang, W., Chen, H., Wang, H., Zhang, H., Puneet, M., Liu, M., Lv, F., Luo, T., Cheng, O., Wang, X., Lu, X., 2016. Essential tremor is associated with disruption of functional connectivity in the ventral intermediate Nucleus-Motor Cortex-Cerebellum circuit: ET Is Associated With Disruption of FC in the VIM. Human Brain Mapping 37, 165–178. DOI: 1002/hbm.23024.
  47. Fang, W., Chen, H., Wang, H., Zhang, H., Liu, M., Puneet, M., Lv, F., Cheng, O., Wang, X., Lu, X., Luo, T., 2015. Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor. Mov. Disord. 30, 1926–1936. DOI: 1002/mds.26375.
  48. Wu, J., Lu, J., Zhang, H., Zhang, J., Yao, C., Zhuang, D., Qiu, T., Guo, Q., Hu, X., Mao, Y., Zhou, L., 2015. Direct evidence from intraoperative electrocortical stimulation indicates shared and distinct speech production center between Chinese and English languages. Hum Brain Mapp 36, 4972–4985. DOI: 1002/hbm.22991.
  49. Liang, P.#, Zhang, H.#, Xu, Y., Jia, W., Zang, Y., Li, K., 2015. Disruption of cortical integration during midazolam-induced light sedation: Effects of Midazolam-Induced Sedation on RSNs. Human Brain Mapping 36, 4247–4261. DOI: 1002/hbm.22914.
  50. Liao, X., Chen, F., Tao, W., Liu, Y., Zhang, H., Li, Y.-J., Zang, Y.-F., 2015. Dynamic observation of baseline brain activities in patients with postherpetic neuralgia revealed by resting-state functional MRI. Progress in Biochemistry and Biophysics 42, 947–954.
  51. Mao, D., Ding, Z., Jia, W., Liao, W., Li, X., Huang, H., Yuan, J., Zang, Y.-F., Zhang, H.*, Low-Frequency Fluctuations of the Resting Brain: High Magnitude Does Not Equal High Reliability. PLOS ONE 10, e0128117. DOI: 10.1371/journal.pone.0128117.
  52. Wang, Z., Wang, J., Zhang, H., Mchugh, R., Sun, X., Li, K., Yang, Q.X., 2015. Interhemispheric Functional and Structural Disconnection in Alzheimer’s Disease: A Combined Resting-State fMRI and DTI Study. PLOS ONE 10, e0126310. DOI: 1371/journal.pone.0126310.
  53. Li, X., Zang, Y.-F., Zhang, H.*, Exploring Dynamic Brain Functional Networks Using Continuous “State-Related” Functional MRI. BioMed Research International 2015, 1–8. DOI: 10.1155/2015/824710.
  54. Zhang, H., Zhang, H., Zang, Y.-F., 2015. Functional connectivity among brain networks in continuous feedback of finger force. Neuroscience 289, 134–143. DOI: 1016/j.neuroscience.2014.12.075.
  55. Gan, H., Zhang, Q., Zhang, H., Chen, Y., Lin, J., Kang, T., Zhang, J., Troy, F.A., Wang, B., 2014. Development of new population-averaged standard templates for spatial normalization and segmentation of MR images for postnatal piglet brains. Magn Reson Imaging 32, 1396–1402. DOI: 1016/j.mri.2014.08.036.
  56. Wu, J., Lu, J., Zhang, H., Zhang, J., Mao, Y., Zhou, L., 2015. Probabilistic map of language regions: challenge and implication. Brain 138, e337–e337. DOI: 1093/brain/awu247.
  57. Liao, W., Zhang, Z., Mantini, D., Xu, Q., Ji, G.-J., Zhang, H., Wang, J., Wang, Z., Chen, G., Tian, L., Jiao, Q., Zang, Y.-F., Lu, G., 2014. Dynamical intrinsic functional architecture of the brain during absence seizures. Brain Struct Funct 219, 2001–2015. DOI: 1007/s00429-013-0619-2.
  58. Ji, G.-J., Zhang, Z., Zhang, H., Wang, J., Liu, D.-Q., Zang, Y.-F., Liao, W., Lu, G., 2013. Disrupted Causal Connectivity in Mesial Temporal Lobe Epilepsy. PLoS ONE 8, e63183. DOI: 1371/journal.pone.0063183.
  59. Fang, W., Lv, F., Luo, T., Cheng, O., Liao, W., Sheng, K., Wang, X., Wu, F., Hu, Y., Luo, J., Yang, Q.X., Zhang, H.*, Abnormal Regional Homogeneity in Patients with Essential Tremor Revealed by Resting-State Functional MRI. PLoS ONE 8, e69199. DOI: 10.1371/journal.pone.0069199.
  60. Lu, J.-F.#, Zhang, H.#, Wu, J.-S., Yao, C.-J., Zhuang, D.-X., Qiu, T.-M., Jia, W.-B., Mao, Y., Zhou, L.-F., 2012. “Awake” intraoperative functional MRI (ai-fMRI) for mapping the eloquent cortex: Is it possible in awake craniotomy? Neuroimage Clin 2, 132–142. DOI: 1016/j.nicl.2012.12.002.
  61. Wang, J., Liu, D.-Q., Zhang, H., Zhu, W.-X., Dong, Z.-Y., Zang, Y.-F., 2013. Asymmetry of the dorsal anterior cingulate cortex: evidences from multiple modalities of MRI. Neuroinformatics 11, 149–157. DOI: 1007/s12021-012-9167-9.
  62. Zhang, Y.-J., Duan, L., Zhang, H., Biswal, B.B., Lu, C.-M., Zhu, C.-Z., 2012. Determination of Dominant Frequency of Resting-State Brain Interaction within One Functional System. PLoS ONE 7, e51584. DOI: 1371/journal.pone.0051584.
  63. Zhang, H., Zhang, Y.-J., Duan, L., Ma, S.-Y., Lu, C.-M., Zhu, C.-Z., 2011. Is resting-state functional connectivity revealed by functional near-infrared spectroscopy test-retest reliable? J Biomed Opt 16, 67008. DOI: 1117/1.3591020.
  64. Zhang, H., Duan, L., Zhang, Y.-J., Lu, C.-M., Liu, H., Zhu, C.-Z., 2011. Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy. Neuroimage 55, 607–615. DOI: 1016/j.neuroimage.2010.12.007.
  65. Zhang, H., Zuo, X.-N., Ma, S.-Y., Zang, Y.-F., Milham, M.P., Zhu, C.-Z., 2010. Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis. Neuroimage 51, 1414–1424. DOI: 1016/j.neuroimage.2010.03.039.
  66. Zhang, H., Zhang, Y.-J., Lu, C.-M., Ma, S.-Y., Zang, Y.-F., Zhu, C.-Z., 2010. Functional connectivity as revealed by . Neuroimage 51, 1150–1161. DOI: 1016/j.neuroimage.2010.02.080.
  67. Wang, L., Zhu, C., He, Y., Zang, Y., Cao, Q., Zhang, H., Zhong, Q., Wang, Y., 2009. Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder. Hum Brain Mapp 30, 638–649. DOI: 1002/hbm.20530.

 

CONFERENCE PAPERS/BOOK CHAPTERS (Peer-reviewed, full-length paper, * corresponding author)

  1. 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).
  2. 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.
  3. 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.
  4. Hu, D., Zhang, H., Wu, Z., Lin, W., Li, G., Shen, D.*, and for the UNC/UMN Baby Connectome Project Consortium, Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.
  5. Taylor, H.P., Wu, Z., Wu, Y., Shen, D., Zhang, H., Yap, P.-T., Automated Parcellation of the Cortex Using Structural Connectome Harmonics, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.
  6. 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.
  7. 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.
  8. Li, G., Liu, Y., Zheng, Y., Wu, Y., Yap, P.-T., Qiu, S., Zhang, H.*, Shen, D.*, Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-state fMRI, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.
  9. Tang, Z., Xu, Y., Jiao, Z., Lu, J., Jin, L., Aibaidula, A., Wu, J., Wang, Q., Zhang, H.*, Shen, D.*, Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.
  10. 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
  11. Jiao, Z., You, H., Yang, F., Li, X., Zhang, H., Shen, D., Decoding EEG by Visual-guided Deep Neural Networks, IJCAI, 2019.
  12. Zhang, L., Zhang, H., Rekik, I., Gao, Y., Wang, Q., Shen, D., Malignant Brain Tumor Classification Using the Random Forest Method. Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) 2018, Beijing, China, Aug. 17-19, 2018 in X. Bai et al. (Eds.): S+SSPR 2018, LNCS 11004, pp. 14-21, 2018.
  13. Yan, W., Zhang, H., Sui, J., Shen, D., Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis. MICCAI 2018, Granada, Spain, Sep. 16-20, 2018.
  14. Kam, T.-E., Zhang, H., Shen, D., A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis. MICCAI 2018, Granada, Spain, Sep. 16-20, 2018.
  15. Wang, L., Li, G., Shi, F., Cao, X., Lian, C., Nie, D., Liu, M., Zhang, H., Li, G., Lin, W., Shen, D., Volume-based Analysis of 6-month-old Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis. MICCAI 2018, Granada, Spain, Sep. 16-20, 2018.
  16. 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.
  17. Chen, J., Zhang, H., Nie, D., Wang, L., Li, G., Lin, W., Shen, D., Automatic Accurate Infant Cerebellar Tissue Segmentation with Densely Connected Convolutional Network. MLMI 2018, Accepted.
  18. Chen, X., Zhang, H., Zhang, Y., Li, Z., Shen, D., Learning Pairwise-Similarity Guided Sparse Functional Connectivity Network for MCI Classification, Asian Conference on Pattern Recognition (ACPR) 2017 (Best paper award).
  19. Zhang, Y., Zhang, H., et al., Shen, D., Constructing Multi-Frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment, 1st International Workshop on Connectomics in NeuroImaging (CNI 2017), MICCAI 2017 Workshop, in Lecture Notes in Computer Science (LNCS). Quebec City, Canada, 10-14, 2017.
  20. Jia, X., Zhang, H., et al., Shen, D., Consciousness Level and Recovery Outcome Prediction using High-Order Brain Functional Connectivity Network, 1st International Workshop on Connectomics in NeuroImaging (CNI 2017), MICCAI 2017 Workshop, in Lecture Notes in Computer Science (LNCS). Quebec City, Canada, 10-14, 2017.
  21. Zhang, L., Zhang, H., et al., Shen, D., Learning-Based Estimation of Functional Correlation Tensors in White Matter for Early Diagnosis of Mild Cognitive Impairment, 3rd International Workshop on Patch-based Techniques in Medical Imaging (PATCHI-MI 2017), MICCAI 2017 Workshop. Quebec City, Canada, 10-14, 2017.
  22. Zhang, Y., Zhang, H., et al., Shen, D., Inter-Subject Similarity Guided Brain Network Modeling for MCI Diagnosis, 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017), MICCAI 2017 Workshop. Quebec City, Canada, 10-14, 2017.
  23. Zhang, H., Weiyan Yin, Weili Lin, Shen, D., Early Brain Functional Segregation and Integration Predict Later Cognitive Performance, 1st International Workshop on Connectomics in NeuroImaging (CNI 2017), MICCAI 2017 Workshop, in Lecture Notes in Computer Science (LNCS). Quebec City, Canada, 10-14, 2017.
  24. Zhou, Y., Yap, P.-T., Zhang, H., Zhang, L., Feng, Q., Shen, D. Improving fMRI registration using white matter functional connectivity tensors. MICCAI 2017, Quebec, Canada, Sep. 10-14, 2017.
  25. Chen, L., Zhang, H., Liu, L., Thung, K., Lu, J., Wu, J., Wang, Q., Shen, D. Multi-label inductive matrix completion for joint MGMT and IDH1 status prediction for glioma patients. MICCAI 2017, Quebec, Canada, Sep. 10-14, 2017.
  26. Chen, X., Zhang, H., Shen, D., Ensemble Hierarchical High-Order Functional Connectivity Networks for MCI Classification, MICCAI 2016, Athens, Greece, 17–21, 2016.
  27. Yu, R., Zhang, H., An, L., Chen, X., Wei, Z., Shen, D., Correlation-Weighted Sparse Group Representation for Brain Network Construction in MCI Classification, MICCAI 2016, Athens, Greece, 17–21, 2016.
  28. Zhu, Y., Zhu, X., Zhang, H., Gao, W., Shen, D., Wu, G., Reveal Consistent Spatial-Temporal Patterns from Dynamic Functional Connectivity for Autism Spectrum Disorder Identification, MICCAI 2016, Athens, Greece, 17–21, 2016.
  29. Liu, L., Wang, Q., Adeli, E., Zhang, L., Zhang, H., Shen, D., Feature selection based on iterative canonical correlation analysis for automatic diagnosis of Parkinson’s Disease, MICCAI 2016, Athens, Greece, 17–21, 2016.
  30. Liu, L., Zhang, H., Rekik, I., Wang, Q., Shen, D., Outcome prediction for patient with high-grade gliomas from brain functional and structural networks, MICCAI 2016, Athens, Greece, 17–21, 2016 (Oral presentation).
  31. Nie, D., Zhang, H., Adeli, E., Liu, L., Shen, D., 3D Deep Learning for Multi-modal Imaging-guided Survival Time Prediction of Brain Tumor Patients, MICCAI 2016, Athens, Greece, 17–21, 2016.
  32. Yang, X., Jin, Y., Chen, X., Zhang, H., Shen, D., Functional Connectivity Network Fusion with Dynamic Thresholding for MCI Diagnosis. MICCAI Workshop on Machine Learning in Medical Imaging (MLMI 2016), Athens, Greece, Oct. 17, 2016.

 

OTHER FULL-LENGTH ARTICLES (ARXIV, BIOXIV, ETC.)

  1. Zhou, Z., Chen, X., Zhang, Y., Qiao, L., Yu, R., Pan, G.*, Zhang, H.*, Shen, D.*, Brain Network Construction and Classification Toolbox (BrainNetClass). arXiv:1906.09908, Jun, 2019.
  2. Wen, X., Wang, R., Lin, W., Zhang, H.*, Shen, D.*, Development of Dynamic Functional Architecture during Early Infancy. bioRxiv, https://doi.org/10.1101/829846, Nov, 2019.
  3. Yan, W., Zhang, H.*, Sui, J.*, Shen, D.*, Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis. arXiv:1808.10383, Aug, 2018.
  4. Jia, X.-Z., Ji, G.-J., Liao, W., Lv, Y.-T., Wang, J., Wang, Z., Zhang, H., Liu, D.-Q., Zang, Y.-F., 2020. Percent amplitude of fluctuation: a simple measure for resting-state fMRI signal at single voxel level. bioRxiv, https://doi.org/10.1101/214098, Nov, 2017.

 

CONFERENCE ABSTRACTS/POSTERS

  1. Jiang, W., Zhang, H.*, Shen, D.*, Development of Graph Theoretical Biomarkers in Early Infancy, 105th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Dec. 1-6, 2019.
  2. Sun, K., Jiao, Z., Yan, X., Zhang, H., Cheng, J.-Z., Yan, F., Shen, D., Comparison of Four Radiomics-based Classification Methods in Diagnosis of Breast Lesions with Multi-b Diffusion-Weighted MR imaging, 105th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Dec. 1-6, 2019. (Oral Presentation).
  3. Sun, K., Jiao, Z., Yan, X., Zhang, H., Cheng, J.-Z., Shen, D., Yan, F., Learning Effective Radiomic Features for Characterization of Breast Lesions with Multi-b Diffusion-Weighted MR Imaging, 105th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Dec. 1-6, 2019. (Oral Presentation).
  4. Li, G., Liu, Y., Zheng, Y., Wu, Y., Yap, P.-T., Qiu, S.*, Zhang, H.*, Shen, D.*, Multiscale Modeling of Intra-Regional and Inter-Regional Connectivities and Their Alterations in Major Depressive Disorder, 105th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Dec. 1-6, 2019. (Oral Presentation).
  5. Huang, P., Zhang, H.*, Jiao, Z., Wei, D., Shi, F., Li, D.*, Shen, D.*, Common-space-learning from Multi-modality for Missing MRI Synthesis and Glioma Grading, 105th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Dec. 1-6, 2019.
  6. Yamashita, K., Zhang, H., Li, T., Wen, X., Jing, B., Kam, T.-E., Hsu, L.-M., Yap, P.-T., Wang, L., Li, G., Baluyot, K.R., Howell, B.R., Styner, M.A., Yacoub, E., Chen, G., Potts, T., Gilmore, J.H., Piven, J., Smith, J.K., Ugurbil, K., Zhu, H., Elison, J.T., Hazlett, H., Zhu, H., Shen, D., Lin, W., Symmetrical Functional Connectivity Strength Between Bilateral Anterior Heschl’s Gyri are Negatively Associated with Receptive Function During Infancy, 105th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Dec. 1-6, 2019. (Oral Presentation).
  7. Li, G., Liu, Y., Zheng, Y., Hsu, L.-M., Zhang, H.*, Shen, D., Large-scale Dynamic Causal Modeling of Major Depressive Disorder based on Resting-state fMRI, OHBM, Rome, Italy, June 9-13, 2019.
  8. Hsu, L.-M., Zhang, H.*, Wen, X., Jing, B., Kam, T.-E., Wang, L., Wu, Z., Yap, P.-T., Baluyot, K.R., Howell, B.R., Styner, M.A., Yacoub, E., Chen, G., Potts, T., Gilmore, J.H., Piven, J., Smith, J.K., Ugurbil, K., Zhu, H., Hazlett, H., Elison, J.T., Lin, W., Shen, D., for UNC/UMN Baby Connectome Project Consortium, Frequency specificity of spontaneous brain activity in developing infant brain, OHBM, Rome, Italy, June 9-13, 2019.
  9. Liu, Y., Hsu, L.-M., Zheng, Y., Qiu, S., Zhang, H.*, Shen, D., Diagnosing Major Depressive Disorder with High-order Local Functional Connectivity, OHBM, Rome, Italy, June 9-13, 2019.
  10. Jiang, W., Zhang, H.*, Zeng, L., Shen, H., Wang, W., Hu, D., Shen, D., Dynamic Neural Disruptions Associated with Antisocial Behavior, OHBM, Rome, Italy, June 9-13, 2019.
  11. Hu, D., Wu, Z., Zhang, H., Lin, W., Gang Li , Shen, D., for UNC/UMN Baby Connectome Project Consortium, Infant Age Prediction Based on Deep Granular Label Distribution Learning of Cortical Features, OHBM, Rome, Italy, June 9-13, 2019.
  12. Zhang, H., Wen, X., Jing, B., Hsu, L.-M., Kam, T.-E., Wu, Z., Wang, L., Li, G., Lin, W., Shen, D., for UNC/UMN Baby Connectome Project Consortium, Infant Resting-state FMRI Analysis Pipeline for UNC/UMN Baby Connectome Project, OHBM, Rome, Italy, June 9-13, 2019.
  13. Zhang, H., Li, G., Wen, X., Jing, B., Hsu, L.-M., Kam, T.-E., Lin, W., Shen, D., for UNC/UMN Baby Connectome Project Consortium, Month-to-month Development of Brain Functional Networks during Early Infancy, OHBM, Rome, Italy, June 9-13, 2019.
  14. Kam, T.-E., Zhang, H.*, Jing, B., Wen, X., Lin, W., Shen, D., for UNC/UMN Baby Connectome Project Consortium, Deep Learning-based Automatic Noisy Component Detection for Automatic Resting-state fMRI Denoising, OHBM, Rome, Italy, June 9-13, 2019.
  15. Yue, L., Hu, D., Zhang, H., Wen, J., Wu, Y., Wang, T., Shen, D., Xiao, S., Prediction of 7-year progression from subjective cognitive decline to MCI, OHBM, Rome, Italy, June 9-13, 2019.
  16. Wen, X., Zhang, H., Wang, R., Lin, W., Shen, D., Increased Functional Connectivity Flexibility During Early Infancy, 27th ISMRM, Montreal, QC, Canada, May 11-16, 2019. (Oral presentation).
  17. Hsu, L.-M., Liu, Y., Zhang, H., Qiu, S., Shen, D., Diagnosing first-episode depression with high-order regional homogeneity, 27th ISMRM, Montreal, QC, Canada, May 11-16, 2019.
  18. Zhang, H., Yin, W., Shen, D., Lin, W., Development- and state-related gradients in infant brain functional connectome, 104th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Nov. 25-30, 2018. (Oral Presentation).
  19. Jing, B., Zhang, H., Lin, W., Shen, D., Age Prediction Using Resting-State Functional Connectivity Characteristics in Typically Developing Infants, 104th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Nov. 25-30, 2018.
  20. Wen, X., Zhang, H., Lin, W., Shen, D., Evolution of Brain Dynamics in the First 2 Years of Life, 104th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Nov. 25-30, 2018. (Oral Presentation).
  21. Zheng, Y., Chen, X., Li, D., Liu, Y., Liang, Y., Qin, C., Zeng, H., Chen, J., Liu, J., Zhang, H.#, Qiu, S.#, Shen, D.#, Treatment-Naïve First Episode Depression Diagnosis Based on Brain Functional Network, 104th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Nov. 25-30, 2018.
  22. Wang, F., Meng, Y., Zhang, H., Yan, J., Wang, L., Lin, W., Shen, D., Li, G., Temporal Evolution of Inter- and Intra-subject Variability of Functional Connectivity in Infants, OHBM, Singapore, 17-21 June, 2018.
  23. Zhang, H., Yin, W., Stanley, N., Mucha, P.J., Lin, W., Shen, D., Multi-Layer Functional Connectome Reveals New Developmental Patterns of the Infant Brain, OHBM, Singapore, 17-21 June, 2018. (Oral Presentation).
  24. Zhang, H., Lin, W., Shen, D., “Multi-Layer Connectome” for Robust Multi-Subject Brain Network Analysis and its Application to Baby Connectome Development Study, Joint Annual Meeting ISMRM-ESMRMB 16-21 June 2018, Paris, France (Oral Presentation).
  25. Wen, X., Zhang, H., Zhang, J., Li, G., Lin, W., Shen, D., Early Development of Modular Organization in Brain Functional Networks at Multiple Scales, Joint Annual Meeting ISMRM-ESMRMB 16-21 June 2018, Paris, France (Oral Presentation).
  26. Zhang, H., Yin, W., Meng, Y., Lin, W., Shen, D., Functional and Structural Developments of Medial Frontal Subdivisions in First 2 Years of Life. OHBM, Vancouver, Canada, 25-29 June 2017.
  27. Zhang, H., Yin, W., Lin, W., Shen, D., The Developing Triple Networks in Infants from 2-Week-Old to 2-Year-Old: A Longitudinal Study. OHBM, Vancouver, Canada, 25-29 June 2017 (Merit Abstract Award).
  28. Zhang, H., Chen, X., Zhang, L., Shen, D., Structured Brain “Chronnectome” Reveals New Brain Dynamic Patterns for Early Detection of Alzheimer’s Disease. ISMRM, Honolulu, Hawaii, 22-27 Apr 2017 (Oral Presentation).
  29. Zhang, H., Yin, W., Lin, W., Shen, D., Growing Apart or Growing Together: a Novel “Developing Triple Network” Hypothesis for Baby Connectome Study. ISMRM, Honolulu, Hawaii, 22-27 Apr 2017.
  30. Zhang, H., Shen, D., Radiation-induced Brain Abnormalities: Plasticity, Progression and Outcome Prediction. ISMRM, Honolulu, Hawaii, 22-27 Apr 2017.
  31. Li, X., Zhang, H.*, Explore dynamic functional networks using independent component analysis and temporal decomposition. OHBM 2015, Honolulu, US.
  32. Zhang, H., Liu, Q., Zang, Y.-F., Searching for optimal language mapping paradigm: is “state-related” fMRI a promising way? OHBM 2015, Honolulu, US.
  33. Zhang, H., Lu, J., Mao, Y., Jia, W., Wu, J., Zhou, L., Mapping language network pre- and intra-operatively using fMRI and electrophysiology: new method. OHBM 2014. Berlin, Germany.
  34. He, H., Chen, S., Zhang, H., Zhong, J., Region and Frequency Dependent Coupling Between Resting-state Power and Task Induced Activity. Joint Annual Meeting ISMRM-ESMRMB 2014, 10-16 May 2014, Milan, Italy.
  35. Zhang, H., Yang, Q.X., Wang, J., Karunanayaka, P., Defining “olfactory matrix” using data-driven and model-driven methods based on resting-state fMRI. OHBM 2013. Seattle, USA.
  36. Zhang, H., Jia, W., Liao, W., Zang, Y.-F., Automatic component identification method based on normalized sensitivity/specificity measurement. OHBM 2013. Seattle, USA.
  37. Jia, W., Zhang H.*, Liao, W., Zang, Y.-F., “Correct” sensorimotor network detected by independent component analysis on resting-state fMRI. OHBM 2013. Seattle, USA.
  38. Karunanayaka, P., Patel, M., Zhang, H. Wang, J., Yang, Q.X., Mapping odor-related and resting state networks using olfactory fMRI. OHBM 2013. Seattle, USA.
  39. Zhang, H., Chen, F., Brain Tumor Localization Using Resting-State FMRI and Independent Component Analysis. 3rd Biennial Conference on Resting-State Brain Connectivity, 2012, Magdeburg, Germany.
  40. Zhang, H., Sun, L., Zuo, X.-N., Wang, J., Qin, Z., Zang, Y.-F., Aberrant Attention and Default Mode Networks in ADHD: a Multicenter Resting-State fMRI Study. OHBM 2012, Beijing, China.
  41. Yang, Q.-X., Zhang, H., Wang, J., Karunanayaka, P., Patel, M., Eslinger, P., Mapping of Olfactory Networks in the Human Brain Using Resting-State fMRI. OHBM 2012, Beijing, China.
  42. Ji, G.J., Zhang, Z.Q., Zhang, H. et al., Granger Causality Analysis in Mesial Temporal Lobe Epilepsy: A Resting-State fMRI Study. OHBM 2012, Beijing, China.
  43. Wang, J., Zhang, H. et al., Gray Matter Asymmetry of Dorsal Anterior Cingulate Cortex in Typical Developmental Children and ADHD. OHBM 2012, Beijing, China.
  44. Zhang, H., Zhu, C.-Z. Brain plasticity due to long-term piano practice as revealed by intrinsic functional-network-connectivity based on resting-state fMRI. 50th Annual Meeting of Society for Psychophysiological Research, 2010, Portland, Oregon, US (Travel award).
  45. Zhang, H., Ma, S.Y., Zhang, Y.J., Zuo, X.N., Zang, Y.-F., Zhu, C.-Z. Concatenating-order Independent Group ICA: MOI-GICA. OHBM 2009, San Fransisco, US.
  46. Zhang, H., Han, Y., Yang, H., Tang, H.H., Gong, Q.Y., Zang, Y.-F., Zhu, C.-Z., Plastic Functional Connectivity due to Music Training: a Resting State fMRI Study. OHBM 2008, Melbourne, Australia.

 

SOFTWARE PATENTS

  1. 2009SR057145, Subject-Order Independent Group ICA Toolbox (COGICAT beta1.0)
  2. 2014SR152791, Dynamic Temporal Decomposition Toolbox (Decom v1.0)
  3. 2014SR152705, Brain Image-based Effect Size Calculator (ES v1.0)
  4. 2014SR152597, Toolkit Resting-state fMRI-based Preoperative Mapping (TUMOR v1.0)
  5. China Invitation Patent, 2017, A Presurgical Brain Functional Network Mapping Technique Based on Resting-state Functional MRI, No. ZL 2014 1 0592967.3.

 

TEACHING

  • Sep 2012 – Jan 2013 Clinical and Experimental Psychiatry, Hangzhou Normal University
  • Sep 2013 – Jan 2014 Clinical and Experimental Psychiatry, Hangzhou Normal University
  • Sep 2013 – Jan 2014 Proceedings on Psychology Research, Hangzhou Normal University
  • Sep 2014 – Jan 2015 Methods for Psychological Research, Hangzhou Normal University
  • Sep 2016 – Jan 2017 “Medical Image Analysis I”, Biomedical Imaging Sciences Seminar (BMME 890/PSYC 795) for doctoral students, UNC Chapel Hill
  • Sep 2017 – Jan 2018 “Medical Image Analysis I”, Biomedical Imaging Sciences Seminar (BMME 890/PSYC 795) for doctoral students, UNC Chapel Hill
  • Lecture on “Infant brain fMRI analysis and pipeline” in workshop “Infant brain processing”, in Flux Satellite Conference 2018, Chapel Hill, United States (5/6/2018)
  • Sep 2018 – Jan 2019 “Medical Image Analysis I”, Biomedical Imaging Sciences Seminar (BMME 890/PSYC 795), for doctoral students, UNC Chapel Hill

 

RESEARCH GRANTS

As P. I.

  • 07/01/2017-05/31/2020, NIH, $271,642, 5R01AG042599 (Liu, Zhang), Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment, as consortium PI.
  • 09/01/2018-04/30/2020, NIH, $412,890, 7R01AG049371 (Huang, Zhang), Subaward PI (subaward #0061817), Imaging Genomics Based Brain Disease Prediction, as consortium PI.
  • 01/01/2019-06/30/2019, A030921 (Zhang), Subcontract award (Duke University), Phenotype-Genotype Predictors of Cognitive Outcomes in Geriatric Depression, as Subcontract PI.
  • 10/27/18-03/31/20, 5R01AG041721 (Shen, Zhang), NIH, $350,863/year, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, as Contact PI.
  • Presurgical Multi-Functional-System Mapping for Glioma Based on Resting-state fMRI and Independent Component Analysis (NSFC 81201156), 2013-2015.
  • Independent Component Analysis on fNIRS/fMRI. Academic Funding For Youth Scientist (Ministry of Education, China), 2010-2011.
  • Independent Component Analysis on Functional Brain Imaging Analyses: methodology and applications, Engagement Fund of Outstanding Doctoral Dissertation, BNU, 2010-2011.

Participation

  • 9/30/16-9/29/19, 1R01MH100217 (Shen, Yap), NIH, $380,000/year, Diagnosis of Alzheimer’s Disease Using Dynamic High-Order Brain Networks, as Investigator
  • 7/1/18-6/30/23, 1R01MH116225-01A1 (Li), NIH, $250,000/year, Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI, as co-Investigator
  • 6/1/16-5/31/20, 1U01MH110274-02 (Lin), NIH, $1,005,361/year, UNC/UMN Baby Connectome Project, as Key Investigator
  • 2013KYB211, Breath-holding fMRI and CVR
  • Y13H180042, Resting-state fMRI-based functional mapping
  • 201342245, DTI-based functional mapping
  • 2014BAI04B00, 2014.1-2016.12, Multimodal imaging based surgical mapping
  • State Key Project of Social Science Foundation: Chinese word processing and disorders: a multidiscipline study
  • Post-stroke Plasticity of Brain Connectivity: a longitudinal fMRI & DTI Study. National Science Foundation of China, 2010-2012. (No. 30970773)
  • FNIRS-based resting-state functional connectivity study: a methodological study and applications to children development, NKLCNL director funding, 2010-2012
  • Investigation on Brain Functional Integration based on Synthetically Medical Intervention on Depressed Patients, International Technical Co-operation Projects of China, 2008-2010. (No. 2007DFA30780)
  • Combination of Patten Recognition Techniques and Multi-modal MR Imaging for Discriminative Analysis of ADHD, National Science Foundation of China, 2006-2008. (No. 30500130)
  • Investigation on Key Problems in Information Processing of Clinic Medicine, State Key Basic Research Projects of China (Fund 973), The Ministry of Science and Technology of China, 2004-2008. (A sub-project No. 2003CB716101)
  • Investigation on functional connectivity in motor system for musicians, National Science Foundation of China, 2005-2007. (No. 30470575)

 

AWARDS

  • 2017     Merit Abstract Award, OHBM2017, Vancouver, Canada, 25-29 June 2017.
  • 2016     Third-class Award of Medical Technique Advances in Zhejiang Province (No. 16073)
  • 2015     Award for Science and Technology Achievements in Zhejiang Province (No. 15001555)
  • 2013     131 Talent Project in Zhejiang Province
  • 2011     Top-Grade of the Excellent Academic Award, NKLCNL, Beijing Normal Univ.
  • 2010     Academic Fund for Youth Scientists, Ministry of Education, China.
  • 2010     “Tomorrow Star” Award, GlaxoSmithKline R&D China.
  • 2005     First class scholarship, Zhejiang Univ.