top of page
본 사이트는
.com
사이트 제작 도구로 제작되었습니다. 지금 나만의 사이트를 만들어보세요.시작하기
Journal articles 

​

  1. Kim, M., Wu, R., Yao, X. Saykin AJ, Moore JH, Shen L. (2022) Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer’s disease. BMC Med Genomics 15, 168. https://doi.org/10.1186/s12920-022-01323-8
     

  2. Baik, J.Y+., Kim, M.+, Bao, J., Long Q, Shen L. (2022). Identifying Alzheimer’s genes via brain transcriptome mapping. BMC Med Genomics 15, 116. https://doi.org/10.1186/s12920-022-01260-6  (+ Equally contributed)
     

  3. Youn J+, Kim M+, Kim JS, Park H*, Cho JW* (2022). Pallidal Structural Changes Related with Levodopa-induced Dyskinesia in Parkinson's disease. Frontiers in Aging Neuroscience, 14, https://doi.org/10.3389/fnagi.2022.781883 (+ Equally contributed)
     

  4. ​Kim M, Min EJ, Liu K, Yan J, Saykin AJ, Moore JH, Long Q, Shen L. (2022). Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics. Medical Image Analysis, 76, 102297. https://doi.org/10.1016/j.media.2021.102297. 
     

  5. Park, B. Y., Park, H., Morys, F., Kim, M., Byeon, K., Lee, H., S.-H Kim, S. L. Valk, A. Dagher, Bernhardt, B. C. (2021). Inter-individual body mass variations relate to fractionated functional brain hierarchies. Communications Biology, 4(1), 1-12. https://doi.org/10.1038/s42003-021-02268-x
     

  6. Kim, M., Bao, J., Liu, K., Park, B. Y., Park, H., Baik, J. Y., Shen, L. (2021). A structural enriched functional network: An application to predict brain cognitive performance. Medical Image Analysis, 71, 102026. https://doi.org/10.1016/j.media.2021.102026.
     

  7. Kim, M., Kim, J. S., Youn, J., Park, H., Cho, J. W. (2020). GraphNet-based imaging biomarker model to explain levodopa-induced dyskinesia in Parkinson's disease. Computer Methods and Programs in Biomedicine, 196, 105713. https://doi.org/10.1016/j.cmpb.2020.105713.
     

  8. Lee, H., Park, B. Y., Byeon, K., Won, J. H., Kim, M., Kim, S. H., Park, H. (2020). Multivariate association between brain function and eating disorders using sparse canonical correlation analysis. Plos one, 15(8), e0237511. https://doi.org/10.1371/journal.pone.0237511.
     

  9. Won, J. H., Kim, M., Youn, J., Park, H. (2020). prediction of age at onset in parkinson’s disease using objective specific neuroimaging genetics based on a sparse canonical correlation analysis. Scientific Reports, 10(1), 1-12. https://doi.org/10.1038/s41598-020-68301-x
     

  10. Kim, M., Won, J. H., Youn, J., Park, H. (2020). Joint-connectivity-based sparse canonical correlation analysis of imaging genetics for detecting biomarkers of Parkinson’s disease. IEEE transactions on medical imaging, 39(1), 23-34. https://doi.org/10.1109/TMI.2019.2918839.
     

  11. Won, J. H., Kim, M., Park, B. Y., Youn, J., Park, H. (2019). Effectiveness of imaging genetics analysis to explain degree of depression in Parkinson’s disease. Plos one, 14(2), e0211699.  https://doi.org/10.1371/journal.pone.0211699.
     

  12. Park, B. Y., Lee, M. J., Kim, M., Kim, S. H., Park, H. (2018). Structural and functional brain connectivity changes between people with abdominal and non-abdominal obesity and their association with behaviors of eating disorders. Frontiers in neuroscience, 12, 741. https://doi.org/10.3389/fnins.2018.00741.
     

  13. Kim, M., Kim, J., Lee, S. H., Park, H. (2017). Imaging genetics approach to Parkinson’s disease and its correlation with clinical score. Scientific reports, 7(1), 1-10. https://doi.org/10.1038/srep46700.
     

  14. Son, S. J., Kim, M., Park, H. (2016). Imaging analysis of Parkinson’s disease patients using SPECT and tractography. Scientific reports, 6(1), 1-11. https://doi.org/10.1038/srep38070.
     

  15. Kim, M., Park, H. (2016). Structural connectivity profile of scans without evidence of dopaminergic deficit (SWEDD) patients compared to normal controls and Parkinson’s disease patients. SpringerPlus, 5(1), 1-15. https://doi.org/10.1186/s40064-016-3110-8.
     

  16. Park, B. Y., Kim, M., Seo, J., Lee, J. M., Park, H. (2016). Connectivity analysis and feature classification in attention deficit hyperactivity disorder sub-types: a task functional magnetic resonance imaging study. Brain topography, 29(3), 429-439. https://doi.org/10.1007/s10548-015-0463-1.
     

  17. Kim, M., Park, H. (2016). Using tractography to distinguish SWEDD from Parkinson’s disease patients based on connectivity. Parkinson’s Disease, 2016. https://doi.org/10.1155/2016/8704910.
     

  18. Lee, S. H., Kim, M., Park, H. (2015). Planning for selective amygdalohippocampectomy involving less neuronal fiber damage based on brain connectivity using tractography. Neural Regeneration Research, 10(7), 1107. https://doi.org/0.4103/1673-5374.160104.

bottom of page