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Journal articles 
  1. M. Kim, R. Wu, X. Yao, A.J. Saykin, J.H. Moore, Q. Long. L. Shen (2022). Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer’s disease. BMC Med Genomics 15 (Suppl 2), 168.

  2. J. Baik+, M. Kim+, J. Bao, Q. Long, L. Shen (2022). Identifying Alzheimer’s genes via brain transcriptome mapping. BMC Med Genomics 15 (Suppl 2), 116.

  3. J. Youn+, M. Kim+, J.S. Kim, H. Park*, J.W. Cho* (2022) Pallidal Structural Changes Related with Levodopa-induced Dyskinesia in Parkinson's disease. Frontiers in Aging Neuroscience. 

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  17. Kim, M., Park, H. (2016). Using tractography to distinguish SWEDD from Parkinson’s disease patients based on connectivity. Parkinson’s Disease, 2016.

  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.

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