AI-Med Lab. @ GIST

Artificial Intelligence on MEDical application Lab. 

2023~Current (Publications @ GIST)

[19] BH Kim, HW Lee, H Ham, HJ Kim, H Jang, JP Kim, YH Park, M Kim*, SW Seo* . "Clinical effects of novel susceptibility genes for beta-amyloid: a gene-based association study in the Korean population." Frontiers in Aging Neuroscience 15 (2023). 

2021~2022 (Publications @ CUK)

[18] 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. https://doi.org/10.1186/s12920-022-01323-8

[17] 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. https://doi.org/10.1186/s12920-022-01260-6

[16] 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. https://doi.org/10.3389/fnagi.2022.781883

[15] 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 (I.F.=10.9, TOP 4.1% in Computer science, interdisciplinary applications), 76, 102297. https://doi.org/10.1016/j.media.2021.102297.

2019~2021 (Publications @ UPENN)

[14] 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 

[13] 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 (I.F.=13.8, TOP 1.3% in Computer science, interdisciplinary applications), 71, 102026. https://doi.org/10.1016/j.media.2021.102026.

2014~2019 (Publications @ SKKU)

[12] 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.

[11] 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.

[10] 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

[9] 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 (I.F.=10.0, TOP 4.0% in Computer science, interdisciplinary applications), 39(1), 23-34. https://doi.org/10.1109/TMI.2019.2918839.

[8] 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.

[7] 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.

[6] 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.

[5] 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.

[4] 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.

[3] 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.

[2] 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.

[1] 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.