S.-J. Son, J. Kim, and H. Park, “Structural and functional connectional fingerprints in mild cognitive impairment and Alzheimer’s disease patients”, PLoS ONE, vol. 12, no. 3, article e0173426, doi:10.1371/journal.pone.0173426, March 2017

S.-J. Son, M. Kim, and H. Park, “Imaging analysis of Parkinson's disease patients using SPECT and tractography”, Scientific Reports, vol. 6, article 38070, doi: 10.1038/srep38070, November 2016

S.-J. Son, J.-H. Kim, J. Seo, J.-M. Lee, and H. Park, “Connectivity Analysis of Normal and Mild Cognitive Impairment Patients based on FDG and PiB-PET Images”, Neuroscience Research, vol. 98, p.50-58, 2015.

손성진, 박현진, “정상 노화군과 경도인지장애 환자군의 18F-FDG-PET과 11C-PIB-PET 영상을 이용한 뇌 연결망 분석”, 의공학회지, vol. 35(3), 68-74, 2014

Y. Choi*, J. Kim*, H. Park, H. K. Kim, J. Kim, J. Y. Jeong, J. H. Ahn, and H. Y. Lee#, “Rethinking a non-predominant pattern in invasive lung adenocarcinoma: Prognostic dissection focusing on a high-grade pattern”, Cancers, vol. 13, no. 11, article 2785, doi: 10.3390/cancers13112785, June 2021.

J. P. Kim*, J. Kim*, H. Jang, J. Kim, S. H. Kang, J. S. Kim, J. Lee, D. L. Na, H. J. Kim, S. W. Seo#, and H. Park#, “Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach”, Scientific Reports, vol. 11, article 6954, doi: 10.1038/s41598-021-86114-4, March 2021. Link

J. Kim, S.-Y. Ryu, S.-H. Lee, H. Y. Lee^, and H. Park^, “Clustering approach to identify intratumor heterogeneity combining FDG PET and diffusion-weighted MRI in lung adenocarcinoma”, European Radiology, vol. 29, no. 1, p.468-475, January 2019 (^ co-corresponding authors).

J. Kim, J. Hong, and H. Park, “Prospects of deep learning for medical imaging”, Precision and Future Medicine, vol. 2, no. 2, p.37-52, June 2018.

J. Kim, S. J. Choi, S.-H. Lee, H. Y. Lee, and H. Park, “Predicting survival using pre-treatment CT for HCC patients treated with TACE: comparison of models using radiomics”, American Journal of Roentgenology, vol. 211, no. 5, p.1026-1034, November 2018.

S. J. Choi*, J. Kim*, H. S. Kim, and H. Park, “Parametric response mapping of dynamic CT: Enhanced prediction of survival in hepatocellular carcinoma patients treated with transarterial chemoembolization”, Abdominal Radiology, Feb. 2017 (* equal contribution)

S.-J. Choi*, J. Kim*, J. Seo, H.-S. Kim, J.-M. Lee, and H. Park, “Parametric Response Mapping of Dynamic CT for Predicting Intrahepatic Recurrence of Hepatocellular Carcinoma after Conventional Transcatheter Arterial Chemoembolization”, European Radiology, vol. 26, no. 1, p.225-34, 2016 (* equal contribution)

S.-J. Choi*, J. Kim*, J. Seo, H.-S. Kim, J.-M. Lee, and H. Park, “Parametric response mapping of dynamic CT as an imaging biomarker to distinguish viability of hepatocellular carcinoma treated with transcatheter arterial chemoembolization”, Abdominal Imaging, vol. 39, no. 3, p.518-525, 2014 (* equal contribution).

M. Kim, J. Bao, K. Liu, B. Park, H. Park, J. Y. Baik, and L. Shen, “A structural enriched functional network: an application to predict brain cognitive performance”, Medical Image Analysis, vol. 71, article 102026, doi: 10.1016/j.media.2021.102026, July 2021. Link

M. Kim, J. H. Won, J. Youn, and H. Park, “Joint-connectivity-based sparse canonical correlation analysis of imaging genetics for detecting biomarkers of Parkinson’s disease”, accepted for IEEE Transaction on Medical Imaging, May 2019

M. Kim, J.Kim, S.H.Lee and H. Park, “Imaging genetics approach to Parkinson’s disease and its correlation with clinical score” Scientific Reports. April 2017

M. Kim and H. Park, “Structural connectivity profile of scans without evidence of dopaminergic deficit (SWEDD) patients compared to normal controls and Parkinson's disease patients”, SpringerPlus, 15:1421; doi: 10.1186/s40064-016-3110-82016, August 2016

M. Kim and H. Park, “Using tractography to distinguish SWEDD from Parkinson’s disease patients based on connectivity”, Parkinson's Disease, Article ID 8704910, 10 pages, 2016. doi:10.1155/2016/8704910. 2016

B.-y Park, C.-S. Chung, M. J. Lee#, and H. Park#, “Accurate neuroimaging biomarkers to predict body mass index in adolescents: a longitudinal study”, accepted for Brain Imaging and Behavior, April 2019 (# corresponding authors).

M. J. Lee*, B.-y Park*, S. Cho, S. T. Kim, H. Park, and C.-S. Chung, “Increased connectivity of pain matrix in chronic migraine: a resting-state functional MRI study”, Journal of Headache and Pain, vol. 20, article 29, doi: 10.1186/s10194-019-0986-z. March 2019

B.-y Park, W. M. Shim, O. James, and H. Park, “Possible links between the lag structure in visual cortex and visual streams using fMRI”, Scientific Reports, vol. 9, article 4283, doi: 10.1038/s41598-019-40728-x, March 2019.

K. Byeon*, B.-y Park*, and H. Park, “Spatially guided functional correlation tensor: A new method to associate body mass index and white matter neuroimaging”, accepted for Computers in Biology and Medicine, February 2019 (* equal contribution). Link

B.-y Park, K. Byeon, and H. Park, “FuNP (Fusion of Neuroimaging Preprocessing) pipelines: A fully automated preprocessing software for functional magnetic resonance imaging”, Frontiers in Neuroinformatics, vol. 13, article 5, doi: 10.3389/fninf.2019.00005, February 2019.

B.-y. Park, M. J. Lee, M. Kim, S.-H. Kim, and H. Park, “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, vol. 12, article 741, doi: 10.3389/fnins.2018.00741, October 2018.

B.-y. Park*, M. J. Lee*, S-H. Lee, J. Cha, C.-S. Chung, S. T. Kim, and H. Park, “DEWS (DEep White matter hyperintensity Segmentation framework): A fully automated pipeline for detecting small deep white matter hyperintensities in migraineurs” Neuroimage: Clinical, vol. 18, p. 638-647, March 2018 (* equal contribution). Link

B.-y. Park, K.-J. Tark, W. M. Shim*, and H. Park*. Functional connectivity based parcellation of early visual cortices, Human Brain Mapping (2017 Dec.). [Epub ahead of print] doi:10.1002/hbm.23926

B.-y Park, T. Moon, and H. Park, “Dynamic functional connectivity analysis reveals improved association between brain networks and eating behaviors compared to static analysis”, Behavioural Brain Research, vol. 337, p.114-121, January 2018.

B.-y Park, J. Hong, and H. Park, “Neuroimaging biomarkers to associate obesity and negative emotions”, Scientific Reports, vol. 7, article 7664, doi:10.1038/s41598-017-08272-8, August 2017.

B.-y. Park, J. Hong, S.-H. Lee, and H. Park, “Functional connectivity of child and adolescent attention deficit hyperactivity disorder patients: correlation with IQ”, Frontiers in Human Neuroscience, 10:565, 2016.

B.-y. Park, M. Kim, J. Seo, J.-M. Lee, and H. Park, “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, 2016.

B.-y. Park, J. Seo, and H. Park, “Functional brain networks associated with eating behaviors in obesity”, Scientific Reports, 6:23891, 2016.

B.-y. Park and H. Park, “Connectivity differences between adult male and female patients with attention deficit hyperactivity disorder according to resting-state functional MRI”, Neural Regeneration Research, 11(1):119-125, 2016.

B.-y. Park, J. Seo, J. Yi, and H. Park, “Structural and functional brain connectivity of people with obesity and prediction of body mass index using connectivity”, PLoS ONE, 10(11):e0141376, 2015

S.-H. Lee, H. Park, and E. S. Ko#, “Radiomics in Breast Imaging from Techniques to Clinical Applications: Review Article”, Korean Journal of Radiology, vol. 21, no. 7, p.779-792, July 2020.

S.-H. Lee, H. Cho, H. Y. Lee#, and H. Park#, “Clinical impact of variability on CT radiomics and suggestions for suitable feature selection: A focus on lung cancer”, Cancer Imaging, vol. 19, article 54, doi: 10.1186/s40644-019-0239-z, July 2019.

S. H. Lee, M. Kim, and H. Park#, “Planning for selective amygdalohippocampectomy involving less neuronal fiber damage based on brain connectivity using tractography”, Neural Regeneration Research, vol. 10, no. 7, p. 1107-1112, 2015.

S.H. Lee, J. Seo, J.-M. Lee, and H. Park#, "Differences in early and late mild cognitive impairment tractography using diffusion tensor MRI", NeuroReport, vol. 25, no.17, p.1393-1398, 2014.

J. Hong, B. Park, M. J. Lee, C.-S. Chung, J. Cha, and H. Park#, “Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs”, Computer Methods and Programs in Biomedicine, vol. 183, article 105065, doi: 10.1016/j.cmpb.2019.105065, January 2020.

J. Hong, B. Park, H. Cho, and H. Park#, “Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects: a resting-state functional MRI study”, Neural Regeneration Research, vol. 12, no. 10, p. 1640-1647, October 2017.

W. Jhang, M. Kim, and H. park,"Handedenss Classification Using Centrality Derived from DTI Tractograhpy ",춘계의공학회,2019

W. Jhang, B. Park, and H. park, "자기공명영상을 이용한 작업기억 상태에서의 연결성 분석", IPIU,2019

H. Lee, J. Kwon, J. Lee, B. Park#, and H. Park#, “Disrupted stepwise functional brain organization in overweight individuals”, Communications Biology, vol. 5, no. 1, article 11, doi: 10.1038/s42003-021-02957-7, January 2022.

H. Lee*, B. Park*, K. Byeon, J. H Won, M. Kim, S. Kim, and H. Park#, “Multivariate association between brain function and eating disorders using sparse canonical correlation analysis”, PLoS ONE, vol. 15, no. 8, August 2020 (* equal contribution).

J. Choi, H. Cho, J. Kwon, H. Y. Lee, and H. Park#, “A cascaded neural network for staging in non-small cell lung cancer using pre-treatment CT”, Diagnostics, vol. 11, no. 6, article 1047, doi: 10.3390/diagnostics11061047, June 2021. Link

H. Cho, C. K. Kim, and H. Park#, “Overview of radiomics in prostate imaging and future directions”, British Journal of Radiology, doi: 10.1259/bjr.20210539, November 2021. Link

H. Cho, H. Y. Lee#, E. Kim, G. Lee, J. Kim, J. Kwon, and H. Park#,  “Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans”, Communications Biology, vol. 4, no. 1, article 1286, doi: 10.1038/s42003-021-02814-7, November 2021. Link

C. Kim*, H. Cho*, J. Y. Choi, T. J. Franks, J. Han, Y. Choi, S. Lee, H. Park#, and K. S. Lee#, “Pleomorphic Carcinoma of the Lung: Prognostic Models of Semantic, Radiomics and Combined Features from CT and PET/CT in 85 Patients”, European Journal of Radiology Open, vol. 8, article 100351, doi: 10.1016/j.ejro.2021.100351, May 2021. Link

H. Cho*, M. Park*, H. Park#, E. S. Ko#, N. Y. Hwang, Y.-H. Im, K. Ko, and S. H. Sim, “The Tumor-Fat Interface Volume of Breast Cancer on Pretreatment MRI Is Associated with Pathologic Response to Neoadjuvant Chemotherapy”, Biology, vol. 9, article 391, doi: 10.3390/biology9110391, November 2020 (* equal contribution).

S. W. Choi*, H. Cho*, H. Koo*, K. R. Cho, K.-H. Nenning, G. Langs, J. Furthner, B. Baumann, A. Woehrer, H. J. Cho, J. K. Sa, D.-S. Kong, H. J. Seol, J. Lee, D. Nam#, and H. Park#, “Multi-habitat radiomics unravels distinct phenotypic subtypes of glioblastoma with clinical and genomic significance”, Cancers, vol. 12, no.7, article 1707, doi: 10.3390/cancers12071707, June 2020 (* equal contribution).

H. W. Lee*, H. Cho*, J.-G. Joung*, H. G. Jeon, B. C. Jeong, S. S. Jeon, H. M. Lee, D.-H. Nam, W.-Y. Park, C. K. Kim#, S. I. Seo#, and H. Park#, “Integrative radiogenomics approach for risk assessment of post-operative metastasis in pathological T1 renal cell carcinoma: a pilot retrospective cohort study”, Cancers, vol. 12, no. 4, article 866, doi: 10.3390/cancers12040866, April 2020 (* equal contribution).

H. Cho*, G. Lee*, H. Y. Lee#, and H. Park#, “Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma”, European Radiology, vol. 30, no. 5, p.2984-2994, May 2020 (* equal contribution).

H. Cho*, Y. Kim*, S. T. Kim, H. Park#, D. Nam, and D.-S. Kong, “Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI”, Neuroradiology, 2018 (* equal contribution, # corresponding author)

H. Cho, S.-H. Lee, J. Kim, and H. Park, “Classification of the glioma grading using radiomics analysis”, PeerJ, vol.6, article 5982, doi:10.7717/peerj.5982, November 2018.

E. Kim*, G. Lee*, S. Lee, H. Cho, H. Y. Lee#, and H. Park#, “Incremental benefits of size-zone matrix-based radiomics features for the prognosis of lung adenocarcinoma: Advantage of spatial partitioning on tumor evaluation”, accepted for European Radiology, April 2022