Kay Park

MSTP in PhD Training

Program: Neurosciences

Current advisor: Eric Claude Leuthardt, MD

Undergraduate university: Washington University, 2017

Enrollment year: 2019

Research summary
Non-invasive characterization of network and metabolism changes in glioblastoma

The long-term goal of my work is to realize the full capabilities of rs-fMRI as functional biomarkers to advance our understanding of glioblastoma and ultimately improve patient outcomes. The overall objective of my projects is to optimize methodological approaches for using rs-fMRI and identify measures that can effectively characterize glioblastomas as a systemic and brain-wide disease. The central hypothesis is that appropriate and effective use of BOLD signals will provide unique insights into glioblastoma biology that complement findings from other approaches like molecular and basic science, thereby improving the interpretability of the clinical tool. The rationale for our proposed research is two-fold: an effective neuroimaging tool that has been methodologically optimized can yield reliable findings, furthering our understanding of the disease; contextualizing rs-fMRI measures within the greater context of glioblastoma pathophysiology can help better realize the potential of rs-fMRI as effective radiomic biomarkers.

Graduate publications
Luckett PH, Lee JJ, Park KY, Raut RV, Meeker KL, Gordon EM, Snyder AZ, Ances BM, Leuthardt EC, Shimony JS. 2023 Resting state network mapping in individuals using deep learning. Front Neurol, 13():1055437. PMCID: PMC9878609

Lamichhane B, Luckett PH, Dierker D, Yun Park K, Burton H, Olufawo M, Trevino G, Lee JJ, Daniel AGS, Hacker CD, Marcus DS, Shimony JS, Leuthardt EC. 2023 Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival. Neurooncol Adv, 5(1):vdad034. PMCID: PMC10162111

Luckett PH, Park KY, Lee JJ, Lenze EJ, Wetherell JL, Eyler LT, Snyder AZ, Ances BM, Shimony JS, Leuthardt EC. 2023 Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning. J Neurosurg, 1-12(epub ahead of print):. PMCID:

Park K Y, Snyder A, Leuthardt E. 2022 Atlas registration for resting state network mapping in patients with brain tumors. , ():

Luckett PH, Maccotta L, Lee JJ, Park KY, U F Dosenbach N, Ances BM, Hogan RE, Shimony JS, Leuthardt EC. 2022 Deep learning resting state functional magnetic resonance imaging lateralization of temporal lobe epilepsy. Epilepsia, 63(6):1542-1552. PMCID: PMC9177812

Daniel AGS, Park KY, Roland JL, Dierker D, Gross J, Humphries JB, Hacker CD, Snyder AZ, Shimony JS, Leuthardt EC. 2021 Functional connectivity within glioblastoma impacts overall survival. Neuro Oncol, 23(3):412-421. PMCID: PMC7992880

Park KY, Lee JJ, Dierker D, Marple LM, Hacker CD, Roland JL, Marcus DS, Milchenko M, Miller-Thomas MM, Benzinger TL, Shimony JS, Snyder AZ, Leuthardt EC. 2020 Mapping language function with task-based vs. resting-state functional MRI. PLoS ONE, 15(7):e0236423. PMCID: PMC7394427

Shimony JS, Leuthardt EC, Dierker D, Park KY, Hacker CD, Snyder AZ. (2020). Resting State Functional MRI for Presurgical Planning. In Ulmer, S., Jansen, O. (eds.), fMRI (pp. 287-301). : Springer, Cham.Luckett P, Lee JJ, Park KY, Dierker D, Daniel AGS, Seitzman BA, Hacker CD, Ances BM, Leuthardt EC, Snyder AZ, Shimony JS. 2020 Mapping of the Language Network With Deep Learning. Front Neurol, 11():819. PMCID: PMC7419701