Moo K. Chung

Dr. Moo K. Chung

Department of Biostatistics, University of Wisconsin, Madison, WI 53706, USA

Research Focus & Metrics

Academic Career Length

27Years (2000-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2001 in NeuroImage
Verified
Journal Article
Closed access
Moo K. ChungMoo K. Chung
Moo K. Chung
KWK.J. Worsley
K.J. Worsley
TPT. Paus
T. Paus
CCC. Cherif
C. Cherif
DCD.L. Collins
D.L. Collins
AEA.C. Evans
A.C. Evans

2 June 2010 in The Journal of Neuroscience
Verified
Journal Article
Open access
PDF file
JHJamie L. Hanson
Jamie L. Hanson
Moo K. ChungMoo K. Chung
Moo K. Chung
BABrian B. Avants
Brian B. Avants
ESElizabeth A. Shirtcliff
Elizabeth A. Shirtcliff
JGJames C. Gee
James C. Gee
SPSeth D. Pollak
Seth D. Pollak
Individuals who experience early adversity, such as child maltreatment, are at heightened risk for a broad array of social and health difficulties. However, little is known about how this behavioral risk is instantiated in the brain. Here we examine a neurobiological contribution to individual differences in human behavior using methodology appropriate for use with pediatric populations paired with an in-depth measure of social behavior. We show that alterations in the orbitofrontal cortex among individuals who experienced physical abuse are related to social difficulties. These data suggest a biological mechanism linking early social learning to later behavioral outcomes.

19 September 2012 in IEEE Transactions on Medical Imaging
Verified
Journal Article
Closed access
HLHyekyoung Lee
Hyekyoung Lee
HKHyejin Kang
Hyejin Kang
Moo K. ChungMoo K. Chung
Moo K. Chung
BKBung-Nyun Kim
Bung-Nyun Kim
DLDong Soo Lee
Dong Soo Lee
The brain network is usually constructed by estimating the connectivity matrix and thresholding it at an arbitrary level. The problem with this standard method is that we do not have any generally accepted criteria for determining a proper threshold. Thus, we propose a novel multiscale framework that models all brain networks generated over every possible threshold. Our approach is based on persistent homology and its various representations such as the Rips filtration, barcodes, and dendrograms. This new persistent homological framework enables us to quantify various persistent topological features at different scales in a coherent manner. The barcode is used to quantify and visualize the evolutionary changes of topological features such as the Betti numbers over different scales. By incorporating additional geometric information to the barcode, we obtain a single linkage dendrogram that shows the overall evolution of the network. The difference between the two networks is then measured by the Gromov-Hausdorff distance over the dendrograms. As an illustration, we modeled and differentiated the FDG-PET based functional brain networks of 24 attention-deficit hyperactivity disorder children, 26 autism spectrum disorder children, and 11 pediatric control subjects.

1 May 2005 in NeuroImage
Verified
Journal Article
Closed access
Moo K. ChungMoo K. Chung
Moo K. Chung
SRSteven M. Robbins
Steven M. Robbins
KDKim M. Dalton
Kim M. Dalton
RDRichard J. Davidson
Richard J. Davidson
AAAndrew L. Alexander
Andrew L. Alexander
AEAlan C. Evans
Alan C. Evans

30 December 2003 in Molecular Psychiatry
Verified
Journal Article
Closed access
PDF file
DPD A Pizzagalli
D A Pizzagalli
TOT R Oakes
T R Oakes
AFA S Fox
A S Fox
Moo K. ChungMoo K. Chung
Moo K. Chung
CLC L Larson
C L Larson
RDR J Davidson
R J Davidson

10 March 2013 in Child Development
Verified
Journal Article
Closed access
PDF file
JHJamie L. Hanson
Jamie L. Hanson
NANagesh Adluru
Nagesh Adluru
Moo K. ChungMoo K. Chung
Moo K. Chung
AAAndrew L. Alexander
Andrew L. Alexander
RDRichard J. Davidson
Richard J. Davidson
SPSeth D. Pollak
Seth D. Pollak