Jie Shi

Dr. Jie Shi

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Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

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

Representative Publications

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1 February 2016 in Biological Psychiatry
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Journal Article
Closed access
SJShantanu H. Joshi
Shantanu H. Joshi
RERandall T. Espinoza
Randall T. Espinoza
TPTara Pirnia
Tara Pirnia
JSJie Shi
Jie Shi
YWYalin Wang
Yalin Wang
KNKatherine L. Narr
Katherine L. Narr

1 September 2013 in NeuroImage
Verified
Journal Article
Closed access
JSJie Shi
Jie Shi
PTPaul M. Thompson
Paul M. Thompson
BGBoris Gutman
Boris Gutman
YWYalin Wang
Yalin Wang

15 September 2011 in IEEE Transactions on Medical Imaging
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Journal Article
Closed access
YWYalin Wang
Yalin Wang
JSJie Shi
Jie Shi
XYXiaotian Yin
Xiaotian Yin
DGDavid Gu
David Gu
TCTony F. Chan
Tony F. Chan
PTPaul M. Thompson
Paul M. Thompson
In brain mapping research, parameterized 3-D surface models are of great interest for statistical comparisons of anatomy, surface-based registration, and signal processing. Here, we introduce the theories of continuous and discrete surface Ricci flow, which can create Riemannian metrics on surfaces with arbitrary topologies with user-defined Gaussian curvatures. The resulting conformal parameterizations have no singularities and they are intrinsic and stable. First, we convert a cortical surface model into a multiple boundary surface by cutting along selected anatomical landmark curves. Secondly, we conformally parameterize each cortical surface to a parameter domain with a user-designed Gaussian curvature arrangement. In the parameter domain, a shape index based on conformal invariants is computed, and inter-subject cortical surface matching is performed by solving a constrained harmonic map. We illustrate various target curvature arrangements and demonstrate the stability of the method using longitudinal data. To map statistical differences in cortical morphometry, we studied brain asymmetry in 14 healthy control subjects. We used a manifold version of Hotelling's T 2 test, applied to the Jacobian matrices of the surface parameterizations. A permutation test, along with the cumulative distribution of p -values, were used to estimate the overall statistical significance of differences. The results show our algorithm's power to detect subtle group differences in cortical surfaces.

22 January 2014 in Human Brain Mapping
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Journal Article
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JSJie Shi
Jie Shi
NLNatasha Leporé
Natasha Leporé
BGBoris A. Gutman
Boris A. Gutman
PTPaul M. Thompson
Paul M. Thompson
LBLeslie C. Baxter
Leslie C. Baxter
FIFor The Alzheimer'S Disease Neuroimaging Initiative
For The Alzheimer'S Disease Neuroimaging Initiative
The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database—the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High‐order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor‐based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T2 test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD. Hum Brain Mapp 35:3903–3918, 2014. © 2014 Wiley Periodicals, Inc.

1 July 2013 in NeuroImage
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Journal Article
Closed access
YWYalin Wang
Yalin Wang
LYLei Yuan
Lei Yuan
JSJie Shi
Jie Shi
AGAlexander Greve
Alexander Greve
JYJieping Ye
Jieping Ye
PTPaul M. Thompson
Paul M. Thompson

1 January 2015 in NeuroImage
Verified
Journal Article
Closed access
JSJie Shi
Jie Shi
CSCynthia M. Stonnington
Cynthia M. Stonnington
PTPaul M. Thompson
Paul M. Thompson
KCKewei Chen
Kewei Chen
BGBoris Gutman
Boris Gutman
YWYalin Wang
Yalin Wang