Title | TwinMARM: two-stage multiscale adaptive regression methods for twin neuroimaging data. |
Publication Type | Journal Article |
Year of Publication | 2012 |
Authors | Li, Yimei, John H. Gilmore, Jiaping Wang, Martin Styner, Weili Lin, and Hongtu Zhu |
Journal | IEEE Trans Med Imaging |
Volume | 31 |
Issue | 5 |
Pagination | 1100-12 |
Date Published | 2012 May |
ISSN | 1558-254X |
Keywords | Algorithms, Brain, Computer Simulation, Female, Humans, Image Processing, Computer-Assisted, Infant, Newborn, Magnetic Resonance Imaging, Male, Neuroimaging, Regression Analysis, Twin Studies as Topic, Twins |
Abstract | Twin imaging studies have been valuable for understanding the relative contribution of the environment and genes on brain structures and their functions. Conventional analyses of twin imaging data include three sequential steps: spatially smoothing imaging data, independently fitting a structural equation model at each voxel, and finally correcting for multiple comparisons. However, conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this paper is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structures and their functions. In each stage, TwinMARM employs hierarchically nested spheres with increasing radii at each location and then captures spatial dependence among imaging observations via consecutively connected spheres across all voxels. Simulation studies show that our TwinMARM significantly outperforms conventional analyses of twin imaging data. Finally, we use our method to detect statistically significant effects of genetic and environmental variations on white matter structures in a neonatal twin study. |
DOI | 10.1109/TMI.2012.2185830 |
Alternate Journal | IEEE Trans Med Imaging |
Original Publication | TwinMARM: Two-stage multiscale adaptive regression methods for twin neuroimaging data. |
PubMed ID | 22287236 |
PubMed Central ID | PMC3380373 |
Grant List | HD 03110 / HD / NICHD NIH HHS / United States R41 NS059095-02 / NS / NINDS NIH HHS / United States R01 MH070890-09 / MH / NIMH NIH HHS / United States HD053000 / HD / NICHD NIH HHS / United States R01 MH086633 / MH / NIMH NIH HHS / United States U01 MH070890 / MH / NIMH NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States R01 MH070890-04 / MH / NIMH NIH HHS / United States R01 MH070890-07 / MH / NIMH NIH HHS / United States R01 HD053000-03S1 / HD / NICHD NIH HHS / United States R21 AG033387-02 / AG / NIA NIH HHS / United States AG033387 / AG / NIA NIH HHS / United States MH070890 / MH / NIMH NIH HHS / United States R01 HD053000-05 / HD / NICHD NIH HHS / United States R01 HD053000-04 / HD / NICHD NIH HHS / United States R01 MH091645-03 / MH / NIMH NIH HHS / United States AS1499 / / Autism Speaks / United States R01 MH070890-03 / MH / NIMH NIH HHS / United States R42 NS059095 / NS / NINDS NIH HHS / United States R01 MH086633-02 / MH / NIMH NIH HHS / United States R01 HD053000-01A1 / HD / NICHD NIH HHS / United States R01 MH070890-08 / MH / NIMH NIH HHS / United States R01 MH091645-01A1 / MH / NIMH NIH HHS / United States U54 EB005149 / EB / NIBIB NIH HHS / United States R01NS055754 / NS / NINDS NIH HHS / United States R41 NS059095 / NS / NINDS NIH HHS / United States R42 NS059095-04 / NS / NINDS NIH HHS / United States R01 HD053000-03 / HD / NICHD NIH HHS / United States R01 MH086633-03 / MH / NIMH NIH HHS / United States R01 MH091645-02 / MH / NIMH NIH HHS / United States MH092335 / MH / NIMH NIH HHS / United States R21 AG033387-01A1 / AG / NIA NIH HHS / United States R01 MH070890-06A1 / MH / NIMH NIH HHS / United States R42 NS059095-03 / NS / NINDS NIH HHS / United States P01CA142538-01 / CA / NCI NIH HHS / United States P50 MH064065 / MH / NIMH NIH HHS / United States R01 MH086633-01A1 / MH / NIMH NIH HHS / United States R01 NS055754 / NS / NINDS NIH HHS / United States RR025747-01 / RR / NCRR NIH HHS / United States R01 MH070890-05S1 / MH / NIMH NIH HHS / United States R01EB5-34816 / EB / NIBIB NIH HHS / United States R01 MH070890-01 / MH / NIMH NIH HHS / United States P30 HD003110 / HD / NICHD NIH HHS / United States R01 MH086633-04 / MH / NIMH NIH HHS / United States R01 MH070890 / MH / NIMH NIH HHS / United States R01 HD053000-02 / HD / NICHD NIH HHS / United States R01 HD053000 / HD / NICHD NIH HHS / United States MH086633 / MH / NIMH NIH HHS / United States R01 MH091645 / MH / NIMH NIH HHS / United States UL1 RR025747 / RR / NCRR NIH HHS / United States R01 MH070890-05 / MH / NIMH NIH HHS / United States R01 MH070890-02 / MH / NIMH NIH HHS / United States R21 AG033387 / AG / NIA NIH HHS / United States R41 NS059095-01 / NS / NINDS NIH HHS / United States MH064065 / MH / NIMH NIH HHS / United States U54 EB005149-01 / EB / NIBIB NIH HHS / United States |
TwinMARM: two-stage multiscale adaptive regression methods for twin neuroimaging data.
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