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Subject-Specific Prediction using Nonlinear Population Modeling: Application to Early Brain Maturation from DTI

Institution:
1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
2GE Global Research, Niskayuna, NY, USA.
3Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2014
Publication Date:
Sep-2014
Journal:
Int Conf Med Image Comput Comput Assist Interv.
Volume Number:
17
Issue Number:
Pt 3
Pages:
33-40
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 3):33-40.
PubMed ID:
25320779
PMCID:
PMC4486206
Appears in Collections:
NA-MIC
Sponsors:
P01 DA022446/DA/NIDA NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
R01 HD055741/HD/NICHD NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
Generated Citation:
Sadeghi N., Fletcher P.T., Prastawa M., Gilmore J.H., Gerig G. Subject-Specific Prediction using Nonlinear Population Modeling: Application to Early Brain Maturation from DTI. Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 3):33-40. PMID: 25320779. PMCID: PMC4486206.
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The term prediction implies expected outcome in the future, often based on a model and statistical inference. Longitudinal imaging studies offer the possibility to model temporal change trajectories of anatomy across populations of subjects. In the spirit of subject-specific analysis, such normative models can then be used to compare data from new subjects to the norm and to study progression of disease or to predict outcome. This paper follows a statistical inference approach and presents a framework for prediction of future observations based on past measurements and population statistics. We describe prediction in the context of nonlinear mixed effects modeling (NLME) where the full reference population's statistics (estimated fixed effects, variance-covariance of random effects, variance of noise) is used along with the individual's available observations to predict its trajectory. The proposed methodology is generic in regard to application domains. Here, we demonstrate analysis of early infant brain maturation from longitudinal DTI with up to three time points. Growth as observed in DTI-derived scalar invariants is modeled with a parametric function, its parameters being input to NLME population modeling. Trajectories of new subject's data are estimated when using no observation, only the first or the first two time points. Leave-one-out experiments result in statistics on differences between actual and predicted observations. We also simulate a clinical scenario of prediction on multiple categories, where trajectories predicted from multiple models are classified based on maximum likelihood criteria.

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