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Subject-specific Estimation of Missing Cortical Thickness Maps in Developing Infant Brains.

Yu Meng1,2, Gang Li2, Yaozong Gao1,2

  • 1Department of Computer Science, University of North Carolina at Chapel Hill, NC, USA.

Medical Computer Vision: Algorithms for Big Data" : International Workshop, MCV 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 9, 2015 : Revised Selected Papers. MCV (Workshop) (5Th : 2015 : Munich, Germany
|December 5, 2017
PubMed
Summary

Researchers developed a new method to estimate missing brain scan data in infants. This approach improves the analysis of brain development trajectories using incomplete longitudinal neuroimaging datasets.

Keywords:
Missing data completioninfant brain developmentlongitudinal cortical thickness

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Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Biostatistics

Background:

  • Longitudinal neuroimaging studies are crucial for understanding infant brain development.
  • Missing data in longitudinal datasets hinders accurate trajectory charting.
  • Existing methods struggle to effectively utilize incomplete infant brain imaging data.

Purpose of the Study:

  • To propose a novel method for estimating subject-specific vertex-wise cortical thickness maps at missing time points in longitudinal infant datasets.
  • To address the challenge of incomplete data in neurodevelopmental studies.
  • To enhance the utilization of available neuroimaging data for charting brain development.

Main Methods:

  • Introduced Dynamically-Assembled Regression Forest (DARF), a customized regression forest model.
  • DARF ensures spatial smoothness of estimated cortical thickness maps.
  • The method efficiently utilizes data from subjects with and without missing scans.

Main Results:

  • Applied DARF to a longitudinal infant dataset (31 healthy subjects, up to 5 scans each).
  • Successfully estimated missing cortical thickness maps.
  • Achieved an average vertex-wise error of less than 0.23 mm for estimations.

Conclusions:

  • DARF is an accurate and computationally efficient method for imputing missing cortical thickness data in longitudinal infant neuroimaging.
  • The proposed method effectively leverages available data to improve the analysis of brain development.
  • This technique offers a valuable tool for researchers studying dynamic brain development trajectories in infants.