SPATIOTEMPORAL MODELING OF DISCRETE-TIME DISTRIBUTION-VALUED DATA APPLIED TO DTI TRACT EVOLUTION IN INFANT NEURODEVELOPMENT.
This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of 'distance' between distributions and an 'average' of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe's disease in comparison with a normative trend estimated from 15 healthy controls.
School of Computing, SCI Institute, University of Utah, Salt Lake City, UT, USA.
Anuja
A
Sharma
School of Computing, SCI Institute, University of Utah, Salt Lake City, UT, USA.
P Thomas
PT
Fletcher
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
John H
JH
Gilmore
Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
Maria L
ML
Escolar
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA ; Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
Aditya
A
Gupta
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
Martin
M
Styner
School of Computing, SCI Institute, University of Utah, Salt Lake City, UT, USA.
Guido
G
Gerig
Mallow’s distance; diffusion tensor imaging; distribution-valued data; early neurodevelopment; spatiotemporal growth trajectory