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Proceedings Paper

Adaptive algorithms to map how brain trauma affects anatomical connectivity in children
Author(s): Emily L. Dennis; Gautam Prasad; Talin Babikian; Claudia Kernan; Richard Mink; Christopher Babbitt; Jeffrey Johnson; Christopher C. Giza; Robert F. Asarnow; Paul M. Thompson
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Paper Abstract

Deficits in white matter (WM) integrity occur following traumatic brain injury (TBI), and often persist long after the visible scars have healed. Heterogeneity in injury types and locations can complicate analyses, making it harder to discover common biomarkers for tracking recovery. Here we apply a newly developed adaptive connectivity method, EPIC (evolving partitions to improve connectomics) to identify differences in structural connectivity that persist longitudinally. This data comes from a longitudinal study, in which we scanned participants (aged 8-19 years) with anatomical and diffusion MRI in both the post-acute and chronic phases (1-6 months and 13-19 months post-injury). To identify patterns of abnormal connectivity, we trained a model on data from 32 TBI patients in the post-acute phase and 45 well-matched healthy controls, reducing an initial 68x68 connectivity matrix to a 14x14 matrix. We then applied this reduced parcellation to the chronic data in participants who had returned for their chronic assessment (21 TBI and 26 healthy controls) and tested for group differences. We found significant differences in two connections, comprising callosal fibers and long anterior-posterior fibers, with the TBI group showing increased fiber density relative to controls. Longitudinal analysis revealed that these were connections that were decreasing over time in the healthy controls, as is a common developmental phenomenon, but they were increasing in the TBI group. While we cannot definitively tell why this may occur with our current data, this study provides targets for longitudinal tracking, and poses questions for future investigation.

Paper Details

Date Published: 22 December 2015
PDF: 7 pages
Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96810B (22 December 2015); doi: 10.1117/12.2207574
Show Author Affiliations
Emily L. Dennis, Imaging Genetics Ctr., Keck USC School of Medicine (United States)
Gautam Prasad, Imaging Genetics Ctr., Keck USC School of Medicine (United States)
Talin Babikian, Semel Institute for Neuroscience and Human Behavior, Univ. of California, Los Angeles (United States)
Claudia Kernan, Semel Institute for Neuroscience and Human Behavior, Univ. of California, Los Angeles (United States)
Richard Mink, Harbor-UCLA Medical Ctr. and Los Angeles Biomedical Research Institute (United States)
Christopher Babbitt, Miller Children’s Hospital (United States)
Jeffrey Johnson, LAC+USC Medical Ctr. (United States)
Christopher C. Giza, UCLA Brain Injury Research Ctr., Mattel Children's Hospital (United States)
Robert F. Asarnow, Semel Institute for Neuroscience and Human Behavior, Univ. of California, Los Angeles (United States)
Univ. of California, Los Angeles (United States)
Paul M. Thompson, Imaging Genetics Ctr., Keck USC School of Medicine (United States)
Semel Institute for Neuroscience and Human Behavior, Univ. of California, Los Angeles (United States)
Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 9681:
11th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Juan D. García-Arteaga; Jorge Brieva, Editor(s)

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