4.7 Article

STEAM - Statistical Template Estimation for Abnormality Mapping: A personalized DTI analysis technique with applications to the screening of preterm infants

Journal

NEUROIMAGE
Volume 125, Issue -, Pages 705-723

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2015.08.079

Keywords

Diffusion tensor imaging; Preterm infants; Brain development; Statistical modeling; Voxel-based analysis; Outcome prediction

Funding

  1. Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN298324]
  2. BGB by NSERC, IODE Canada
  3. Government of Alberta
  4. Canadian Institutes of Health Research (CIHR) [MOP-79262, MOP-86489]
  5. Tier 2 Canada Research Chair in Neonatal Neuroscience
  6. Michael Smith Foundation for Health Research Scholar
  7. Child & Family Research Institute

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We introduce the STEAMDTI analysis engine: a whole brain voxel-based analysis technique for the examination of diffusion tensor images (DTIs). Our STEAM analysis technique consists of two parts. First, we introduce a collection of statistical templates that represent the distribution of DTIs for a normative population. These templates include various diffusionmeasures from the full tensor, to fractional anisotropy, to 12 other tensor features. Second, we propose a voxel-based analysis (VBA) pipeline that is reliable enough to identify areas in individual DTI scans that differ significantly from the normative group represented in the STEAM statistical templates. We identify and justify choices in the VBA pipeline relating to multiple comparison correction, image smoothing, and dealing with non-normally distributed data. Finally, we provide a proof of concept for the utility of STEAM on a cohort of 134 very preterm infants. We generated templates from scans of 55 very preterm infants whose T1 MRI scans show no abnormalities and who have normal neurodevelopmental outcome. The remaining 79 infants were then compared to the templates using our VBA technique. We show: (a) that our statistical templates display the white matter development expected over the modeled time period, and (b) that our VBA results detect abnormalities in the diffusion measurements that relate significantly with both the presence of white matter lesions and with neurodevelopmental outcomes at 18 months. Most notably, we show that STEAM produces personalized results while also being able to highlight abnormalities across the whole brain and at the scale of individual voxels. While we show the value of STEAM on DTI scans from a preterm infant cohort, STEAM can be equally applied to other cohorts as well. To facilitate this whole-brain personalized DTI analysis, we made STEAM publicly available at http://www.sfu.ca/bgb2/steam. (C) 2015 Elsevier Inc. All rights reserved.

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