A protocol for the analysis of DTI data collected from young children

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http://hdl.handle.net/10138/316922

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Tokariev , M , Vuontela , V , Perkola , J , Lönnberg , P , Lano , A , Andersson , S , Metsäranta , M & Carlson , S 2020 , ' A protocol for the analysis of DTI data collected from young children ' , MethodsX , vol. 7 , 100878 . https://doi.org/10.1016/j.mex.2020.100878

Title: A protocol for the analysis of DTI data collected from young children
Author: Tokariev, Maksym; Vuontela, Virve; Perkola, Jaana; Lönnberg, Piia; Lano, Aulikki; Andersson, Sture; Metsäranta, Marjo; Carlson, Synnöve
Contributor: University of Helsinki, Department of Physiology
University of Helsinki, Department of Psychology and Logopedics
University of Helsinki, Kliinisen neurofysiologian yksikkö
University of Helsinki, HUS Children and Adolescents
University of Helsinki, HUS Medical Imaging Center
University of Helsinki, Clinicum
University of Helsinki, HUS Medical Imaging Center
University of Helsinki, Department of Physiology
Date: 2020-04-09
Language: eng
Number of pages: 9
Belongs to series: MethodsX
ISSN: 2215-0161
URI: http://hdl.handle.net/10138/316922
Abstract: Analysis of scalar maps obtained by diffusion tensor imaging (DTI) produce valuable information about the microstructure of the brain white matter. The DTI scanning of child populations, compared with adult groups, requires specifically designed data acquisition protocols that take into consideration the trade-off between the scanning time, diffusion strength, number of diffusion directions, and the applied analysis techniques. Furthermore, inadequate normalization of DTI images and non-robust tensor reconstruction have profound effects on data analyses and may produce biased statistical results. Here, we present an acquisition sequence that was specifically designed for pediatric populations, and describe the analysis steps of the DTI data collected from extremely preterm-born young school-aged children and their age- and gender-matched controls. The protocol utilizes multiple software packages to address the effects of artifacts and to produce robust tensor estimation. The computation of a population-specific template and the nonlinear registration of tensorial images with this template were implemented to improve alignment of brain images from the children.
Subject: Diffusion tensor imaging (DTI)
Nonlinear registration
Tract-based spatial statistics (TBSS)
Pediatric
3112 Neurosciences
1184 Genetics, developmental biology, physiology
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