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A protocol for the analysis of DTI data collected from young children.
Tokariev, Maksym; Vuontela, Virve; Perkola, Jaana; Lönnberg, Piia; Lano, Aulikki; Andersson, Sture; Metsäranta, Marjo; Carlson, Synnöve.
Afiliación
  • Tokariev M; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
  • Vuontela V; Department of Physiology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
  • Perkola J; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
  • Lönnberg P; Department of Physiology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
  • Lano A; Department of Clinical Neurophysiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Andersson S; Department of Child Neurology, Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Metsäranta M; Department of Child Neurology, Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Carlson S; Department of Pediatrics, Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
MethodsX ; 7: 100878, 2020.
Article en En | MEDLINE | ID: mdl-32382519
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MethodsX Año: 2020 Tipo del documento: Article País de afiliación: Finlandia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MethodsX Año: 2020 Tipo del documento: Article País de afiliación: Finlandia
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