A protocol for the analysis of DTI data collected from young children
Maksym Tokariev,
Virve Vuontela,
Jaana Perkola,
Piia Lönnberg,
Aulikki Lano,
Sture Andersson,
Marjo Metsäranta,
Synnöve Carlson
Affiliations
Maksym Tokariev
Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Department of Physiology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
Virve Vuontela
Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Department of Physiology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
Jaana Perkola
Department of Clinical Neurophysiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
Piia Lönnberg
Department of Child Neurology, Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
Aulikki Lano
Department of Child Neurology, Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
Sture Andersson
Department of Pediatrics, Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
Marjo Metsäranta
Department of Pediatrics, Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
Synnöve Carlson
Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Department of Physiology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Advanced Magnetic Imaging Centre, Aalto University School of Science, Espoo, Finland; Corresponding author.
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.
