Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data

Abstract

Spatial normalization and morphological studies of pediatric brain imaging data based on adult reference data may not be appropriate due to the developmental differences between the two populations. In this study, we set out to create pediatric templates and a priori brain tissue data from a large collection of normal, healthy children to compare it to standard adult data available within a widely used imaging software solution (SPM99, WDOCN, London, UK). Employing four different processing strategies, we found considerable differences between our pediatric data and the adult data. We conclude that caution should be used when analyzing pediatric brain data using adult a priori information. To assess the effects of using pediatric a priori brain information, the data obtained in this study is available to the scientific community from our website (www.irc.cchmc.org).Magn MRI provides excellent spatial resolution and tissue contrast and is thus ideally suited for morphological analyses of the brain. To allow for the comparison of imaging studies across individuals, imaging data has to be spatially normalized. The most widely used approach to spatial frameworks is the one described by Talairach and Tournoux (1), based on a single (elderly) female brain, which is different from larger collections of normal brains used in later collections (2). Spatial normalization is generally based on adult data and thus poses special problems in children, since pediatric brains differ in size, composition, and shape from adult brains (3-5).With regard to morphological studies of the human brain, several methods utilize a priori brain information to classify brain tissue, including a widespread method for the systematic and automated analysis of structural brain data (voxel based morphometry, VBM) put forward by Ashburner and Friston (6). This method utilizes tissue a priori information from an adult reference population, and the applicability of this data to a pediatric population will depend on the differences between these populations.The question of how substantial the differences are between reference data derived from a normal pediatric population and adult data is the subject of this study. We set out to construct normative pediatric brain data and this dataset was compared to standard adult data, which is available in a widely used VBM software solution, SPM99 (Wellcome Department, University College, London, UK). MATERIALS AND METHODS SubjectsMR images of 200 healthy children were used in this study. Subjects were recruited as part of an ongoing study on normal language development (7). Institutional review board approval and informed consent were obtained for all subjects. Rigid exclusion criteria were applied to ensure a normal pediatric population (5). All MRI scans were read for structural abnormalities by a qualified pediatric neuroradiologist and excluded from further processing if abnormal findings were present. Data Acquisition and PreparationChildren were imaged with a Bruker Biospec 30/60 3 Tesla MRI scanner equ...

Reprint of: Minimizing noise in pediatric task-based functional MRI; Adolescents with developmental disabilities and typical development

“…However, in some populations such as pediatric groups, individuals with disorders like schizophrenia, or healthy young or older adults, this could be problematic due to greater variability in anatomy as well as localization of functional activation (Brett et al, 2002;Park and Reuter-Lorenz, 2009). Thus approaches including population specific templates (Lee et al, 2005;Sanchez et al, 2012;Wilke et al, 2002Wilke et al, , 2003aWilke et al, , 2003bZhang et al, 2014) study-specific templates, or subject matched templates (Rohlfing et al, 2009) have been suggested instead (Huang et al, 2010;Padilla et al, 2011). However, these might prevent generalization of results across studies, and the need for such approaches has also been questioned given that depending on the resolution and population this may not pose a big problem (Burgund et al, 2002;Kang et al, 2003) and many standard templates may contain enough variability to cause concern (For review, see Evans et al (2012) and Mandal et al (2012)).…”

What is where and why it is important

“…Any given imaging experiment will be better served by mapping to a population based atlas that closely resembles the cohort under study. We suggest that population atlases for groups such as Alzheimer's (Thompson et al, 2000); (Mega et al, 2005), schizophrenia ; (Cannon et al, 2006)), pediatric populations (Wilke et al, 2003); ; (Evans and Group, 2006)), autism (Joshi et al, 2004), even decades of life (Mazziotta et al, 2001) should be utilized, as appropriate, for the subject group.…”

A Digital Pediatric Brain Structure Atlas from T1-Weighted MR Images

“…The object function used was normalized mutual information [9] and was maximized using Powell's golden search algorithm [10]. The pediatric brain template was available from Cincinnati Children's Hospital Medical Center (www.irc.cchmc.org) [7]. Templates are available for three age groups: young children (aged 5-9.5 years old), older children (aged 9.6-12.9 years old), and adolescents (aged 13-18.8 years).…”

“…A pediatric atlas is needed because the human brain continues to develop throughout childhood and adolescence [6] and the use of an adult template in pediatric neuroimaging studies may introduce significant bias [7]. Kates et al [8] revised the original Talairach atlas grid to increase its applicability to pediatric brains.…”