Advances in structural and functional neuroimaging allow for unprecedented opportunities to discover how the development of the child’s brain supports the growth of the child’s mind, both in typical development and in developmental disorders. Measurement of structural and functional brain development from childhood to adulthood necessitates quantitative comparisons of structure across ages, either to examine structural changes or to localize functional changes. Such quantitative comparisons are typically achieved by registering or mapping individual brains into a common or normalized stereotactic space (Miller et al., 1993
; exceptions are functional or structural regions-of-interest approaches). A potential hazard in developmental studies is that the many changes in brain morphology that occur in development may confound such normalization across ages in both cross-sectional and longitudinal neuroimaging studies. Here, we examined two important issues regarding such normalization in developmental cognitive neuroscience. First, we assessed whether normalization is valid from ages 4 to 11, a period of major brain and mental growth. Normalization appears to be valid in 7-to-8 year olds (Burgund et al., 2002
), but there is no evidence in younger ages. With increasingly early identification and treatment of developmental disorders or risks for developmental disorders such as attention-deficit/hyperactivity disorder (ADHD), dyslexia, and bipolar disorder, it is important to know if brains of children ages 4–6 can be studied developmentally with standard spatial normalization procedures. Second, we examined whether a surface-based approach for normalization offers superior accuracy for cortical regions relative to more commonly used volume-based approaches. Such quantification of the accuracy of normalization approaches, as investigated in this study, is essential for precise characterization of developmental changes in structure and function.
The first decade of life represents a period of extensive anatomical changes and functional maturation of the human brain. Most major fissures or sulci are visible on the surface of the brain at the time of birth (Cowan, 1979
). However, the brain continues to expand in volume and morphological changes persist for years after birth (see Toga et al., 2006
, for a review). Cellular (e.g., generation and pruning of neurons and synapses, myelination) and macroanatomical (e.g., cortical thinning, white matter expansion, changes in sulci and gyri) changes are closely associated with cognitive development. Magnetic resonance imaging (MRI) provides a window into these developmental changes, but is currently limited to detecting changes mainly at a macroscopic level, such as positions of sulci and gyri, cortical thickness, connectivity, and curvature. Prior studies (Rademacher et al., 1993
; Hinds et al., 2008
; Fischl et al., 2008
) have reported that sulci and gyri correspond well to cytoarchitectonic features in several regions of the brain. Therefore much can be learned even from such coarse information. However, in order to compare these features across children of different ages, brain images from different participants are registered or spatially normalized into a common coordinate system.
Registration or spatial normalization is the process of transforming brains from different participants into a common reference frame. Registering brains helps in comparing: (i) structural and functional properties across the participants within a study; and (ii) results from different brain imaging studies. Currently, in most fMRI group analyses, volume-based registration is used to transform brain-imaging data into canonical spaces (e.g., Talairach space - Talairach and Tournoux, 1988
; MNI space – Evans et al., 1992
). Several algorithms exist for performing volume-based registration (reviewed in Ardekani et al., 2005
; Gholipour et al., 2007
; Klein et al., 2009
). These approaches typically employ a transformation that matches the overall extents of the brains to one another or to an average brain template (e.g., MNI152, MNI305 spaces; Evans et al., 1993
). In general, they use intensity differences to guide registration. However, such approaches tend to ignore the topological properties and geometric features (e.g., sulci and gyri) of the cortex. As a result, these normalization procedures that are meant to align or register anatomical regions across participants leave a large amount of residual inter-subject anatomical variability (Amunts et al., 1999
; Nieto-Castanon et al., 2003
; Hinds et al., 2008
) and therefore blur individual anatomical distinctions.
Surface-based algorithms (Fischl et al., 1999
; Davatzikos et al., 1996
; Drury et al., 1996
, Thompson and Toga, 1996
, Cointepas et al., 2001
, Tosun and Prince, 2008
) were developed to improve the accuracy of cortical registration and thereby reduce inter-subject variability. These approaches account for morphological and topological properties of the human brain. When performing registrations, surface-based approaches treat the cerebral cortex as a sheet and seek to find an alignment that matches sulcal and gyral patterns typically quantified using some type of curvature of the cortex. Surface-based registration has been shown to map cytoarchitectonic borders more accurately between brains than affine volume-based registration (Fischl et al., 2008
). Hinds et al. (2008)
reported significant reduction in prediction error of locating V1 using an atlas constructed via surfaced-based registration over a nonlinear volume-based approach (Hömke, 2006
; Schormann and Zilles, 1998
). Using this surface-based atlas Hinds et al. (2009)
demonstrated that predicted- and histologically defined structural boundaries of primary visual cortex align well with functionally defined boundaries.
In this study, we chose four registration algorithms: three volume-based and one surface-based. Two volume-based algorithms were chosen on the basis of being among the most commonly used methods in published literature. These were SPM 5 nonlinear normalization (Ashburner et al., 1999
) and a 12-parameter affine transform (e.g., similar to FLIRT - Jenkinson et al., 2002
). In addition, we chose ANTS (Avants et al., 2006
), a diffeomorphic-registration algorithm, which consistently ranked highest among nonlinear volume-registration algorithms evaluated in Klein et al. (2009)
and which showed no significant difference in registration accuracy compared to FreeSurfer in a study comprising labeled data in adults (Klein et al., 2010
). For surface-based registration, we used FreeSurfer, a fully automated, freely available morphological analysis software package that does not require manually created landmarks to perform registration. Currently, other surface registration methods require manually assigned landmarks (e.g., Caret - Van Essen et al., 2001
; curve-LDDMM - Qui and Miller, 2007
) or are unable to apply nonlinear transforms to arbitrary labels (e.g., BrainVisa; Cointepas et al., 2001
). They were not included in this study.
Evaluating the accuracy of registration algorithms on brain images typically requires comparison of the automatic registration to an objective criterion based on individual anatomy. Prior studies have used consistent, manual labeling of cortical landmarks or features such as gyri and sulci as such a criterion to compare the accuracy of volume-based (Klein et al., 2009
) and surface-based (Fischl et al., 2004
; Desikan et al., 2006
) registration approaches. However, all the underlying brain images used in these studies were from adult participants. In contrast, the current study aimed to evaluate the accuracy of volume- and surface-based registration in aligning macroanatomically defined brain regions across a set of pediatric brain images of varying ages.
Only one previous study (Burgund et al., 2002
) examined the accuracy of registering anatomical landmarks from a pediatric population. The study investigated the feasibility of using a common volume-based stereotactic coordinate system for comparing functional studies involving adults and children between 7 and 8 years of age. In that study, a 12-parameter affine transform was used to normalize the structural MR images of 20 children and 20 adults to a 12-subject average that was conformed to Talairach space (Talairach and Tournoux, 1988
). The investigators manually traced points along 10 different sulci identifiable in specific planar sections on each of these normalized volumes, as well as points along the outer boundaries of the brain in the three cardinal orientations (axial, sagittal and coronal). They observed that the location and variability of these manually traced positions after normalization was fairly consistent across the age groups. Furthermore, using computer simulations of fMRI data with 5mm resolution, they demonstrated that the observed variability did not generate any significant spurious effects. Based on these observations, they concluded that: (i) stereotactic normalization does not significantly distort brain morphology between adults and children; and (ii) the small distortions observed do not limit the ability to compare functional activation between adults and children in such a space. Furthermore, they indicated that “more work comparing younger children’s brains (below 7 years) to adult brains is needed before similar stereotactic approaches should be applied to that group.”
In this study, we extend the work described above by: i) using more anatomically precise delineations of boundaries of cortical regions based on surface geometry as opposed to picking points on a particular (anatomically arbitrary) imaging plane; ii) investigating a younger and larger age-range of children (4.2–11.1 years of age); and iii) using FreeSurfer's surface-based registration approach in addition to two commonly used volume-based registrations (a linear 12-parameter affine transform and a nonlinear normalization approach from SPM 5) and a diffeomorphic volume-registration method (ANTS; Avants et al., 2006
). The critical questions were whether it is valid to compare structural and functional brain images in child development (ages 4 to 11), and whether there are advantages for any particular kind of cortical image registration in this age range.