Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection
Introduction
Magnetic Resonance Imaging (MRI) has been one of the most widely used imaging modalities to describe the macroscopic anatomical changes during the development process because of its capability to capture the anatomy three-dimensionally, quantitatively, and non-invasively. Using MRI, dynamic changes can be characterized in vivo on a larger sample size. Previous studies have described a biphasic development of the brain — rapid growth in the first 2 years of life, followed by slower and more subtle developmental changes. During the first 2 years of life, histological studies have described the classical temporo-spatial gradients of myelinization. In the first months of life, the signal intensities of gray and white matter in T1- and T2-weighted images are the reverse of those seen in an adult brain. As the white matter (WM) myelinates, it changes from hypointense to hyperintense relative to the gray matter in T1-weighted images, and from hyperintense to hypointense relative to the gray matter in T2-weighted images (Ballesteros et al., 1993, Barkovich et al., 1988, Konishi et al., 1993, van der Knaap & Valk, 1990).
Although these patterns are generally true, the information they provided on brain maturation is limited (Barkovich, 2000, Brody et al., 1987, Kinney et al., 1988, Petanjek et al., 2008). Recently, diffusion tensor imaging (DTI) has been shown to provide additional information about the changes in the brain's microstructure with maturation (Alexander et al., 2007, Bartha et al., 2007, Cascio et al., 2007, Ding et al., 2008, Dubois et al., 2006, Engelbrecht et al., 2002, Gilmore et al., 2007, Hasan et al., 2007a, Hasan et al., 2007b, Hasan et al., 2008, Hermoye et al., 2006, Huang et al., 2006, Huppi & Dubois, 2006, Le Bihan, 2003, Moseley, 2002, Mukherjee et al., 2001, Mukherjee et al., 2002, Neil et al., 1998, Snook et al., 2005, Stegemann et al., 2006). Taking advantage of anisotropic diffusion, DTI can demonstrate brain axonal organization in detail beyond the resolution of conventional MRI (Mori and Zhang, 2006). By increasing the contrast within the WM, regional connectivity can be investigated in both normal and pathological conditions.
After the second year, the developmental changes become much more subtle. The average brain weight at 2 years old has already reached approximately 80% of that of adults weight, and at 5 years old, there is no significant difference (Dekaban, 1978, Lenroot & Giedd, 2006). In terms of MR contrasts, T1-weighted imaging studies found changes in a confined area of the brain (Paus et al., 1999, Snook et al., 2005, Thompson et al., 2000). DTI is sensitive enough to show a pattern of maturation with considerable regional variation, generally characterized by an increase in fractional anisotropy (FA) and a decrease in mean diffusivity (Dubois et al., 2008, Hasan et al., 2007a, Hasan et al., 2007b, Hasan et al., 2008, Klingberg et al., 1999, Lebel et al., 2008, Qiu et al., 2008, Snook et al., 2005). Previous studies are primarily based on measurements in pre-defined brain regions, and a comprehensive whole-brain spatio-temporal study has not been undertaken.
In this study, we investigated developmental changes in the WM after the second year using DTI. Characterization of brain development by MRI consists of five dimensions: one for time; three for locations; and one for measured parameters, which include volume, FA, apparent coefficient of diffusion, and radial/axial diffusivities. For the location information, the finest unit is the voxel. In voxel-based analyses, we can establish standard voxel coordinates and monitor anatomical changes at each voxel. However, this type of analysis is based on the assumption that the brain normalization procedure (identification of corresponding voxels between the standard brain and individual brains) is accurate, and also, often suffers from poor statistical power due to the high level of noise. An alternative approach based on regions of interest (ROIs) ameliorates these shortcomings by grouping the anatomically related voxels within the same anatomical unit, thus systematically reducing the location information from hundreds of thousands of voxels to a limited number of ROIs. By manually defining the ROIs, severe inaccuracy issues can also be avoided. The manual ROI-based analyses, however, is known to have reproducibility issues (manual ROI drawing is not perfectly repeatable), and it is not suitable for the whole-brain analyses (too time-consuming to define a large number of 3D ROIs). In this study, we adopted a voxel-based and an atlas-based analysis, in which the entire brain was automatically segmented to 176 anatomical units after normalization. For each defined area, the time courses of various MR parameters were characterized. For the standard coordinates, we used our JHU-DTI-MNI single-subject atlas (also known as the “Eve Atlas”) (Mori et al., 2008), which consists of multiple MR contrasts (T1/T2/DTI) and 176 pre-segmented regions (Oishi et al., 2008). The relatively small anatomical changes after the age of two allowed us to use the adult atlas for the standard coordinates.
We expect that this type of five-dimensional analyses would generate complex and often difficult-to-interpret results; some regions may not have any time-dependent change in one parameter (e.g., FA), while there is a clear age-related change in another parameter (e.g., ADC). Such behavior may be completely different in the adjacent anatomical areas. While this type of information will provide further insight into brain development, one of our primary goals is to enrich the brain atlas. Characterization of the normal development process would provide the average values and the degree of normal variability of each measured parameter at each location. This, in turn, allows us to perform power analysis to detect abnormalities in brain growth. This would be an essential step toward automated detection of abnormalities in the future. To demonstrate the utility of the enriched atlas, DTI data from cerebral palsy patients were analyzed for automated detection of abnormalities.
Section snippets
Subjects
Data from a total of 35 subjects from a pediatric database (lbam.med.jhmi.edu) (Hermoye et al., 2006) were used in this study. This included nine healthy pediatric volunteers (> 4 years old) and 17 pediatric patients referred for a clinical MR examination for extra-cranial symptoms (14 male; > 2 years old; mean age: 6.7 years). The clinical indications were pathologies related to the internal ear, the orbits, the spine, epilepsy, trauma, infectious disease, genetic disease, and vascular/cisternal
Macroscopic analysis of brain volumes
Linear fitting showed statistical significance for the logarithmic age-dependent increase in the whole-brain, WM, and cerebrospinal (CSF) volumes (Fig. 3), although the correlation for the whole-brain volume was weak (R2 = 0.3). At age two, the average brain volume was 1,076,727 mm3, 78% of the adult's mean volume. After 5 years of age, there was no significant time-dependency (p-value > 0.05) and the average brain volume was 1,179,396 mm3. The WM compartment (FA ≥ 0.25) increased, with a slightly
Macroscopic analysis of brain development
In Fig. 3, macroscopic characterization of brain development is displayed. The time-dependency of the brain, WM, gray matter, and CSF volumes closely follow the results in previous publications (Hasan et al., 2007b, Hua et al., 2009, Wilke et al., 2007). It is well known that the human brain grows rapidly during the first 2 years of life, by which time it has achieved 80% of its adult weight and, at 5 years of age, it is approximately 90% of the adult weight (Dekaban, 1978, Lenroot & Giedd, 2006
Conclusion
LDDMM and atlas-based analyses of DTI allowed us to detect brain anatomical changes from 2 years of age to adulthood, and provided a comprehensive investigation of brain development. Each brain area followed a temporally distinct maturational trajectory in both size and diffusivity of water molecules. The quantitative and regional characterization of the normal maturation process is the first step in characterizing abnormal brain development. The reported tools and data provide important
Acknowledgments
This publication was made possible by the grant number F05NS059230 (AVF) from the National Institute of Neurological Disorders and Stroke (NINDS), a component of the National Institutes of Health (NIH). This research was also supported by NIH grants P41 RR015241, AG20012 and RR015241 (SM). Dr. Peter C.M. van Zijl is a paid lecturer for Philips Medical Systems. This arrangement has been approved by Johns Hopkins University in accordance with its conflict of interest policies.
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