Multi-Manifold Diffeomorphic Metric Mapping for Cortical Registration with its Applications in Brain Structural and Functional Studies

Ph.DDOCTOR OF PHILOSOPH

Abnormal surface morphology of the central sulcus in children with attention-deficit/hyperactivity disorder

The central sulcus (CS) divides the primary motor and somatosensory areas, and its three-dimensional (3D) anatomy reveals the structural changes of the sensorimotor regions. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is associated with sensorimotor and executive function deficits. However, it is largely unknown whether the morphology of the CS alters due to inappropriate development in the ADHD brain. Here, we employed the sulcus-based morphometry approach to investigate the 3D morphology of the CS in 42 children whose ages spanned from 8.8 to 13.5 years (21 with ADHD and 21 controls). After automatic labeling of each CS, we computed 7 regional shape metrics for each CS, including the global average length, average depth, maximum depth, average span, surface area, average cortical thickness and local sulcal profile. We found that the average depth and maximum depth of the left CS as well as the average cortical thickness of bilateral CS in the ADHD group were significantly larger than those in the healthy children. Moreover, significant between-group differences in the sulcal profile had been found in middle sections of the CSs bilaterally, and these changes were positively correlated with the hyperactivity-impulsivity scores in the children with ADHD. Altogether, our results provide evidence for the abnormity of the CS anatomical morphology in children with ADHD due to the structural changes in the motor cortex, which significantly contribute to the clinical symptomatology of the disorder

Evaluating the validity of volume-based and surface-based brain image registration for developmental cognitive neuroscience studies in children 4 to 11 years of age

Understanding the neurophysiology of human cognitive development relies on methods that enable accurate comparison of structural and functional neuroimaging data across brains from people of different ages. A fundamental question is whether the substantial brain growth and related changes in brain morphology that occur in early childhood permit valid comparisons of brain structure and function across ages. Here we investigated whether valid comparisons can be made in children from ages 4 to 11, and whether there are differences in the use of volume-based versus surface-based registration approaches for aligning structural landmarks across these ages. Regions corresponding to the calcarine sulcus, central sulcus, and Sylvian fissure in both the hemispheres were manually labeled on T1-weighted structural magnetic resonance images from 31 children ranging in age from 4.2 to 11.2 years old. Quantitative measures of shape similarity and volumetric-overlap of these manually labeled regions were calculated when brains were aligned using a 12-parameter affine transform, SPM's nonlinear normalization, a diffeomorphic registration (ANTS), and FreeSurfer's surface-based registration. Registration error for normalization into a common reference framework across participants in this age range was lower than commonly used functional imaging resolutions. Surface-based registration provided significantly better alignment of cortical landmarks than volume-based registration. In addition, registering children's brains to a common space does not result in an age-associated bias between older and younger children, making it feasible to accurately compare structural properties and patterns of brain activation in children from ages 4 to 11

MULTISCALE KERNELS FOR DIFFEOMORPHIC BRAIN IMAGE AND SURFACE MATCHING

Ph.DDOCTOR OF PHILOSOPH

Evaluating the validity of volume-based and surface-based brain image registration for developmental cognitive neuroscience studies in children 4 to 11 years of age

Understanding the neurophysiology of human cognitive development relies on methods that enable accurate comparison of structural and functional neuroimaging data across brains from people of different ages. A fundamental question is whether the substantial brain growth and related changes in brain morphology that occur in early childhood permit valid comparisons of brain structure and function across ages. Here we investigated whether valid comparisons can be made in children from ages 4 to 11, and whether there are differences in the use of volume-based versus surface-based registration approaches for aligning structural landmarks across these ages. Regions corresponding to the calcarine sulcus, central sulcus, and Sylvian fissure in both the hemispheres were manually labeled on T1-weighted structural magnetic resonance images from 31 children ranging in age from 4.2 to 11.2 years old. Quantitative measures of shape similarity and volumetric-overlap of these manually labeled regions were calculated when brains were aligned using a 12-parameter affine transform, SPM's nonlinear normalization, a diffeomorphic registration (ANTS), and FreeSurfer's surface-based registration. Registration error for normalization into a common reference framework across participants in this age range was lower than commonly used functional imaging resolutions. Surface-based registration provided significantly better alignment of cortical landmarks than volume-based registration. In addition, registering children's brains to a common space does not result in an age-associated bias between older and younger children, making it feasible to accurately compare structural properties and patterns of brain activation in children from ages 4 to 11

Magnetic resonance imaging and surface-based analysis techniques to quantify growth of the developing brain

Human brains, and those of most higher mammals, are gyrencephalic: folded) to accommodate a large cortical surface within the limited volume of the skull. Abnormal folding of the cerebral cortex in humans is associated with a number of neurological dysfunctions and diseases such as schizophrenia and Williams syndrome. To understand the mechanism of gyrification, and to illuminate the underlying causes of abnormal folding, objective, quantitative methods to characterize normal and abnormal development must be developed. The ferret is an excellent model in which to study the development of convolutions in the brain because folding occurs post-natally over a period of several weeks, and the brain can be imaged conveniently in small-animal magnetic resonance: MR) scanners. Here, MR imaging was used to acquire three-dimensional image volumes of the ferret brain in vivo at different stages during the period of cortical folding. Through segmentation of these volumes, surface representations of the cortex are generated at each time point. A novel intra-subject registration algorithm: LAndmark Correspondence and Relaxation Of Surface Strain: LACROSS), which provides a point-to-point correspondence between two surfaces, is applied to the cortical surfaces from two ferret kits. The resulting calculations of growth show regional patterns within the cortex, and temporal variations over this period of early brain development

Landmarking the brain for geometric morphometric analysis: An error study

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm-5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm-3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes. © 2014 Chollet et al

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure