Anqi Qiu

Basal Ganglia Volume and Shape in Children With Attention Deficit Hyperactivity Disorder

OBJECTIVE Volumetric abnormalities of basal ganglia have been associated with attention deficit hyperactivity disorder (ADHD), especially in boys. To specify localization of these abnormalities, large deformation diffeomorphic metric mapping (LDDMM) was used to examine the effects of ADHD, sex, and their interaction on basal ganglia shapes. METHOD The basal ganglia (caudate, putamen, globus pallidus) were manually delineated on magnetic resonance imaging from 66 typically developing children (35 boys) and 47 children (27 boys) with ADHD. LDDMM mappings from 35 typically developing children were used to generate basal ganglia templates. Shape variations of each structure relative to the template were modeled for each subject as a random field using Laplace-Beltrami basis functions in the template coordinates. Linear regression was used to examine group differences in volumes and shapes of the basal ganglia. RESULTS Boys with ADHD showed significantly smaller basal ganglia volumes compared with typically developing boys, and LDDMM revealed the groups remarkably differed in basal ganglia shapes. Volume compression was seen bilaterally in the caudate head and body and anterior putamen as well as in the left anterior globus pallidus and right ventral putamen. Volume expansion was most pronounced in the posterior putamen. No volume or shape differences were revealed in girls with ADHD. CONCLUSIONS The shape compression pattern of basal ganglia in boys with ADHD suggests that ADHD-associated deviations from typical brain development involve multiple frontal-subcortical control loops, including circuits with premotor, oculomotor, and prefrontal cortices. Further investigations employing brain-behavior analyses will help to discern the task-dependent contributions of these circuits to impaired response control that is characteristic of ADHD.

Regional shape abnormalities in mild cognitive impairment and Alzheimer's disease

Magnetic resonance (MR) based shape analysis provides an opportunity to detect regional specificity of volumetric changes that may distinguish mild cognitive impairment (MCI) and Alzheimers disease (AD) from healthy elderly controls (CON), and predict future conversion to AD. We assessed the surface deformation of seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, body and temporal horn of the lateral ventricles) in 383 MRI volumes, based on data shared through the publicly available Alzheimers Disease Neuroimaging Initiative (ADNI), to identify regionally-specific shape abnormalities in MCI and AD. Large deformation diffeomorphic metric mapping (LDDMM) was used to generate the shapes of seven structures based on template shapes injected into segmented subcortical volumes. LDDMM then constructed the surface deformation maps encoding the local shape variation of each subject relative to the template. Hierarchical models were developed to detect differences in local shape in MCI and AD relative to CON. Our findings revealed that surface inward-deformation in MCI and AD is most prominent in the anterior hippocampal segment and the basolateral complex of the amygdala. Most pronounced surface outward-deformation in MCI and AD occurs in the lateral ventricles. Mild surface inward-deformation in MCI and AD occurs in the anterior-lateral and ventral-lateral aspects of the thalamus, with no evidence of regionally-specific deformation in the putamen or globus pallidus. Although the locations of the shape abnormalities in MCI and AD are primarily within the mesial temporal region, analyses support distinct components of correlated shape variation that may help predict future MCI conversion.

Large Deformation Diffeomorphic Metric Curve Mapping

We present a matching criterion for curves and integrate it into the large deformation diffeomorphic metric mapping (LDDMM) scheme for computing an optimal transformation between two curves embedded in Euclidean space ℝd. Curves are first represented as vector-valued measures, which incorporate both location and the first order geometric structure of the curves. Then, a Hilbert space structure is imposed on the measures to build the norm for quantifying the closeness between two curves. We describe a discretized version of this, in which discrete sequences of points along the curve are represented by vector-valued functionals. This gives a convenient and practical way to define a matching functional for curves. We derive and implement the curve matching in the large deformation framework and demonstrate mapping results of curves in ℝ2 and ℝ3. Behaviors of the curve mapping are discussed using 2D curves. The applications to shape classification is shown and experiments with 3D curves extracted from brain cortical surfaces are presented.

Smooth functional and structural maps on the neocortex via orthonormal bases of the Laplace-Beltrami operator

Functional and structural maps, such as a curvature, cortical thickness, and functional magnetic resonance imaging (MRI) maps, indexed over the local coordinates of the cortical manifold play an important role in neuropsychiatric studies. Due to the highly convoluted nature of the cerebral cortex and image quality, these functions are generally uninterpretable without proper methods of association and smoothness onto the local coordinate system. In this paper, we generalized the spline smoothing problem (Wahba, 1990) from a sphere to any arbitrary two-dimensional (2-D) manifold with boundaries. We first seek a numerical solution to orthonormal basis functions of the Laplace-Beltrami (LB) operator with Neumann boundary conditions for a 2-D manifold M then solve the spline smoothing problem in a reproducing kernel Hilbert space (r.k.h.s.) of real-valued functions on manifold M with kernel constructed from the basis functions. The explicit discrete LB representation is derived using the finite element method calculated directly on the manifold coordinates so that finding discrete LB orthonormal basis functions is equivalent to solving an algebraic eigenvalue problem. And then smoothed functions in r.k.h.s can be represented as a linear combination of the basis functions. We demonstrate numerical solutions of spherical harmonics on a unit sphere and brain orthonormal basis functions on a planum temporale manifold. Then synthetic data is used to quantify the goodness of the smoothness compared with the ground truth and discuss how many basis functions should be incorporated in the smoothing. We present applications of our approach to smoothing sulcal mean curvature, cortical thickness, and functional statistical maps on submanifolds of the neocortex

Multi-structure network shape analysis via normal surface momentum maps.

We present a shape analysis pipeline for the assessment of anatomical variations in subcortical networks in MR images. The shape analysis pipeline injects the global shape properties of the CFA subcortical template into the subcortical parcellations generated from FreeSurfer via large deformation diffeomorphic metric mapping (LDDMM). Examples are shown for this injection in several subcortical structures whose raw MR images were sampled from the database of Open Access Series of Imaging Studies (OASIS). The shape analysis is performed on random field representation of the template surface momentum maps that encode the shape variation of subcortical structure targets of each individual subject relative to the template. The momentum maps have the optimum property that they are supported only on the boundary of the subcortical structures with the direction normal to the subcortical nuclei boundary thereby reducing the dimension of shape variation significantly. A two-level statistical model was built on these momentum maps to assess anatomical connectivity among the subcortical structures on the basis of similar surface deformation (compression or expansion). Results in the study of healthy aging on the hippocampus-amygdala network indicate the anatomical connectivity between the basolateral complex of the amygdala and the subiculum of the hippocampus on the basis of shape compression in healthy elders relative to young adults.

Basal Ganglia Shapes Predict Social, Communication, and Motor Dysfunctions in Boys With Autism Spectrum Disorder

OBJECTIVE Basal ganglia abnormalities have been suggested as contributing to motor, social, and communicative impairments in autism spectrum disorder (ASD). Volumetric analyses offer limited ability to detect localized differences in basal ganglia structure. Our objective was to investigate basal ganglia shape abnormalities and their association with behavioral features of ASD, which may involve multiple frontal-subcortical circuits. METHOD Basal ganglia were manually delineated from MR images of 32 boys with ASD and 45 typically developing (TD) boys. Large deformation diffeomorphic metric mapping (LDDMM) was used to assess between-group differences in basal ganglia shape and to examine associations with motor, praxis, and reciprocal social and communicative impairments in ASD. RESULTS Boys with ASD showed changes in right basal ganglia shape as compared with TD boys; surface deformation was present in the caudate, putamen, and globus pallidus but did not stand up to correction for multiple comparisons. Brain-behavior correlation findings were more robust; analyses accounting for multiple comparisons revealed, in boys with ASD, surface inward deformation of the right posterior putamen predicted poorer motor skill, whereas surface inward deformation of the bilateral anterior and posterior putamen predicted poorer praxis. Surface outward deformation in the bilateral medial caudate head predicted greater reciprocal social and communicative impairment. CONCLUSIONS Motor, social, and communicative impairments in boys with ASD are associated with shape abnormalities in the basal ganglia. The findings suggest abnormalities within parallel frontal-subcortical circuits are differentially associated with impaired acquisition of motor and reciprocal social and communicative skills in ASD.

Prenatal maternal depression associates with microstructure of right amygdala in neonates at birth.

BACKGROUND Antenatal maternal cortisol levels associate with alterations in the amygdala, a structure associated with emotion regulation, in the offspring. However, because offspring brain and behavior are commonly assessed years after birth, the timing of such maternal influences is unclear. This study aimed to examine the association between antenatal maternal depressive symptomatology and neonatal amygdala volume and microstructure and thus establish evidence for the transgenerational transmission of vulnerability for affective disorders during prenatal development. METHODS Our study recruited Asian mothers at 10 to 13 weeks pregnancy and assessed maternal depression at 26 weeks gestation using the Edinburgh Postnatal Depression Scale. Structural magnetic resonance imaging and diffusion tensor imaging were performed with 157 nonsedated, 6- to 14-day-old newborns and then analyzed to extract the volume, fractional anisotropy, and axial diffusivity values of the amygdala. RESULTS Adjusting for household income, maternal age, and smoking exposure, postconceptual age at magnetic resonance imaging, and birth weight, we found significantly lower fractional anisotropy (p = .009) and axial diffusivity (p = .028), but not volume (p = .993), in the right amygdala in the infants of mothers with high compared with those with low-normal Edinburgh Postnatal Depression Scale scores. CONCLUSIONS The results reveal a significant relation between antenatal maternal depression and the neonatal microstructure of the right amygdala, a brain region closely associated with stress reactivity and vulnerability for mood anxiety disorders. These findings suggest the prenatal transmission of vulnerability for depression from mother to child and that interventions targeting maternal depression should begin early in pregnancy.

Diffeomorphic metric surface mapping in subregion of the superior temporal gyrus

This paper describes the application of large deformation diffeomorphic metric mapping to cortical surfaces based on the shape and geometric properties of subregions of the superior temporal gyrus in the human brain. The anatomical surfaces of the cortex are represented as triangulated meshes. The diffeomorphic matching algorithm is implemented by defining a norm between the triangulated meshes, based on the algorithms of Vaillant and Glaunès. The diffeomorphic correspondence is defined as a flow of the extrinsic three dimensional coordinates containing the cortical surface that registers the initial and target geometry by minimizing the norm. The methods are demonstrated in 40 high-resolution MRI cortical surfaces of planum temporale (PT) constructed from subsets of the superior temporal gyrus (STG). The effectiveness of the algorithm is demonstrated via the Euclidean positional distance, distance of normal vectors, and curvature before and after the surface matching as well as the comparison with a landmark matching algorithm. The results demonstrate that both the positional and shape variability of the anatomical configurations are being represented by the diffeomorphic maps.

Parallel Transport in Diffeomorphisms Distinguishes the Time-Dependent Pattern of Hippocampal Surface Deformation due to Healthy Aging and the Dementia of the Alzheimer’s Type

Hippocampal surface structure was assessed at twice 2 years apart in 26 nondemented subjects (CDR 0), in 18 subjects with early dementia of Alzheimer type (DAT, CDR 0.5), and in 9 subjects who converted from the nondemented (CDR 0) to the demented (CDR 0.5) state using magnetic resonance (MR) imaging. We used parallel transport in diffeomorphisms under the large deformation diffeomorphic metric mapping framework to translate within-subject deformation of the hippocampal surface as represented in the MR images between the two time points in a global template coordinate system. We then performed hypothesis testing on the longitudinal variation of hippocampal shape in the global template. Both subjects with early DAT and converters showed greater rates of hippocampal deformation across time than nondemented controls within every subfield of the hippocampus. In a random field analysis, inward surface deformation across time occurred in a non-uniform manner across the hippocampal surface in subjects with early DAT relative to the nondemented controls. Also, compared to the controls, the lateral aspect of the left hippocampal tail showed inward surface deformation in the converters. Using surface deformation patterns as features in a linear discriminant analysis, we were able to respectively distinguish converters and patients with early DAT from healthy nondemented controls at classification rates of 0.77 and 0.87, which were obtained in the same training set using the leave-one-out cross validation approach.

Correction of B0 susceptibility induced distortion in diffusion-weighted images using large-deformation diffeomorphic metric mapping.

Geometric distortion caused by B0 inhomogeneity is one of the most important problems for diffusion-weighted images (DWI) using single-shot, echo planar imaging (SS-EPI). In this study, large-deformation, diffeomorphic metric mapping (LDDMM) algorithm has been tested for the correction of geometric distortion in diffusion tensor images (DTI). Based on data from nine normal subjects, the amount of distortion caused by B0 susceptibility in the 3-T magnet was characterized. The distortion quality was validated by manually placing landmarks in the target and DTI images before and after distortion correction. The distortion was found to be up to 15 mm in the population-averaged map and could be more than 20 mm in individual images. Both qualitative demonstration and quantitative statistical results suggest that the highly elastic geometric distortion caused by spatial inhomogeneity of the B0 field in DTI using SS-EPI can be effectively corrected by LDDMM. This postprocessing method is especially useful for correcting existent DTI data without phase maps.