Faculty Bibliography

The use of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) in animal models of neuropathology is of increasing interest to the neuroscience community. In this work, we present our approach to create optimal translational studies that include both animal and human neuroimaging data within the frameworks of a study of post-natal neuro-development in intra-uterine cocaine-exposure. We propose the use of non-invasive neuroimaging to study developmental brain structural and white matter pathway abnormalities via sMRI and DTI, as advanced MR imaging technology is readily available and automated image analysis methodology have recently been transferred from the human to animal imaging setting. For this purpose, we developed a synergistic, parallel approach to imaging and image analysis for the human and the rodent branch of our study. We propose an equivalent design in both the selection of the developmental assessment stage and the neuroimaging setup. This approach brings significant advantages to study neurobiological features of early brain development that are common to animals and humans but also preserve analysis capabilities only possible in animal research. This paper presents the main framework and individual methods for the proposed cross-species study design, as well as preliminary DTI cross-species comparative results in the intra-uterine cocaine-exposure study.

In this paper, we propose a novel approach for intensity based atlas construction from a population of anatomical images, that estimates not only a template representative image but also a common optimal parameterization of the anatomical variations evident in the population. First, we introduce a discrete parameterization of large diffeomorphic deformations based on a finite set of control points, so that deformations are characterized by a low dimensional geometric descriptor. Second, we optimally estimate the position of the control points in the template image domain. As a consequence, control points move to where they are needed most to capture the geometric variability evident in the population. Third, the optimal number of control points is estimated by using a log - L1 sparsity penalty. The estimation of the template image, the template-to-subject mappings and their optimal parameterization is done via a single gradient descent optimization, and at the same computational cost as independent template-to-subject registrations. We present results that show that the anatomical variability of the population can be encoded efficiently with these compact and adapted geometric descriptors.

Deformable image registration in the presence of considerable contrast differences and large size and shape changes presents significant research challenges. First, it requires a robust registration framework that does not depend on intensity measurements and can handle large nonlinear shape variations. Second, it involves the expensive computation of nonlinear deformations with high degrees of freedom. Often it takes a significant amount of computation time and thus becomes infeasible for practical purposes. In this paper, we present a solution based on two key ideas: a new registration method that generates a mapping between anatomies represented as a multicompartment model of class posterior images and geometries and an implementation of the algorithm using particle mesh approximation on Graphical Processing Units (GPUs) to fulfill the computational requirements. We show results on the registrations of neonatal to 2-year old infant MRIs. Quantitative validation demonstrates that our proposed method generates registrations that better maintain the consistency of anatomical structures over time and provides transformations that better preserve structures undergoing large deformations than transformations obtained by standard intensity-only registration. We also achieve the speedup of three orders of magnitudes compared to a CPU reference implementation, making it possible to use the technique in time-critical applications.

Longitudinal shape analysis often relies on the estimation of a realistic continuous growth scenario from data sparsely distributed in time. In this paper, we propose a new type of growth model parameterized by acceleration, whereas standard methods typically control the velocity. This mimics the behavior of biological tissue as a mechanical system driven by external forces. The growth trajectories are estimated as smooth flows of deformations, which are twice differentiable. This differs from piecewise geodesic regression, for which the velocity may be discontinuous. We evaluate our approach on a set of anatomical structures of the same subject, scanned 16 times between 4 and 8 years of age. We show our acceleration based method estimates smooth growth, demonstrating improved regularity compared to piecewise geodesic regression. Leave-several-out experiments show that our method is robust to missing observations, as well as being less sensitive to noise, and is therefore more likely to capture the underlying biological growth.

The degree of white matter (WM) myelination is rather inhomogeneous across the brain. As a consequence, white matter appears differently across the cortical lobes in MR images acquired during early postnatal development. At 1 year old specifically, the gray/white matter contrast of MR images in prefrontal and temporal lobes is limited and thus tissue segmentation results show commonly reduce edaccuracy in these lobes. In this novel work, we propose the use of spatial intensity growth maps (IGM) for T1 and T2 weighted image to compensate for local appearance inhomogeneity. The IGM captures expected intensity changes from 1 to 2 years of age, as appearance inhomogeneity is highly reduced by the age of 24 months. For that purpose, we employ MRI data from a large dataset of longitudinal (12 and 24 month old subjects) MR study of Autism. The IGM creation is based on automatically co-registered images at 12 months, corresponding registered 24 months images, and a final registration of all image to a prior average template. In template space, voxelwise correspondence is thus achieved and the IGM is computed as the coefficient of a voxelwise linear regression model between corresponding intensities at 1-year and 2-years. The proposed IGM shows low regression values of 1-10% in GM and CSF regions, as well as in WM regions at advanced stage of myelination at 1-year. However, in the prefrontal and temporal lobe we observed regression values of 20-25%, indicating that the IGM appropriately captures the expected large intensity change in these lobes due to myelination. The IGM is applied to cross-sectional MRI datasets of 1-year old subjects via registration, correction and tissue segmentation of the corrected dataset. We validated our approach in a small study of images with known, manual "ground truth" segmentations. We furthermore present an EM-like optimization of adapting existing non-optimal prior atlas probability maps to fit known expert rater segmentations.

OBJECTIVE: Schizophrenia is a neurodevelopmental disorder associated with abnormalities of brain structure and white matter, although little is known about when these abnormalities arise. This study was conducted to identify structural brain abnormalities in the prenatal and neonatal periods associated with genetic risk for schizophrenia. METHOD: Prenatal ultrasound scans and neonatal structural magnetic resonance imaging (MRI) and diffusion tensor imaging were prospectively obtained in the offspring of mothers with schizophrenia or schizoaffective disorder (N=26) and matched comparison mothers without psychiatric illness (N=26). Comparisons were made for prenatal lateral ventricle width and head circumference, for neonatal intracranial, CSF, gray matter, white matter, and lateral ventricle volumes, and for neonatal diffusion properties of the genu and splenium of the corpus callosum and corticospinal tracts. RESULTS: Relative to the matched comparison subjects, the offspring of mothers with schizophrenia did not differ in prenatal lateral ventricle width or head circumference. Overall, the high-risk neonates had nonsignificantly larger intracranial, CSF, and lateral ventricle volumes. Subgroup analysis revealed that male high-risk infants had significantly larger intracranial, CSF, total gray matter, and lateral ventricle volumes; the female high-risk neonates were similar to the female comparison subjects. There were no group differences in white matter diffusion tensor properties. CONCLUSIONS: Male neonates at genetic risk for schizophrenia had several larger than normal brain volumes, while females did not. To the authors' knowledge, this study provides the first evidence, in the context of its limitations, that early neonatal brain development may be abnormal in males at genetic risk for schizophrenia.

Twin studies have found that global brain volumes, including total intracranial volume (ICV), total gray matter, and total white matter volumes are highly heritable in adults and older children. Very little is known about genetic and environmental contributions to brain structure in very young children and whether these contributions change over the course of development. We performed structural imaging on a 3T MR scanner of 217 neonatal twins, 41 same-sex monozygotic, 50 same-sex dizygotic pairs, and 35 "single" twins-neonates with brain scans unavailable for their co-twins. Tissue segmentation and parcellation was performed, and structural equation modeling was used to estimate additive genetic, common environmental, and unique environmental effects on brain structure. Heritability of ICV (0.73) and total white matter volume (0.85) was high and similar to that described in older children and adults; the heritability of total gray matter (0.56) was somewhat lower. Heritability of lateral ventricle volume was high (0.71), whereas the heritability of cerebellar volume was low (0.17). Comparison with previous twin studies in older children and adults reveal that three general patterns of how heritability can change during postnatal brain development: (1) for global white matter volumes, heritability is comparable to reported heritability in adults, (2) for global gray matter volume and cerebellar volume, heritability increases with age, and (3) for lateral ventricle volume, heritability decreases with age. More detailed studies of the changes in the relative genetic and environmental effects on brain structure throughout early childhood development are needed.

BACKGROUND: We sought to test the hypothesis that deficits in grey matter volume are characteristic of psychotic youth with early-onset schizophrenia-spectrum disorders (EOSS) but not of psychotic youth with early-onset mood disorders (EOMD). METHODS: We used magnetic resonance imaging to examine brain volume in 24 psychotic youth (13 male, 11 female) with EOSS (n = 12) or EOMD (n = 12) and 17 healthy controls (10 male, 7 female). We measured the volume of grey and white matter using an automated segmentation program. RESULTS: After adjustment for age and intracranial volume, whole brain volume was lower in the EOSS patients than in the healthy controls (p = 0.001) and EOMD patients (p = 0.002). The EOSS patients had a deficit in grey matter volume (p = 0.005), especially in the frontal (p = 0.003) and parietal (p = 0.006) lobes, with no significant differences in white matter volume. LIMITATIONS: The main limitations of our study were its small sample size and the inclusion of patients with depression and mania in the affective group. CONCLUSION: Adolescents with EOSS have grey matter deficits compared with healthy controls and psychotic adolescents with EOMD. Our results suggest that grey matter deficits are not generally associated with psychosis but may be specifically associated with schizophrenia. Larger studies with consistent methods are needed to reconcile the contradictory findings among imaging studies involving psychotic youth.