Dongrong Xu

Thinning of sensorimotor cortices in children with Tourette syndrome

The basal ganglia portions of cortico-striato-thalamo-cortical (CSTC) circuits have consistently been implicated in the pathogenesis of Tourette syndrome, whereas motor and sensorimotor cortices in these circuits have been relatively overlooked. Using magnetic resonance imaging, we detected cortical thinning in frontal and parietal lobes in groups of Tourette syndrome children relative to controls. This thinning was most prominent in ventral portions of the sensory and motor homunculi that control the facial, orolingual and laryngeal musculature that is commonly involved in tic symptoms. Correlations of cortical thickness in sensorimotor regions with tic symptoms suggest that these brain regions are important in the pathogenesis of Tourette syndrome.

Spatial normalization of diffusion tensor fields

A method for the spatial normalization and reorientation of diffusion tensor (DT) fields is presented. Spatial normalization of tensor fields requires an appropriate reorientation of the tensor on each voxel, in addition to its relocation into the standardized space. This appropriate tensor reorientation is determined from the spatial normalization transformation and from an estimate of the underlying fiber direction. The latter is obtained by treating the principal eigenvectors of the tensor field around each voxel as random samples drawn from the probability distribution that represents the direction of the underlying fiber. This approach was applied to DT images from nine normal volunteers, and the results show a significant improvement in signal‐to‐noise ratio (SNR) after spatial normalization and averaging of tensor fields across individuals. The statistics of the spatially normalized tensor field, which represents the tensor characteristics of normal individuals, may be useful for quantitatively characterizing individual variations of white matter structures revealed by DT imaging (DTI) and deviations caused by pathology. Simulated experiments using this methodology are also described. Magn Reson Med 50:175–182, 2003.

A Framework for Callosal Fiber Distribution Analysis

This paper presents a framework for analyzing the spatial distribution of neural fibers in the brain, with emphasis on interhemispheric fiber bundles crossing through the corpus callosum. The proposed approach combines methodologies for fiber tracking and spatial normalization and is applied on diffusion tensor images and standard magnetic resonance images.

Anatomical Brain Images Alone Can Accurately Diagnose Chronic Neuropsychiatric Illnesses

Objective Diagnoses using imaging-based measures alone offer the hope of improving the accuracy of clinical diagnosis, thereby reducing the costs associated with incorrect treatments. Previous attempts to use brain imaging for diagnosis, however, have had only limited success in diagnosing patients who are independent of the samples used to derive the diagnostic algorithms. We aimed to develop a classification algorithm that can accurately diagnose chronic, well-characterized neuropsychiatric illness in single individuals, given the availability of sufficiently precise delineations of brain regions across several neural systems in anatomical MR images of the brain. Methods We have developed an automated method to diagnose individuals as having one of various neuropsychiatric illnesses using only anatomical MRI scans. The method employs a semi-supervised learning algorithm that discovers natural groupings of brains based on the spatial patterns of variation in the morphology of the cerebral cortex and other brain regions. We used split-half and leave-one-out cross-validation analyses in large MRI datasets to assess the reproducibility and diagnostic accuracy of those groupings. Results In MRI datasets from persons with Attention-Deficit/Hyperactivity Disorder, Schizophrenia, Tourette Syndrome, Bipolar Disorder, or persons at high or low familial risk for Major Depressive Disorder, our method discriminated with high specificity and nearly perfect sensitivity the brains of persons who had one specific neuropsychiatric disorder from the brains of healthy participants and the brains of persons who had a different neuropsychiatric disorder. Conclusions Although the classification algorithm presupposes the availability of precisely delineated brain regions, our findings suggest that patterns of morphological variation across brain surfaces, extracted from MRI scans alone, can successfully diagnose the presence of chronic neuropsychiatric disorders. Extensions of these methods are likely to provide biomarkers that will aid in identifying biological subtypes of those disorders, predicting disease course, and individualizing treatments for a wide range of neuropsychiatric illnesses.

Review of spectral imaging technology in biomedical engineering: achievements and challenges

Abstract. Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

Correction of eddy-current distortions in diffusion tensor images using the known directions and strengths of diffusion gradients

To correct eddy‐current artifacts in diffusion tensor (DT) images without the need to obtain auxiliary scans for the sole purpose of correction.

Association of Cerebral Networks in Resting State with Sexual Preference of Homosexual Men: A Study of Regional Homogeneity and Functional Connectivity

Recent imaging studies have shown that brain morphology and neural activity during sexual arousal differ between homosexual and heterosexual men. However, functional differences in neural networks at the resting state is unknown. The study is to characterize the association of homosexual preference with measures of regional homogeneity and functional connectivity in the resting state. Participants were 26 healthy homosexual men and 26 age-matched healthy heterosexual men in whom we collected echo planar magnetic resonance imaging data in the resting state. The sexual orientation was evaluated using the Kinsey Scale. We first assessed group differences in regional homogeneity and then, taking the identified differences as seed regions, we compared groups in measures of functional connectivity from those seeds. The behavioral significances of the differences in regional homogeneity and functional connectivity were assessed by examining their associations with Kinsey Scores. Homosexual participants showed significantly reduced regional homogeneity in the left inferior occipital gyrus, right middle occipital gyrus, right superior occipital gyrus, left cuneus, right precuneus, and increased regional homogeneity in rectal gyrus, bilateral midbrain, and left temporal lobe. Regional homogeneity correlated positively with Kinsey scores in the left inferior occipital gyrus. The homosexual group also showed reduced functional connectivity between left middle temporal gyrus, left supra-marginal gyrus, right cuneus and the seed region, i.e. left inferior occipital gyrus. Additionly, the connection between the left inferior occipital gyrus and right thalamus correlated positively with Kinsey scores. These differences in regional homogeneity and functional connectivity may contribute to a better understanding of the neural basis of male sexual orientation.

Cerebellar morphology in Tourette syndrome and obsessive-compulsive disorder.

Neuroanatomical and functional imaging studies have identified the cerebellum as an integral component of motor and language control. Few studies, however, have investigated the role of the cerebellum in Tourette syndrome (TS), a condition defined by the presence of semi‐involuntary movements and sounds.

Resting-state functional MRI: Functional connectivity analysis of the visual cortex in primary open-angle glaucoma patients

Purpose: To analyze functional connectivity (FC) of the visual cortex using resting‐state functional MRI in human primary open‐angle glaucoma (POAG) patients. Materials and Methods: Twenty‐two patients with known POAG and 22 age‐matched controls were included in this IRB‐approved study. Subjects were evaluated by 3 T MR using resting‐state blood oxygenation level dependent and three‐dimensional brain volume imaging (3D‐BRAVO) MRI. Data processing was performed with standard software. FC maps were generated from Brodmann areas (BA) 17/18/19/7 in a voxel‐wise fashion. Region of interest analysis was used to specifically examine FC among each pair of BA17/18/19/7. Results: Voxel‐wise analyses demonstrated decreased FC in the POAG group between the primary visual cortex (BA17) and the right inferior temporal, left fusiform, left middle occipital, right superior occipital, left postcentral, right precentral gyri, and anterior lobe of the left cerebellum. Increased FC was found between BA17 and the left cerebellum, right middle cerebellar peduncle, right middle frontal gyrus, and extra‐nuclear gyrus (P < 0.05). In terms of the higher visual cortices (BA18/19), positive FC was disappeared with the cerebellar vermis, right middle temporal, and right superior temporal gyri (P < 0.05). Negative FC was disappeared between BA18/19 and the right insular gyrus (P < 0.05). Region of interest analysis demonstrated no statistically significant differences in FC between the POAG patients relative to the controls (P > 0.05). Conclusion: Changes in FC of the visual cortex are found in patients with POAG. These include alterations in connectivity between the visual cortex and associative visual areas along with disrupted connectivity between the primary and higher visual areas. Hum Brain Mapp 34:2455–2463, 2013.

Statistical Analyses of Brain Surfaces Using Gaussian Random Fields on 2-D Manifolds

Interest in the morphometric analysis of the brain and its subregions has recently intensified because growth or degeneration of the brain in health or illness affects not only the volume but also the shape of cortical and subcortical brain regions, and new image processing techniques permit detection of small and highly localized perturbations in shape or localized volume, with remarkable precision. An appropriate statistical representation of the shape of a brain region is essential, however, for detecting, localizing, and interpreting variability in its surface contour and for identifying differences in volume of the underlying tissue that produce that variability across individuals and groups of individuals. Our statistical representation of the shape of a brain region is defined by a reference region for that region and by a Gaussian random field (GRF) that is defined across the entire surface of the region. We first select a reference region from a set of segmented brain images of healthy individuals. The GRF is then estimated as the signed Euclidean distances between points on the surface of the reference region and the corresponding points on the corresponding region in images of brains that have been coregistered to the reference. Correspondences between points on these surfaces are defined through deformations of each region of a brain into the coordinate space of the reference region using the principles of fluid dynamics. The warped, coregistered region of each subject is then unwarped into its native space, simultaneously bringing into that space the map of corresponding points that was established when the surfaces of the subject and reference regions were tightly coregistered. The proposed statistical description of the shape of surface contours makes no assumptions, other than smoothness, about the shape of the region or its GRF. The description also allows for the detection and localization of statistically significant differences in the shapes of the surfaces across groups of subjects at both a fine and coarse scale. We demonstrate the effectiveness of these statistical methods by applying them to study differences in shape of the amygdala and hippocampus in a large sample of normal subjects and in subjects with attention deficit/hyperactivity disorder (ADHD)