Publications by Year: 2013

Despite the fact that several theories link cortical development and function to the development of white matter and its geometrical structure, the relationship between gray and white matter morphology has not been widely researched. In this paper, we propose a novel framework for investigating this relationship. Given a set of fiber tracts which connect to a particular cortical region, the key idea is to compute two scalar fields that represent geometrical characteristics of the white matter and of the surface of the cortical region. The distributions of these scalar values are then linked via Mutual Information, which results in a quantitative marker that can be used in the study of normal and pathological brain structure and development. We apply this framework to a population study on autism spectrum disorder in children.

A significant percentage of individuals diagnosed with mild traumatic brain injury (mTBI) experience persistent post-concussive symptoms (PPCS). Little is known about the pathology of these symptoms and there is often no radiological evidence based on conventional clinical imaging. We aimed to utilize methods to evaluate microstructural tissue changes and to determine whether or not a link with PPCS was present. A novel analysis method was developed to identify abnormalities in high-resolution diffusion tensor imaging (DTI) when the location of brain injury is heterogeneous across subjects. A normative atlas with 145 brain regions of interest (ROI) was built from 47 normal controls. Comparing each subject’s diffusion measures to the atlas generated subject-specific profiles of injury. Abnormal ROIs were defined by absolute z-score values above a given threshold. The method was applied to 11 PPCS patients following mTBI and 11 matched controls. Z-score information for each individual was summarized with two location-independent measures: "load" (number of abnormal regions) and "severity" (largest absolute z-score). Group differences were then computed using Wilcoxon rank sum tests. Results showed statistically significantly higher load (p = 0.018) and severity (p = 0.006) for fractional anisotropy (FA) in patients compared with controls. Subject-specific profiles of injury evinced abnormally high FA regions in gray matter (30 occurrences over 11 patients), and abnormally low FA in white matter (3 occurrences over 11 subjects). Subject-specific profiles provide important information regarding the pathology associated with PPCS. Increased gray matter (GM) anisotropy is a novel in-vivo finding, which is consistent with an animal model of brain trauma that associates increased FA in GM with pathologies such as gliosis. In addition, the individualized analysis shows promise for enhancing the clinical care of PPCS patients as it could play a role in the diagnosis of brain injury not revealed using conventional imaging.

In image segmentation, we are often interested in using certain quantities to characterize the object, and perform the classification based on criteria such as mean intensity, gradient magnitude, and responses to certain predefined filters. Unfortunately, in many cases such quantities are not adequate to model complex textured objects. Along a different line of research, the sparse characteristic of natural signals has been recognized and studied in recent years. Therefore, how such sparsity can be utilized, in a non-parametric way, to model the object texture and assist the textural image segmentation process is studied in this paper, and a segmentation scheme based on the sparse representation of the texture information is proposed. More explicitly, the texture is encoded by the dictionaries constructed from the user initialization. Then, an active contour is evolved to optimize the fidelity of the representation provided by the dictionary of the target. In doing so, not only a non-parametric texture modeling technique is provided, but also the sparsity of the representation guarantees the computation efficiency. The experiments are carried out on the publicly available image data sets which contain a large variety of texture images, to analyze the user interaction, performance statistics, and to highlight the algorithm’s capability of robustly extracting textured regions from an image.

CNTNAP2 is a gene on chromosome 7 that has shown associations with autism and schizophrenia, and there is evidence that it plays an important role for neuronal synchronization and brain connectivity. In this study, we assessed the relationship between Diffusion Tensor Imaging (DTI), a putative marker of anatomical brain connectivity, and multiple single nucleotide polymorphisms (SNPs) spread out over this large gene. 81 healthy controls and 44 patients with schizophrenia (all Caucasian) underwent DTI and genotyping of 31 SNPs within CNTNAP2. We employed Tract-based Spatial Statistics (TBSS) for inter-subject brain registration and computed average diffusivity values for six major white matter tracts. Analyses of Covariance (ANCOVAs) were computed to test for possible associations with genotypes. The strongest association, which survived rigorous Bonferroni correction, was between rs2710126 genotype and Fractional Anisotropy (FA) in the uncinate fasciculus (p = .00003). This anatomical location is particularly interesting given the enriched fronto-temporal expression of CNTNAP2 in the developing brain. For this SNP, no phenotype association has been reported before. There were several further genotype-DTI associations that were nominally significant but did not survive Bonferroni correction, including an association between axial diffusivity in the dorsal cingulum bundle and a region in intron 13 (represented by rs2710102, rs759178, rs2538991), which has previously been reported to be associated with anterior-posterior functional connectivity. We present new evidence about the effects of CNTNAP2 on brain connectivity, whose disruption has been hypothesized to be central to schizophrenia pathophysiology.

Patients with 22q11.2 deletion syndrome (22q11.2DS) represent a population at high risk for developing schizophrenia, as well as learning disabilities. Deficits in visuo-spatial memory are thought to underlie some of the cognitive disabilities. Neuronal substrates of visuo-spatial memory include the inferior fronto-occipital fasciculus (IFOF) and the inferior longitudinal fasciculus (ILF), two tracts that comprise the ventral visual stream. Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is an established method to evaluate white matter (WM) connections in vivo. DT-MRI scans of nine 22q11.2DS young adults and nine matched healthy subjects were acquired. Tractography of the IFOF and the ILF was performed. DT-MRI indices, including Fractional anisotropy (FA, measure of WM changes), axial diffusivity (AD, measure of axonal changes) and radial diffusivity (RD, measure of myelin changes) of each of the tracts and each group were measured and compared. The 22q11.2DS group showed statistically significant reductions of FA in IFOF in the left hemisphere. Additionally, reductions of AD were found in the IFOF and the ILF in both hemispheres. These findings might be the consequence of axonal changes, which is possibly due to fewer, thinner, or less organized fibers. No changes in RD were detected in any of the tracts delineated, which is in contrast to findings in schizophrenia patients where increases in RD are believed to be indicative of demyelination. We conclude that reduced axonal changes may be key to understanding the underlying pathology of WM leading to the visuo-spatial phenotype in 22q11.2DS.

IMPORTANCE: Some, but not all, previous magnetic resonance imaging studies have indicated smaller cortical and local gray matter volumes (GMVs) in men with schizotypal personality disorder (SPD) compared with healthy control (HC) subjects. Thus, there is need for a whole-brain comparison to resolve inconsistencies and provide hitherto generally absent data on the association between GMV and symptoms. OBJECTIVE: To use voxel-based morphometry to evaluate a large sample of neuroleptic-naive men with SPD compared with group-matched HC subjects on local and global GMV and to identify associations with symptoms, especially negative symptoms. Also, to determine whether age-related GMV reductions are greater in men with SPD than HC subjects, providing presumptive evidence on possible progression. DESIGN, SETTING, AND PARTICIPANTS: This naturalistic study involved 54 neuroleptic-naive men with SPD and 54 male HC subjects aged 18 to 55 years recruited from the community and scanned on the same 1.5-T GE magnetic resonance imaging scanner. Participants were group matched on age, socioeconomic status, handedness, and IQ. MAIN OUTCOME MEASURES: Cross-sectional voxel-based morphometry, GMV in subjects with SPD and HC participants, and the relationship to clinical symptoms. RESULTS: A voxelwise analysis showed participants with SPD had significantly smaller GMV in the left superior temporal gyrus and widespread frontal, frontolimbic, and parietal regions compared with HC subjects. Most of these regional volumes were strikingly and significantly correlated with negative symptoms: the more the volume reduction, the more negative symptoms. Global cortical GMV and most regional GMV showed significant negative relationships with age in both those with SPD and HC subjects, without any group by age interactions. CONCLUSIONS AND RELEVANCE: Men with SPD showed global and widespread smaller regional GMV. The regional structural abnormalities were correlated with the severity of a participant’s negative symptoms. While the pattern of GMV loss is similar to that in schizophrenia, the similar patterns of HC-SPD age-related GMV reduction suggest that SPD showed no progressive GMV loss, pointing to an important difference in the biological mechanisms of SPD and schizophrenia.

We report our design and implementation of a quadruple pulsed-field gradient (qPFG) diffusion MRI pulse sequence on a whole-body clinical scanner and demonstrate its ability to non-invasively detect restriction-induced microscopic anisotropy in human brain tissue. The microstructural information measured using qPFG diffusion MRI in white matter complements that provided by diffusion tensor imaging (DTI) and exclusively characterizes diffusion of water trapped in microscopic compartments with unique measures of average cell geometry. We describe the effect of white matter fiber orientation on the expected MR signal and highlight the importance of incorporating such information in the axon diameter measurement using a suitable mathematical framework. Integration of qPFG diffusion-weighted images (DWI) with fiber orientations measured using high-resolution DTI allows the estimation of average axon diameters in the corpus callosum of healthy human volunteers. Maps of inter-hemispheric average axon diameters reveal an anterior-posterior variation in good topographical agreement with anatomical measurements reported in previous post-mortem studies. With further technical refinements and additional clinical validation, qPFG diffusion MRI could provide a quantitative whole-brain histological assessment of white and gray matter, enabling a wide range of neuroimaging applications for improved diagnosis of neurodegenerative pathologies, monitoring neurodevelopmental processes, and mapping brain connectivity.

Diffusion-weighted magnetic resonance (MR) signals reflect information about underlying tissue microstructure and cytoarchitecture. We propose a quantitative, efficient, and robust mathematical and physical framework for representing diffusion-weighted MR imaging (MRI) data obtained in "q-space," and the corresponding "mean apparent propagator (MAP)" describing molecular displacements in "r-space." We also define and map novel quantitative descriptors of diffusion that can be computed robustly using this MAP-MRI framework. We describe efficient analytical representation of the three-dimensional q-space MR signal in a series expansion of basis functions that accurately describes diffusion in many complex geometries. The lowest order term in this expansion contains a diffusion tensor that characterizes the Gaussian displacement distribution, equivalent to diffusion tensor MRI (DTI). Inclusion of higher order terms enables the reconstruction of the true average propagator whose projection onto the unit "displacement" sphere provides an orientational distribution function (ODF) that contains only the orientational dependence of the diffusion process. The representation characterizes novel features of diffusion anisotropy and the non-Gaussian character of the three-dimensional diffusion process. Other important measures this representation provides include the return-to-the-origin probability (RTOP), and its variants for diffusion in one- and two-dimensions-the return-to-the-plane probability (RTPP), and the return-to-the-axis probability (RTAP), respectively. These zero net displacement probabilities measure the mean compartment (pore) volume and cross-sectional area in distributions of isolated pores irrespective of the pore shape. MAP-MRI represents a new comprehensive framework to model the three-dimensional q-space signal and transform it into diffusion propagators. Experiments on an excised marmoset brain specimen demonstrate that MAP-MRI provides several novel, quantifiable parameters that capture previously obscured intrinsic features of nervous tissue microstructure. This should prove helpful for investigating the functional organization of normal and pathologic nervous tissue.

We compare two strategies for modeling the connections of the brain’s white matter: fiber clustering and the parcellation-based connectome. Both methods analyze diffusion magnetic resonance imaging fiber tractography to produce a quantitative description of the brain’s connections. Fiber clustering is designed to reconstruct anatomically-defined white matter tracts, while the parcellation-based white matter segmentation enables the study of the brain as a network. From the perspective of white matter segmentation, we compare and contrast the goals and methods of the parcellation-based and clustering approaches, with special focus on reviewing the field of fiber clustering. We also propose a third category of new hybrid methods that combine the aspects of parcellation and clustering, for joint analysis of connection structure and anatomy or function. We conclude that these different approaches for segmentation and modeling of the white matter can advance the neuroscientific study of the brain’s connectivity in complementary ways.

Brain abnormalities in Williams syndrome (WS) have been consistently reported, despite few studies have devoted attention to connectivity between different brain regions in WS. In this study, we evaluated corpus callosum (CC) morphometry: bending angle, length, thickness and curvature of CC using a new shape analysis method in a group of 17 individuals with WS matched with a typically developing group. We used this multimethod approach because we hypothesized that neurodevelopmental abnormalities might result in both volume changes and structure deformation. Overall, we found reduced absolute CC cross-sectional area and volume in WS (mean CC and subsections). In parallel, we observed group differences regarding CC shape and thickness. Specifically, CC of WS is morphologically different, characterized by a larger bending angle and being more curved in the posterior part. Moreover, although CC in WS is shorter, a larger relative thickness of CC was found in all callosal sections. Finally, groups differed regarding the association between CC measures, age, white matter volume and cognitive performance. In conclusions, abnormal patterns of CC morphology and shape may be implicated in WS cognitive and behavioural phenotype.