Publications

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.

Traditional models of the human language circuitry encompass three cortical areas, Broca’s, Geschwind’s and Wernicke’s, and their connectivity through white matter fascicles. The neural connectivity deep to these cortical areas remains poorly understood, as does the macroscopic functional organization of the cortico-subcortical language circuitry. In an effort to expand current knowledge, we combined functional MRI (fMRI) and diffusion tensor imaging to explore subject-specific structural and functional macroscopic connectivity, focusing on Broca’s area. Fascicles were studied using diffusion tensor imaging fiber tracking seeded from volumes placed manually within the white matter. White matter fascicles and fMRI-derived clusters (antonym-generation task) of positive and negative blood-oxygen-level-dependent (BOLD) signal were co-registered with 3-D renderings of the brain in 12 healthy subjects. Fascicles connecting BOLD-derived clusters were analyzed within specific cortical areas: Broca’s, with the pars triangularis, the pars opercularis, and the pars orbitaris; Geschwind’s and Wernicke’s; the premotor cortex, the dorsal supplementary motor area, the middle temporal gyrus, the dorsal prefrontal cortex and the frontopolar region. We found a functional connectome divisible into three systems-anterior, superior and inferior-around the insula, more complex than previously thought, particularly with respect to a new extended Broca’s area. The extended Broca’s area involves two new fascicles: the operculo-premotor fascicle comprised of well-organized U-shaped fibers that connect the pars opercularis with the premotor region; and (2) the triangulo-orbitaris system comprised of intermingled U-shaped fibers that connect the pars triangularis with the pars orbitaris. The findings enhance our understanding of language function.

The accurate diagnosis of Alzheimer’s disease (AD) at different stages is essential to identify patients at high risk of dementia and plan prevention or treatment measures accordingly. In this study, we proposed a new AD staging method for the entire spectrum of AD including the AD, Mild Cognitive Impairment with and without AD conversions, and Cognitive Normal groups. Our method embedded the high dimensional multi-view features derived from neuroimaging data into a low dimensional feature space and could form a more distinctive representation than the naive concatenated features. It also updated the testing data based on the Localized Sparse Code Gradients (LSCG) to further enhance the classification. The LSCG algorithm, validated using Magnetic Resonance Imaging data from the ADNI baseline cohort, achieved significant improvements on all diagnosis groups compared to using the original sparse coding method.

The accurate diagnosis of Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) is important in early dementia detection and treatment planning. Most of current studies formulate the AD diagnosis scenario as a classification problem and solve it using various machine learners trained with multi-modal biomarkers. However, the diagnosis accuracy is usually constrained by the performance of the machine learners as well as the methods of integrating the multi-modal data. In this study, we propose a novel diagnosis algorithm, the Multifold Bayesian Kernelization (MBK), which models the diagnosis process as a synthesis analysis of multi-modal biomarkers. MBK constructs a kernel for each biomarker that maximizes the local neighborhood affinity, and further evaluates the contribution of each biomarker based on a Bayesian framework. MBK adopts a novel diagnosis scheme that could infer the subject’s diagnosis by synthesizing the output diagnosis probabilities of individual biomarkers. The proposed algorithm, validated using multimodal neuroimaging data from the ADNI baseline cohort with 85 AD, 169 MCI and 77 cognitive normal subjects, achieves significant improvements on all diagnosis groups compared to the state-of-the-art methods.

The ability to perform complex as well as simple cognitive tasks engages a network of brain regions that is mediated by the white matter fiber bundles connecting them. Different cognitive tasks employ distinctive white matter fiber bundles. The temporal lobe and its projections subserve a variety of key functions known to deteriorate during aging. In a cohort of 52 healthy subjects (ages 25-82 years), we performed voxel-wise regression analysis correlating performance in higher-order cognitive domains (executive function, information processing speed, and memory) with white matter integrity, as measured by diffusion tensor imaging (DTI) fiber tracking in the temporal lobe projections [uncinate fasciculus (UF), fornix, cingulum, inferior longitudinal fasciculus (ILF), and superior longitudinal fasciculus (SLF)]. The fiber tracts were spatially registered and statistical parametric maps were produced to spatially localize the significant correlations. Results showed that performance in the executive function domain is correlated with DTI parameters in the left SLF and right UF; performance in the information processing speed domain is correlated with fractional anisotropy (FA) in the left cingulum, left fornix, right and left ILF and SLF; and the memory domain shows significant correlations with DTI parameters in the right fornix, right cingulum, left ILF, left SLF and right UF. These findings suggest that DTI tractography enables anatomical definition of region of interest (ROI) for correlation of behavioral parameters with diffusion indices, and functionality can be correlated with white matter integrity.

BACKGROUND: Previous studies have shown that frontostriatal networks, especially those involving dorsolateral prefrontal cortex (DLPFC) and ventrolateral prefrontal cortex (VLPFC) mediate cognitive functions some of which are abnormal in schizophrenia. This study examines white matter integrity of the tracts connecting DLPFC/VLPFC and striatum in patients with first-episode schizophrenia (FESZ), and their associations with cognitive and clinical correlates. METHODS: Diffusion tensor and structural magnetic resonance images were acquired on a 3T GE Echospeed system from 16 FESZ and 18 demographically comparable healthy controls. FreeSurfer software was used to parcellate regions of interest. Two-tensor tractography was applied to extract fibers connecting striatum with rostral middle frontal gyrus (rMFG) and inferior frontal gyrus (IFG), representing DLPFC and VLPFC respectively. DTI indices, including fractional anisotropy (FA), trace, axial diffusivity (AD) and radial diffusivity (RD), were used for group comparisons. Additionally, correlations were evaluated between these diffusion indices and the Wisconsin Card Sorting Task (WCST) and the Brief Psychiatric Rating Scale (BPRS). RESULTS: FA was significantly reduced in the left IFG-striatum tract, whereas trace and RD were significantly increased in rMFG-striatum and IFG-striatum tracts, bilaterally. The number of WCST categories completed correlated positively with FA of the right rMFG-striatum tract, and negatively with trace and RD of right rMFG-striatum and right IFG-striatum tracts in FESZ. The BPRS scores did not correlate with these indices. CONCLUSIONS: These data suggest that white matter tract abnormalities between rMFG/IFG and striatum are present in FESZ and appear to be significantly associated with executive dysfunction but not with symptom severity.