Publications by Year: 2020

Several prominent theories of schizophrenia suggest that structural white matter pathologies may follow a developmental, maturational, and/or degenerative process. However, a lack of lifespan studies has precluded verification of these theories. Here, we analyze the largest sample of carefully harmonized diffusion MRI data to comprehensively characterize age-related white matter trajectories, as measured by fractional anisotropy (FA), across the course of schizophrenia. Our analysis comprises diffusion scans of 600 schizophrenia patients and 492 healthy controls at different illness stages and ages (14-65 years), which were gathered from 13 sites. We determined the pattern of age-related FA changes by cross-sectionally assessing the timing of the structural neuropathology associated with schizophrenia. Quadratic curves were used to model between-group FA differences across whole-brain white matter and fiber tracts at each age; fiber tracts were then clustered according to both the effect-sizes and pattern of lifespan white matter FA differences. In whole-brain white matter, FA was significantly lower across the lifespan (up to 7%; p

BACKGROUND: Extracellular free water within cerebral white matter tissue has been shown to increase with age and pathology, yet the cognitive consequences of free water in typical aging prior to the development of neurodegenerative disease remains unclear. Understanding the contribution of free water to cognitive function in older adults may provide important insight into the neural mechanisms of the cognitive aging process.

PURPOSE: To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T values within a two-compartment model of white matter, and to explore the approach in vivo. METHODS: Sampling protocols featuring different b-values (b), b-tensor shapes (b ), and echo times (TE) were optimized using Cram\ er-Rao lower bounds (CRLB). Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin." RESULTS: Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/b /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm /ms) but higher T values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm /ms) and T values (150 ms). CONCLUSIONS: Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities.

RATIONALE AND OBJECTIVES: We have developed a technique to measure ventilation heterogeneity (VH) on low dose chest CT scan that we hypothesize may be associated with the development of lung nodules, and perhaps cancer. If true, such an analysis may improve screening by identifying regional areas of higher risk. MATERIALS AND METHODS: Using the National Lung Screening Trial database, we identified a small subset of those participants who were labeled as having a positive screening test at 1 year (T1) but not at baseline (T0). We isolated the region in which the nodule would form on the T0 scan ("target region") and measured VH as the standard deviation of the linear dimension of a virtual cubic airspace based on measurement of lung attenuation within the region.

PURPOSE: To evaluate the feasibility of a 3-minutes protocol for assessment of the microscopic anisotropy and tissue heterogeneity based on tensor-valued diffusion MRI in a wide range of intracranial tumors. METHODS: B-tensor encoding was performed in 42 patients with intracranial tumors (gliomas, meningiomas, adenomas, and metastases). Microscopic anisotropy and tissue heterogeneity were evaluated by estimating the anisotropic kurtosis (MK ) and isotropic kurtosis (MK ), respectively. An extensive imaging protocol was compared with a 3-minutes protocol.

Diffusion MRI may enable non-invasive mapping of axonal microstructure. Most approaches infer axon diameters from effects of time-dependent diffusion on the diffusion-weighted MR signal by modeling axons as straight cylinders. Axons do not, however, propagate in straight trajectories, and so far the impact of the axonal trajectory on diameter estimation has been insufficiently investigated. Here, we employ a toy model of axons, which we refer to as the undulating thin fiber model, to analyze the impact of undulating trajectories on the time dependence of diffusion. We study time-dependent diffusion in the frequency domain and characterize the diffusion spectrum by its height, width, and low-frequency behavior (power law exponent). Results show that microscopic orientation dispersion of the thin fibers is the main parameter that determines the characteristics of the diffusion spectra. At lower frequencies (longer diffusion times), straight cylinders and undulating thin fibers can have virtually identical spectra. If the straight-cylinder assumption is used to interpret data from undulating thin axons, the diameter is overestimated by an amount proportional to the undulation amplitude and microscopic orientation dispersion of the fibers. At higher frequencies (shorter diffusion times), spectra from cylinders and undulating thin fibers differ. The low-frequency behavior of the spectra from the undulating thin fibers may also differ from that of cylinders, because the power law exponent of undulating fibers can reach values below 2 for experimentally relevant frequency ranges. In conclusion, we argue that the non-straight nature of axonal trajectories should not be overlooked when analyzing and interpreting diffusion MRI data.

Motion is a major confound in diffusion-weighted imaging (DWI) in the body, and it is a common cause of image artefacts. The effects are particularly severe in cardiac applications, due to the nonrigid cyclical deformation of the myocardium. Spin echo-based DWI commonly employs gradient moment-nulling techniques to desensitise the acquisition to velocity and acceleration, ie, nulling gradient moments up to the 2nd order (M2-nulled). However, current M2-nulled DWI scans are limited to encode diffusion along a single direction at a time. We propose a method for designing b-tensors of arbitrary shapes, including planar, spherical, prolate and oblate tensors, while nulling gradient moments up to the 2nd order and beyond. The design strategy comprises initialising the diffusion encoding gradients in two encoding blocks about the refocusing pulse, followed by appropriate scaling and rotation, which further enables nulling undesired effects of concomitant gradients. Proof-of-concept assessment of in vivo mean diffusivity (MD) was performed using linear and spherical tensor encoding (LTE and STE, respectively) in the hearts of five healthy volunteers. The results of the M2-nulled STE showed that (a) the sequence was robust to cardiac motion, and (b) MD was higher than that acquired using standard M2-nulled LTE, where diffusion-weighting was applied in three orthogonal directions, which may be attributed to the presence of restricted diffusion and microscopic diffusion anisotropy. Provided adequate signal-to-noise ratio, STE could significantly shorten estimation of MD compared with the conventional LTE approach. Importantly, our theoretical analysis and the proposed gradient waveform design may be useful in microstructure imaging beyond diffusion tensor imaging where the effects of motion must be suppressed.

To date, few studies have evaluated the contribution of early life experiences to neurocognitive abnormalities observed in posttraumatic stress disorder (PTSD). Childhood maltreatment is common among individuals with PTSD and is thought to catalyze stress-related biobehavioral changes that might impact both brain structure and function in adulthood. The current study examined differences in brain morphology (brain volume, cortical thickness) and neuropsychological performance in individuals with PTSD characterized by low or high self-reported childhood maltreatment, compared with healthy comparison participants. Data were drawn from the INjury and TRaUmatic STress (INTRuST) Clinical Consortium imaging repository, which contains MRI and self-report data for individuals classified as PTSD positive (with and without a history of mild traumatic brain injury [mTBI]), individuals with mTBI only, and healthy comparison participants. The final sample included 36 individuals with PTSD without childhood maltreatment exposure (PTSD, n = 30 with mTBI), 31 individuals with PTSD and childhood maltreatment exposure (PTSD + M, n = 26 with mTBI), and 114 healthy comparison participants without history of childhood maltreatment exposure (HC). The PTSD + M and PTSD groups demonstrated cortical thinning in prefrontal and occipital regions, and poorer verbal memory and processing speed compared to the HC group. PTSD + M participants demonstrated cortical thinning in frontal and cingulate regions, and poorer executive functioning relative to the PTSD and HC groups. Thus, neurocognitive features varied between individuals with PTSD who did versus did not have exposure to childhood maltreatment, highlighting the need to assess developmental history of maltreatment when examining biomarkers in PTSD.

We present an open-source framework for pulmonary fissure completeness assessment. Fissure incompleteness has been shown to associate with emphysema treatment outcomes, motivating the development of tools that facilitate completeness estimation. Generally, the task of fissure completeness assessment requires accurate detection of fissures and definition of the boundary surfaces separating the lung lobes. The framework we describe acknowledges a) the modular nature of fissure detection and lung lobe segmentation (lobe boundary detection), and b) that methods to address these challenges are varied and continually developing. It is designed to be readily deployable on existing lung lobe segmentation and fissure detection data sets. The framework consists of multiple components: a flexible quality control module that enables rapid assessment of lung lobe segmentations, an interactive lobe segmentation tool exposed through 3D Slicer for handling challenging cases, a flexible fissure representation using particles-based sampling that can handle fissure feature-strength or binary fissure detection images, and a module that performs fissure completeness estimation using voxel counting and a novel surface area estimation approach. We demonstrate the usage of the proposed framework by deploying on 100 cases exhibiting various levels of fissure completeness. We compare the two completeness level approaches and also compare to visual reads. The code is available to the community via github as part of the Chest Imaging Platform and a 3D Slicer extension module.

The central nervous system is known to have limited regenerative capacity. Not only does this halt the human body’s reparative processes after central nervous system lesions, but it also impedes the establishment of effective and safe therapeutic options for such patients. Despite the high prevalence of stroke and spinal cord injury in the general population, these conditions remain incurable and place a heavy burden on patients’ families and on society more broadly. Neuroregeneration and neural engineering are diverse biomedical fields that attempt reparative treatments, utilizing stem cells-based strategies, biologically active molecules, nanotechnology, exosomes and highly tunable biodegradable systems (e.g., certain hydrogels). Although there are studies demonstrating promising preclinical results, safe clinical translation has not yet been accomplished. A key gap in clinical translation is the absence of an ideal animal or ex vivo model that can perfectly simulate the human microenvironment, and also correspond to all the complex pathophysiological and neuroanatomical factors that affect functional outcomes in humans after central nervous system injury. Such an ideal model does not currently exist, but it seems that the nonhuman primate model is uniquely qualified for this role, given its close resemblance to humans. This review considers some regenerative therapies for central nervous system repair that hold promise for future clinical translation. In addition, it attempts to uncover some of the main reasons why clinical translation might fail without the implementation of nonhuman primate models in the research pipeline.