Publications by Year: 2018

BACKGROUND AND PURPOSE: Free water in the posterior substantia nigra obtained from a bi-tensor diffusion MR imaging model has been shown to significantly increase over 1- and 4-year periods in patients with early-stage idiopathic Parkinson disease compared with healthy controls, which suggests that posterior substantia nigra free water may be an idiopathic Parkinson disease progression biomarker. Due to the known temporal posterior-to-anterior substantia nigra degeneration in idiopathic Parkinson disease, we assessed longitudinal changes in free water in both the posterior and anterior substantia nigra in patients with later-stage idiopathic Parkinson disease and age-matched healthy controls for comparison. MATERIALS AND METHODS: Nineteen subjects with idiopathic Parkinson disease and 19 age-matched healthy control subjects were assessed on the same 3T MR imaging scanner at baseline and after approximately 3 years. RESULTS: Baseline mean idiopathic Parkinson disease duration was 7.1 years. Both anterior and posterior substantia nigra free water showed significant intergroup differences at baseline (< .001 and= .014, respectively, idiopathic Parkinson disease versus healthy controls); however, only anterior substantia nigra free water showed significant longitudinal group × time interaction increases (= .021, idiopathic Parkinson disease versus healthy controls). There were no significant longitudinal group × time interaction differences found for conventional diffusion tensor imaging or free water-corrected DTI assessments in either the anterior or posterior substantia nigra. CONCLUSIONS: Results from this study provide further evidence supporting substantia nigra free water as a promising disease-progression biomarker in idiopathic Parkinson disease that may help to identify disease-modifying therapies if used in future clinical trials. Our novel finding of longitudinal increases in anterior but not posterior substantia nigra free water is potentially a result of the much longer disease duration of our cohort compared with previously studied cohorts and the known posterior-to-anterior substantia nigra degeneration that occurs over time in idiopathic Parkinson disease.

INTRODUCTION: Diffusion tensor imaging detects early tissue alterations in Alzheimer’s disease and cerebral small vessel disease (SVD). However, the origin of diffusion alterations in SVD is largely unknown.

Despite their widespread use in non-invasive studies of porous materials, conventional MRI methods yield ambiguous results for microscopically heterogeneous materials such as brain tissue. While the forward link between microstructure and MRI observables is well understood, the inverse problem of separating the signal contributions from different microscopic pores is notoriously difficult. Here, we introduce an experimental protocol where heterogeneity is resolved by establishing 6D correlations between the individual values of isotropic diffusivity, diffusion anisotropy, orientation of the diffusion tensor, and relaxation rates of distinct populations. Such procedure renders the acquired signal highly specific to the sample’s microstructure, and allows characterization of the underlying pore space without prior assumptions on the number and nature of distinct microscopic environments. The experimental feasibility of the suggested method is demonstrated on a sample designed to mimic the properties of nerve tissue. If matched to the constraints of whole body scanners, this protocol could allow for the unconstrained determination of the different types of tissue that compose the living human brain.

Characterization of anisotropy via diffusion MRI reveals fiber crossings in a substantial portion of voxels within the white-matter (WM) regions of the human brain. A considerable number of such voxels could exhibit asymmetric features such as bends and junctions. However, widely employed reconstruction methods yield symmetric Orientation Distribution Functions (ODFs) even when the underlying geometry is asymmetric. In this paper, we employ inter-voxel directional filtering approaches through a cone model to reveal more information regarding the cytoarchitectural organization within the voxel. The cone model facilitates a sharpening of the ODFs in some directions while suppressing peaks in other directions, thus yielding an Asymmetric ODF (AODF) field. We also show that a scalar measure of AODF asymmetry can be employed to obtain new contrast within the human brain. The feasibility of the technique is demonstrated on in vivo data obtained from the MGH-USC Human Connectome Project (HCP) and Parkinson’s Progression Markers Initiative (PPMI) Project database. Characterizing asymmetry in neural tissue cytoarchitecture could be important for localizing and quantitatively assessing specific neuronal pathways.

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.

The rate of water exchange across cell membranes is a parameter of biological interest and can be measured by diffusion magnetic resonance imaging (dMRI). In this work, we investigate a stochastic model for the diffusion-and-exchange of water molecules. This model provides a general solution for the temporal evolution of dMRI signal using any type of gradient waveform, thereby generalizing the signal expressions for the Kärger model. Moreover, we also derive a general nth order cumulant expansion of the dMRI signal accounting for water exchange, which has not been explored in earlier studies. Based on this analytical expression, we compute the cumulant expansion for dMRI signals for the special case of single diffusion encoding (SDE) and double diffusion encoding (DDE) sequences. Our results provide a theoretical guideline on optimizing experimental parameters for SDE and DDE sequences, respectively. Moreover, we show that DDE signals are more sensitive to water exchange at short-time scale but provide less attenuation at long-time scale than SDE signals. Our theoretical analysis is also validated using Monte Carlo simulations on synthetic structures.

BACKGROUND: Exposure to ambient air pollution has been associated with lower lung function in adults, but few studies have investigated associations with radiographic lung and airway measures. METHODS: We ascertained lung volume, mass, density, visual emphysema, airway size, and airway wall area by computed tomography (CT) among 2,545 nonsmoking Framingham CT substudy participants. We examined associations of home distance to major road and PM2.5 (2008 average from a spatiotemporal model using satellite data) with these outcomes using linear and logistic regression models adjusted for age, sex, height, weight, census tract median household value and population density, education, pack-years of smoking, household tobacco exposure, cohort, and date. We tested for differential susceptibility by sex, smoking status (former vs. never), and cohort. RESULTS: The mean participant age was 60.1 years (standard deviation 11.9 years). Median PM2.5 level was 9.7 µg/m (interquartile range, 1.6). Living

A single-nucleotide polymorphism (rs35705950) in the mucin 5B () gene promoter is associated with pulmonary fibrosis and interstitial features on chest CT but may also have beneficial effects. In non-Hispanic whites in the COPDGene cohort with interstitial features (n=454), the promoter polymorphism was associated with a 61% lower odds of a prospectively reported acute respiratory disease event (P=0.001), a longer time-to-first event (HR=0.57; P=0.006) and 40% fewer events (P=0.016). The promoter polymorphism may have a beneficial effect on the risk of acute respiratory disease events in smokers with interstitial CT features.

BACKGROUND: Previous investigations in adult smokers from the COPDGene Study have shown that early-life respiratory disease is associated with reduced lung function, COPD, and airway thickening. Using 5-year follow-up data, we assessed disease progression in subjects who had experienced early-life respiratory disease. We hypothesized that there are alternative pathways to reaching reduced FEVand that subjects who had childhood pneumonia, childhood asthma, or asthma-COPD overlap (ACO) would have less lung function decline than subjects without these conditions. METHODS: Subjects returning for 5-year follow-up were assessed. Childhood pneumonia was defined by self-reported pneumonia at < 16 years. Childhood asthma was defined as self-reported asthma diagnosed by a health professional at < 16 years. ACO was defined as subjects with COPD who self-reported asthma diagnosed by a health-professional at = 40 years. Smokers with and those without these early-life respiratory diseases were compared on measures of disease progression. RESULTS: Follow-up data from 4,915 subjects were examined, including 407 subjects who had childhood pneumonia, 323 subjects who had childhood asthma, and 242 subjects with ACO. History of childhood asthma or ACO was associated with an increased exacerbation frequency (childhood asthma, P < .001; ACO, P = .006) and odds of severe exacerbations (childhood asthma, OR, 1.41; ACO, OR, 1.42). History of childhood pneumonia was associated with increased exacerbations in subjects with COPD (absolute difference [β], 0.17; P = .04). None of these early-life respiratory diseases were associated with an increased rate of lung function decline or progression on CT scans. CONCLUSIONS: Subjects who had early-life asthma are at increased risk of developing COPD and of having more active disease with more frequent and severe respiratory exacerbations without an increased rate of lung function decline over a 5-year period. TRIAL REGISTRY: ClinicalTrials.gov; No. NCT00608764; https://clinicaltrials.gov.

PURPOSE: In Parkinson’s disease (PD), pathological microstructural changes occur that may be detected using diffusion magnetic resonance imaging (dMRI). However, there are few longitudinal studies that explore the effect of disease progression on diffusion indices. METHODS: We prospectively included 76 patients with PD and 38 healthy controls (HC), all of whom underwent diffusion kurtosis imaging (DKI) as part of the prospective Swedish BioFINDER study at baseline and 2 years later. Annualized rates of change in DKI parameters, including fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK), were estimated in the gray matter (GM) by placing regions of interest (ROIs) in the basal ganglia and the thalamus, and in the white matter (WM) by tract-based spatial statistics (TBSS) analysis.