Publications by Year: 2020

BACKGROUND: Relative enlargement of the pulmonary artery (PA) on chest CT imaging is associated with respiratory exacerbations in patients with COPD or cystic fibrosis. We sought to determine whether similar findings were present in patients with asthma and whether these findings were explained by differences in ventricular size. METHODS: We measured the PA and aorta diameters in 233 individuals from the Severe Asthma Research Program III cohort. We also estimated right, left, and total epicardial cardiac ventricular volume indices (eERVVI, eELVVI, and eETVVI, respectively). Associations between the cardiac and PA measures (PA-to-aorta [PA/A] ratio, eERVVI-to-eELVVI [eRV/eLV] ratio, eERVVI, eELVVI, eETVVI) and clinical measures of asthma severity were assessed by Pearson correlation, and associations with asthma severity and exacerbation rate were evaluated by multivariable linear and zero-inflated negative binomial regression.

The first episode of psychosis is typically preceded by a prodromal phase with subthreshold symptoms and functional decline. Improved outcome prediction in this stage is needed to allow targeted early intervention. This study assesses a combined clinical and resting-state fMRI prediction model in 137 adolescents and young adults at Clinical High Risk (CHR) for psychosis from the Shanghai At Risk for Psychosis (SHARP) program. Based on outcome at one-year follow-up, participants were separated into three outcome categories including good outcome (symptom remission, N = 71), intermediate outcome (ongoing CHR symptoms, N = 30), and poor outcome (conversion to psychosis or treatment-refractory, N = 36). Validated clinical predictors from the psychosis-risk calculator were combined with measures of resting-state functional connectivity. Using multinomial logistic regression analysis and leave-one-out cross-validation, a clinical-only prediction model did not achieve a significant level of outcome prediction (F = 0.32, p = .154). An imaging-only model yielded a significant prediction model (F = 0.41, p = .016), but a combined model including both clinical and connectivity measures showed the best performance (F = 0.46, p 

BACKGROUND: The cingulum bundle (CB) is a major white matter fiber tract of the limbic system that underlies cingulate cortex, passing longitudinally over the corpus callosum. The connectivity of this white matter fiber tract plays a major role in emotional expression, attention, motivation, and working memory, all of which are affected in schizophrenia. Myelin related CB abnormalities have also been implicated in schizophrenia. The purpose of this study is to determine whether or not CB abnormalities are evident in individuals at clinical high risk (CHR) for psychosis, and whether or not cognitive deficits in the domains subserved by CB are related to its structural abnormalities. METHODS: Diffusion Tensor Imaging (DTI) was performed on a 3 T magnet. DT tractography was used to evaluate CB in 20 individuals meeting CHR criteria (13 males/7 females) and 23 healthy controls (12 males/11 females) group matched on age, gender, parental socioeconomic status, education, and handedness. Fractional anisotropy (FA), a measure of white matter coherence and integrity, radial diffusivity (RD), thought to reflect myelin integrity, trace, a possible marker of atrophy, and axial diffusivity (AD), thought to reflect axonal integrity, were averaged over the entire tract and used to investigate CB abnormalities in individuals at CHR for psychosis compared with healthy controls.

The so-called "dot-compartment" is conjectured in diffusion MRI to represent small spherical spaces, such as cell bodies, in which the diffusion is restricted in all directions. Previous investigations inferred its existence from data acquired with directional diffusion encoding which does not permit a straightforward separation of signals from ’sticks’ (axons) and signals from ’dots’. Here we combine isotropic diffusion encoding with ultra-strong diffusion gradients (240 ​mT/m) to achieve high diffusion-weightings with high signal to noise ratio, while suppressing signal arising from anisotropic water compartments with significant mobility along at least one axis (e.g., axons). A dot-compartment, defined to have apparent diffusion coefficient equal to zero and no exchange, would result in a non-decaying signal at very high b-values (b≳7000s/mm). With this unique experimental setup, a residual yet slowly decaying signal above the noise floor for b-values as high as 15000s/mm was seen clearly in the cerebellar grey matter (GM), and in several white matter (WM) regions to some extent. Upper limits of the dot-signal-fraction were estimated to be 1.8% in cerebellar GM and 0.5% in WM. By relaxing the assumption of zero diffusivity, the signal at high b-values in cerebellar GM could be represented more accurately by an isotropic water pool with a low apparent diffusivity of 0.12 μm/ms and a substantial signal fraction of 9.7%. The T2 of this component was estimated to be around 61ms. This remaining signal at high b-values has potential to serve as a novel and simple marker for isotropically-restricted water compartments in cerebellar GM.

In this work we investigate the use of sum of squares constraints for various diffusion-weighted MRI models, with a goal of enforcing strict, global non-negativity of the diffusion propagator. We formulate such constraints for the mean apparent propagator model and for spherical deconvolution, guaranteeing strict non-negativity of the corresponding diffusion propagators. For the cumulant expansion similar constraints cannot exist, and we instead derive a set of auxiliary constraints that are necessary but not sufficient to guarantee non-negativity. These constraints can all be verified and enforced at reasonable computational costs using semidefinite programming. By verifying our constraints on standard reconstructions of the different models, we show that currently used weak constraints are largely ineffective at ensuring non-negativity. We further show that if strict non-negativity is not enforced then estimated model parameters may suffer from significant errors, leading to serious inaccuracies in important derived quantities such as the main fiber orientations, mean kurtosis, etc. Finally, our experiments confirm that the observed constraint violations are mostly due to measurement noise, which is difficult to mitigate and suggests that properly constrained optimization should currently be considered the norm in many cases.

OBJECTIVE: Several studies suggested that obsessive-compulsive disorder (OCD) patients display increased impulsivity, impaired decision-making, and reward system dysfunction. In a Research Domain Criteria (RDoC) perspective, these findings are prototypical for addiction and have led some authors to view OCD as a behavioral addiction. Thus, the aim of this study was to investigate similarities and differences on impulsivity, decision-making, and reward system, as core dimensions of addiction, across OCD and gambling disorder (GD) patients. METHODS: Forty-four OCD patients, 26 GD patients, and 40 healthy controls (HCs) were included in the study. Impulsivity was assessed through the Barratt Impulsiveness Scale, decision-making through the Iowa Gambling Task, and reward system through a self-report clinical instrument (the Shaps-Hamilton Anhedonia Scale) assessing hedonic tone and through an olfactory test assessing hedonic appraisal to odors. RESULTS: Both OCD and GD patients showed increased impulsivity when compared to HCs. More specifically, the OCD patients showed cognitive impulsivity, and the GD patients showed both increased cognitive and motor impulsivity. Furthermore, both OCD and GD patients showed impaired decision-making performances when compared to HCs. Finally, GD patients showed increased anhedonia and blunted hedonic response to pleasant odors unrelated to gambling or depression/anxiety symptoms, while OCD patients showed only increased anhedonia levels related to OC and depression/anxiety symptoms. CONCLUSION: OCD patients showed several similarities and some differences with GD patients when compared to HCs on impulsivity, decision-making, and reward system, three core dimensions of addiction. These results could have relevant implications for the research of new treatment targets for OCD.

BACKGROUND: Peak lung function and rate of decline predict future airflow obstruction and nonrespiratory comorbid conditions. Associations between lung function trajectories and emphysema have not been explored. METHODS: Using data from the population-based CARDIA Study, we sought to describe the prevalence of visually ascertained emphysema at multiple time points and contextualize its development based upon participant’s adult life course measures of lung function. There were 3171 men and women enrolled at a mean age of 25 years, who underwent serial spirometric examinations through a mean age of 55 years. Trajectories for the change in percent-predicted forced expiratory volume in one second (FEV) were determined by fitting a mixture model via maximum likelihood. Emphysema was visually identified on computed tomographic scans and its prevalence reported at mean ages of 40, 45, and 50 years.

BACKGROUND: The trigeminal nerve (TGN) is the largest cranial nerve and can be involved in multiple inflammatory, compressive, ischemic or other pathologies. Currently, imaging-based approaches to identify the TGN mostly rely on T2-weighted magnetic resonance imaging (MRI), which provides localization of the cisternal portion of the TGN where the contrast between nerve and cerebrospinal fluid (CSF) is high enough to allow differentiation. The course of the TGN within the brainstem as well as anterior to the cisternal portion, however, is more difficult to display on traditional imaging sequences. An advanced imaging technique, diffusion MRI (dMRI), enables tracking of the trajectory of TGN fibers and has the potential to visualize anatomical regions of the TGN not seen on T2-weighted imaging. This may allow a more comprehensive assessment of the nerve in the context of pathology. To date, most work in TGN tracking has used clinical dMRI acquisitions with a b-value of 1000 s/mm and conventional diffusion tensor MRI (DTI) tractography methods. Though higher b-value acquisitions and multi-tensor tractography methods are known to be beneficial for tracking brain white matter fiber tracts, there have been no studies conducted to evaluate the performance of these advanced approaches on nerve tracking of the TGN, in particular on tracking different anatomical regions of the TGN. OBJECTIVE: We compare TGN tracking performance using dMRI data with different b-values, in combination with both single- and multi-tensor tractography methods. Our goal is to assess the advantages and limitations of these different strategies for identifying the anatomical regions of the TGN.

Biomarker estimation methods from medical images have traditionally followed a segment-and-measure strategy. Deep-learning regression networks have changed such a paradigm, enabling the direct estimation of biomarkers in databases where segmentation masks are not present. While such methods achieve high performance, they operate as a black-box. In this work, we present a novel deep learning network structure that, when trained with only the value of the biomarker, can perform biomarker regression and the generation of an accurate localization mask simultaneously, thus enabling a qualitative assessment of the image locus that relates to the quantitative result. We showcase the proposed method with three different network structures and compare their performance against direct regression networks in four different problems: pectoralis muscle area (PMA), subcutaneous fat area (SFA), liver mass area in single slice computed tomography (CT), and Agatston score estimated from non-contrast thoracic CT images (CAC). Our results show that the proposed method improves the performance with respect to direct biomarker regression methods (correlation coefficient of 0.978, 0.998, and 0.950 for the proposed method in comparison to 0.971, 0.982, and 0.936 for the reference regression methods on PMA, SFA and CAC respectively) while achieving good localization (DICE coefficients of 0.875, 0.914 for PMA and SFA respectively, p 0.05 for all pairs). We observe the same improvement in regression results comparing the proposed method with those obtained by quantify the outputs using an U-Net segmentation network (0.989 and 0.951 respectively). We, therefore, conclude that it is possible to obtain simultaneously good biomarker regression and localization when training biomarker regression networks using only the biomarker value.