Publications

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.

A large proportion (range of 44-75%) of women who experience intimate-partner violence (IPV) have been shown to sustain repetitive mild traumatic brain injuries (mTBIs) from their abusers. Further, despite requests for research on TBI-related health outcomes, there are currently only a handful of studies addressing this issue and only one prior imaging study that has investigated the neural correlates of IPV-related TBIs. In response, we examined specific regions of white matter microstructure in 20 women with histories of IPV. Subjects were imaged on a 3-Tesla Siemens Magnetom TrioTim scanner using diffusion magnetic resonance imaging. We investigated the association between a score reflecting number and recency of IPV-related mTBIs and fractional anisotropy (FA) in the posterior and superior corona radiata as well as the posterior thalamic radiation, brain regions shown previously to be involved in mTBI. We also investigated the association between several cognitive measures, namely learning, memory, and cognitive flexibility, and FA in the white matter regions of interest. We report a negative correlation between the brain injury score and FA in regions of the posterior and superior corona radiata. We failed to find an association between our cognitive measures and FA in these regions, but the interpretation of these results remains inconclusive due to possible power issues. Overall, these data build upon the small but growing literature demonstrating potential consequences of mTBIs for women experiencing IPV, and further underscore the urgent need for larger and more comprehensive studies in this area.

The "cognitive dysmetria" hypothesis suggests that impairments in cognition and behavior in patients with schizophrenia can be explained by disruptions in the cortico-cerebellar-thalamic-cortical circuit. In this study we examine thalamo-cortical connections in patients with first-episode schizophrenia (FESZ). White matter pathways are investigated that connect the thalamus with three frontal cortex regions including the anterior cingulate cortex (ACC), ventrolateral prefrontal cortex (VLPFC), and lateral oribitofrontal cortex (LOFC). We use a novel method of two-tensor tractography in 26 patients with FESZ compared to 31 healthy controls (HC), who did not differ on age, sex, or education. Dependent measures were fractional anisotropy (FA), Axial Diffusivity (AD), and Radial Diffusivity (RD). Subjects were also assessed using clinical functioning measures including the Global Assessment of Functioning (GAF) Scale, the Global Social Functioning Scale (GF: Social), and the Global Role Functioning Scale (GF: Role). FESZ patients showed decreased FA in the right thalamus-right ACC and right-thalamus-right LOFC pathways compared to healthy controls (HCs). In the right thalamus-right VLPFC tract, we found decreased FA and increased RD in the FESZ group compared to HCs. After correcting for multiple comparisons, reductions in FA in the right thalamus- right ACC and the right thalamus- right VLPC tracts remained significant. Moreover, reductions in FA were significantly associated with lower global functioning scores as well as lower social and role functioning scores. We report the first diffusion tensor imaging study of white matter pathways connecting the thalamus to three frontal regions. Findings of white matter alterations and clinical associations in the thalamic-cortical component of the cortico-cerebellar-thalamic-cortical circuit in patients with FESZ support the cognitive dysmetria hypothesis and further suggest the possible involvement of myelin sheath pathology and axonal membrane disruption in the pathogenesis of the disorder.

Schizophrenia has been characterized as a neurodevelopmental disorder, with structural brain abnormalities reported at all stages. However, at present, it remains unclear whether gray and white matter abnormalities represent related or independent pathologies in schizophrenia. In this study, we present findings from an integrative analysis exploring the morphological relationship between gray and white matter in 45 schizophrenia participants and 49 healthy controls. We utilized mutual information (MI), a measure of how much information two variables share, to assess the morphological dependence between gray and white matter in three segments of the corpus callsoum, and the gray matter regions these segments connect: (1) the genu and the left and right rostral middle frontal gyrus (rMFG), (2) the isthmus and the left and right superior temporal gyrus (STG), (3) the splenium and the left and right lateral occipital gyrus (LOG). We report significantly reduced MI between white matter tract dispersion of the right hemispheric callosal connections to the STG and both cortical thickness and area in the right STG in schizophrenia patients, despite a lack of group differences in cortical thickness, surface area, or dispersion. We believe that this reduction in morphological dependence between gray and white matter may reflect a possible decoupling of the developmental processes that shape morphological features of white and gray matter early in life. The present study also demonstrates the importance of studying the relationship between gray and white matter measures, as opposed to restricting analyses to gray and white matter measures independently.

This article gives an overview of microstructure imaging of the brain with diffusion MRI and reviews the state of the art. The microstructure-imaging paradigm aims to estimate and map microscopic properties of tissue using a model that links these properties to the voxel scale MR signal. Imaging techniques of this type are just starting to make the transition from the technical research domain to wide application in biomedical studies. We focus here on the practicalities of both implementing such techniques and using them in applications. Specifically, the article summarizes the relevant aspects of brain microanatomy and the range of diffusion-weighted MR measurements that provide sensitivity to them. It then reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure, as well as the expanding areas of application. Next we focus on practicalities of designing a working microstructure imaging technique: model selection, experiment design, parameter estimation, validation, and the pipeline of development of this class of technique. The article concludes with some future perspectives on opportunities in this topic and expectations on how the field will evolve in the short-to-medium term.

The white matter connections between the midbrain dopamine neurons and the striatum are part of a neural system involved in reward-based learning, a process that is impaired in patients with schizophrenia. The striato-nigro-striatal (SNS) tract, which participates in this process, has not as yet been explored. The present study aimed to use diffusion MRI (dMRI) to delineate the SNS tract, and to compare the application of two dMRI measures, Tract Dispersion (TD), an index of white matter morphology, and Fractional Anisotropy (FA), an index of white matter integrity, to detect group differences between patients with chronic schizophrenia (CSZ) and healthy controls (HC). dMRI scans were acquired in 22 male patients with CSZ and 23 age-matched HC. Two-tensor tractography was used in addition to manually-delineated regions of interest to extract the SNS tract. A mixed-model analysis of variance was used to investigate differences in TD and FA between CSZ patients and HC. The associations between TD and behavioral measures were also explored. Patients and controls differed significantly in TD (P = 0.04), but not in FA (P = 0.69). The group differences in TD were driven by a higher TD in the right hemisphere in the CSZ group. Higher TD correlated significantly with poorer performance in the Iowa Gambling Task (IGT) when combining the scores of both groups. The findings suggest that dysconnectiviy of the SNS tract which is associated with schizophrenia, could arise from abnormalities in white matter morphology. These abnormalities may potentially reflect irregularities in brain development.

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease associated with exposure to repetitive head impacts. CTE has been linked to disruptions in cognition, mood, and behavior. Unfortunately, the diagnosis of CTE can only be made post-mortem. Neuropathological evidence suggests limbic structures may provide an opportunity to characterize CTE in the living. Using 3 T magnetic resonance imaging, we compared select limbic brain regional volumes - the amygdala, hippocampus, and cingulate gyrus - between symptomatic former National Football League (NFL) players (n = 86) and controls (n = 22). Moreover, within the group of former NFL players, we examined the relationship between those limbic structures and neurobehavioral functioning (n = 75). The former NFL group comprised eighty-six men (mean age = 55.2 ± 8.0 years) with at least 12 years of organized football experience, at least 2 years of active participation in the NFL, and self-reported declines in cognition, mood, and behavior within the last 6 months. The control group consisted of men (mean age = 57.0 ± 6.6 years) with no history of contact-sport involvement or traumatic brain injury. All control participants provided neurobehavioral data. Compared to controls, former NFL players exhibited reduced volumes of the amygdala, hippocampus, and cingulate gyrus. Within the NFL group, reduced bilateral cingulate gyrus volume was associated with worse attention and psychomotor speed (r = 0.4 (right), r = 0.42 (left); both p