Cardiovascular Magnetic Resonance Imaging in Patients With Ibrutinib-Associated Cardiotoxicity.

Importance: Ibrutinib has been associated with serious cardiotoxic arrhythmias. In preclinical models, these events are paralleled or proceeded by diffuse myocardial injury (inflammation and fibrosis). Yet whether this is seen in patients or has implications for future cardiotoxic risk is unknown. Objective: To assess the incidence and outcomes of myocardial injury among patients with ibrutinib-related cardiotoxicity. Design, Setting, and Participants: This cohort study included consecutive patients treated with ibrutinib from 2012 to 2019, phenotyped using cardiovascular magnetic resonance (CMR) from a large US Comprehensive Cancer Center registry. Exposures: Ibrutinib treatment for cancer control. Main Outcomes and Measures: The primary outcome was the presence of late gadolinium enhancement (LGE) fibrosis. The secondary outcome was the occurrence of major adverse cardiac events (MACE), defined as atrial fibrillation, heart failure, symptomatic ventricular arrhythmias, and sudden death of probable or definite ibrutinib association after CMR. We also assessed parametric-mapping subclinical fibrosis (native-T1, extracellular volume fraction) and inflammation/edema (max-T2) measures. Cardiovascular magnetic resonance measures were compared with those obtained in similar consecutive patients with cancer without ibrutinib treatment (pretreatment controls). Observed measures were also compared with similar-aged broad population rates (general-population controls) and a broader pool of cardiovascular disease (CVD) risk-matched cancer controls. Multivariable regression was used to assess the association between CMR measures and MACE. Results: Overall, 49 patients treated with ibrutinib were identified, including 33 imaged after treatment initiation (mean [SD] age, 65 [10] years, 9 [27%] with hypertension, and 23 [69.7%] with index-arrhythmias); median duration of ibrutinib-use was 14 months. The mean (SD) pretreatment native T1 was 977.0 (73.0) ms, max-T2 56.5 (4.0) ms, and 4 (13.3%) had LGE. Posttreatment initiation, mean (SD) native T1 was 1033.7 (48.2) ms, max-T2 61.5 (4.8) ms, and 17 (54.8%) had LGE (P < .001, P = .01, and P < .001, respectively, pre- vs post-ibrutinib treatment). Native T12SDs was elevated in 9 (28.6%), and max-T22SDs in 21 (63.0%), respectively. Cardiovascular magnetic resonance measures were highest in those with suspected toxic effects (P = .01 and P = .01, respectively). There was no association between traditional CVD-risk or cancer-treatment status and abnormal CMR measures. Among those without traditional CVD, 16 (58.6%) had LGE vs 38 (13.3%) in matched-controls (relative-risk, 4.8; P < .001). Over a median follow-up of 19 months, 13 (39.4%) experienced MACE. In multivariable models inclusive of traditional CVD risk factors, LGE (hazard ratio [HR], 4.9; P = .04), and native-T12SDs (HR, 3.3; P = .05) associated with higher risks of MACE. Conclusions and Relevance: In this cohort study, myocardial injury was common in ibrutinib users, and its presence was associated with higher cardiotoxic risk.

RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI.

Real-time fMRI (RT-fMRI) neurofeedback has been shown to be effective in treating neuropsychiatric disorders and holds tremendous promise for future breakthroughs, both with regard to basic science and clinical applications. However, the prevalence of its use has been hampered by computing hardware requirements, the complexity of setting up and running an experiment, and a lack of standards that would foster collaboration. To address these issues, we have developed RT-Cloud (https://github.com/brainiak/rt-cloud), a flexible, cloud-based, open-source Python software package for the execution of RT-fMRI experiments. RT-Cloud uses standardized data formats and adaptable processing streams to support and expand open science in RT-fMRI research and applications. Cloud computing is a key enabling technology for advancing RT-fMRI because it eliminates the need for on-premise technical expertise and high-performance computing; this allows installation, configuration, and maintenance to be automated and done remotely. Furthermore, the scalability of cloud computing makes it easier to deploy computationally-demanding multivariate analyses in real time. In this paper, we describe how RT-Cloud has been integrated with open standards, including the Brain Imaging Data Structure (BIDS) standard and the OpenNeuro database, how it has been applied thus far, and our plans for further development and deployment of RT-Cloud in the coming years.

Cloud-Based Functional Magnetic Resonance Imaging Neurofeedback to Reduce the Negative Attentional Bias in Depression: A Proof-of-Concept Study.

Individuals with depression show an attentional bias toward negatively valenced stimuli and thoughts. In this proof-of-concept study, we present a novel closed-loop neurofeedback procedure intended to remediate this bias. Internal attentional states were detected in real time by applying machine learning techniques to functional magnetic resonance imaging data on a cloud server; these attentional states were externalized using a visual stimulus that the participant could learn to control. We trained 15 participants with major depressive disorder and 12 healthy control participants over 3 functional magnetic resonance imaging sessions. Exploratory analysis showed that participants with major depressive disorder were initially more likely than healthy control participants to get stuck in negative attentional states, but this diminished with neurofeedback training relative to controls. Depression severity also decreased from pre- to posttraining. These results demonstrate that our method is sensitive to the negative attentional bias in major depressive disorder and showcase the potential of this novel technique as a treatment that can be evaluated in future clinical trials.

Diet-induced metabolic improvements in a hamster model of hypercholesterolemia are strongly linked to alterations of the gut microbiota.

The mammalian gastrointestinal microbiota exerts a strong influence on host lipid and cholesterol metabolism. In this study, we have characterized the interplay among diet, gut microbial ecology, and cholesterol metabolism in a hamster model of hypercholesterolemia. Previous work in this model had shown that grain sorghum lipid extract (GSL) included in the diet significantly improved the high-density lipoprotein (HDL)/non-HDL cholesterol equilibrium (T. P. Carr, C. L. Weller, V. L. Schlegel, S. L. Cuppett, D. M. Guderian, Jr., and K. R. Johnson, J. Nutr. 135:2236-2240, 2005). Molecular analysis of the hamsters' fecal bacterial populations by pyrosequencing of 16S rRNA tags, PCR-denaturing gradient gel electrophoresis, and Bifidobacterium-specific quantitative real-time PCR revealed that the improvements in cholesterol homeostasis induced through feeding the hamsters GSL were strongly associated with alterations of the gut microbiota. Bifidobacteria, which significantly increased in abundance in hamsters fed GSL, showed a strong positive association with HDL plasma cholesterol levels (r = 0.75; P = 0.001). The proportion of members of the family Coriobacteriaceae decreased when the hamsters were fed GSL and showed a high positive association with non-HDL plasma cholesterol levels (r = 0.84; P = 0.0002). These correlations were more significant than those between daily GSL intake and animal metabolic markers, implying that the dietary effects on host cholesterol metabolism are conferred, at least in part, through an effect on the gut microbiota. This study provides evidence that modulation of the gut microbiota-host metabolic interrelationship by dietary intervention has the potential to improve mammalian cholesterol homeostasis, which has relevance for cardiovascular health.