Free-running 5D coronary MR angiography at 1.5T using LIBRE water excitation pulses.

PURPOSE: To implement, optimize, and characterize lipid-insensitive binomial off-resonant RF excitation (LIBRE) pulses for fat-suppressed fully self-gated free-running 5D cardiac MRI. METHODS: Bloch equation simulations were used to optimize LIBRE parameter settings in non-interrupted bSSFP prior to in vitro validation. Thus, optimized LIBRE pulses were subsequently applied to free-running coronary MRA in 20 human adult subjects, where resulting images were quantitatively compared to those obtained with non-fat-suppressing excitation (SP), conventional 1-2-1 water excitation (WE), and a previously published interrupted free-running (IFR) sequence. SAR and scan times were recorded. Respiratory-and-cardiac-motion-resolved images were reconstructed with XD-GRASP, and contrast ratios, coronary artery detection rate, vessel length, and vessel sharpness were computed. RESULTS: The numerically optimized LIBRE parameters were successfully validated in vitro. In vivo, LIBRE had the lowest SAR and a scan time that was similar to that of WE yet 18% shorter than that of IFR. LIBRE improved blood-fat contrast when compared to SP, WE, and IFR, vessel detection relative to SP and IFR, and vessel sharpness when compared to WE and IFR (for example, for the left main and anterior descending coronary artery, 51.5% ± 10.2% [LIBRE] versus 42.1% ± 6.8% [IFR]). Vessel length measurements remained unchanged for all investigated methods. CONCLUSION: LIBRE enabled fully self-gated non-interrupted free-running 5D bSSFP imaging of the heart at 1.5T with suppressed fat signal. Measures of image quality, vessel conspicuity, and scan time compared favorably to those obtained with the more conventional non-interrupted WE and the previously published IFR, while SAR reduction offers added flexibility.

Data-driven tensor independent component analysis for model-based connectivity neurofeedback.

Neurofeedback based on real-time functional MRI is an emerging technique to train voluntary control over brain activity in healthy and disease states. Recent developments even allow for training of brain networks using connectivity feedback based on dynamic causal modeling (DCM). DCM is an influential hypothesis-driven approach that requires prior knowledge about the target brain network dynamics and the modulatory influences. Data-driven approaches, such as tensor independent component analysis (ICA), can reveal spatiotemporal patterns of brain activity without prior assumptions. Tensor ICA allows flexible data decomposition and extraction of components consisting of spatial maps, time-series, and session/subject-specific weights, which can be used to characterize individual neurofeedback regulation per regulation trial, run, or session. In this study, we aimed to better understand the spatiotemporal brain patterns involved and affected by model-based feedback regulation using data-driven tensor ICA. We found that task-specific spatiotemporal brain patterns obtained using tensor ICA were highly consistent with model-based feedback estimates. However, we found that the DCM approach captured specific network interdependencies that went beyond what could be detected with either general linear model (GLM) or ICA approaches. We also found that neurofeedback-guided regulation resulted in activity changes that were characteristic of the mental strategies used to control the feedback signal, and that these activity changes were not limited to periods of active self-regulation, but were also evident in distinct gradual recovery processes during subsequent rest periods. Complementary data-driven and model-based approaches could aid in interpretation of the neurofeedback data when applied post-hoc, and in the definition of the target brain area/pattern/network/model prior to the neurofeedback training study when applied to the pilot data. Systematically investigating the triad of mental effort, spatiotemporal brain network changes, and activity and recovery processes might lead to a better understanding of how learning with neurofeedback is accomplished, and how such learning can cause plastic brain changes along with specific behavioral effects.

Characterization of full-length recombinant human Proteoglycan 4 as an ocular surface boundary lubricant.

Proteoglycan 4 (PRG4, or lubricin) is a lubricating mucin-like glycoprotein recently discovered at the ocular surface, where it functions as a boundary lubricant and appears to play a protective role. Recent technological advances have enabled abundant expression of full-length recombinant human PRG4 (rhPRG4). The objectives of this study were to 1) biochemically characterize the gross structure and glycosylations of full-length rhPRG4, and 2) assess the ocular surface boundary lubricating ability of rhPRG4 at both human cornea-eyelid and human cornea-polydimethylsiloxane (PDMS) biointerfaces. rhPRG4 expressed by a Chinese hamster ovary cell line was characterized and compared to native bovine PRG4 by SDS-PAGE western blotting, and protein identity was assessed by tandem mass spectrometry (MS/MS). Human corneas were articulated against PDMS or human eyelids, at effective sliding velocities of 0.3-30 mm/s under physiological loads of ∼15 kPa, to assess and compare the ocular lubricating ability of rhPRG4 to PRG4. Samples were tested serially in PRG4, rhPRG4 (both 300 µg/ml), then saline. Western blotting indicated that rhPRG4 had immunoreactivity at the appropriate apparent molecular weight, and possessed O-linked glycosylation consistent with that of PRG4. rhPRG4 protein identity was confirmed by MS/MS. Both PRG4 and rhPRG4 significantly, and similarly, reduced friction compared to saline at both human cornea - PDMS and human cornea-eyelid biointerfaces. In conclusion, the rhPRG4 studied here demonstrated appropriate higher order structure, O-linked glycosylations, and ocular surface boundary lubricating. Purified rhPRG4 may have clinical utility as a topical treatment of dry eye disease or contact lens biomaterial coating to promote more comfortable wear.