Acute Mechanical Consequences of Vessel-Specific Coronary Bypass Combinations.

PURPOSE: Premature coronary artery bypass graft (CABG) failure has been linked to geometric, mechanical, and compositional discrepancies between host and graft tissues. Acute hemodynamic disturbances and the introduction of wall stress gradients trigger a myriad of mechanobiological processes at the anastomosis that can be associated with restenosis and graft failure. Although the origins of coronary artery disease dictate the anastomotic target, an opportunity exists for graft-vessel optimization through rationale graft selection. METHODS: Here we explored the four distinct regions of the left (L) and right (R) ITA (1 = proximal, 2 = submuscular, 3 = middle, 4 = distal), and four common target vessels in the coronary circulation including the proximal and distal left anterior descending (PLAD & DLAD), right coronary (RCA), and left circumflex (LCX) arteries. Benchtop biaxial mechanical data was used to acquire constitutive model parameters of these tissues and enable vessel-specific computational models to elucidate the mechanical consequences of 32 unique graft-target combinations. RESULTS: Simulations revealed the maximum principal wall stresses for the PLAD, RCA, and LCX occurred when anastomosed with LITA1, and the maximum flow-induced shear stress occurred with LITA4. The DLAD, on the other hand, reached stress maximums when anastomosed to LITA4. Using a normalized objective function of simulation output variables, we found LITA2 to be the best graft choice for both LADs, RITA3 for the RCA, and LITA3 for the LCX. CONCLUSION: Although mechanical compatibility is just one of many factors determining bypass graft outcomes, our data suggests improvements can be made to the grafting process through vessel-specific regional optimization.

Complexity Analysis of Resting-State and Task fMRI Using Multiscale Sample Entropy.

Functional magnetic resonance imaging (fMRI) is a powerful tool that allows for analysis of neural activity via the measurement of blood-oxygenation-level-dependent (BOLD) signal. The BOLD fluctuations can exhibit different levels of complexity, depending upon the conditions under which they are measured. We examined the complexity of both resting-state and task-based fMRI using sample entropy (SampEn) as a surrogate for signal predictability. We found that within most tasks, regions of the brain that were deemed task-relevant displayed significantly low levels of SampEn, and there was a strong negative correlation between parcel entropy and amplitude.

A method to mitigate spatio-temporally varying task-correlated motion artifacts from overt-speech fMRI paradigms in aphasia.

Quantifying accurate functional magnetic resonance imaging (fMRI) activation maps can be dampened by spatio-temporally varying task-correlated motion (TCM) artifacts in certain task paradigms (e.g., overt speech). Such real-world tasks are relevant to characterize longitudinal brain reorganization poststroke, and removal of TCM artifacts is vital for improved clinical interpretation and translation. In this study, we developed a novel independent component analysis (ICA)-based approach to denoise spatio-temporally varying TCM artifacts in 14 persons with aphasia who participated in an overt language fMRI paradigm. We compared the new methodology with other existing approaches such as "standard" volume registration, nonselective motion correction ICA packages (i.e., AROMA), and combining the novel approach with AROMA. Results show that the proposed methodology outperforms other approaches in removing TCM-related false positive activity (i.e., improved detectability power) with high spatial specificity. The proposed method was also effective in maintaining a balance between removal of TCM-related trial-by-trial variability and signal retention. Finally, we show that the TCM artifact is related to clinical metrics, such as speech fluency and aphasia severity, and the implication of TCM denoising on such relationship is also discussed. Overall, our work suggests that routine bulkhead motion based denoising packages cannot effectively account for spatio-temporally varying TCM. Further, the proposed TCM denoising approach requires a one-time front-end effort to hand label and train the classifiers that can be cost-effectively utilized to denoise large clinical data sets.