Perivascular spaces mediate a relationship between diabetes and other cerebral small vessel disease markers in cerebrovascular and neurodegenerative diseases.

Type 2 diabetes mellitus (T2DM) and hypertension are risk factors for cerebral small vessel disease (SVD); however, few studies have characterised their relationships with MRI-visible perivascular spaces (PVS). MRI was used to quantify deep (d) and periventricular (p) white matter hyperintensities (WMH), lacunes, PVS in the white matter (wmPVS) or basal ganglia (bgPVS), and diffusion metrics in white matter. Patients with T2DM had greater wmPVS volume and there were greater wmPVS volumes in patients with T2DM and hypertension together. Counterfactual moderated mediation models found indirect effects of T2DM on volumes of other SVD and diffusion markers that were mediated by wmPVS: pWMH, dWMH, periventricular lacunes, and deep lacunes, and progression of deep lacunes over 1 year, in patients with hypertension, but not in patients without hypertension. Studying the regulation of cortical perivascular fluid dynamics may reveal mechanisms that mediate the impact of T2DM on cerebral small vessels.

Association of Dual-Task Gait Cost and White Matter Hyperintensity Burden Poststroke: Results From the ONDRI.

BACKGROUND: Acute change in gait speed while performing a mental task [dual-task gait cost (DTC)], and hyperintensity magnetic resonance imaging signals in white matter are both important disability predictors in older individuals with history of stroke (poststroke). It is still unclear, however, whether DTC is associated with overall hyperintensity volume from specific major brain regions in poststroke. METHODS: This is a cohort study with a total of 123 older (69 ± 7 years of age) participants with history of stroke were included from the Ontario Neurodegenerative Disease Research Initiative. Participants were clinically assessed and had gait performance assessed under single- and dual-task conditions. Structural neuroimaging data were analyzed to measure both, white matter hyperintensity (WMH) and normal appearing volumes. Percentage of WMH volume in frontal, parietal, occipital, and temporal lobes as well as subcortical hyperintensities in basal ganglia + thalamus were the main outcomes. Multivariate models investigated associations between DTC and hyperintensity volumes, adjusted for age, sex, years of education, global cognition, vascular risk factors, APOE4 genotype, residual sensorimotor symptoms from previous stroke and brain volume. RESULTS: There was a significant positive global linear association between DTC and hyperintensity burden (adjusted Wilks' λ = .87, P = .01). Amongst all WMH volumes, hyperintensity burden from basal ganglia + thalamus provided the most significant contribution to the global association (adjusted ß = .008, η2 = .03; P = .04), independently of brain atrophy. CONCLUSIONS: In poststroke, increased DTC may be an indicator of larger white matter damages, specifically in subcortical regions, which can potentially affect the overall cognitive processing and decrease gait automaticity by increasing the cortical control over patients' locomotion.

Localized White Matter Tract Integrity Measured by Diffusion Tensor Imaging Is Altered in People with Mild Cognitive Impairment and Associated with Dual-Task and Single-Task Gait Speed.

BACKGROUND: Altered white matter (WM) tract integrity may contribute to mild cognitive impairment (MCI) and gait abnormalities. OBJECTIVE: The purpose of this study was to determine whether diffusion tensor imaging (DTI) metrics were altered in specific portions of WM tracts in people with MCI and to determine whether gait speed variations were associated with the specific DTI metric changes. METHODS: DTI was acquired in 44 people with MCI and 40 cognitively normal elderly controls (CNCs). Fractional anisotropy (FA) and radial diffusivity (RD) were measured along 18 major brain WM tracts using probabilistic tractography. The average FA and RD along the tracts were compared between the groups using MANCOVA and post-hoc tests. The tracts with FA or RD differences between the groups were examined using an along-tract exploratory analysis to identify locations that differed between the groups. Associations between FA and RD in whole tracts and in the segments of the tracts that differed between the groups and usual/dual-task gait velocities and gross cognition were examined. RESULTS: Lower FA and higher RD was observed in right cingulum-cingulate gyrus endings (rh.ccg) of the MCI group compared to the CNC group. These changes were localized to the posterior portions of the rh.ccg and correlated with gait velocities. CONCLUSION: Lower FA and higher RD in the posterior portion of the rh.ccg adjacent to the posterior cingulate suggests decreased microstructural integrity in the MCI group. The correlation of these metrics with gait velocities suggests an important role for this tract in maintaining normal cognitive-motor function.

Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity.

Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.

Increased brain volumetric measurement precision from multi-site 3D T1-weighted 3 T magnetic resonance imaging by correcting geometric distortions.

PURPOSE: Magnetic resonance imaging (MRI) scanner-specific geometric distortions may contribute to scanner induced variability and decrease volumetric measurement precision for multi-site studies. The purpose of this study was to determine whether geometric distortion correction increases the precision of brain volumetric measurements in a multi-site multi-scanner study. METHODS: Geometric distortion variation was quantified over a one-year period at 10 sites using the distortion fields estimated from monthly 3D T1-weighted MRI geometrical phantom scans. The variability of volume and distance measurements were quantified using synthetic volumes and a standard quantitative MRI (qMRI) phantom. The effects of geometric distortion corrections on MRI derived volumetric measurements of the human brain were assessed in two subjects scanned on each of the 10 MRI scanners and in 150 subjects with cerebrovascaular disease (CVD) acquired across imaging sites. RESULTS: Geometric distortions were found to vary substantially between different MRI scanners but were relatively stable on each scanner over a one-year interval. Geometric distortions varied spatially, increasing in severity with distance from the magnet isocenter. In measurements made with the qMRI phantom, the geometric distortion correction decreased the standard deviation of volumetric assessments by 35% and distance measurements by 42%. The average coefficient of variance decreased by 16% in gray matter and white matter volume estimates in the two subjects scanned on the 10 MRI scanners. CONCLUSION: Geometric distortion correction using an up-to-date correction field is recommended to increase precision in volumetric measurements made from MRI images.

Estimation of the hyperelastic parameters of fresh human oropharyngeal soft tissues using indentation testing.

Patient-specific finite element (FE) modeling of the upper airway is an effective tool for accurate assessment of obstructive sleep apnea (OSA) syndrome. It is also useful for planning minimally invasive surgical procedures under severe OSA conditions. A major requirement of FE modeling is having reliable data characterizing the biomechanical properties of the upper airway tissues, particularly oropharyngeal soft tissue. While some data characterizing this tissue's linear elastic regime is available, reliable data characterizing its hyperelasticity is scarce. The aim of the current study is to estimate the hyperelastic mechanical properties of the oropharyngeal soft tissues, including the palatine tonsil, soft palate, uvula, and tongue base. Fresh tissue specimens of human oropharyngeal tissue were acquired from 13 OSA patients who underwent standard surgical procedures. Indentation testing was performed on the specimens to obtain their force-displacement data. To determine the specimens' hyperelastic parameters using these data, an inverse FE framework was utilized. In this work, the hyperelastic parameters corresponding to the commonly used Yeoh and 2nd order Ogden models were obtained. Both models captured the experimental force-displacement data of the tissue specimens reasonably accurately with mean errors of 11.65% or smaller. This study has provided estimates of the hyperelastic parameters of all upper airway soft tissues using fresh human tissue specimens for the first time.

Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines.

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

Estimation of the Young's moduli of fresh human oropharyngeal soft tissues using indentation testing.

Finite element (FE)-based biomechanical simulations of the upper airway are promising computational tools to study abnormal upper airway deformations under obstructive sleep apnea (OSA) conditions and to help guide minimally invasive surgical interventions in case of upper airway collapse. To this end, passive biomechanical properties of the upper airway tissues, especially oropharyngeal soft tissues, are indispensable. This research aimed at characterizing the linear elastic mechanical properties of the oropharyngeal soft tissues including palatine tonsil, soft palate, uvula, and tongue base. For this purpose, precise indentation experiments were conducted on freshly harvested human tissue samples accompanied by FE-based inversion schemes. To minimize the impact of the probable nonlinearities of the tested tissue samples, only the first quarter of the measured force-displacement data corresponding to the linear elastic regime was utilized in the FE-based inversion scheme to improve the accuracy of the tissue samples' Young's modulus calculations. Measured Young's moduli of the oropharyngeal soft tissues obtained in this study are presented. They include first estimates for palatine tonsil tissue samples while measured Young's moduli of other upper airway tissues were obtained for the first time using fresh human tissue samples.

A finite element model of myocardial infarction using a composite material approach.

Computational models are effective tools to study cardiac mechanics under normal and pathological conditions. They can be used to gain insight into the physiology of the heart under these conditions while they are adaptable to computer assisted patient-specific clinical diagnosis and therapeutic procedures. Realistic cardiac mechanics models incorporate tissue active/passive response in conjunction with hyperelasticity and anisotropy. Conventional formulation of such models leads to mathematically-complex problems usually solved by custom-developed non-linear finite element (FE) codes. With a few exceptions, such codes are not available to the research community. This article describes a computational cardiac mechanics model developed such that it can be implemented using off-the-shelf FE solvers while tissue pathologies can be introduced in the model in a straight-forward manner. The model takes into account myocardial hyperelasticity, anisotropy, and active contraction forces. It follows a composite tissue modeling approach where the cardiac tissue is decomposed into two major parts: background and myofibers. The latter is modelled as rebars under initial stresses mimicking the contraction forces. The model was applied in silico to study the mechanics of infarcted left ventricle (LV) of a canine. End-systolic strain components, ejection fraction, and stress distribution attained using this LV model were compared quantitatively and qualitatively to corresponding data obtained from measurements as well as to other corresponding LV mechanics models. This comparison showed very good agreement.

Constructing a patient-specific computer model of the upper airway in sleep apnea patients.

OBJECTIVE: The use of computer simulation to develop a high-fidelity model has been proposed as a novel and cost-effective alternative to help guide therapeutic intervention in sleep apnea surgery. We describe a computer model based on patient-specific anatomy of obstructive sleep apnea (OSA) subjects wherein the percentage and sites of upper airway collapse are compared to findings on drug-induced sleep endoscopy (DISE). STUDY DESIGN: Basic science computer model generation. METHODS: Three-dimensional finite element techniques were undertaken for model development in a pilot study of four OSA patients. Magnetic resonance imaging was used to capture patient anatomy and software employed to outline critical anatomical structures. A finite-element mesh was applied to the volume enclosed by each structure. Linear and hyperelastic soft-tissue properties for various subsites (tonsils, uvula, soft palate, and tongue base) were derived using an inverse finite-element technique from surgical specimens. Each model underwent computer simulation to determine the degree of displacement on various structures within the upper airway, and these findings were compared to DISE exams performed on the four study patients. RESULTS: Computer simulation predictions for percentage of airway collapse and site of maximal collapse show agreement with observed results seen on endoscopic visualization. CONCLUSION: Modeling the upper airway in OSA patients is feasible and holds promise in aiding patient-specific surgical treatment. LEVEL OF EVIDENCE: NA. Laryngoscope, 128:277-282, 2018.

A novel micro-to-macro approach for cardiac tissue mechanics.

For studying cardiac mechanics, hyperelastic anisotropic computational models have been developed which require the tissue anisotropic and hyperelastic parameters. These parameters are obtained by tissue samples mechanically testing. The validity of such parameters are limited to the specific tissue sample only. They are not adaptable for pathological tissues commonly associated with tissue microstructure alterations. To investigate cardiac tissue mechanics, a novel approach is proposed to model hyperelasticity and anisotropy. This approach is adaptable to various tissue microstructural constituent's distributions in normal and pathological tissues. In this approach, the tissue is idealized as composite material consisting of cardiomyocytes distributed in extracellular matrix (ECM). The major myocardial tissue constituents are mitochondria and myofibrils while the main ECM's constituents are collagen fibers and fibroblasts. Accordingly, finite element simulations of uniaxial and equibiaxial tests of normal and infarcted tissue samples with known amounts of these constituents were conducted, leading to corresponding tissue stress-strain data that were fitted to anisotropic/hyperelastic models. The models were validated where they showed good agreement characterized by maximum average stress-strain errors of 16.17 and 10.01% for normal and infarcted cardiac tissue, respectively. This demonstrate the effectiveness of the proposed models in accurate characterization of healthy and pathological cardiac tissues.

Comparative biomechanical study of using decellularized human adipose tissues for post-mastectomy and post-lumpectomy breast reconstruction.

Developing suitable biomaterials for post-mastectomy or post-lumpectomy breast reconstruction is highly important. This study is aimed at evaluating biomechanical suitability of decellularized adipose tissue (DAT) for this purpose. The study involves computational experiments for evaluating deformation of the breast reconstructed using DAT under loading conditions pertaining to two common body position changes of prone-to-supine and prone-to-upright. This was conducted using nonlinear finite element models where the breast geometry was obtained from MRI image of a female breast. The experiments were performed using DAT sourced from various adipose tissue depots in comparison to natural adipose tissue. Data obtained from the conducted experiments showed no contour defects with various DAT materials for simulated post-mastectomy or post-lumpectomy breast reconstruction under the loading conditions. They also demonstrated that a breast reconstructed using DAT derived from the breast or subcutaneous abdominal depots exhibit significantly closer deformation, both qualitatively and quantitatively, to that of a normal breast under the same loading conditions. Similarity of DAT deformation to that of natural breast tissue in post-surgery breast reconstruction was assessed using nonlinear finite element analysis. Our results provide evidence that DAT derived from subcutaneous abdominal and breast depots yield more analogous deformation pattern to the natural tissue in post-mastectomy breast reconstruction applications. This is quite encouraging, as breast and subcutaneous adipose tissue can be readily obtained in large quantities from breast or abdominal lipo-reduction surgery procedures. Furthermore, in post-lumpectomy cases all DAT samples used in this research showed similar deformation, and thus are suitable as breast tissue substituents.