1
|
Upadhyay K, Jagani R, Giovanis DG, Alshareef A, Knutsen AK, Johnson CL, Carass A, Bayly PV, Shields MD, Ramesh KT. Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-based Machine Learning Approach. Mil Med 2024:usae199. [PMID: 38739497 DOI: 10.1093/milmed/usae199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/06/2024] [Accepted: 04/02/2024] [Indexed: 05/16/2024] Open
Abstract
INTRODUCTION Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant variability of head shapes exists in U.S. Army soldiers, evident from the Anthropometric Survey of U.S. Army Personnel (ANSUR II). The objective of this study is to elucidate the effects of head shape on the predicted risk of traumatic brain injury from computational head injury models. MATERIALS AND METHODS Magnetic resonance imaging scans of 25 human subjects are collected. These images are registered to the standard MNI152 brain atlas, and the resulting transformation matrix components (called head shape parameters) are used to quantify head shapes of the subjects. A generative machine learning model is used to generate 25 additional head shape parameter datasets to augment our database. Head injury models are developed for these head shapes, and a rapid injurious head rotation event is simulated to obtain several brain injury predictor variables (BIPVs): Peak cumulative maximum principal strain (CMPS), average CMPS, and the volume fraction of brain exceeding an injurious CMPS threshold. A Gaussian process regression model is trained between head shape parameters and BIPVs, which is then used to study the relative sensitivity of the various BIPVs on individual head shape parameters. We distinguish head shape parameters into 2 types: Scaling components ${T_{xx}}$, ${T_{yy}}$, and ${T_{zz}}$ that capture the breadth, length, and height of the head, respectively, and shearing components (${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$) that capture the relative skewness of the head shape. RESULTS An overall positive correlation is evident between scaling components and BIPVs. Notably, a very high, positive correlation is seen between the BIPVs and the head volume. As an example, a 57% increase in peak CMPS was noted between the smallest and the largest investigated head volume parameters. The variation in shearing components ${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$ on average does not cause notable changes in the BIPVs. From the Gaussian process regression model, all 3 BIPVs showed an increasing trend with each of the 3 scaling components, but the BIPVs are found to be most sensitive to the height dimension of the head. From the Sobol sensitivity analysis, the ${T_{zz}}$ scaling parameter contributes nearly 60% to the total variance in peak and average CMPS; ${T_{yy}}$ contributes approximately 20%, whereas ${T_{xx}}$ contributes less than 5%. The remaining contribution is from the 6 shearing components. Unlike peak and average CMPS, the VF-CMPS BIPV is associated with relatively evenly distributed Sobol indices across the 3 scaling parameters. Furthermore, the contribution of shearing components on the total variance in this case is negligible. CONCLUSIONS Head shape has a considerable influence on the injury predictions of computational head injury models. Available "average" head injury models based on a 50th-percentile U.S. male are likely associated with considerable uncertainty. In general, larger head sizes correspond to greater BIPV magnitudes, which point to potentially a greater injury risk under rapid neck rotation for people with larger heads.
Collapse
Affiliation(s)
- Kshitiz Upadhyay
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Roshan Jagani
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Dimitris G Giovanis
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ahmed Alshareef
- Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD 20817, USA
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19713, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Philip V Bayly
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Michael D Shields
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - K T Ramesh
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| |
Collapse
|
2
|
Lowder JL, Zhao P, Bradley MS, Giugale LE, Xu H, Abramowitch SD, Bayly PV. Preoperative prolapse phenotype is predictive of surgical outcome with minimally invasive sacrocolpopexy. Am J Obstet Gynecol 2024:S0002-9378(24)00523-4. [PMID: 38642697 DOI: 10.1016/j.ajog.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/31/2024] [Accepted: 04/11/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND The gold-standard treatment for advanced pelvic organ prolapse is sacrocolpopexy. However, the preoperative features of prolapse that predict optimal outcomes are unknown. OBJECTIVE This study aimed to develop a clinical prediction model that uses preoperative scores on the Pelvic Organ Prolapse Quantification examination to predict outcomes after minimally invasive sacrocolpopexy for stages 2, 3, and 4 uterovaginal prolapse and vaginal vault prolapse. STUDY DESIGN A 2-institution database of pre- and postoperative variables from 881 cases of minimally invasive sacrocolpopexy was analyzed. Data from patients were analyzed in the following 4 groups: stage 2 uterovaginal prolapse, stage 3 to 4 uterovaginal prolapse, stage 2 vaginal vault prolapse, and stage 3 to 4 vaginal vault prolapse. Unsupervised machine learning was used to identify clusters and investigate associations between clusters and outcome. The k-means clustering analysis was performed with preoperative Pelvic Organ Prolapse Quantification points and stratified by previous hysterectomy status. The "optimal" surgical outcome was defined as postoperative Pelvic Organ Prolapse Quantification stage <2. Demographic variables were compared by cluster with Student t and chi-square tests. Odds ratios were calculated to determine whether clusters could predict the outcome. Age at surgery, body mass index, and previous prolapse surgery were used for adjusted odds ratios. RESULTS Five statistically distinct prolapse clusters (phenotypes C, A, A>P, P, and P>A) were found. These phenotypes reflected the predominant region of prolapse (apical, anterior, or posterior) and whether support was preserved in the nonpredominant region. Phenotype A (anterior compartment prolapse predominant, posterior support preserved) was found in all 4 groups of patients and was considered the reference in the analysis. In 111 patients with stage 2 uterovaginal prolapse, phenotypes A and A>P (greater anterior prolapse than posterior prolapse) were found, and patients with phenotype A were more likely than those with phenotype A>P to have an optimal surgical outcome. In 401 patients with stage 3 to 4 uterovaginal prolapse, phenotypes C (apical compartment predominant, prolapse in all compartments), A, and A>P were found, and patients with phenotype A>P were more likely than those with phenotype A to have ideal surgical outcome. In 72 patients with stage 2 vaginal vault prolapse, phenotypes A, A>P, and P (posterior compartment predominant, anterior support preserved) were found, and those with phenotype A>P were less likely to have an ideal outcome than patients with phenotype A. In 297 patients with stage 3 to 4 vaginal vault prolapse, phenotypes C, A, and P>A (prolapse greater in posterior than in anterior compartment) were found, but there were no significant differences in rate of ideal outcome between phenotypes. CONCLUSION Five anatomic phenotypes based on preoperative Pelvic Organ Prolapse Quantification scores were present in patients with stages 2 and 3 to 4 uterovaginal prolapse and vaginal vault prolapse. These phenotypes are predictive of surgical outcome after minimally invasive sacrocolpopexy. Further work needs to confirm the presence and predictive nature of these phenotypes. In addition, whether the phenotypes represent a progression of prolapse or discrete prolapse presentations resulting from different anatomic and life course risk profiles is unknown. These phenotypes may be useful in surgical counseling and planning.
Collapse
Affiliation(s)
- Jerry L Lowder
- Division of Urogynecology and Reconstructive Pelvic Surgery, Department of Obstetrics and Gynecology, Washington University School of Medicine in St. Louis, St. Louis, MO.
| | - Peinan Zhao
- Division of Clinical Research, Department of Obstetrics and Gynecology, Washington University School of Medicine in St. Louis, St. Louis, MO
| | - Megan S Bradley
- Division of Female Pelvic Medicine and Reconstructive Surgery, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Lauren E Giugale
- Division of Female Pelvic Medicine and Reconstructive Surgery, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Haonan Xu
- Department of Obstetrics and Gynecology, Washington University School of Medicine in St. Louis, St. Louis, MO
| | - Steven D Abramowitch
- Departments of Bioengineering and Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO
| |
Collapse
|
3
|
Wang S, Okamoto RJ, McGarry MDJ, Bayly PV. Shear wave speeds in a nearly incompressible fibrous material with two unequal fiber families. J Acoust Soc Am 2024; 155:2327-2338. [PMID: 38557738 PMCID: PMC10987194 DOI: 10.1121/10.0025467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/21/2024] [Accepted: 03/14/2024] [Indexed: 04/04/2024]
Abstract
The mechanical properties of soft biological tissues can be characterized non-invasively by magnetic resonance elastography (MRE). In MRE, shear wave fields are induced by vibration, imaged by magnetic resonance imaging, and inverted to estimate tissue properties in terms of the parameters of an underlying material model. Most MRE studies assume an isotropic material model; however, biological tissue is often anisotropic with a fibrous structure, and some tissues contain two or more families of fibers-each with different orientations and properties. Motivated by the prospect of using MRE to characterize such tissues, this paper describes the propagation of shear waves in soft fibrous material with two unequal fiber families. Shear wave speeds are expressed in terms of material parameters, and the effect of each parameter on the shear wave speeds is investigated. Analytical expressions of wave speeds are confirmed by finite element simulations of shear wave transmission with various polarization directions. This study supports the feasibility of estimating parameters of soft fibrous tissues with two unequal fiber families in vivo from local shear wave speeds and advances the prospects for the mechanical characterization of such biological tissues by MRE.
Collapse
Affiliation(s)
- Shuaihu Wang
- Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, Missouri 63130, USA
| | - Ruth J Okamoto
- Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, Missouri 63130, USA
| | - Matthew D J McGarry
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755, USA
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, Missouri 63130, USA
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63130, USA
| |
Collapse
|
4
|
Walter C, Balouchzadeh R, Garcia KE, Kroenke CD, Pathak A, Bayly PV. Multi-scale measurement of stiffness in the developing ferret brain. Sci Rep 2023; 13:20583. [PMID: 37996465 PMCID: PMC10667369 DOI: 10.1038/s41598-023-47900-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/20/2023] [Indexed: 11/25/2023] Open
Abstract
Cortical folding is an important process during brain development, and aberrant folding is linked to disorders such as autism and schizophrenia. Changes in cell numbers, size, and morphology have been proposed to exert forces that control the folding process, but these changes may also influence the mechanical properties of developing brain tissue. Currently, the changes in tissue stiffness during brain folding are unknown. Here, we report stiffness in the developing ferret brain across multiple length scales, emphasizing changes in folding cortical tissue. Using rheometry to measure the bulk properties of brain tissue, we found that overall brain stiffness increases with age over the period of cortical folding. Using atomic force microscopy to target the cortical plate, we found that the occipital cortex increases in stiffness as well as stiffness heterogeneity over the course of development and folding. These findings can help to elucidate the mechanics of the cortical folding process by clarifying the concurrent evolution of tissue properties.
Collapse
Affiliation(s)
- Christopher Walter
- Mechanical Engineering and Materials Science, Washington University, St. Louis, USA.
| | - Ramin Balouchzadeh
- Mechanical Engineering and Materials Science, Washington University, St. Louis, USA
| | - Kara E Garcia
- Radiology and Imaging Sciences, Indiana University School of Medicine, Evansville, IN, USA
| | - Christopher D Kroenke
- Advanced Imaging Research Center and Oregon National Primate Research Center Division of Neuroscience, Oregon Health and Science University, Portland, OR, USA
| | - Amit Pathak
- Mechanical Engineering and Materials Science, Washington University, St. Louis, USA
| | - Philip V Bayly
- Mechanical Engineering and Materials Science, Washington University, St. Louis, USA.
| |
Collapse
|
5
|
Okamoto RJ, Escarcega JD, Alshareef A, Carass A, Prince JL, Johnson CL, Bayly PV. Effect of Direction and Frequency of Skull Motion on Mechanical Vulnerability of the Human Brain. J Biomech Eng 2023; 145:111005. [PMID: 37432674 PMCID: PMC10578077 DOI: 10.1115/1.4062937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/26/2023] [Accepted: 07/06/2023] [Indexed: 07/12/2023]
Abstract
Strain energy and kinetic energy in the human brain were estimated by magnetic resonance elastography (MRE) during harmonic excitation of the head, and compared to characterize the effect of loading direction and frequency on brain deformation. In brain MRE, shear waves are induced by external vibration of the skull and imaged by a modified MR imaging sequence; the resulting harmonic displacement fields are typically "inverted" to estimate mechanical properties, like stiffness or damping. However, measurements of tissue motion from MRE also illuminate key features of the response of the brain to skull loading. In this study, harmonic excitation was applied in two different directions and at five different frequencies from 20 to 90 Hz. Lateral loading induced primarily left-right head motion and rotation in the axial plane; occipital loading induced anterior-posterior head motion and rotation in the sagittal plane. The ratio of strain energy to kinetic energy (SE/KE) depended strongly on both direction and frequency. The ratio of SE/KE was approximately four times larger for lateral excitation than for occipital excitation and was largest at the lowest excitation frequencies studied. These results are consistent with clinical observations that suggest lateral impacts are more likely to cause injury than occipital or frontal impacts, and also with observations that the brain has low-frequency (∼10 Hz) natural modes of oscillation. The SE/KE ratio from brain MRE is potentially a simple and powerful dimensionless metric of brain vulnerability to deformation and injury.
Collapse
Affiliation(s)
- Ruth J. Okamoto
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, One Brookings Drive, MSC 1185-208-125, St. Louis, MO 63130
| | - Jordan D. Escarcega
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130
| | - Ahmed Alshareef
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Curtis L. Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19713
| | - Philip V. Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130
| |
Collapse
|
6
|
Wang S, Guertler CA, Okamoto RJ, Johnson CL, McGarry MDJ, Bayly PV. Mechanical stiffness and anisotropy measured by MRE during brain development in the minipig. Neuroimage 2023; 277:120234. [PMID: 37369255 PMCID: PMC11081136 DOI: 10.1016/j.neuroimage.2023.120234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/12/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
The relationship between brain development and mechanical properties of brain tissue is important, but remains incompletely understood, in part due to the challenges in measuring these properties longitudinally over time. In addition, white matter, which is composed of aligned, myelinated, axonal fibers, may be mechanically anisotropic. Here we use data from magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI) to estimate anisotropic mechanical properties in six female Yucatan minipigs at ages from 3 to 6 months. Fiber direction was estimated from the principal axis of the diffusion tensor in each voxel. Harmonic shear waves in the brain were excited by three different configurations of a jaw actuator and measured using a motion-sensitive MR imaging sequence. Anisotropic mechanical properties are estimated from displacement field and fiber direction data with a finite element- based, transversely-isotropic nonlinear inversion (TI-NLI) algorithm. TI-NLI finds spatially resolved TI material properties that minimize the error between measured and simulated displacement fields. Maps of anisotropic mechanical properties in the minipig brain were generated for each animal at all four ages. These maps show that white matter is more dissipative and anisotropic than gray matter, and reveal significant effects of brain development on brain stiffness and structural anisotropy. Changes in brain mechanical properties may be a fundamental biophysical signature of brain development.
Collapse
Affiliation(s)
- Shuaihu Wang
- Mechanical Engineering and Material Science, Washington University in St. Louis, United States
| | - Charlotte A Guertler
- Mechanical Engineering and Material Science, Washington University in St. Louis, United States
| | - Ruth J Okamoto
- Mechanical Engineering and Material Science, Washington University in St. Louis, United States
| | | | | | - Philip V Bayly
- Mechanical Engineering and Material Science, Washington University in St. Louis, United States; Biomedical Engineering, Washington University in St. Louis, United States.
| |
Collapse
|
7
|
Kailash KA, Guertler CA, Johnson CL, Okamoto RJ, Bayly PV. Measurement of relative motion of the brain and skull in the mini-pig in-vivo. J Biomech 2023; 156:111676. [PMID: 37329640 PMCID: PMC11086683 DOI: 10.1016/j.jbiomech.2023.111676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 06/19/2023]
Abstract
The mechanical role of the skull-brain interface is critical to the pathology of concussion and traumatic brain injury (TBI) and may evolve with age. Here we characterize the skull-brain interface in juvenile, female Yucatan mini-pigs from 3 to 6 months old using techniques from magnetic resonance elastography (MRE). The displacements of the skull and brain were measured by a motion-sensitive MR imaging sequence during low-amplitude harmonic motion of the head. Each animal was scanned four times at 1-month intervals. Harmonic motion at 100 Hz was excited by three different configurations of a jaw actuator in order to vary the direction of loading. Rigid-body linear motions of the brain and skull were similar, although brain rotations were consistently smaller than corresponding skull rotations. Relative displacements between the brain and skull were estimated for voxels on the surface of the brain. Amplitudes of relative displacements between skull and brain were 1-3 μm, approximately 25-50% of corresponding skull displacements. Maps of relative displacement showed variations by anatomical region, and the normal component of relative displacement was consistently 25-50% of the tangential component. These results illuminate the mechanics of the skull-brain interface in a gyrencephalic animal model relevant to human brain injury and development.
Collapse
Affiliation(s)
- Keshav A Kailash
- Washington University in St. Louis, Biomedical Engineering, United States
| | - Charlotte A Guertler
- Washington University in St. Louis, Mechanical Engineering and Material Science, United States
| | | | - Ruth J Okamoto
- Washington University in St. Louis, Mechanical Engineering and Material Science, United States
| | - Philip V Bayly
- Washington University in St. Louis, Biomedical Engineering, United States; Washington University in St. Louis, Mechanical Engineering and Material Science, United States.
| |
Collapse
|
8
|
Escarcega J, Knutsen AK, Alshareef A, Johnson CL, Okamoto RJ, Pham DL, Bayly PV. Comparison of Deformation Patterns Excited in the Human Brain in vivo by Harmonic and Impulsive Skull Motion. J Biomech Eng 2023:1-43. [PMID: 37345977 DOI: 10.1115/1.4062809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 06/12/2023] [Indexed: 06/23/2023]
Abstract
Noninvasive measurements of brain deformation in human participants in vivo are needed to develop models of brain biomechanics and understand traumatic brain injury (TBI). Tagged magnetic resonance imaging (tagged MRI) and magnetic resonance elastography (MRE) are two techniques to study human brain deformation; these techniques differ in the type of motion and difficulty of implementation. In this study, oscillatory strain fields in the human brain caused by impulsive head acceleration and measured by tagged MRI were compared quantitatively to strain fields measured by MRE during harmonic head motion at 10 and 50 Hz. Strain fields were compared by registering to a common anatomical template, then computing correlations between the registered strain fields. Correlations were computed between tagged MRI strain fields in six participants and MRE strain fields at 10 Hz and 50 Hz in six different participants. Correlations among strain fields within the same experiment type were compared statistically to correlations from different experiment types. Strain fields from harmonic head motion at 10 Hz imaged by MRE were qualitatively and quantitatively similar to modes excited by impulsive head motion, imaged by tagged MRI. Notably, correlations between strain fields from 10 Hz MRE and tagged MRI did not differ significantly from correlations between strain fields from tagged MRI. These results suggest that low-frequency modes of oscillation dominate the response of the brain during impact. Thus, low-frequency MRE, which is simpler and more widely available than tagged MRI, can be used to illuminate the brain's response to head impact.
Collapse
Affiliation(s)
- Jordan Escarcega
- Mechanical Engineering and Materials Science, Washington University, 1 Brookings Drive, MSC 1185-208-125, St. Louis, MO 63130
| | - Andrew K Knutsen
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817
| | - Ahmed Alshareef
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817
| | - Curtis L Johnson
- Biomedical Engineering, University of Delaware, Newark, DE 19716
| | - Ruth J Okamoto
- Mechanical Engineering and Materials Science, Washington University, St. Louis, MO 63130
| | - Dzung L Pham
- Radiology and Radiological Sciences, Uniformed Services University, Bethesda, MD 20814
| | - Philip V Bayly
- Mechanical Engineering and Materials Science, Washington University, St. Louis, MO 63130
| |
Collapse
|
9
|
Yoon D, Ruding M, Guertler CA, Okamoto RJ, Bayly PV. Design and characterization of 3-D printed hydrogel lattices with anisotropic mechanical properties. J Mech Behav Biomed Mater 2023; 138:105652. [PMID: 36610282 PMCID: PMC10159757 DOI: 10.1016/j.jmbbm.2023.105652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/20/2022] [Accepted: 01/01/2023] [Indexed: 01/04/2023]
Abstract
The goal of this study was to design, fabricate, and characterize hydrogel lattice structures with consistent, controllable, anisotropic mechanical properties. Lattices, based on three unit-cell types (cubic, diamond, and vintile), were printed using stereolithography (SLA) of polyethylene glycol diacrylate (PEGDA). To create structural anisotropy in the lattices, unit cell design files were scaled by a factor of two in one direction in each layer and then printed. The mechanical properties of the scaled lattices were measured in shear and compression and compared to those of the unscaled lattices. Two apparent shear moduli of each lattice were measured by dynamic shear tests in two planes: (1) parallel and (2) perpendicular to the scaling direction, or cell symmetry axis. Three apparent Young's moduli of each lattice were measured by compression in three different directions: (1) the "build" direction or direction of added layers, (2) the scaling direction, and (3) the unscaled direction perpendicular to both scaling and build directions. For shear deformation in unscaled lattices, the apparent shear moduli were similar in the two perpendicular directions. In contrast, scaled lattices exhibit clear differences in apparent shear moduli. In compression of unscaled lattices, apparent Young's moduli were independent of direction in cubic and vintile lattices; in diamond lattices Young's moduli differed in the build direction, but were similar in the other two directions. Scaled lattices in compression exhibited additional differences in apparent Young's moduli in the scaled and unscaled directions. Notably, the effects of scaling on apparent modulus differed between each lattice type (cubic, diamond, or vintile) and deformation mode (shear or compression). Scaling of 3D-printed, hydrogel lattices may be harnessed to create tunable, structures of desired shape, stiffness, and mechanical anisotropy, in both shear and compression.
Collapse
Affiliation(s)
- Daniel Yoon
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri, USA
| | - Margrethe Ruding
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri, USA
| | - Charlotte A Guertler
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri, USA
| | - Ruth J Okamoto
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri, USA
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri, USA.
| |
Collapse
|
10
|
Abstract
Motile cilia beat with an asymmetric waveform consisting of a power stroke that generates a propulsive force and a recovery stroke that returns the cilium back to the start. Cilia are anchored to the cell cortex by basal bodies (BBs) that are directly coupled to the ciliary doublet microtubules (MTs). We find that, consistent with ciliary forces imposing on BBs, bending patterns in BB triplet MTs are responsive to ciliary beating. BB bending varies as environmental conditions change the ciliary waveform. Bending occurs where striated fibers (SFs) attach to BBs and mutants with short SFs that fail to connect to adjacent BBs exhibit abnormal BB bending, supporting a model in which SFs couple ciliary forces between BBs. Finally, loss of the BB stability protein Poc1, which helps interconnect BB triplet MTs, prevents the normal distributed BB and ciliary bending patterns. Collectively, BBs experience ciliary forces and manage mechanical coupling of these forces to their surrounding cellular architecture for normal ciliary beating.
Collapse
Affiliation(s)
- Anthony D. Junker
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
| | - Louis G. Woodhams
- Department of Mechanical Engineering and Material Science, Washington University in St. Louis, St. Louis, MO 63130
| | - Adam W. J. Soh
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
| | - Eileen T. O’Toole
- Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80302
| | - Philip V. Bayly
- Department of Mechanical Engineering and Material Science, Washington University in St. Louis, St. Louis, MO 63130
| | - Chad G. Pearson
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045,*Address correspondence to: Chad G. Pearson ()
| |
Collapse
|
11
|
Upadhyay K, Alshareef A, Knutsen AK, Johnson CL, Carass A, Bayly PV, Pham DL, Prince JL, Ramesh KT. Development and validation of subject-specific 3D human head models based on a nonlinear visco-hyperelastic constitutive framework. J R Soc Interface 2022; 19:20220561. [PMCID: PMC9554734 DOI: 10.1098/rsif.2022.0561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Computational head models are promising tools for understanding and predicting traumatic brain injuries. Most available head models are developed using inputs (i.e. head geometry, material properties and boundary conditions) from experiments on cadavers or animals and employ hereditary integral-based constitutive models that assume linear viscoelasticity in part of the rate-sensitive material response. This leads to high uncertainty and poor accuracy in capturing the nonlinear brain tissue response. To resolve these issues, a framework for the development of subject-specific three-dimensional head models is proposed, in which all inputs are derived in vivo from the same living human subject: head geometry via magnetic resonance imaging (MRI), brain tissue properties via magnetic resonance elastography (MRE), and full-field strain-response of the brain under rapid head rotation via tagged MRI. A nonlinear, viscous dissipation-based visco-hyperelastic constitutive model is employed to capture brain tissue response. Head models are validated using quantitative metrics that compare spatial strain distribution, temporal strain evolution, and the magnitude of strain maxima, with the corresponding experimental observations from tagged MRI. Results show that our head models accurately capture the strain-response of the brain. Further, employment of the nonlinear visco-hyperelastic constitutive framework provides improvements in the prediction of peak strains and temporal strain evolution over hereditary integral-based models.
Collapse
Affiliation(s)
- Kshitiz Upadhyay
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218, USA,Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ahmed Alshareef
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218, USA,Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Andrew K. Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20814, USA
| | - Curtis L. Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Philip V. Bayly
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Dzung L. Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20814, USA
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - K. T. Ramesh
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218, USA,Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| |
Collapse
|
12
|
Cho JH, Li ZA, Zhu L, Muegge BD, Roseman HF, Lee EY, Utterback T, Woodhams LG, Bayly PV, Hughes JW. Islet primary cilia motility controls insulin secretion. Sci Adv 2022; 8:eabq8486. [PMID: 36149960 PMCID: PMC9506710 DOI: 10.1126/sciadv.abq8486] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 07/13/2022] [Indexed: 06/16/2023]
Abstract
Primary cilia are specialized cell-surface organelles that mediate sensory perception and, in contrast to motile cilia and flagella, are thought to lack motility function. Here, we show that primary cilia in human and mouse pancreatic islets exhibit movement that is required for glucose-dependent insulin secretion. Islet primary cilia contain motor proteins conserved from those found in classic motile cilia, and their three-dimensional motion is dynein-driven and dependent on adenosine 5'-triphosphate and glucose metabolism. Inhibition of cilia motion blocks beta cell calcium influx and insulin secretion. Human beta cells have enriched ciliary gene expression, and motile cilia genes are altered in type 2 diabetes. Our findings redefine primary cilia as dynamic structures having both sensory and motile function and establish that pancreatic islet cilia movement plays a regulatory role in insulin secretion.
Collapse
Affiliation(s)
- Jung Hoon Cho
- Department of Medicine, Washington University School of Medicine, 660 South Euclid Ave, St. Louis, MO, USA
| | - Zipeng A. Li
- Department of Medicine, Washington University School of Medicine, 660 South Euclid Ave, St. Louis, MO, USA
| | - Lifei Zhu
- Department of Medicine, Washington University School of Medicine, 660 South Euclid Ave, St. Louis, MO, USA
| | - Brian D. Muegge
- Department of Medicine, Washington University School of Medicine, 660 South Euclid Ave, St. Louis, MO, USA
- Department of Medicine, VA Medical Center, 915 North Grand Blvd, St. Louis, MO, USA
| | - Henry F. Roseman
- Department of Medicine, Washington University School of Medicine, 660 South Euclid Ave, St. Louis, MO, USA
| | - Eun Young Lee
- Department of Medicine, Washington University School of Medicine, 660 South Euclid Ave, St. Louis, MO, USA
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, South Korea
| | - Toby Utterback
- Department of Mechanical Engineering and Materials Science, Washington University McKelvey School of Engineering, 1 Brookings Drive, St. Louis, MO, USA
| | - Louis G. Woodhams
- Department of Mechanical Engineering and Materials Science, Washington University McKelvey School of Engineering, 1 Brookings Drive, St. Louis, MO, USA
| | - Philip V. Bayly
- Department of Mechanical Engineering and Materials Science, Washington University McKelvey School of Engineering, 1 Brookings Drive, St. Louis, MO, USA
| | - Jing W. Hughes
- Department of Medicine, Washington University School of Medicine, 660 South Euclid Ave, St. Louis, MO, USA
| |
Collapse
|
13
|
Soh AWJ, Woodhams LG, Junker AD, Enloe CM, Noren BE, Harned A, Westlake CJ, Narayan K, Oakey JS, Bayly PV, Pearson CG. Intracellular connections between basal bodies promote the coordinated behavior of motile cilia. Mol Biol Cell 2022; 33:br18. [PMID: 35767367 DOI: 10.1091/mbc.e22-05-0150] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Hydrodynamic flow produced by multiciliated cells is critical for fluid circulation and cell motility. Hundreds of cilia beat with metachronal synchrony for fluid flow. Cilia-driven fluid flow produces extracellular hydrodynamic forces that cause neighboring cilia to beat in a synchronized manner. However, hydrodynamic coupling between neighboring cilia is not the sole mechanism that drives cilia synchrony. Cilia are nucleated by basal bodies (BBs) that link to each other and to the cell's cortex via BB-associated appendages. The intracellular BB and cortical network is hypothesized to synchronize ciliary beating by transmitting cilia coordination cues. The extent of intracellular ciliary connections and the nature of these stimuli remain unclear. Moreover, how BB connections influence the dynamics of individual cilia has not been established. We show by focused ion beam scanning electron microscopy imaging that cilia are coupled both longitudinally and laterally in the ciliate Tetrahymena thermophila by the underlying BB and cortical cytoskeletal network. To visualize the behavior of individual cilia in live, immobilized Tetrahymena cells, we developed Delivered Iron Particle Ubiety Live Light (DIPULL) microscopy. Quantitative and computer analyses of ciliary dynamics reveal that BB connections control ciliary waveform and coordinate ciliary beating. Loss of BB connections reduces cilia-dependent fluid flow forces.
Collapse
Affiliation(s)
- Adam W J Soh
- Department of Cell and Developmental Biology, Anschutz Medical Campus, University of Colorado, Aurora, CO 80045
| | - Louis G Woodhams
- Department of Mechanical Engineering and Material Science, Washington University in St. Louis, St. Louis, MO 63130
| | - Anthony D Junker
- Department of Cell and Developmental Biology, Anschutz Medical Campus, University of Colorado, Aurora, CO 80045
| | - Cassidy M Enloe
- Department of Chemical Engineering, College of Engineering and Applied Science, University of Wyoming, Laramie, WY 82071
| | - Benjamin E Noren
- Department of Chemical Engineering, College of Engineering and Applied Science, University of Wyoming, Laramie, WY 82071
| | - Adam Harned
- Center for Molecular Microscopy and Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892.,Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, and
| | - Christopher J Westlake
- Laboratory of Cell and Developmental Signaling, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702
| | - Kedar Narayan
- Center for Molecular Microscopy and Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892.,Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, and
| | - John S Oakey
- Department of Chemical Engineering, College of Engineering and Applied Science, University of Wyoming, Laramie, WY 82071
| | - Philip V Bayly
- Department of Mechanical Engineering and Material Science, Washington University in St. Louis, St. Louis, MO 63130
| | - Chad G Pearson
- Department of Cell and Developmental Biology, Anschutz Medical Campus, University of Colorado, Aurora, CO 80045
| |
Collapse
|
14
|
Upadhyay K, Giovanis DG, Alshareef A, Knutsen AK, Johnson CL, Carass A, Bayly PV, Shields MD, Ramesh K. Data-driven Uncertainty Quantification in Computational Human Head Models. Comput Methods Appl Mech Eng 2022; 398:115108. [PMID: 37994358 PMCID: PMC10664838 DOI: 10.1016/j.cma.2022.115108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
Computational models of the human head are promising tools for estimating the impact-induced response of the brain, and thus play an important role in the prediction of traumatic brain injury. The basic constituents of these models (i.e., model geometry, material properties, and boundary conditions) are often associated with significant uncertainty and variability. As a result, uncertainty quantification (UQ), which involves quantification of the effect of this uncertainty and variability on the simulated response, becomes critical to ensure reliability of model predictions. Modern biofidelic head model simulations are associated with very high computational cost and high-dimensional inputs and outputs, which limits the applicability of traditional UQ methods on these systems. In this study, a two-stage, data-driven manifold learning-based framework is proposed for UQ of computational head models. This framework is demonstrated on a 2D subject-specific head model, where the goal is to quantify uncertainty in the simulated strain fields (i.e., output), given variability in the material properties of different brain substructures (i.e., input). In the first stage, a data-driven method based on multi-dimensional Gaussian kernel-density estimation and diffusion maps is used to generate realizations of the input random vector directly from the available data. Computational simulations of a small number of realizations provide input-output pairs for training data-driven surrogate models in the second stage. The surrogate models employ nonlinear dimensionality reduction using Grassmannian diffusion maps, Gaussian process regression to create a low-cost mapping between the input random vector and the reduced solution space, and geometric harmonics models for mapping between the reduced space and the Grassmann manifold. It is demonstrated that the surrogate models provide highly accurate approximations of the computational model while significantly reducing the computational cost. Monte Carlo simulations of the surrogate models are used for uncertainty propagation. UQ of the strain fields highlights significant spatial variation in model uncertainty, and reveals key differences in uncertainty among commonly used strain-based brain injury predictor variables.
Collapse
Affiliation(s)
- Kshitiz Upadhyay
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Dimitris G. Giovanis
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ahmed Alshareef
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Andrew K. Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20814, USA
| | - Curtis L. Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Philip V. Bayly
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Michael D. Shields
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - K.T. Ramesh
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| |
Collapse
|
15
|
Woodhams LG, Shen Y, Bayly PV. Generation of ciliary beating by steady dynein activity: the effects of inter-filament coupling in multi-filament models. J R Soc Interface 2022; 19:20220264. [PMID: 35857924 PMCID: PMC9257587 DOI: 10.1098/rsif.2022.0264] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 06/20/2022] [Indexed: 09/05/2023] Open
Abstract
The structure of the axoneme in motile cilia and flagella is emerging with increasing detail from high-resolution imaging, but the mechanism by which the axoneme creates oscillatory, propulsive motion remains mysterious. It has recently been proposed that this motion may be caused by a dynamic 'flutter' instability that can occur under steady dynein loading, and not by switching or modulation of dynein motor activity (as commonly assumed). In the current work, we have built an improved multi-filament mathematical model of the axoneme and implemented it as a system of discrete equations using the finite-element method. The eigenvalues and eigenvectors of this model predict the emergence of oscillatory, wave-like solutions in the absence of dynein regulation and specify the associated frequencies and waveforms of beating. Time-domain simulations with this model illustrate the behaviour predicted by the system's eigenvalues. This model and analysis allow us to efficiently explore the potential effects of difficult to measure biophysical parameters, such as elasticity of radial spokes and inter-doublet links, on the ciliary waveform. These results support the idea that dynamic instability without dynamic dynein regulation is a plausible and robust mechanism for generating ciliary beating.
Collapse
Affiliation(s)
- Louis G. Woodhams
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130-4899, USA
| | - Yenan Shen
- Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544, USA
| | - Philip V. Bayly
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130-4899, USA
| |
Collapse
|
16
|
Jyoti D, McGarry M, Van Houten E, Sowinski D, Bayly PV, Johnson CL, Paulsen K. Quantifying stability of parameter estimates for in vivonearly incompressible transversely-isotropic brain MR elastography. Biomed Phys Eng Express 2022; 8. [PMID: 35299161 PMCID: PMC9272913 DOI: 10.1088/2057-1976/ac5ebe] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/17/2022] [Indexed: 11/12/2022]
Abstract
Easily computable quality metrics for measured medical data at point-of-care are important for imaging technologies involving offline reconstruction. Accordingly, we developed a new data quality metric forin vivotransversely-isotropic (TI) magnetic resonance elastography (MRE) based on a generalization of the widely accepted octahedral shear-strain calculation. The metric uses MRE displacement data and an estimate of the TI property field to yield a 'stability map' which predicts regions of low versus high accuracy in the resulting material property reconstructions. We can also calculate an average TI parameter stability (TIPS) score over all voxels in a region of interest for a given measurement to indicate how reliable the recovered mechanical property estimate for the region is expected to be. The calculation is rapid and places little demand on computing resources compared to the computationally intensive material property reconstruction from non-linear inversion (TI-NLI) of displacement fields, making it ideal for point-of-care evaluation of data quality. We test the predictions of the stability map for both simulated phantoms andin vivohuman brain data. We used a range of different displacement datasets from vibrations applied in the anterior-posterior (AP), left-right (LR) and combined AP + LR directions. The TIPS and variability maps (noise sensitivity or variation from the mean of repeated MRE scans) were consistently anti-correlated. Notably, Spearman correlation coefficients ∣R∣>0.6 were found between variability and TIPS score for individual white matter tracts within vivodata. These observations demonstrate the reliability and promise of this data quality metric to screen data rapidly in realistic clinical MRE applications.
Collapse
Affiliation(s)
- Dhrubo Jyoti
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Matthew McGarry
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | | | - Damian Sowinski
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Philip V Bayly
- Washington University in St Louis, St Louis, MO, 63130, United States of America
| | - Curtis L Johnson
- University of Delaware, Newark, DE 19716, United States of America
| | - Keith Paulsen
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America.,Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, United States of America
| |
Collapse
|
17
|
Hou Z, Guertler CA, Okamoto RJ, Chen H, Garbow JR, Kamilov US, Bayly PV. Estimation of the mechanical properties of a transversely isotropic material from shear wave fields via artificial neural networks. J Mech Behav Biomed Mater 2022; 126:105046. [PMID: 34953435 PMCID: PMC8875313 DOI: 10.1016/j.jmbbm.2021.105046] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/24/2021] [Accepted: 12/11/2021] [Indexed: 02/03/2023]
Abstract
Artificial neural networks (ANN), established tools in machine learning, are applied to the problem of estimating parameters of a transversely isotropic (TI) material model using data from magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI). We use neural networks to estimate parameters from experimental measurements of ultrasound-induced shear waves after training on analogous data from simulations of a computer model with similar loading, geometry, and boundary conditions. Strain ratios and shear-wave speeds (from MRE) and fiber direction (the direction of maximum diffusivity from diffusion tensor imaging (DTI)) are used as inputs to neural networks trained to estimate the parameters of a TI material (baseline shear modulus μ, shear anisotropy φ, and tensile anisotropy ζ). Ensembles of neural networks are applied to obtain distributions of parameter estimates. The robustness of this approach is assessed by quantifying the sensitivity of property estimates to assumptions in modeling (such as assumed loss factor) and choices in fitting (such as the size of the neural network). This study demonstrates the successful application of simulation-trained neural networks to estimate anisotropic material parameters from complementary MRE and DTI imaging data.
Collapse
Affiliation(s)
- Zuoxian Hou
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA, Corresponding author:
| | - Charlotte A. Guertler
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Ruth J. Okamoto
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Hong Chen
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA,Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO 63108, USA
| | - Joel R. Garbow
- Biomedical Magnetic Resonance Laboratory, Washington University School of Medicine, 4525 Scott Avenue, CB 8227, St. Louis, MO 63110, USA
| | - Ulugbek S. Kamilov
- Department of Electrical and Systems Engineering and Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Philip V. Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
| |
Collapse
|
18
|
Smith DR, Caban-Rivera DA, McGarry MD, Williams LT, McIlvain G, Okamoto RJ, Van Houten EE, Bayly PV, Paulsen KD, Johnson CL. Anisotropic mechanical properties in the healthy human brain estimated with multi-excitation transversely isotropic MR elastography. Brain Multiphysics 2022; 3. [DOI: 10.1016/j.brain.2022.100051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
|
19
|
Bayly PV, Alshareef A, Knutsen AK, Upadhyay K, Okamoto RJ, Carass A, Butman JA, Pham DL, Prince JL, Ramesh KT, Johnson CL. MR Imaging of Human Brain Mechanics In Vivo: New Measurements to Facilitate the Development of Computational Models of Brain Injury. Ann Biomed Eng 2021; 49:2677-2692. [PMID: 34212235 PMCID: PMC8516723 DOI: 10.1007/s10439-021-02820-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/22/2021] [Indexed: 01/04/2023]
Abstract
Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of which differ from the living human brain. Here we describe efforts to noninvasively measure the biomechanical response of the human brain with MRI-at non-injurious strain levels-and generate data that can be used to develop, calibrate, and evaluate computational brain biomechanics models. Specifically, this paper reports on a project supported by the National Institute of Neurological Disorders and Stroke to comprehensively image brain anatomy and geometry, mechanical properties, and brain deformations that arise from impulsive and harmonic skull loadings. The outcome of this work will be a publicly available dataset ( http://www.nitrc.org/projects/bbir ) that includes measurements on both males and females across an age range from adolescence to older adulthood. This article describes the rationale and approach for this study, the data available, and how these data may be used to develop new computational models and augment existing approaches; it will serve as a reference to researchers interested in using these data.
Collapse
Affiliation(s)
- Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA.
| | - Ahmed Alshareef
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Kshitiz Upadhyay
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Ruth J Okamoto
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - John A Butman
- Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - K T Ramesh
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE, USA.
| |
Collapse
|
20
|
Gomez AD, Bayly PV, Butman JA, Pham DL, Prince JL, Knutsen AK. Group characterization of impact-induced, in vivo human brain kinematics. J R Soc Interface 2021; 18:20210251. [PMID: 34157896 DOI: 10.1098/rsif.2021.0251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Brain movement during an impact can elicit a traumatic brain injury, but tissue kinematics vary from person to person and knowledge regarding this variability is limited. This study examines spatio-temporal brain-skull displacement and brain tissue deformation across groups of subjects during a mild impact in vivo. The heads of two groups of participants were imaged while subjected to a mild (less than 350 rad s-2) impact during neck extension (NE, n = 10) and neck rotation (NR, n = 9). A kinematic atlas of displacement and strain fields averaged across all participants was constructed and compared against individual participant data. The atlas-derived mean displacement magnitude was 0.26 ± 0.13 mm for NE and 0.40 ± 0.26 mm for NR, which is comparable to the displacement magnitudes from individual participants. The strain tensor from the atlas displacement field exhibited maximum shear strain (MSS) of 0.011 ± 0.006 for NE and 0.017 ± 0.009 for NR and was lower than the individual MSS averaged across participants. The atlas illustrates common patterns, containing some blurring but visible relationships between anatomy and kinematics. Conversely, the direction of the impact, brain size, and fluid motion appear to underlie kinematic variability. These findings demonstrate the biomechanical roles of key anatomical features and illustrate common features of brain response for model evaluation.
Collapse
Affiliation(s)
- Arnold D Gomez
- School of Medicine, Department of Neurology, Johns Hopkins University, 600 North Wolfe Street, 200 Carnegie Hall, Baltimore, MD, USA
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St Louis, 1 Brookings Drive, Box 1185, Saint Louis, MI, USA
| | - John A Butman
- Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, Henry M Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, Henry M Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| |
Collapse
|
21
|
Escarcega JD, Knutsen AK, Okamoto RJ, Pham DL, Bayly PV. Natural oscillatory modes of 3D deformation of the human brain in vivo. J Biomech 2021; 119:110259. [PMID: 33618329 PMCID: PMC8055167 DOI: 10.1016/j.jbiomech.2021.110259] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 01/04/2021] [Accepted: 01/10/2021] [Indexed: 12/12/2022]
Abstract
Natural modes and frequencies of three-dimensional (3D) deformation of the human brain were identified from in vivo tagged magnetic resonance images (MRI) acquired dynamically during transient mild acceleration of the head. Twenty 3D strain fields, estimated from tagged MRI image volumes in 19 adult subjects, were analyzed using dynamic mode decomposition (DMD). These strain fields represented dynamic, 3D brain deformations during constrained head accelerations, either involving rotation about the vertical axis of the neck or neck extension. DMD results reveal fundamental oscillatory modes of deformation at damped frequencies near 7 Hz (in neck rotation) and 11 Hz (in neck extension). Modes at these frequencies were found consistently among all subjects. These characteristic features of 3D human brain deformation are important for understanding the response of the brain in head impacts and provide valuable quantitative criteria for the evaluation and use of computer models of brain mechanics.
Collapse
Affiliation(s)
- J D Escarcega
- Mechanical Engineering and Materials Science, Washington University in St. Louis, MO, United States.
| | - A K Knutsen
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States
| | - R J Okamoto
- Mechanical Engineering and Materials Science, Washington University in St. Louis, MO, United States
| | - D L Pham
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States
| | - P V Bayly
- Mechanical Engineering and Materials Science, Washington University in St. Louis, MO, United States
| |
Collapse
|
22
|
Abstract
Acoustic microfluidics has emerged as a versatile solution for particle manipulation in medicine and biology. However, current technologies are largely confined to specialized research laboratories. The translation of acoustofluidics from research to clinical and industrial settings requires improved consistency and repeatability across different platforms. Performance comparisons will require straightforward experimental assessment tools that are not yet available. We introduce a method for characterizing acoustofluidic devices in real-time by exploiting the capacity of swimming microorganisms to respond to changes in their environment. The unicellular alga, Chlamydomonas reinhardtii, is used as an active probe to visualize the evolving acoustic pressure field within microfluidic channels and chambers. In contrast to more familiar mammalian cells, C. reinhardtii are simple to prepare and maintain, and exhibit a relatively uniform size distribution that more closely resembles calibration particles; however, unlike passive particles, these motile cells naturally fill complex chamber geometries and redistribute when the acoustic field changes or is turned off. In this way, C. reinhardtii cells offer greater flexibility than conventional polymer or glass calibration beads for in situ determination of device operating characteristics. To illustrate the technique, the varying spatial density and distribution of swimming cells are correlated to the acoustic potential to automatically locate device resonances within a specified frequency range. Peaks in the correlation coefficient of successive images not only identify the resonant frequencies for various geometries, but the peak shape can be related to the relative strength of the resonances. Qualitative mapping of the acoustic field strength with increasing voltage amplitude is also shown. Thus, we demonstrate that dynamically responsive C. reinhardtii enable real-time measurement and continuous monitoring of acoustofluidic device performance.
Collapse
Affiliation(s)
- Minji Kim
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri 63130, USA.
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri 63130, USA.
| | - J Mark Meacham
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri 63130, USA.
| |
Collapse
|
23
|
Hou Z, Bayly PV, Okamoto RJ. Shear wave speeds in nearly-incompressible fibrous materials with two fiber families. J Acoust Soc Am 2021; 149:1097. [PMID: 33639778 PMCID: PMC7878018 DOI: 10.1121/10.0003528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/13/2021] [Accepted: 01/25/2021] [Indexed: 06/12/2023]
Abstract
An analytical and numerical investigation of shear wave behavior in nearly-incompressible soft materials with two fiber families was performed, focusing on the effects of material parameters and imposed pre-deformations on wave speed. This theoretical study is motivated by the emerging ability to image shear waves in soft biological tissues by magnetic resonance elastography (MRE). In MRE, the relationships between wave behavior and mechanical properties can be used to characterize tissue properties non-invasively. We demonstrate these principles in two material models, each with two fiber families. One model is a nearly-incompressible linear elastic model that exhibits both shear and tensile anisotropy; the other is a two-fiber-family version of the widely-used Holzapfel-Gasser-Ogden (HGO) model, which is nonlinear. Shear waves can be used to probe nonlinear material behavior using infinitesimal dynamic deformations superimposed on larger, quasi-static "pre-deformations." In this study, closed-form expressions for shear wave speeds in the HGO model are obtained in terms of the model parameters and imposed pre-deformations. Analytical expressions for wave speeds are confirmed by finite element simulations of shear waves with various polarizations and propagation directions. These studies support the feasibility of estimating the parameters of an HGO material model noninvasively from measured shear wave speeds.
Collapse
Affiliation(s)
- Zuoxian Hou
- Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, Missouri 63130, USA
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, Missouri 63130, USA
| | - Ruth J Okamoto
- Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, Missouri 63130, USA
| |
Collapse
|
24
|
Alshareef A, Knutsen AK, Johnson CL, Carass A, Upadhyay K, Bayly PV, Pham DL, Prince JL, Ramesh K. Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain. Brain Multiphysics 2021; 2. [PMID: 37168236 PMCID: PMC10168673 DOI: 10.1016/j.brain.2021.100038] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95th percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions.
Collapse
|
25
|
Knutsen AK, Gomez AD, Gangolli M, Wang WT, Chan D, Lu YC, Christoforou E, Prince JL, Bayly PV, Butman JA, Pham DL. In vivo estimates of axonal stretch and 3D brain deformation during mild head impact. Brain Multiphys 2020; 1. [PMID: 33870238 DOI: 10.1016/j.brain.2020.100015] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
The rapid deformation of brain tissue in response to head impact can lead to traumatic brain injury. In vivo measurements of brain deformation during non-injurious head impacts are necessary to understand the underlying mechanisms of traumatic brain injury and compare to computational models of brain biomechanics. Using tagged magnetic resonance imaging (MRI), we obtained measurements of three-dimensional strain tensors that resulted from a mild head impact after neck rotation or neck extension. Measurements of maximum principal strain (MPS) peaked shortly after impact, with maximal values of 0.019-0.053 that correlated strongly with peak angular velocity. Subject-specific patterns of MPS were spatially heterogeneous and consistent across subjects for the same motion, though regions of high deformation differed between motions. The largest MPS values were seen in the cortical gray matter and cerebral white matter for neck rotation and the brainstem and cerebellum for neck extension. Axonal fiber strain (Ef) was estimated by combining the strain tensor with diffusion tensor imaging data. As with MPS, patterns of Ef varied spatially within subjects, were similar across subjects within each motion, and showed group differences between motions. Values were highest and most strongly correlated with peak angular velocity in the corpus callosum for neck rotation and in the brainstem for neck extension. The different patterns of brain deformation between head motions highlight potential areas of greater risk of injury between motions at higher loading conditions. Additionally, these experimental measurements can be directly compared to predictions of generic or subject-specific computational models of traumatic brain injury.
Collapse
Affiliation(s)
- Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Arnold D Gomez
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mihika Gangolli
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Wen-Tung Wang
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Deva Chan
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Yuan-Chiao Lu
- Center for the Developing Brain, Children's National Hospital, Washington, D.C., USA
| | | | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri, USA
| | - John A Butman
- Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| |
Collapse
|
26
|
Smith DR, Guertler CA, Okamoto RJ, Romano AJ, Bayly PV, Johnson CL. Multi-Excitation Magnetic Resonance Elastography of the Brain: Wave Propagation in Anisotropic White Matter. J Biomech Eng 2020; 142:1074133. [PMID: 32006012 DOI: 10.1115/1.4046199] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Indexed: 12/13/2022]
Abstract
Magnetic resonance elastography (MRE) has emerged as a sensitive imaging technique capable of providing a quantitative understanding of neural microstructural integrity. However, a reliable method for the quantification of the anisotropic mechanical properties of human white matter is currently lacking, despite the potential to illuminate the pathophysiology behind neurological disorders and traumatic brain injury. In this study, we examine the use of multiple excitations in MRE to generate wave displacement data sufficient for anisotropic inversion in white matter. We show the presence of multiple unique waves from each excitation which we combine to solve for parameters of an incompressible, transversely isotropic (ITI) material: shear modulus, μ, shear anisotropy, ϕ, and tensile anisotropy, ζ. We calculate these anisotropic parameters in the corpus callosum body and find the mean values as μ = 3.78 kPa, ϕ = 0.151, and ζ = 0.099 (at 50 Hz vibration frequency). This study demonstrates that multi-excitation MRE provides displacement data sufficient for the evaluation of the anisotropic properties of white matter.
Collapse
Affiliation(s)
- Daniel R Smith
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716
| | - Charlotte A Guertler
- Department of Mechanical Engineering and Material Science, Washington University, St. Louis, MO 63130
| | - Ruth J Okamoto
- Department of Mechanical Engineering and Material Science, Washington University, St. Louis, MO 63130
| | | | - Philip V Bayly
- Department of Mechanical Engineering and Material Science, Washington University, St. Louis, MO 63130
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716
| |
Collapse
|
27
|
Guertler CA, Okamoto RJ, Ireland JA, Pacia CP, Garbow JR, Chen H, Bayly PV. Estimation of Anisotropic Material Properties of Soft Tissue by MRI of Ultrasound-Induced Shear Waves. J Biomech Eng 2020; 142:1073942. [PMID: 31980814 DOI: 10.1115/1.4046127] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Indexed: 11/08/2022]
Abstract
This paper describes a new method for estimating anisotropic mechanical properties of fibrous soft tissue by imaging shear waves induced by focused ultrasound (FUS) and analyzing their direction-dependent speeds. Fibrous materials with a single, dominant fiber direction may exhibit anisotropy in both shear and tensile moduli, reflecting differences in the response of the material when loads are applied in different directions. The speeds of shear waves in such materials depend on the propagation and polarization directions of the waves relative to the dominant fiber direction. In this study, shear waves were induced in muscle tissue (chicken breast) ex vivo by harmonically oscillating the amplitude of an ultrasound beam focused in a cylindrical tissue sample. The orientation of the fiber direction relative to the excitation direction was varied by rotating the sample. Magnetic resonance elastography (MRE) was used to visualize and measure the full 3D displacement field due to the ultrasound-induced shear waves. The phase gradient (PG) of radially propagating "slow" and "fast" shear waves provided local estimates of their respective wave speeds and directions. The equations for the speeds of these waves in an incompressible, transversely isotropic (TI), linear elastic material were fitted to measurements to estimate the shear and tensile moduli of the material. The combination of focused ultrasound and MR imaging allows noninvasive, but comprehensive, characterization of anisotropic soft tissue.
Collapse
Affiliation(s)
- Charlotte A Guertler
- Department of Mechanical Engineering & Materials Science, Washington University in St. Louis, 1 Brookings Drive, CB 1185 St. Louis, MO 63130
| | - Ruth J Okamoto
- Department of Mechanical Engineering & Materials Science, Washington University in St. Louis, 1 Brookings Drive, CB 1185 St. Louis, MO 63130
| | - Jake A Ireland
- Department of Mechanical Engineering & Materials Science, Washington University in St. Louis, 1 Brookings Drive, CB 1185 St. Louis, MO 63130
| | - Christopher P Pacia
- Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Drive, CB 1097, St. Louis, MO 63130
| | - Joel R Garbow
- Biomedical Magnetic Resonance Laboratory, Washington University in St. Louis, 4525 Scott Avenue, CB 8227, St. Louis, MO 63110
| | - Hong Chen
- Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Drive, CB 1097, St. Louis, MO 63130
| | - Philip V Bayly
- Department of Mechanical Engineering & Materials Science, Washington University in St. Louis, 1 Brookings Drive, CB 1185 St. Louis, MO 63130; Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Drive, CB 1097, St. Louis, MO 63130
| |
Collapse
|
28
|
Hou Z, Okamoto RJ, Bayly PV. Shear Wave Propagation and Estimation of Material Parameters in a Nonlinear, Fibrous Material. J Biomech Eng 2020; 142:958441. [PMID: 31513702 DOI: 10.1115/1.4044504] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Indexed: 11/08/2022]
Abstract
This paper describes the propagation of shear waves in a Holzapfel-Gasser-Ogden (HGO) material and investigates the potential of magnetic resonance elastography (MRE) for estimating parameters of the HGO material model from experimental data. In most MRE studies the behavior of the material is assumed to be governed by linear, isotropic elasticity or viscoelasticity. In contrast, biological tissue is often nonlinear and anisotropic with a fibrous structure. In such materials, application of a quasi-static deformation (predeformation) plays an important role in shear wave propagation. Closed form expressions for shear wave speeds in an HGO material with a single family of fibers were found in a reference (undeformed) configuration and after imposed predeformations. These analytical expressions show that shear wave speeds are affected by the parameters (μ0, k1, k2, κ) of the HGO model and by the direction and amplitude of the predeformations. Simulations of corresponding finite element (FE) models confirm the predicted influence of HGO model parameters on speeds of shear waves with specific polarization and propagation directions. Importantly, the dependence of wave speeds on the parameters of the HGO model and imposed deformations could ultimately allow the noninvasive estimation of material parameters in vivo from experimental shear wave image data.
Collapse
Affiliation(s)
- Zuoxian Hou
- Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, MO, 63130
| | - Ruth J Okamoto
- Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, MO, 63130
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, MO, 63130
| |
Collapse
|
29
|
Gomez AD, Knutsen AK, Pham DL, Bayly PV, Prince JL. Quantitative Validation of MRI-Based Motion Estimation for Brain Impact Biomechanics. Computational Biomechanics for Medicine 2020; 2020:61-71. [PMID: 37067891 PMCID: PMC10103905 DOI: 10.1007/978-3-030-15923-8_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Head impact can cause traumatic brain injury (TBI) through axonal overstretch or subsequent inflammation and understanding the biomechanics of the impact event is useful for TBI prevention research. Tagged magnetic resonance imaging (MRI) acquired during a mild-acceleration impact has enabled measurement and visualization of brain deformation in vivo. However, measurements using MRI are subject to error, and having independent validation while imaging in vivo is very difficult. Thus, characterizing the accuracy of these measurements needs to be done in a separate experiment using a phantom where a gold standard is available. This study describes a method for error quantification using a calibration phantom compatible with MRI and high-speed video (the gold standard). During linear acceleration, the maximum shear strain (MSS) in the phantom ranged from 0 to 12%, which is similar to in vivo brain deformation at a similar acceleration. The mean displacement error against video was 0.3±0.3 mm, and the MSS error was 1.4±0.3%. To match resolutions, video data was filtered temporally using an averaging filter. Compared to the unfiltered results, resolution matching improved the agreement between MRI and video results by 15%. In conclusion, tagged MRI analysis compares well to video data provided that resolutions are matched-a finding that is also applicable when using MRI to validate simulations.
Collapse
Affiliation(s)
- Arnold D Gomez
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, USA
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, USA
| | - Philip V Bayly
- Mechanical Engineering Department, Washington University in St. Louis, St. Louis, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| |
Collapse
|
30
|
Chan DD, Knutsen AK, Lu YC, Yang SH, Magrath E, Wang WT, Bayly PV, Butman JA, Pham DL. Statistical Characterization of Human Brain Deformation During Mild Angular Acceleration Measured In Vivo by Tagged Magnetic Resonance Imaging. J Biomech Eng 2019; 140:2681445. [PMID: 30029236 DOI: 10.1115/1.4040230] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Indexed: 01/17/2023]
Abstract
Understanding of in vivo brain biomechanical behavior is critical in the study of traumatic brain injury (TBI) mechanisms and prevention. Using tagged magnetic resonance imaging, we measured spatiotemporal brain deformations in 34 healthy human volunteers under mild angular accelerations of the head. Two-dimensional (2D) Lagrangian strains were examined throughout the brain in each subject. Strain metrics peaked shortly after contact with a padded stop, corresponding to the inertial response of the brain after head deceleration. Maximum shear strain of at least 3% was experienced at peak deformation by an area fraction (median±standard error) of 23.5±1.8% of cortical gray matter, 15.9±1.4% of white matter, and 4.0±1.5% of deep gray matter. Cortical gray matter strains were greater in the temporal cortex on the side of the initial contact with the padded stop and also in the contralateral temporal, frontal, and parietal cortex. These tissue-level deformations from a population of healthy volunteers provide the first in vivo measurements of full-volume brain deformation in response to known kinematics. Although strains differed in different tissue type and cortical lobes, no significant differences between male and female head accelerations or strain metrics were found. These cumulative results highlight important kinematic features of the brain's mechanical response and can be used to facilitate the evaluation of computational simulations of TBI.
Collapse
Affiliation(s)
- Deva D Chan
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892
| | - Yuan-Chiao Lu
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892
| | - Sarah H Yang
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892
| | - Elizabeth Magrath
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892
| | - Wen-Tung Wang
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892
| | - Philip V Bayly
- Professor Department of Mechanical Engineering and Materials Science, Washington University at St. Louis, St. Louis, MO 63130
| | - John A Butman
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, , Bethesda, MD 20892-1182 e-mail:
| |
Collapse
|
31
|
Kim M, Huff E, Bottier M, Dutcher SK, Bayly PV, Meacham JM. Acoustic trap-and-release for rapid assessment of cell motility. Soft Matter 2019; 15:4266-4275. [PMID: 30968924 DOI: 10.1039/c9sm00184k] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Functional cilia and flagella are crucial to the propulsion of physiological fluids, motile cells, and microorganisms. Motility assessment of individual cells allows discrimination of normal from dysfunctional behavior, but cell-scale analysis of individual trajectories to represent a population is laborious and impractical for clinical, industrial, and even research applications. We introduce an assay that quantifies swimming capability as a function of the variation in polar moment of inertia of cells released from an acoustic trap. Acoustic confinement eliminates the need to trace discrete trajectories and enables automated analysis of hundreds of cells in minutes. The approach closely approximates the average speed estimated from the mean squared displacement of individual cells for wild-type Chlamydomonas reinhardtii and two mutants (ida3 and oda5) that display aberrant swimming behaviors. Large-population acoustic trap-and-release rapidly differentiates these cell types based on intrinsic motility, which provides a highly sensitive and efficient alternative to conventional particle tracing.
Collapse
Affiliation(s)
- Minji Kim
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri 63130, USA.
| | | | | | | | | | | |
Collapse
|
32
|
Okamoto RJ, Romano AJ, Johnson CL, Bayly PV. Insights Into Traumatic Brain Injury From MRI of Harmonic Brain Motion. J Exp Neurosci 2019; 13:1179069519840444. [PMID: 31001064 PMCID: PMC6454654 DOI: 10.1177/1179069519840444] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/07/2019] [Indexed: 12/14/2022] Open
Abstract
Measurements of dynamic deformation of the human brain, induced by external
harmonic vibration of the skull, were analyzed to illuminate the mechanics of
mild traumatic brain injury (TBI). Shear wave propagation velocity vector fields
were obtained to illustrate the role of the skull and stiff internal membranes
in transmitting motion to the brain. Relative motion between the cerebrum and
cerebellum was quantified to assess the vulnerability of connecting structures.
Mechanical deformation was quantified throughout the brain to investigate
spatial patterns of strain and axonal stretch. Strain magnitude was generally
attenuated as shear waves propagated into interior structures of the brain; this
attenuation was greater at higher frequencies. Analysis of shear wave
propagation direction indicates that the stiff membranes (falx and tentorium)
greatly affect brain deformation during imposed skull motion as they serve as
sites for both initiation and reflection of shear waves. Relative motion between
the cerebellum and cerebrum was small in comparison with the overall motion of
both structures, which suggests that such relative motion might play only a
minor role in TBI mechanics. Strain magnitudes and the amount of axonal stretch
near the bases of sulci were similar to those in other areas of the cortex, and
local strain concentrations at the gray-white matter boundary were not observed.
We tentatively conclude that observed differences in neuropathological response
in these areas might be due to heterogeneity in the response to mechanical
deformation rather than heterogeneity of the deformation itself.
Collapse
Affiliation(s)
- Ruth J Okamoto
- Department of Mechanical Engineering & Materials Science, Washington University in St. Louis, St. Louis, MO, USA
| | - Anthony J Romano
- Acoustics Division, U.S. Naval Research Laboratory, Washington, DC, USA
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE, USA
| | - Philip V Bayly
- Department of Mechanical Engineering & Materials Science, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
33
|
Bottier M, Thomas KA, Dutcher SK, Bayly PV. How Does Cilium Length Affect Beating? Biophys J 2019; 116:1292-1304. [PMID: 30878201 PMCID: PMC6451027 DOI: 10.1016/j.bpj.2019.02.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 01/23/2019] [Accepted: 02/13/2019] [Indexed: 12/21/2022] Open
Abstract
The effects of cilium length on the dynamics of cilia motion were investigated by high-speed video microscopy of uniciliated mutants of the swimming alga, Chlamydomonas reinhardtii. Cells with short cilia were obtained by deciliating cells via pH shock and allowing cilia to reassemble for limited times. The frequency of cilia beating was estimated from the motion of the cell body and of the cilium. Key features of the ciliary waveform were quantified from polynomial curves fitted to the cilium in each image frame. Most notably, periodic beating did not emerge until the cilium reached a critical length between 2 and 4 μm. Surprisingly, in cells that exhibited periodic beating, the frequency of beating was similar for all lengths with only a slight decrease in frequency as length increased from 4 μm to the normal length of 10-12 μm. The waveform average curvature (rad/μm) was also conserved as the cilium grew. The mechanical metrics of ciliary propulsion (force, torque, and power) all increased in proportion to length. The mechanical efficiency of beating appeared to be maximal at the normal wild-type length of 10-12 μm. These quantitative features of ciliary behavior illuminate the biophysics of cilia motion and, in future studies, may help distinguish competing hypotheses of the underlying mechanism of oscillation.
Collapse
Affiliation(s)
- Mathieu Bottier
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri; Department of Genetics, Washington University in St. Louis, St. Louis, Missouri
| | - Kyle A Thomas
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri
| | - Susan K Dutcher
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri.
| |
Collapse
|
34
|
Lu YC, Daphalapurkar NP, Knutsen AK, Glaister J, Pham DL, Butman JA, Prince JL, Bayly PV, Ramesh KT. A 3D Computational Head Model Under Dynamic Head Rotation and Head Extension Validated Using Live Human Brain Data, Including the Falx and the Tentorium. Ann Biomed Eng 2019; 47:1923-1940. [PMID: 30767132 DOI: 10.1007/s10439-019-02226-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Accepted: 02/05/2019] [Indexed: 10/27/2022]
Abstract
We employ an advanced 3D computational model of the head with high anatomical fidelity, together with measured tissue properties, to assess the consequences of dynamic loading to the head in two distinct modes: head rotation and head extension. We use a subject-specific computational head model, using the material point method, built from T1 magnetic resonance images, and considering the anisotropic properties of the white matter which can predict strains in the brain under large rotational accelerations. The material model now includes the shear anisotropy of the white matter. We validate the model under head rotation and head extension motions using live human data, and advance a prior version of the model to include biofidelic falx and tentorium. We then examine the consequences of incorporating the falx and tentorium in terms of the predictions from the computational head model.
Collapse
Affiliation(s)
- Y-C Lu
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA
| | - N P Daphalapurkar
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA.,Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - A K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - J Glaister
- Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - D L Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - J A Butman
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - J L Prince
- Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - P V Bayly
- Department of Mechanical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - K T Ramesh
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA. .,Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA.
| |
Collapse
|
35
|
Badachhape AA, Okamoto RJ, Durham RS, Efron D, Nadell SJ, Johnson CL, Bayly PV. Erratum: "The Relationship of Three-Dimensional Human Skull Motion to Brain Tissue Deformation in Magnetic Resonance Elastography Studies" (ASME J. Biomech. Eng., 2017, 139(5), p. 051002; DOI: 10.1115/1.4036146) to Paper Number BIO-16-1363. J Biomech Eng 2018; 140:2694846. [PMID: 30264155 DOI: 10.1115/1.4040947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Indexed: 11/08/2022]
|
36
|
Beauchemin PF, Bayly PV, Garbow JR, Schmidt JLS, Okamoto RJ, Chériet F, Périé D. Frequency-dependent shear properties of annulus fibrosus and nucleus pulposus by magnetic resonance elastography. NMR Biomed 2018; 31:e3918. [PMID: 29727498 DOI: 10.1002/nbm.3918] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 12/23/2017] [Accepted: 02/15/2018] [Indexed: 05/22/2023]
Abstract
Aging and degeneration are associated with changes in mechanical properties in the intervertebral disc, generating interest in the establishment of mechanical properties as early biomarkers for the degenerative cascade. Magnetic resonance elastography (MRE) of the intervertebral disc is usually limited to the nucleus pulposus, as the annulus fibrosus is stiffer and less hydrated. The objective of this work was to adapt high-frequency needle MRE to the characterization of the shear modulus of both the nucleus pulposus and annulus fibrosus. Bovine intervertebral discs were removed from fresh oxtails and characterized by needle MRE. The needle was inserted in the center of the disc and vibrations were generated by an amplified piezoelectric actuator. MRE acquisitions were performed on a 4.7-T small-animal MR scanner using a spin echo sequence with sinusoidal motion encoding gradients. Acquisitions were repeated over a frequency range of 1000-1800 Hz. The local frequency estimation inversion algorithm was used to compute the shear modulus. Stiffness maps allowed the visualization of the soft nucleus pulposus surrounded by the stiffer annulus fibrosus surrounded by the homogeneous gel. A significant difference in shear modulus between the nucleus pulposus and annulus fibrosus, and an increase in the shear modulus with excitation frequency, were observed, in agreement with the literature. This study demonstrates that global characterization of both the nucleus pulposus and annulus fibrosus of the intervertebral disc is possible with needle MRE using a preclinical magnetic resonance imaging (MRI) scanner. MRE can be a powerful method for the mapping of the complex properties of the intervertebral disc. The developed method could be adapted for in situ use by preserving adjacent vertebrae and puncturing the side of the intervertebral disc, thereby allowing an assessment of the contribution of osmotic pressure to the mechanical behavior of the intervertebral disc.
Collapse
Affiliation(s)
- P F Beauchemin
- Mechanical Engineering, Polytechnique de Montréal, Montréal, QC, Canada
| | - P V Bayly
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA
| | - J R Garbow
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - J L S Schmidt
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA
| | - R J Okamoto
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA
| | - F Chériet
- Mechanical Engineering, Polytechnique de Montréal, Montréal, QC, Canada
- Research Center, CHU Sainte-Justine, Montréal, QC, Canada
| | - D Périé
- Mechanical Engineering, Polytechnique de Montréal, Montréal, QC, Canada
- Research Center, CHU Sainte-Justine, Montréal, QC, Canada
| |
Collapse
|
37
|
Oltean A, Schaffer AJ, Bayly PV, Brody SL. Quantifying Ciliary Dynamics during Assembly Reveals Stepwise Waveform Maturation in Airway Cells. Am J Respir Cell Mol Biol 2018; 59:511-522. [PMID: 29851510 PMCID: PMC6178159 DOI: 10.1165/rcmb.2017-0436oc] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 05/31/2018] [Indexed: 11/24/2022] Open
Abstract
Motile cilia are essential for clearance of particulates and pathogens from airways. For effective transport, ciliary motor proteins and axonemal structures interact to generate the rhythmic, propulsive bending, but the mechanisms that produce a dynamic waveform remain incompletely understood. Biomechanical measures of human ciliary motion and their relationships to ciliary assembly are needed to illuminate the biophysics of normal ciliary function and to quantify dysfunction in ciliopathies. To these ends, we analyzed ciliary motion by high-speed video microscopy of ciliated cells sampled from human lung airways compared with primary culture cells that undergo ciliogenesis in vitro. Quantitative assessment of waveform parameters showed variations in waveform shape between individual cilia; however, general trends in waveform parameters emerged, associated with progression of cilia length and stage of differentiation. When cilia emerged from cultured cells, beat frequency was initially elevated, then fell and remained stable as cilia lengthened. In contrast, the average bending amplitude and the ability to generate force gradually increased and eventually approached values observed in ex vivo samples. Dynein arm motor proteins DNAH5, DNAH9, DNAH11, and DNAH6 were localized within specific regions of the axoneme in the ex vivo cells; however, distinct stages of in vitro waveform development identified by biomechanical features were associated with the progressive movement of dyneins to the appropriate proximal or distal sections of the cilium. These observations suggest that the stepwise variation in waveform development during ciliogenesis is dependent on cilia length and potentially on outer dynein arm assembly.
Collapse
Affiliation(s)
- Alina Oltean
- Department of Medicine and
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri
| | | | - Philip V. Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri
| | | |
Collapse
|
38
|
Garcia KE, Kroenke CD, Bayly PV. Mechanics of cortical folding: stress, growth and stability. Philos Trans R Soc Lond B Biol Sci 2018; 373:rstb.2017.0321. [PMID: 30249772 DOI: 10.1098/rstb.2017.0321] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2018] [Indexed: 12/17/2022] Open
Abstract
Cortical folding, or gyrification, coincides with several important developmental processes. The folded shape of the human brain allows the cerebral cortex, the thin outer layer of neurons and their associated projections, to attain a large surface area relative to brain volume. Abnormal cortical folding has been associated with severe neurological, cognitive and behavioural disorders, such as epilepsy, autism and schizophrenia. However, despite decades of study, the mechanical forces that lead to cortical folding remain incompletely understood. Leading hypotheses have focused on the roles of (i) tangential growth of the outer cortex, (ii) spatio-temporal patterns in the birth and migration of neurons, and (iii) internal tension in axons. Recent experimental studies have illuminated not only the fundamental cellular and molecular processes underlying cortical development, but also the stress state, mechanical properties and spatio-temporal patterns of growth in the developing brain. The combination of mathematical modelling and physical measurements has allowed researchers to evaluate hypothesized mechanisms of folding, to determine whether each is consistent with physical laws. This review summarizes what physical scientists have learned from models and recent experimental observations, in the context of recent neurobiological discoveries regarding cortical development. Here, we highlight evidence of a combined mechanism, in which spatio-temporal patterns bias the locations of primary folds (i), but tangential growth of the cortical plate induces mechanical instability (ii) to propagate primary and higher-order folds.This article is part of the Theo Murphy meeting issue 'Mechanics of development'.
Collapse
Affiliation(s)
- K E Garcia
- Mechanical Engineering and Materials Science, Washington University in Saint Louis, Saint Louis, MO, USA.,Engineering, University of Southern Indiana, Evansville, IN, USA
| | - C D Kroenke
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - P V Bayly
- Mechanical Engineering and Materials Science, Washington University in Saint Louis, Saint Louis, MO, USA
| |
Collapse
|
39
|
Gomez AD, Stone ML, Bayly PV, Prince JL. Quantifying Tensor Field Similarity With Global Distributions and Optimal Transport. Med Image Comput Comput Assist Interv 2018; 11071:428-436. [PMID: 33196063 DOI: 10.1007/978-3-030-00934-2_48] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Strain tensor fields quantify tissue deformation and are important for functional analysis of moving organs such as the heart and the tongue. Strain data can be readily obtained using medical imaging. However, quantification of similarity between different data sets is difficult. Strain patterns vary in space and time, and are inherently multidimensional. Also, the same type of mechanical deformation can be applied to different shapes; hence, automatic quantification of similarity should be unaffected by the geometry of the objects being deformed. This work introduces the application of global distributions used to classify shapes and vector fields in the pattern recognition literature, in the context of tensorial strain data. In particular, the distribution of mechanical properties of a field are approximated using a 3D histogram, and the Wasserstein distance from optimal transport theory is used to measure the similarity between histograms. To measure the method's consistency in matching deformations across different objects, the proposed approach was evaluated by sorting strain fields according to their similarity. Performance was compared to sorting via maximum shear distribution (a 1D histogram) and tensor residual magnitude (in perfectly registered objects). The technique was also applied to correlate muscle activation to muscular contraction observed via tagged MRI. The results show that the proposed approach accurately matches deformation regardless of the shape of the object being deformed. Sorting accuracy surpassed 1D shear distribution and was on par with residual magnitude, but without the need for registration between objects.
Collapse
Affiliation(s)
- Arnold D Gomez
- Electrical and Computer Engineerng Department, Jonhs Hopkins University, Baltimore, USA
| | - Maureen L Stone
- Department of Neural and Pain Sciences, University of Maryland Baltimore, USA
| | - Philip V Bayly
- Mechanical Engineering Department, Washington University in St. Louis, St. Louis, USA
| | - Jerry L Prince
- Electrical and Computer Engineerng Department, Jonhs Hopkins University, Baltimore, USA
| |
Collapse
|
40
|
Bayly PV, Garbow JR. Pre-clinical MR elastography: Principles, techniques, and applications. J Magn Reson 2018; 291:73-83. [PMID: 29705042 PMCID: PMC5943171 DOI: 10.1016/j.jmr.2018.01.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 01/07/2018] [Indexed: 05/09/2023]
Abstract
Magnetic resonance elastography (MRE) is a method for measuring the mechanical properties of soft tissue in vivo, non-invasively, by imaging propagating shear waves in the tissue. The speed and attenuation of waves depends on the elastic and dissipative properties of the underlying material. Tissue mechanical properties are essential for biomechanical models and simulations, and may serve as markers of disease, injury, development, or recovery. MRE is already established as a clinical technique for detecting and characterizing liver disease. The potential of MRE for diagnosing or characterizing disease in other organs, including brain, breast, and heart is an active research area. Studies involving MRE in the pre-clinical setting, in phantoms and artificial biomaterials, in the mouse, and in other mammals, are critical to the development of MRE as a robust, reliable, and useful modality.
Collapse
Affiliation(s)
- P V Bayly
- Mechanical Engineering and Materials Science, Washington University in Saint Louis, MO, USA.
| | - J R Garbow
- Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| |
Collapse
|
41
|
Garcia KE, Robinson EC, Alexopoulos D, Dierker DL, Glasser MF, Coalson TS, Ortinau CM, Rueckert D, Taber LA, Van Essen DC, Rogers CE, Smyser CD, Bayly PV. Dynamic patterns of cortical expansion during folding of the preterm human brain. Proc Natl Acad Sci U S A 2018; 115:3156-3161. [PMID: 29507201 PMCID: PMC5866555 DOI: 10.1073/pnas.1715451115] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
During the third trimester of human brain development, the cerebral cortex undergoes dramatic surface expansion and folding. Physical models suggest that relatively rapid growth of the cortical gray matter helps drive this folding, and structural data suggest that growth may vary in both space (by region on the cortical surface) and time. In this study, we propose a unique method to estimate local growth from sequential cortical reconstructions. Using anatomically constrained multimodal surface matching (aMSM), we obtain accurate, physically guided point correspondence between younger and older cortical reconstructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with unprecedented precision. By considering 30 preterm infants scanned two to four times during the period of rapid cortical expansion (28-38 wk postmenstrual age), we observe significant regional differences in growth across the cortical surface that are consistent with the emergence of new folds. Furthermore, these growth patterns shift over the course of development, with noninjured subjects following a highly consistent trajectory. This information provides a detailed picture of dynamic changes in cortical growth, connecting what is known about patterns of development at the microscopic (cellular) and macroscopic (folding) scales. Since our method provides specific growth maps for individual brains, we are also able to detect alterations due to injury. This fully automated surface analysis, based on tools freely available to the brain-mapping community, may also serve as a useful approach for future studies of abnormal growth due to genetic disorders, injury, or other environmental variables.
Collapse
Affiliation(s)
- Kara E Garcia
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130;
| | - Emma C Robinson
- Department of Computer Science, Imperial College London, London SW7 2AZ, United Kingdom
- Department of Biomedical Engineering, Division of Imaging Sciences, St. Thomas' Hospital, King's College London, London SE1 7EH, United Kingdom
- Department of Perinatal Imaging and Health, Division of Imaging Sciences, St. Thomas' Hospital, King's College London, London SE1 7EH, United Kingdom
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Donna L Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Matthew F Glasser
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110
- Internal Medicine, St. Luke's Hospital, St. Louis, MO 63017
| | - Timothy S Coalson
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110
| | - Cynthia M Ortinau
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
| | - Daniel Rueckert
- Department of Computer Science, Imperial College London, London SW7 2AZ, United Kingdom
| | - Larry A Taber
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110
| | - Cynthia E Rogers
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Christopher D Smyser
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130
| |
Collapse
|
42
|
Badachhape AA, Okamoto RJ, Johnson CL, Bayly PV. Relationships between scalp, brain, and skull motion estimated using magnetic resonance elastography. J Biomech 2018; 73:40-49. [PMID: 29580689 DOI: 10.1016/j.jbiomech.2018.03.028] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 02/03/2018] [Accepted: 03/09/2018] [Indexed: 11/27/2022]
Abstract
The objective of this study was to characterize the relationships between motion in the scalp, skull, and brain. In vivo estimates of motion transmission from the skull to the brain may illuminate the mechanics of traumatic brain injury. Because of challenges in directly sensing skull motion, it is useful to know how well motion of soft tissue of the head, i.e., the scalp, can approximate skull motion or predict brain tissue deformation. In this study, motion of the scalp and brain were measured using magnetic resonance elastography (MRE) and separated into components due to rigid-body displacement and dynamic deformation. Displacement estimates in the scalp were calculated using low motion-encoding gradient strength in order to reduce "phase wrapping" (an ambiguity in displacement estimates caused by the 2 π-periodicity of MRE phase contrast). MRE estimates of scalp and brain motion were compared to skull motion estimated from three tri-axial accelerometers. Comparison of the relative amplitudes and phases of harmonic motion in the scalp, skull, and brain of six human subjects indicate that data from scalp-based sensors should be used with caution to estimate skull kinematics, but that fairly consistent relationships exist between scalp, skull, and brain motion. In addition, the measured amplitude and phase relationships of scalp, skull, and brain can be used to evaluate and improve mathematical models of head biomechanics.
Collapse
Affiliation(s)
- Andrew A Badachhape
- Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States.
| | - Ruth J Okamoto
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, United States
| | - Curtis L Johnson
- Biomedical Engineering, University of Delaware, Newark, DE, United States
| | - Philip V Bayly
- Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States; Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, United States
| |
Collapse
|
43
|
Bottier M, Dutcher SK, Bayly PV. Frequency and Curvature of the Flagellar Waveform of Chlamydomonas Reinhardtii are Stable during Regrowth. Biophys J 2018. [DOI: 10.1016/j.bpj.2017.11.1810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
|
44
|
Oltean A, Bayly PV, Brody SL. Maturation of the Human Motile Cilia Waveform in Airway Cells. Biophys J 2018. [DOI: 10.1016/j.bpj.2017.11.3504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
|
45
|
Guertler CA, Okamoto RJ, Schmidt JL, Badachhape AA, Johnson CL, Bayly PV. Mechanical properties of porcine brain tissue in vivo and ex vivo estimated by MR elastography. J Biomech 2018; 69:10-18. [PMID: 29395225 DOI: 10.1016/j.jbiomech.2018.01.016] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 11/27/2017] [Accepted: 01/08/2018] [Indexed: 11/29/2022]
Abstract
The mechanical properties of brain tissue in vivo determine the response of the brain to rapid skull acceleration. These properties are thus of great interest to the developers of mathematical models of traumatic brain injury (TBI) or neurosurgical simulations. Animal models provide valuable insight that can improve TBI modeling. In this study we compare estimates of mechanical properties of the Yucatan mini-pig brain in vivo and ex vivo using magnetic resonance elastography (MRE) at multiple frequencies. MRE allows estimations of properties in soft tissue, either in vivo or ex vivo, by imaging harmonic shear wave propagation. Most direct measurements of brain mechanical properties have been performed using samples of brain tissue ex vivo. It has been observed that direct estimates of brain mechanical properties depend on the frequency and amplitude of loading, as well as the time post-mortem and condition of the sample. Using MRE in the same animals at overlapping frequencies, we observe that porcine brain tissue in vivo appears stiffer than porcine brain tissue samples ex vivo at frequencies of 100 Hz and 125 Hz, but measurements show closer agreement at lower frequencies.
Collapse
Affiliation(s)
- Charlotte A Guertler
- Washington University in St. Louis, Mechanical Engineering and Materials Science, United States.
| | - Ruth J Okamoto
- Washington University in St. Louis, Mechanical Engineering and Materials Science, United States
| | - John L Schmidt
- Washington University in St. Louis, Mechanical Engineering and Materials Science, United States
| | | | | | - Philip V Bayly
- Washington University in St. Louis, Mechanical Engineering and Materials Science, United States; Washington University in St. Louis, Biomedical Engineering, United States
| |
Collapse
|
46
|
Hu T, Bayly PV. Finite element models of flagella with sliding radial spokes and interdoublet links exhibit propagating waves under steady dynein loading. Cytoskeleton (Hoboken) 2018; 75:185-200. [PMID: 29316355 DOI: 10.1002/cm.21432] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 12/21/2017] [Accepted: 12/22/2017] [Indexed: 12/11/2022]
Abstract
It remains unclear how flagella generate propulsive, oscillatory waveforms. While it is well known that dynein motors, in combination with passive cytoskeletal elements, drive the bending of the axoneme by applying shearing forces and bending moments to microtubule doublets, the origin of rhythmicity is still mysterious. Most conceptual models of flagellar oscillation involve dynein regulation or switching, so that dynein activity first on one side of the axoneme, then the other, drives bending. In contrast, a "viscoelastic flutter" mechanism has recently been proposed, based on a dynamic structural instability. Simple mathematical models of coupled elastic beams in viscous fluid, subjected to steady, axially distributed, dynein forces of sufficient magnitude, can exhibit oscillatory motion without any switching or dynamic regulation. Here we introduce more realistic finite element (FE) models of 6-doublet and 9-doublet flagella, with radial spokes and interdoublet links that slide along the central pair or corresponding doublet. These models demonstrate the viscoelastic flutter mechanism. Above a critical force threshold, these models exhibit an abrupt onset of propulsive, wavelike oscillations typical of flutter instability. Changes in the magnitude and spatial distribution of steady dynein force, or to viscous resistance, lead to behavior qualitatively consistent with experimental observations. This study demonstrates the ability of FE models to simulate nonlinear interactions between axonemal components during flagellar beating, and supports the plausibility of viscoelastic flutter as a mechanism of flagellar oscillation.
Collapse
Affiliation(s)
- Tianchen Hu
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri
| |
Collapse
|
47
|
Schmidt JL, Tweten DJ, Badachhape AA, Reiter AJ, Okamoto RJ, Garbow JR, Bayly PV. Measurement of anisotropic mechanical properties in porcine brain white matter ex vivo using magnetic resonance elastography. J Mech Behav Biomed Mater 2017; 79:30-37. [PMID: 29253729 DOI: 10.1016/j.jmbbm.2017.11.045] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 11/12/2017] [Accepted: 11/27/2017] [Indexed: 02/05/2023]
Abstract
The mechanical properties of brain tissue, particularly those of white matter (WM), need to be characterized accurately for use in finite element (FE) models of brain biomechanics and traumatic brain injury (TBI). Magnetic resonance elastography (MRE) is a powerful tool for non-invasive estimation of the mechanical properties of soft tissues. While several studies involving direct mechanical tests of brain tissue have shown mechanical anisotropy, most MRE studies of brain tissue assume an isotropic model. In this study, an incompressible transversely isotropic (TI) material model parameterized by minimum shear modulus (μ2), shear anisotropy parameter (ϕ), and tensile anisotropy parameter (ζ) is applied to analyze MRE measurements of ex vivo porcine white matter (WM) brain tissue. To characterize shear anisotropy, "slow" (pure transverse) shear waves were propagated at 100, 200 and 300Hz through sections of ex vivo brain tissue including both WM and gray matter (GM). Shear waves were found to propagate with elliptical fronts, consistent with TI material behavior. Shear wave fields were also analyzed within regions of interest (ROI) to find local shear wavelengths parallel and perpendicular to fiber orientation. FE simulations of a TI material with a range of plausible shear modulus (μ2) and shear anisotropy parameters (ϕ) were run and the results were analyzed in the same fashion as the experimental case. Parameters of the FE simulations which most closely matched each experiment were taken to represent the mechanical properties of that particular sample. Using this approach, WM in the ex vivo porcine brain was found to be mildly anisotropic in shear with estimates of minimum shear modulus (actuation frequencies listed in parenthesis): μ2= 1.04 ± 0.12 kPa (at 100Hz), μ2= 1.94 ± 0.29 kPa (at 200Hz), and μ2= 2.88 ± 0.34 kPa (at 300Hz) and corresponding shear anisotropy factors of ϕ= 0.27 ± 0.09 (at 100Hz), ϕ= 0.29 ± 0.14 (at 200Hz) and ϕ= 0.34 ± 0.13 (at 300Hz). Future MRE studies will focus on tensile anisotropy, which will require both slow and fast shear waves for accurate estimation.
Collapse
Affiliation(s)
- J L Schmidt
- Mechanical Engineering and Materials Science, Washington University in Saint Louis, MO 63130, United States.
| | - D J Tweten
- Mechanical Engineering and Materials Science, Washington University in Saint Louis, MO 63130, United States
| | - A A Badachhape
- Biomedical Engineering, Washington University in Saint Louis, MO 63130, United States
| | - A J Reiter
- Mechanical Engineering and Materials Science, Washington University in Saint Louis, MO 63130, United States
| | - R J Okamoto
- Mechanical Engineering and Materials Science, Washington University in Saint Louis, MO 63130, United States
| | - J R Garbow
- Radiology, Washington University in Saint Louis, MO 63130, United States
| | - P V Bayly
- Mechanical Engineering and Materials Science, Washington University in Saint Louis, MO 63130, United States; Biomedical Engineering, Washington University in Saint Louis, MO 63130, United States
| |
Collapse
|
48
|
Badachhape AA, Okamoto RJ, Durham RS, Efron BD, Nadell SJ, Johnson CL, Bayly PV. The Relationship of Three-Dimensional Human Skull Motion to Brain Tissue Deformation in Magnetic Resonance Elastography Studies. J Biomech Eng 2017; 139:2610238. [PMID: 28267188 DOI: 10.1115/1.4036146] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Indexed: 12/13/2022]
Abstract
In traumatic brain injury (TBI), membranes such as the dura mater, arachnoid mater, and pia mater play a vital role in transmitting motion from the skull to brain tissue. Magnetic resonance elastography (MRE) is an imaging technique developed for noninvasive estimation of soft tissue material parameters. In MRE, dynamic deformation of brain tissue is induced by skull vibrations during magnetic resonance imaging (MRI); however, skull motion and its mode of transmission to the brain remain largely uncharacterized. In this study, displacements of points in the skull, reconstructed using data from an array of MRI-safe accelerometers, were compared to displacements of neighboring material points in brain tissue, estimated from MRE measurements. Comparison of the relative amplitudes, directions, and temporal phases of harmonic motion in the skulls and brains of six human subjects shows that the skull-brain interface significantly attenuates and delays transmission of motion from skull to brain. In contrast, in a cylindrical gelatin "phantom," displacements of the rigid case (reconstructed from accelerometer data) were transmitted to the gelatin inside (estimated from MRE data) with little attenuation or phase lag. This quantitative characterization of the skull-brain interface will be valuable in the parameterization and validation of computer models of TBI.
Collapse
Affiliation(s)
- Andrew A Badachhape
- Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63105 e-mail:
| | - Ruth J Okamoto
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63105
| | - Ramona S Durham
- Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63105
| | - Brent D Efron
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63105
| | - Sam J Nadell
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63105
| | - Curtis L Johnson
- Biomedical Engineering, University of Delaware, Newark, DE 19716
| | - Philip V Bayly
- Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63105;Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63105
| |
Collapse
|
49
|
Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, Bozek J, Wright R, Schuh A, Webster M, Hutter J, Price A, Cordero Grande L, Hughes E, Tusor N, Bayly PV, Van Essen DC, Smith SM, Edwards AD, Hajnal J, Jenkinson M, Glocker B, Rueckert D. Multimodal surface matching with higher-order smoothness constraints. Neuroimage 2017; 167:453-465. [PMID: 29100940 DOI: 10.1016/j.neuroimage.2017.10.037] [Citation(s) in RCA: 147] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 10/13/2017] [Accepted: 10/17/2017] [Indexed: 02/05/2023] Open
Abstract
In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies, and cortical surface-based alignment has generally been accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas relative to these folds. This makes alignment of cortical areas a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible, spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that driving cross-subject surface alignment, using areal features, such as resting state-networks and myelin maps, improves group task fMRI statistics and map sharpness. However, the initial implementation of MSM's regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits, unless regularisation strength was increased to a level which prevented the algorithm from fully maximizing surface alignment. Here we propose and implement a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multimodal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints that enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modelling of cortical development for neonates (born between 31 and 43 weeks of post-menstrual age) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.
Collapse
Affiliation(s)
- Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Kara Garcia
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Matthew F Glasser
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA; St. Luke's Hospital, St Louis, MO, USA
| | - Zhengdao Chen
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy S Coalson
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Robert Wright
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Matthew Webster
- Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Lucilio Cordero Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Philip V Bayly
- Department of Mechanical Engineering and Material Science, Washington University in St. Louis, St. Louis, MO, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Stephen M Smith
- Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, United Kingdom
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| |
Collapse
|
50
|
Bayly PV, Dutcher SK. Steady dynein forces induce flutter instability and propagating waves in mathematical models of flagella. J R Soc Interface 2017; 13:rsif.2016.0523. [PMID: 27798276 DOI: 10.1098/rsif.2016.0523] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Accepted: 09/26/2016] [Indexed: 12/26/2022] Open
Abstract
Cilia and flagella are highly conserved organelles that beat rhythmically with propulsive, oscillatory waveforms. The mechanism that produces these autonomous oscillations remains a mystery. It is widely believed that dynein activity must be dynamically regulated (switched on and off, or modulated) on opposite sides of the axoneme to produce oscillations. A variety of regulation mechanisms have been proposed based on feedback from mechanical deformation to dynein force. In this paper, we show that a much simpler interaction between dynein and the passive components of the axoneme can produce coordinated, propulsive oscillations. Steady, distributed axial forces, acting in opposite directions on coupled beams in viscous fluid, lead to dynamic structural instability and oscillatory, wave-like motion. This 'flutter' instability is a dynamic analogue to the well-known static instability, buckling. Flutter also occurs in slender beams subjected to tangential axial loads, in aircraft wings exposed to steady air flow and in flexible pipes conveying fluid. By analysis of the flagellar equations of motion and simulation of structural models of flagella, we demonstrate that dynein does not need to switch direction or inactivate to produce autonomous, propulsive oscillations, but must simply pull steadily above a critical threshold force.
Collapse
Affiliation(s)
- P V Bayly
- Department of Mechanical Engineering and Materials Science, 1 Brookings Drive, Box 1185, Saint Louis, MO 63130, USA
| | - S K Dutcher
- Department of Genetics, Washington University in Saint Louis, 1 Brookings Drive, Box 1185, Saint Louis, MO 63130, USA
| |
Collapse
|