Brain stiffness increases with myelin content

Role of Brain Elastography in the Neonatal Setting: State of the Art of Ultrasonographic Techniques and Future Perspectives

Magnetic resonance imaging is currently used in the neonatal setting for assessing features of the neonatal brain. However, its utilization is constrained by logistic, technical, or clinical challenges. Brain elastography is a new research technique which enhances the diagnostic capability of traditional imaging, and can be paired with both ultrasonography and magnetic resonance imaging. In particular, brain elastography adds objective and quantitative information to traditional imaging by detecting differences in tissue elasticity/stiffness, which may represent a surrogate marker of the physiologic and pathologic features of the neonatal brain. To date, very limited experience exists about the use of brain elastography specifically in the neonatal setting. The aim of the present review was to describe the most recent information about the feasibility and diagnostic accuracy of brain ultrasound elastography (USE) in neonates, and to provide information about the possible future applications and perspectives of brain elastography.

The Networking Brain: How Extracellular Matrix, Cellular Networks, and Vasculature Shape the In Vivo Mechanical Properties of the Brain

Mechanically, the brain is characterized by both solid and fluid properties. The resulting unique material behavior fosters proliferation, differentiation, and repair of cellular and vascular networks, and optimally protects them from damaging shear forces. Magnetic resonance elastography (MRE) is a noninvasive imaging technique that maps the mechanical properties of the brain in vivo. MRE studies have shown that abnormal processes such as neuronal degeneration, demyelination, inflammation, and vascular leakage lead to tissue softening. In contrast, neuronal proliferation, cellular network formation, and higher vascular pressure result in brain stiffening. In addition, brain viscosity has been reported to change with normal blood perfusion variability and brain maturation as well as disease conditions such as tumor invasion. In this article, the contributions of the neuronal, glial, extracellular, and vascular networks are discussed to the coarse-grained parameters determined by MRE. This reductionist multi-network model of brain mechanics helps to explain many MRE observations in terms of microanatomical changes and suggests that cerebral viscoelasticity is a suitable imaging marker for brain disease.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Mechanobiological insight into brain diseases based on mechanosensitive channels: Common mechanisms and clinical potential

BACKGROUND

As physical signals, mechanical cues regulate the neural cells in the brain. The mechanosensitive channels (MSCs) perceive the mechanical cues and transduce them by permeating specific ions or molecules across the plasma membrane, and finally trigger a series of intracellular bioelectrical and biochemical signals. Emerging evidence supports that wide-distributed, high-expressed MSCs like Piezo1 play important roles in several neurophysiological processes and neurological disorders.

AIMS

To systematically conclude the functions of MSCs in the brain and provide a novel mechanobiological perspective for brain diseases.

METHOD

We summarized the mechanical cues and MSCs detected in the brain and the research progress on the functional roles of MSCs in physiological conditions. We then concluded the pathological activation and downstream pathways triggered by MSCs in two categories of brain diseases, neurodegenerative diseases and place-occupying damages. Finally, we outlined the methods for manipulating MSCs and discussed their medical potential with some crucial outstanding issues.

RESULTS

The MSCs present underlying common mechanisms in different brain diseases by acting as the "transportation hubs" to transduce the distinct signal patterns: the upstream mechanical cues and the downstream intracellular pathways. Manipulating the MSCs is feasible to alter the complicated downstream processes, providing them promising targets for clinical treatment.

CONCLUSIONS

Recent research on MSCs provides a novel insight into brain diseases. The common mechanisms mediated by MSCs inspire a wide range of therapeutic potentials targeted on MSCs in different brain diseases.

Post-mortem changes of anisotropic mechanical properties in the porcine brain assessed by MR elastography

Knowledge of the mechanical properties of brain tissue in vivo is essential to understanding the mechanisms underlying traumatic brain injury (TBI) and to creating accurate computational models of TBI and neurosurgical simulation. Brain white matter, which is composed of aligned, myelinated, axonal fibers, is structurally anisotropic. White matter in vivo also exhibits mechanical anisotropy, as measured by magnetic resonance elastography (MRE), but measurements of anisotropy obtained by mechanical testing of white matter ex vivo have been inconsistent. The minipig has a gyrencephalic brain with similar white matter and gray matter proportions to humans and therefore provides a relevant model for human brain mechanics. In this study, we compare estimates of anisotropic mechanical properties of the minipig brain obtained by identical, non-invasive methods in the live (in vivo) and dead animals (in situ). To do so, we combine wave displacement fields from MRE and fiber directions derived from diffusion tensor imaging (DTI) with a finite element-based, transversely-isotropic nonlinear inversion (TI-NLI) algorithm. Maps of anisotropic mechanical properties in the minipig brain were generated for each animal alive and at specific times post-mortem. These maps show that white matter is stiffer, more dissipative, and more anisotropic than gray matter when the minipig is alive, but that these differences largely disappear post-mortem, with the exception of tensile anisotropy. Overall, brain tissue becomes stiffer, less dissipative, and less mechanically anisotropic post-mortem. These findings emphasize the importance of testing brain tissue properties in vivo.

Statement of Significance

In this study, MRE and DTI in the minipig were combined to estimate, for the first time, anisotropic mechanical properties in the living brain and in the same brain after death. Significant differences were observed in the anisotropic behavior of brain tissue post-mortem. These results demonstrate the importance of measuring brain tissue properties in vivo as well as ex vivo, and provide new quantitative data for the development of computational models of brain biomechanics.

Transfontanellar shear wave elastography of the neonatal brain for quantitative evaluation of white matter damage

Cerebral white matter damage (WMD) is the most frequent brain lesion observed in infants surviving premature birth. Qualitative B-mode cranial ultrasound (cUS) is widely used to assess brain integrity at bedside. Its limitations include lower discriminatory power to predict long-term outcomes compared to magnetic resonance imaging (MRI). Shear wave elastography (SWE), a promising ultrasound imaging modality, might improve this limitation by detecting quantitative differences in tissue stiffness. The study enrolled 90 neonates (52% female, mean gestational age = 30.1  ± 4.5 weeks), including 78 preterm and 12 term controls. Preterm neonates underwent B-mode and SWE assessments in frontal white matter (WM), parietal WM, and thalami on day of life (DOL) 3, DOL8, DOL21, 40 weeks, and MRI at term equivalent age (TEA). Term infants were assessed on DOL3 only. Our data revealed that brain stiffness increased with gestational age in preterm infants but remained lower at TEA compared to the control group. In the frontal WM, elasticity values were lower in preterm infants with WMD detected on B-mode or MRI at TEA and show a good predictive value at DOL3. Thus, brain stiffness measurement using SWE could be a useful screening method for early identification of preterm infants at high WMD risk.Registration numbers: EudraCT number ID-RCB: 2012-A01530-43, ClinicalTrial.gov number NCT02042716.

Mechanical Properties of White Matter Tracts in Aging Assessed via Anisotropic MR Elastography

Magnetic resonance elastography (MRE) is a promising neuroimaging technique to probe tissue microstructure, which has revealed widespread softening with loss of structural integrity in the aging brain. Traditional MRE approaches assume mechanical isotropy. However, white matter is known to be anisotropic from aligned, myelinated axonal bundles, which can lead to uncertainty in mechanical property estimates in these areas when using isotropic MRE. Recent advances in anisotropic MRE now allow for estimation of shear and tensile anisotropy, along with substrate shear modulus, in white matter tracts. The objective of this study was to investigate age-related differences in anisotropic mechanical properties in human brain white matter tracts for the first time. Anisotropic mechanical properties in all tracts were found to be significantly lower in older adults compared to young adults, with average property differences ranging between 0.028-0.107 for shear anisotropy and between 0.139-0.347 for tensile anisotropy. Stiffness perpendicular to the axonal fiber direction was also significantly lower in older age, but only in certain tracts. When compared with fractional anisotropy measures from diffusion tensor imaging, we found that anisotropic MRE measures provided additional, complementary information in describing differences between the white matter integrity of young and older populations. Anisotropic MRE provides a new tool for studying white matter structural integrity in aging and neurodegeneration.

Low-frequency MR elastography reveals altered deep gray matter viscoelasticity in multiple sclerosis

INTRODUCTION

Brain viscoelasticity as assessed by magnetic resonance elastography (MRE) has been discussed as a promising surrogate of microstructural alterations due to neurodegenerative processes. Existing studies indicate that multiple sclerosis (MS) is associated with a global reduction in brain stiffness. However, no study to date systematically investigated the MS-related characteristics of brain viscoelasticity separately in normal-appearing white matter (NAWM), deep gray matter (DGM) and T2-hyperintense white matter (WM) lesions.

METHODS

70 MS patients and 42 healthy volunteers underwent whole-cerebral MRE using a stimulated echo sequence (DENSE) with a low-frequency mechanical excitation at 20 Hertz. The magnitude |G∗| (Pa) and phase angle φ (rad) of the complex shear modulus G∗ were reconstructed by multifrequency dual elasto-visco (MDEV) inversion and related to structural imaging and clinical parameters.

RESULTS

We observed φ in the thalamus to be higher by 4.3 % in patients relative to healthy controls (1.11 ± 0.07 vs. 1.06 ± 0.07, p < 0.0001). Higher Expanded Disability Status Scale (EDSS) scores were negatively associated with φ in the basal ganglia (p = 0.01). We measured φ to be lower in MS lesions compared to surrounding NAWM (p = 0.001), which was most prominent for lesions in the temporal lobe (1.01 ± 0.22 vs. 1.06 ± 0.19, p = 0.003). Age was associated with lower values of |G∗| (p = 0.04) and φ (p = 0.004) in the thalamus of patients. No alteration in NAWM stiffness relative to WM in healthy controls was observed.

CONCLUSION

Low-frequency elastography in MS patients reveals age-independent alterations in the viscoelasticity of deep gray matter at early stages of disease.

Insights into the Mechanical Characterization of Mouse Brain Tissue Using Microindentation Testing

Indentation testing is the most common approach to quantify mechanical brain tissue properties. Despite a myriad of studies conducted already, reported stiffness values vary extensively and continue to be subject of study. Moreover, the growing interest in the relationship between the brain's spatially heterogeneous microstructure and local tissue stiffness warrants the development of standardized measurement protocols to enable comparability between studies and assess repeatability of reported data. Here, we present three individual protocols that outline (1) sample preparation of a 1000-µm thick coronal slice, (2) a comprehensive list of experimental parameters associated with the FemtoTools FT-MTA03 Micromechanical Testing System for spherical indentation, and (3) two different approaches to derive the elastic modulus from raw force-displacement data. Lastly, we demonstrate that our protocols deliver a robust experimental framework that enables us to determine the spatially heterogeneous microstructural properties of (mouse) brain tissue. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Mouse brain sample preparation Basic Protocol 2: Indentation testing of mouse brain tissue using the FemtoTools FT-MTA03 Micromechanical Testing and Assembly System Basic Protocol 3: Tissue stiffness identification from force-displacement data.

Mechanical characteristics of glioblastoma and peritumoral tumor-free human brain tissue

BACKGROUND

The diagnosis of brain tumor is a serious event for the affected patient. Surgical resection is a crucial part in the treatment of brain tumors. However, the distinction between tumor and brain tissue can be difficult, even for experienced neurosurgeons. This is especially true in the case of gliomas. In this project we examined whether the biomechanical parameters elasticity and stress relaxation behavior are suitable as additional differentiation criteria between tumorous (glioblastoma multiforme; glioblastoma, IDH-wildtype; GBM) and non-tumorous, peritumoral tissue.

METHODS

Indentation measurements were used to examine non-tumorous human brain tissue and GBM samples for the biomechanical properties of elasticity and stress-relaxation behavior. The results of these measurements were then used in a classification algorithm (Logistic Regression) to distinguish between tumor and non-tumor.

RESULTS

Differences could be found in elasticity spread and relaxation behavior between tumorous and non-tumorous tissue. Classification was successful with a sensitivity/recall of 83% (sd = 12%) and a precision of 85% (sd = 9%) for detecting tumorous tissue.

CONCLUSION

The findings imply that the data on mechanical characteristics, with particular attention to stress relaxation behavior, can serve as an extra element in differentiating tumorous brain tissue from non-tumorous brain tissue.

Evaluating the Effects of Pulsed Electrical Stimulation on the Mechanical Behavior and Microstructure of Medulla Oblongata Tissues

The development of remote surgery hinges on comprehending the mechanical properties of the tissue at the surgical site. Understanding the mechanical behavior of the medulla oblongata tissue is instrumental for precisely determining the remote surgery implementation site. Additionally, exploring this tissue's response under electric fields can inform the creation of electrical stimulation therapy regimens. This could potentially reduce the extent of medulla oblongata tissue damage from mechanical compression. Various types of pulsed electric fields were integrated into a custom-built indentation device for this study. Experimental findings suggested that applying pulsed electric fields amplified the shear modulus of the medulla oblongata tissue. In the electric field, the elasticity and viscosity of the tissue increased. The most significant influence was noted from the low-frequency pulsed electric field, while the burst pulsed electric field had a minimal impact. At the microstructural scale, the application of an electric field led to the concentration of myelin in areas distant from the surface layer in the medulla oblongata, and the orderly structure of proteoglycans became disordered. The alterations observed in the myelin and proteoglycans under an electric field were considered to be the fundamental causes of the changes in the mechanical behavior of the medulla oblongata tissue. Moreover, cell polarization and extracellular matrix cavitation were observed, with transmission electron microscopy results pointing to laminar separation within the myelin at the ultrastructure scale. This study thoroughly explored the impact of electric field application on the mechanical behavior and microstructure of the medulla oblongata tissue, delving into the underlying mechanisms. This investigation delved into the changes and mechanisms in the mechanical behavior and microstructure of medulla oblongata tissue under the influence of electric fields. Furthermore, this study could serve as a reference for the development of electrical stimulation regimens in the central nervous system. The acquired mechanical behavior data could provide valuable baseline information to aid in the evolution of remote surgery techniques involving the medulla oblongata tissue.

Mechanics in the nervous system: From development to disease

Physical forces are ubiquitous in biological processes across scales and diverse contexts. This review highlights the significance of mechanical forces in nervous system development, homeostasis, and disease. We provide an overview of mechanical signals present in the nervous system and delve into mechanotransduction mechanisms translating these mechanical cues into biochemical signals. During development, mechanical cues regulate a plethora of processes, including cell proliferation, differentiation, migration, network formation, and cortex folding. Forces then continue exerting their influence on physiological processes, such as neuronal activity, glial cell function, and the interplay between these different cell types. Notably, changes in tissue mechanics manifest in neurodegenerative diseases and brain tumors, potentially offering new diagnostic and therapeutic target opportunities. Understanding the role of cellular forces and tissue mechanics in nervous system physiology and pathology adds a new facet to neurobiology, shedding new light on many processes that remain incompletely understood.

Viscoelastic response of gray matter and white matter brain tissues under creep and relaxation

Accurate measurement of the mechanical properties of brain tissue is of paramount importance for understanding its mechanics-biology relationship. Most published studies on brain viscoelasticity have been conducted using a single relaxation test, without validating the validity of linear viscoelasticity, which is insufficient to establish an accurate constitutive equation for brain tissue. We obtained the creep and relaxation profiles of fresh adult porcine white matter (N = 120) and gray matter (N = 56) under finite step-and-hold uniaxial compression, using a mechanical testing machine, with 16.67 mm/s loading rate and 80 s hold time. These curves were employed to determine viscoelastic properties and demonstrated an excellent fit with a concise power-law function. The average initial modulus for gray matter (GM) was 6.619 kPa, higher than that for white matter under transverse loading (WM-2D) at 5.579 kPa (p < 0.01), yet lower than that for white matter under axial loading (WM-1D) at 6.759 kPa (p = 0.0121). Notably, WM-2D exhibited the highest degree of fluidity (β = 0.216). Our findings reveal that gray matter behaves as a linear viscoelastic material with power-law creep compliance and relaxation modulus. Conversely, the creep and relaxation behavior of white matter deviates from the verification relationship derived from linear viscoelastic theory, indicating its nonlinearity. This fact underscores the inaccuracy of assuming a linear constitutive relationship to characterize the viscoelastic properties of white matter. By combining the power-law function with the experimentally obtained creep compliance and relaxation modulus, we offer a unique approach to determining the viscoelastic characteristics of brain tissue.

Estimating the viscoelastic properties of the human brain at 7 T MRI using intrinsic MRE and nonlinear inversion

Intrinsic actuation magnetic resonance elastography (MRE) is a phase-contrast MRI technique that allows for in vivo quantification of mechanical properties of the brain by exploiting brain motion that arise naturally due to the cardiac pulse. The mechanical properties of the brain reflect its tissue microstructure, making it a potentially valuable parameter in studying brain disease. The main purpose of this study was to assess the feasibility of reconstructing the viscoelastic properties of the brain using high-quality 7 T MRI displacement measurements, obtained using displacement encoding with stimulated echoes (DENSE) and intrinsic actuation. The repeatability and sensitivity of the method for detecting normal regional variation in brain tissue properties was assessed as secondary goal. The displacement measurements used in this analysis were previously acquired for a separate study, where eight healthy subjects (27 ± 7 years) were imaged with repeated scans (spatial resolution approx. 2 mm isotropic, temporal resolution 75 ms, motion sensitivity 0.35 mm/2π for displacements in anterior-posterior and left-right directions, and 0.7 mm/2π for feet-head displacements). The viscoelastic properties of the brain were estimated using a subzone based non-linear inversion scheme. The results show comparable consistency to that of extrinsic MRE between the viscoelastic property maps obtained from repeated displacement measurements. The shear stiffness maps showed fairly consistent spatial patterns. The whole-brain repeatability coefficient (RC) for shear stiffness was (mean ± standard deviation) 8 ± 8% relative to the mean whole-brain stiffness, and the damping ratio RC was 28 ± 17% relative to the whole-brain damping ratio. The shear stiffness maps showed similar statistically significant regional trends as demonstrated in a publicly available atlas of viscoelastic properties obtained with extrinsic actuation MRE at 50 Hz. The damping ratio maps showed less consistency, likely due to data-model mismatch of describing the brain as a viscoelastic material under low frequencies. While artifacts induced by fluid flow within the brain remain a limitation of the technique in its current state, intrinsic actuation based MRE allow for consistent and repeatable estimation of the mechanical properties of the brain. The method provides enough sensitivity to investigate regional variation in such properties in the normal brain, which is likely sufficient to also investigate pathological changes.

A multiphysics model to predict periventricular white matter hyperintensity growth during healthy brain aging

Periventricular white matter hyperintensities (WMH) are a common finding in medical images of the aging brain and are associated with white matter damage resulting from cerebral small vessel disease, white matter inflammation, and a degeneration of the lateral ventricular wall. Despite extensive work, the etiology of periventricular WMHs remains unclear. We pose that there is a strong coupling between age-related ventricular expansion and the degeneration of the ventricular wall which leads to a dysregulated fluid exchange across this brain-fluid barrier. Here, we present a multiphysics model that couples cerebral atrophy-driven ventricular wall loading with periventricular WMH formation and progression. We use patient data to create eight 2D finite element models and demonstrate the predictive capabilities of our damage model. Our simulations show that we accurately capture the spatiotemporal features of periventricular WMH growth. For one, we observe that damage appears first in both the anterior and posterior horns and then spreads into deeper white matter tissue. For the other, we note that it takes up to 12 years before periventricular WMHs first appear and derive an average annualized periventricular WMH damage growth rate of 15.2 ± 12.7 mm2/year across our models. A sensitivity analysis demonstrated that our model parameters provide sufficient sensitivity to rationalize subject-specific differences with respect to onset time and damage growth. Moreover, we show that the septum pellucidum, a membrane that separates the left and right lateral ventricles, delays the onset of periventricular WMHs at first, but leads to a higher WMH load in the long-term.

Multi-scale measurement of stiffness in the developing ferret brain

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.

Histology-informed multiscale modeling of human brain white matter

In this study, we propose a novel micromechanical model for the brain white matter, which is described as a heterogeneous material with a complex network of axon fibers embedded in a soft ground matrix. We developed this model in the framework of RVE-based multiscale theories in combination with the finite element method and the embedded element technique for embedding the fibers. Microstructural features such as axon diameter, orientation and tortuosity are incorporated into the model through distributions derived from histological data. The constitutive law of both the fibers and the matrix is described by isotropic one-term Ogden functions. The hyperelastic response of the tissue is derived by homogenizing the microscopic stress fields with multiscale boundary conditions to ensure kinematic compatibility. The macroscale homogenized stress is employed in an inverse parameter identification procedure to determine the hyperelastic constants of axons and ground matrix, based on experiments on human corpus callosum. Our results demonstrate the fundamental effect of axon tortuosity on the mechanical behavior of the brain's white matter. By combining histological information with the multiscale theory, the proposed framework can substantially contribute to the understanding of mechanotransduction phenomena, shed light on the biomechanics of a healthy brain, and potentially provide insights into neurodegenerative processes.

Vascular determinants of hippocampal viscoelastic properties in healthy adults across the lifespan

Arterial stiffness and cerebrovascular pulsatility are non-traditional risk factors of Alzheimer's disease. However, there is a gap in understanding the earliest mechanisms that link these vascular determinants to brain aging. Changes to mechanical tissue properties of the hippocampus (HC), a brain structure essential for memory encoding, may reflect the impact of vascular dysfunction on brain aging. We tested the hypothesis that arterial stiffness and cerebrovascular pulsatility are related to HC tissue properties in healthy adults across the lifespan. Twenty-five adults underwent measurements of brachial blood pressure (BP), large elastic artery stiffness, middle cerebral artery pulsatility index (MCAv PI), and magnetic resonance elastography (MRE), a sensitive measure of HC viscoelasticity. Individuals with higher carotid pulse pressure (PP) exhibited lower HC stiffness (β = -0.39, r = -0.41, p = 0.05), independent of age and sex. Collectively, carotid PP and MCAv PI significantly explained a large portion of the total variance in HC stiffness (adjusted R2 = 0.41, p = 0.005) in the absence of associations with HC volumes. These cross-sectional findings suggest that the earliest reductions in HC tissue properties are associated with alterations in vascular function.

Photoacoustic elasto-viscography and optical coherence microscopy for multi-parametric ex vivo brain imaging

Optical coherence microscopy (OCM) has shown the importance of imaging ex vivo brain slices at the microscopic level for a better understanding of the disease pathology and mechanism. However, the current OCM-based techniques are mainly limited to providing the tissue's optical properties, such as the attenuation coefficient, scattering coefficient, and cell architecture. Imaging the tissue's mechanical properties, including the elasticity and viscosity, in addition to the optical properties, to provide a comprehensive multi-parametric assessment of the sample has remained a challenge. Here, we present an integrated photoacoustic elasto-viscography (PAEV) and OCM imaging system to measure the sample's optical absorption coefficient, attenuation coefficient, and mechanical properties, including elasticity and viscosity. The obtained mechanical and optical properties were consistent with anatomical features observed in the PAEV and OCM images. The elasticity and viscosity maps showed rich variations of microstructural mechanical properties of mice brain. In the reconstructed elasto-viscogram of brain slices, greater elasticity, and lower viscosity were observed in white matter than in gray matter. With the ability to provide multi-parametric properties of the sample, the PAEV-OCM system holds the potential for a more comprehensive study of brain disease pathology.

Advancing the interfacing performances of chronically implantable neural probes in the era of CMOS neuroelectronics

Tissue penetrating microelectrode neural probes can record electrophysiological brain signals at resolutions down to single neurons, making them invaluable tools for neuroscience research and Brain-Computer-Interfaces (BCIs). The known gradual decrease of their electrical interfacing performances in chronic settings, however, remains a major challenge. A key factor leading to such decay is Foreign Body Reaction (FBR), which is the cascade of biological responses that occurs in the brain in the presence of a tissue damaging artificial device. Interestingly, the recent adoption of Complementary Metal Oxide Semiconductor (CMOS) technology to realize implantable neural probes capable of monitoring hundreds to thousands of neurons simultaneously, may open new opportunities to face the FBR challenge. Indeed, this shift from passive Micro Electro-Mechanical Systems (MEMS) to active CMOS neural probe technologies creates important, yet unexplored, opportunities to tune probe features such as the mechanical properties of the probe, its layout, size, and surface physicochemical properties, to minimize tissue damage and consequently FBR. Here, we will first review relevant literature on FBR to provide a better understanding of the processes and sources underlying this tissue response. Methods to assess FBR will be described, including conventional approaches based on the imaging of biomarkers, and more recent transcriptomics technologies. Then, we will consider emerging opportunities offered by the features of CMOS probes. Finally, we will describe a prototypical neural probe that may meet the needs for advancing clinical BCIs, and we propose axial insertion force as a potential metric to assess the influence of probe features on acute tissue damage and to control the implantation procedure to minimize iatrogenic injury and subsequent FBR.

Interactions between ploidy and resource availability shape clonal interference at initiation and recurrence of glioblastoma

Glioblastoma (GBM) is the most aggressive form of primary brain tumor. Complete surgical resection of GBM is almost impossible due to the infiltrative nature of the cancer. While no evidence for recent selection events have been found after diagnosis, the selective forces that govern gliomagenesis are strong, shaping the tumor's cell composition during the initial progression to malignancy with late consequences for invasiveness and therapy response. We present a mathematical model that simulates the growth and invasion of a glioma, given its ploidy level and the nature of its brain tissue micro-environment (TME), and use it to make inferences about GBM initiation and response to standard-of-care treatment. We approximate the spatial distribution of resource access in the TME through integration of in-silico modelling, multi-omics data and image analysis of primary and recurrent GBM. In the pre-malignant setting, our in-silico results suggest that low ploidy cancer cells are more resistant to starvation-induced cell death. In the malignant setting, between first and second surgery, simulated tumors with different ploidy compositions progressed at different rates. Whether higher ploidy predicted fast recurrence, however, depended on the TME. Historical data supports this dependence on TME resources, as shown by a significant correlation between the median glucose uptake rates in human tissues and the median ploidy of cancer types that arise in the respective tissues (Spearman r = -0.70; P = 0.026). Taken together our findings suggest that availability of metabolic substrates in the TME drives different cell fate decisions for cancer cells with different ploidy and shapes GBM disease initiation and relapse characteristics.

Brain Stiffness Follows Cuprizone-Induced Variations in Local Myelin Content

Brain maturation and neurological diseases are intricately linked to microstructural changes that inherently affect the brain's mechanical behavior. Animal models are frequently used to explore relative brain stiffness changes as a function of underlying microstructure. Here, we are using the cuprizone mouse model to study indentation-derived stiffness changes resulting from acute and chronic demyelination during a 15-week observation period. We focus on the corpus callosum, cingulum, and cortex which undergo different degrees of de- and remyelination and, therefore, result in region-specific stiffness changes. Mean stiffness of the corpus callosum starts at 1.1 ± 0.3 kPa in untreated mice, then cuprizone treatment causes stiffness to drop to 0.6 ± 0.1 kPa by week 3, temporarily increase to 0.9 ± 0.3 kPa by week 6, and ultimately stabilize around 0.7 ± 0.1 kPa by week 9 for the rest of the observation period. The cingulum starts at 3.2 ± 0.9 kPa, then drops to 1.6 ± 0.4 kPa by week 3, and then gradually stabilizes around 1.4 ± 0.3 kPa by week 9. Cortical stiffness exhibits less stiffness variations overall; it starts at 4.2 ± 1.3 kPa, drops to 2.4 ± 0.6 kPa by week 3, and stabilizes around 2.7 ± 0.9 kPa by week 6. We also assess the impact of tissue fixation on indentation-based mechanical tissue characterization. On the one hand, fixation drastically increases untreated mean tissue stiffness by a factor of 3.3 for the corpus callosum, 2.9 for the cingulum, and 3.6 for the cortex; on the other hand, fixation influences interregional stiffness ratios during demyelination, thus suggesting that fixation affects individual brain tissues differently. Lastly, we determine the spatial correlation between stiffness measurements and myelin density and observe a region-specific proportionality between myelin content and tissue stiffness. STATEMENT OF SIGNIFICANCE: Despite extensive work, the relationship between microstructure and mechanical behavior in the brain remains mostly unknown. Additionally, the existing variation of measurement results reported in literature requires in depth investigation of the impact of individual cell and protein populations on tissue stiffness and interregional stiffness ratios. Here, we used microindentation measurements to show that brain stiffness changes with myelin density in the cuprizone-based demyelination mouse model. Moreover, we explored the impact of tissue fixation prior to mechanical characterization because of conflicting results reported in literature. We observe that fixation has a distinctly different impact on our three regions of interest, thus causing region-specific tissue stiffening and, more importantly, changing interregional stiffness ratios.

Modulation of Methacrylated Hyaluronic Acid Hydrogels Enables Their Use as 3D Cultured Model

Bioengineered hydrogels represent physiologically relevant platforms for cell behaviour studies in the tissue engineering and regenerative medicine fields, as well as in in vitro disease models. Hyaluronic acid (HA) is an ideal platform since it is a natural biocompatible polymer that is widely used to study cellular crosstalk, cell adhesion and cell proliferation, and is one of the major components of the extracellular matrix (ECM). We synthesised chemically modified HA with photo-crosslinkable methacrylated groups (HA-MA) in aqueous solutions and in strictly monitored pH and temperature conditions to obtain hydrogels with controlled bulk properties. The physical and chemical properties of the different HA-MA hydrogels were investigated via rheological studies, mechanical testing and scanning electron microscopy (SEM) imaging, which allowed us to determine the optimal biomechanical properties and develop a biocompatible scaffold. The morphological evolution processes and proliferation rates of glioblastoma cells (U251-MG) cultured on HA-MA surfaces were evaluated by comparing 2D structures with 3D structures, showing that the change in dimensionality impacted cell functions and interactions. The cell viability assays and evaluation of mitochondrial metabolism showed that the hydrogels did not interfere with cell survival. In addition, morphological studies provided evidence of cell-matrix interactions that promoted cell budding from the spheroids and the invasiveness in the surrounding environment.

Three-dimensional bioprinting of stem cell-derived central nervous system cells enables astrocyte growth, vasculogenesis, and enhances neural differentiation/function

Current research tools for preclinical drug development such as rodent models and two-dimensional immortalized monocultures have failed to serve as effective translational models for human central nervous system (CNS) disorders. Recent advancements in the development of induced pluripotent stem cells (iPSCs) and three-dimensional (3D) culturing can improve the in vivo-relevance of preclinical models, while generating 3D cultures though novel bioprinting technologies can offer increased scalability and replicability. As such, there is a need to develop platforms that combine iPSC-derived cells with 3D bioprinting to produce scalable, tunable, and biomimetic cultures for preclinical drug discovery applications. We report a biocompatible poly(ethylene glycol)-based matrix which incorporates Arg-Gly-Asp and Tyr-Ile-Gly-Ser-Arg peptide motifs and full-length collagen IV at a stiffness similar to the human brain (1.5 kPa). Using a high-throughput commercial bioprinter we report the viable culture and morphological development of monocultured iPSC-derived astrocytes, brain microvascular endothelial-like cells, neural progenitors, and neurons in our novel matrix. We also show that this system supports endothelial-like vasculogenesis and enhances neural differentiation and spontaneous activity. This platform forms a foundation for more complex, multicellular models to facilitate high-throughput translational drug discovery for CNS disorders.

A Bayesian method with nonlinear noise model to calibrate constitutive parameters of soft tissue

The measured mechanical responses of soft tissue exhibit large variability and errors, especially for the softest brain tissue, while calibrating its constitutive parameters in a deterministic way remains a common practice. Here we implement a Bayesian method considering the nonlinear noise model to calibrate constitutive parameters of brain tissue. A probability model is first developed based on the measured experimental data, likelihood function, and prior function, from which the posterior distributions of model parameters are formulated. The likelihood function considers the nonlinear behaviors of the constitutive response and noise distribution of the experimentally measured data. Meanwhile, the prior predictive distribution is computed to check the probability model preliminarily. Secondly, the Markov Chain Monte Carlo (MCMC) method is used to compute the posterior distributions of model parameters, enabling assessment of parameter uncertainty, correlation, and model calibration error. Finally, the posterior predictive distributions of the overall response, constitutive response, and noise response are computed to validate the probabilistic model, all of which are consistent with the corresponding data. Furthermore, the effect of the prior distribution, experimental data, and noise model on posterior distribution is studied. Our study provides a general approach to calibrating constitutive parameters of soft tissue despite errors and large variability in experimental data.

AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials

Nano-indentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nano-indentation allows mechanical interrogation where traditional methods may fail. However, because nano-indentation does not yield a homogeneous deformation field, interpreting the resulting load-displacement curves is non-trivial and most investigators resort to simplified approaches based on the Hertzian solution. Unfortunately, for small samples and large indentation depths, these solutions are inaccurate. We set out to use machine learning to provide an alternative strategy. We first used the finite element method to create a large synthetic data set. We then used these data to train neural networks to inversely identify material parameters from load-displacement curves. To this end, we took two different approaches. First, we learned the indentation forward problem, which we then applied within an iterative framework to identify material parameters. Second, we learned the inverse problem of directly identifying material parameters. We show that both approaches are effective at identifying the parameters of the neo-Hookean and Gent models. Specifically, when applied to synthetic data, our approaches are accurate even for small sample sizes and at deep indentation. Additionally, our approaches are fast, especially compared to the inverse finite element approach. Finally, our approaches worked on unseen experimental data from thin mouse brain samples. Here, our approaches proved robust to experimental noise across over 1000 samples. By providing open access to our data and code, we hope to support others that conduct nano-indentation on soft materials.

Mechanosensitive Piezo1 channel in physiology and pathophysiology of the central nervous system

Since the discovery of the mechanosensitive Piezo1 channel in 2010, there has been a significant amount of research conducted to explore its regulatory role in the physiology and pathology of various organ systems. Recently, a growing body of compelling evidence has emerged linking the activity of the mechanosensitive Piezo1 channel to health and disease of the central nervous system. However, the exact mechanisms underlying these associations remain inadequately comprehended. This review systematically summarizes the current research on the mechanosensitive Piezo1 channel and its implications for central nervous system mechanobiology, retrospects the results demonstrating the regulatory role of the mechanosensitive Piezo1 channel on various cell types within the central nervous system, including neural stem cells, neurons, oligodendrocytes, microglia, astrocytes, and brain endothelial cells. Furthermore, the review discusses the current understanding of the involvement of the Piezo1 channel in central nervous system disorders, such as Alzheimer's disease, multiple sclerosis, glaucoma, stroke, and glioma.

Mechanical stiffness and anisotropy measured by MRE during brain development in the minipig

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.

Mechanical loading of the ventricular wall as a spatial indicator for periventricular white matter degeneration

Progressive white matter degeneration in periventricular and deep white matter regions appears as white matter hyperintensities (WMH) on MRI scans. To date, periventricular WMHs are often associated with vascular dysfunction. Here, we demonstrate that ventricular inflation resulting from cerebral atrophy and hemodynamic pulsation with every heartbeat leads to a mechanical loading state of periventricular tissues that significantly affects the ventricular wall. Specifically, we present a physics-based modeling approach that provides a rationale for ependymal cell involvement in periventricular WMH formation. Building on eight previously created 2D finite element brain models, we introduce novel mechanomarkers for ependymal cell loading and geometric measures that characterize lateral ventricular shape. We show that our novel mechanomarkers, such as maximum ependymal cell deformations and maximum curvature of the ventricular wall, spatially overlap with periventricular WMH locations and are sensitive predictors for WMH formation. We also explore the role of the septum pellucidum in mitigating mechanical loading of the ventricular wall by constraining the radial expansion of the lateral ventricles during loading. Our models consistently show that ependymal cells are stretched thin only in the horns of the ventricles irrespective of ventricular shape. We therefore pose that periventricular WMH etiology is strongly linked to the deterioration of the over-stretched ventricular wall resulting in CSF leakage into periventricular white matter. Subsequent secondary damage mechanisms, including vascular degeneration, exacerbate lesion formation and lead to progressive growth into deep white matter regions.

Changes in stiffness of the optic nerve and involvement of neurofilament light chains in the course of experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis

Multiple sclerosis and its animal model, experimental autoimmune encephalomyelitis (EAE), are often accompanied by optic neuritis associated with neurofilament disruption. In this study, the stiffness of the optic nerve was investigated by atomic force microscopy (AFM) in mice with induced EAE in the successive phases of the disease: onset, peak, and chronic. AFM results were compared with the intensity of the main pathological processes in the optic nerve: inflammation, demyelination, and axonal loss, as well as with the density of astrocytes, assessed by quantitative histology and immunohistochemistry. Optic nerve tissue and serum levels of neurofilament light chain protein (NEFL) were also examined by immunostaining and ELISA, respectively. The stiffness of the optic nerve in EAE mice was lower than that in control and naïve animals. It increased in the onset and peak phases and sharply decreased in the chronic phase. Serum NEFL level showed similar dynamics, while tissue NEFL level decreased in the onset and peak phases, indicating a leak of NEFL from the optic nerve to body fluids. Inflammation and demyelination gradually increased to reach the maximum in the peak phase of EAE, and inflammation slightly declined in the chronic phase, while demyelination did not. The axonal loss also gradually increased and had the highest level in the chronic phase. Among these processes, demyelination and especially axonal loss most effectively decrease the stiffness of the optic nerve. NEFL level in serum can be regarded as an early indicator of EAE, as it rapidly grows in the onset phase of the disease.

Matrix stiffness regulates the tight junction phenotypes and local barrier properties in tricellular regions in an iPSC-derived BBB model

The blood-brain barrier (BBB) can respond to various mechanical cues such as shear stress and substrate stiffness. In the human brain, the compromised barrier function of the BBB is closely associated with a series of neurological disorders that are often also accompanied by the alteration of brain stiffness. In many types of peripheral vasculature, higher matrix stiffness decreases barrier function of endothelial cells through mechanotransduction pathways that alter cell-cell junction integrity. However, human brain endothelial cells are specialized endothelial cells that largely resist changes in cell morphology and key BBB markers. Therefore, it has remained an open question how matrix stiffness affects barrier integrity in the human BBB. To gain insight into the effects of matrix stiffness on BBB permeability, we differentiated brain microvascular endothelial-like cells from human induced pluripotent stem cells (iBMEC-like cells) and cultured the cells on extracellular matrix-coated hydrogels of varying stiffness. We first detected and quantified the junction presentation of key tight junction (TJ) proteins. Our results show matrix-dependent junction phenotypes in iBMEC-like cells, where cells on softer gels (1 kPa) have significantly lower continuous and total TJ coverages. We also determined that these softer gels also lead to decreased barrier function in a local permeability assay. Furthermore, we found that matrix stiffness regulates the local permeability of iBMEC-like cells through the balance of continuous ZO-1 TJs and no junction regions ZO-1 in tricellular regions. Together, these findings provide valuable insights into the effects of matrix stiffness on TJ phenotypes and local permeability of iBMEC-like cells. STATEMENT OF SIGNIFICANCE: Brain mechanical properties, including stiffness, are particularly sensitive indicators for pathophysiological changes in neural tissue. The compromised function of the blood-brain barrier is closely associated with a series of neurological disorders often accompanied by altered brain stiffness. In this study, we use polymeric biomaterials and provide new evidence that biomaterial stiffness regulates the local permeability in iPSC-derived brain endothelial cells in tricellular regions through the tight junction protein ZO-1. Our findings provide valuable insights into the changes in junction architecture and barrier permeability in response to different substrate stiffnesses. Since BBB dysfunction has been linked to many diseases, understanding the influence of substrate stiffness on junction presentations and barrier permeability could lead to the development of new treatments for diseases associated with BBB dysfunction or drug delivery across BBB systems.

Mechanics of axon growth and damage: A systematic review of computational models

Normal axon development depends on the action of mechanical forces both generated within the cytoskeleton and outside the cell, but forces of large magnitude or rate cause damage instead. Computational models aid scientists in studying the role of mechanical forces in axon growth and damage. These studies use simulations to evaluate how different sources of force generation within the cytoskeleton interact with each other to regulate axon elongation and retraction. Furthermore, mathematical models can help optimize externally applied tension to promote axon growth without causing damage. Finally, scientists also use simulations of axon damage to investigate how forces are distributed among different components of the axon and how the tissue surrounding an axon influences its susceptibility to injury. In this review, we discuss how computational studies complement experimental studies in the areas of axon growth, regeneration, and damage.

Exploring the multiphysics of the brain during development, aging, and in neurological diseases

The human brain remains an endless source of wonder and represents an intruiging scientific frontier. Multiphysics approaches naturally lend themselves to combine our extensive knowledge about the neurobiology of aging and diseases with mechanics to better capture the multiscale behavior of the brain. Our group uses experimental methods, medical image analysis, and constitutive modeling to develop better disease models with the long-term goal to improve diagnosis, treatment, and ultimately enable prevention of many prevalent age- and disease-related brain changes. In the present perspective, we outline on-going work related to neurodevelopment, aging, and neurodegenerative disease.

Fluid compartments influence elastography of the aging mouse brain

Objective. Elastography of the brain has the potential to reveal subtle but clinically important changes in the structure and composition as a function of age, disease, and injury.Approach. In order to quantify the specific effects of aging on mouse brain elastography, and to determine the key factors influencing observed changes, we applied optical coherence tomography reverberant shear wave elastography at 2000 Hz to a group of wild-type healthy mice ranging from young to old age.Main results. We found a strong trend towards increasing stiffness with age, with an approximately 30% increase in shear wave speed from 2 months to 30 months within this sampled group. Furthermore, this appears to be strongly correlated with decreasing measures of whole brain fluid content, so older brains have less water and are stiffer. Rheological models are applied, and the strong effect is captured by specific assignment of changes to the glymphatic compartment of the brain fluid structures along with a correlated change in the parenchymal stiffness.Significance. Short-term and longer-term changes in elastography measures may provide a sensitive biomarker of progressive and fine-scale changes in the glymphatic fluid channels and parenchymal components of the brain.

Automated model discovery for human brain using Constitutive Artificial Neural Networks

The brain is our softest and most vulnerable organ, and understanding its physics is a challenging but significant task. Throughout the past decade, numerous competing models have emerged to characterize its response to mechanical loading. However, selecting the best constitutive model remains a heuristic process that strongly depends on user experience and personal preference. Here we challenge the conventional wisdom to first select a constitutive model and then fit its parameters to data. Instead, we propose a new strategy that simultaneously discovers both model and parameters. We integrate more than a century of knowledge in thermodynamics and state-of-the-art machine learning to build a Constitutive Artificial Neural Network that enables automated model discovery. Our design paradigm is to reverse engineer the network from a set of functional building blocks that are, by design, a generalization of popular constitutive models, including the neo Hookean, Blatz Ko, Mooney Rivlin, Demiray, Gent, and Holzapfel models. By constraining input, output, activation functions, and architecture, our network a priori satisfies thermodynamic consistency, objectivity, symmetry, and polyconvexity. We demonstrate that-out of more than 4000 models-our network autonomously discovers the model and parameters that best characterize the behavior of human gray and white matter under tension, compression, and shear. Importantly, our network weights translate naturally into physically meaningful parameters, such as shear moduli of 1.82kPa, 0.88kPa, 0.94kPa, and 0.54kPa for the cortex, basal ganglia, corona radiata, and corpus callosum. Our results suggest that Constitutive Artificial Neural Networks have the potential to induce a paradigm shift in soft tissue modeling, from user-defined model selection to automated model discovery. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN. STATEMENT OF SIGNIFICANCE: Human brain is ultrasoft, difficult to test, and challenging to model. Numerous competing constitutive models exist, but selecting the best model remains a matter of personal preference. Here we automate the process of model selection. We formulate the problem of autonomous model discovery as a neural network and capitalize on the powerful optimizers in deep learning. However, rather than using a conventional neural network, we reverse engineer our own Constitutive Artificial Neural Network from a set of modular building blocks, which we rationalize from common constitutive models. When trained with tension, compression, and shear experiments of gray and white matter, our network simultaneously discovers both model and parameters that describes the data better than any existing invariant-based model. Our network could induce a paradigm shift from user-defined model selection to automated model discovery.

Unravelling the mechanotransduction pathways in Alzheimer's disease

Alzheimer's disease (AD) represents one of the most common and debilitating neurodegenerative disorders. By the end of 2040, AD patients might reach 11.2 million in the USA, around 70% higher than 2022, with severe consequences on the society. As now, we still need research to find effective methods to treat AD. Most studies focused on the tau and amyloid hypothesis, but many other factors are likely involved in the pathophysiology of AD. In this review, we summarize scientific evidence dealing with the mechanotransduction players in AD to highlight the most relevant mechano-responsive elements that play a role in AD pathophysiology. We focused on the AD-related role of extracellular matrix (ECM), nuclear lamina, nuclear transport and synaptic activity. The literature supports that ECM alteration causes the lamin A increment in the AD patients, leading to the formation of nuclear blebs and invaginations. Nuclear blebs have consequences on the nuclear pore complexes, impairing nucleo-cytoplasmic transport. This may result in tau hyperphosphorylation and its consequent self-aggregation in tangles, which impairs the neurotransmitters transport. It all exacerbates in synaptic transmission impairment, leading to the characteristic AD patient's memory loss. Here we related for the first time all the evidence associating the mechanotransduction pathway with neurons. In addition, we highlighted the entire pathway influencing neurodegenerative diseases, paving the way for new research perspectives in the context of AD and related pathologies.

Linear and nonlinear rheology of liberase-treated breast cancer tumors

Extracellular matrix (ECM) rigidity has been shown to increase the invasive properties of breast cancer cells, promoting transformation and metastasis through mechanotransduction. Reducing ECM stiffness via enzymatic digestion could be a promising approach to slowing breast cancer development by de-differentiation of breast cancer cells to less aggressive phenotypes and enhancing the effectiveness of existing chemotherapeutics via improved drug penetrance throughout the tumor. In this study, we examine the effects of injectable liberase (a blend of collagenase and thermolysin enzymes) treatments on the linear and nonlinear rheology of allograft 4T1 mouse mammary tumors. We perform two sets of in vivo mouse studies, in which either one or multiple treatment injections occur before the tumors are harvested for rheological analysis. The treatment groups in each study consist of a buffer control, free liberase enzyme in buffer, a thermoresponsive copolymer called LiquoGel (LQG) in buffer, and a combined, localized injection of LQG and liberase. All tumor samples exhibit gel-like linear rheological behavior with the elastic modulus significantly larger than the viscous modulus and both independent of frequency. Tumors that receive a single injection of localized liberase have significantly lower tumor volumes and lower tissue moduli at both the center and edge compared to buffer- and free liberase-injected control tumors, while tissue viscoelasticity remains relatively unaffected. Tumors injected multiple times with LQG and liberase also have lower tissue volumes but possess higher tissue moduli and lower viscoelasticities compared to the other treatment groups. We propose that a mechanotransductive mechanism could cause the formation of smaller but stiffer tumors after repeated, localized liberase injections. Large amplitude oscillatory shear (LAOS) experiments are also performed on tissues from the multiple injection study and the results are analyzed using MITlaos. LAOS analysis reveals that all 4T1 tumors from the multiple injection study exhibit nonlinear rheological behavior at high strains and strain rates. Examination of the Lissajous-Bowditch curves, Chebyshev coefficient ratios, elastic moduli, and dynamic viscosities demonstrate that the onset and type of nonlinear behavior is independent of treatment type and elastic modulus, suggesting that multiple liberase injections do not affect the nonlinear viscoelasticity of 4T1 tumors.

Substrate stiffness in nerve cells

Recently, substrate stiffness has been involved in the physiology and pathology of the nervous system. However, the role and function of substrate stiffness remain unclear. Here, we review known effects of substrate stiffness on nerve cell morphology and function in the central and peripheral nervous systems and their involvement in pathology. We hope this review will clarify the research status of substrate stiffness in nerve cells and neurological disorder.

Label-free differential imaging of cellular components in mouse brain tissue by wide-band photoacoustic microscopy

Mapping diverse cellular components with high spatial resolution is important to interrogate biological systems and study disease pathogenesis. Conventional optical imaging techniques for mapping biomolecular profiles with differential staining and labeling methods are cumbersome. Different types of cellular components exhibit distinctive characteristic absorption spectra across a wide wavelength range. By virtue of this property, a lab-made wide-band optical-resolution photoacoustic microscopy (wbOR-PAM) system, which covers wavelengths from the ultraviolet and visible to the shortwave infrared regions, was designed and developed to capture multiple cellular components in 300-μm-thick brain slices at nine different wavelengths without repetitive staining and complicated processing. This wbOR-PAM system provides abundant spectral information. A reflective objective lens with an infinite conjugate design was applied to focus laser beams with different wavelengths, avoiding chromatic aberration. The molecular components of complex brain slices were probed without labeling. The findings of the present study demonstrated a distinctive absorption of phospholipids, a major component of the cell membrane, brain, and nervous system, at 1690 nm and revealed their precise distribution with microscopic resolution in a mouse brain, for the first time. This novel imaging modality provides a new opportunity to investigate important biomolecular components without either labeling or lengthy specimen processing, thus, laying the groundwork for revealing cellular mechanisms involved in disease pathogenesis.

The epigenetic landscape of oligodendrocyte lineage cells

The epigenetic landscape of oligodendrocyte lineage cells refers to the cell-specific modifications of DNA, chromatin, and RNA that define a unique gene expression pattern of functionally specialized cells. Here, we focus on the epigenetic changes occurring as progenitors differentiate into myelin-forming cells and respond to the local environment. First, modifications of DNA, RNA, nucleosomal histones, key principles of chromatin organization, topologically associating domains, and local remodeling will be reviewed. Then, the relationship between epigenetic modulators and RNA processing will be explored. Finally, the reciprocal relationship between the epigenome as a determinant of the mechanical properties of cell nuclei and the target of mechanotransduction will be discussed. The overall goal is to provide an interpretative key on how epigenetic changes may account for the heterogeneity of the transcriptional profiles identified in this lineage.

Can machine learning accelerate soft material parameter identification from complex mechanical test data?

Identifying the constitutive parameters of soft materials often requires heterogeneous mechanical test modes, such as simple shear. In turn, interpreting the resulting complex deformations necessitates the use of inverse strategies that iteratively call forward finite element solutions. In the past, we have found that the cost of repeatedly solving non-trivial boundary value problems can be prohibitively expensive. In this current work, we leverage our prior experimentally derived mechanical test data to explore an alternative approach. Specifically, we investigate whether a machine learning-based approach can accelerate the process of identifying material parameters based on our mechanical test data. Toward this end, we pursue two different strategies. In the first strategy, we replace the forward finite element simulations within an iterative optimization framework with a machine learning-based metamodel. Here, we explore both Gaussian process regression and neural network metamodels. In the second strategy, we forgo the iterative optimization framework and use a stand alone neural network to predict the entire material parameter set directly from experimental results. We first evaluate both approaches with simple shear experiments on blood clot, an isotropic, homogeneous material. Next, we evaluate both approaches against simple shear and uniaxial loading experiments on right ventricular myocardium, an anisotropic, heterogeneous material. We find that replacing the forward finite element simulations with metamodels significantly accelerates the parameter identification process with excellent results in the case of blood clot, and with satisfying results in the case of right ventricular myocardium. On the other hand, we find that replacing the entire optimization framework with a neural network yielded unsatisfying results, especially for right ventricular myocardium. Overall, the importance of our work stems from providing a baseline example showing how machine learning can accelerate the process of material parameter identification for soft materials from complex mechanical data, and from providing an open access experimental and simulation dataset that may serve as a benchmark dataset for others interested in applying machine learning techniques to soft tissue biomechanics.

Glioblastoma Spheroid Invasion through Soft, Brain-Like Matrices Depends on Hyaluronic Acid-CD44 Interactions

Increased secretion of hyaluronic acid (HA), a glycosaminoglycan abundant in the brain extracellular matrix (ECM), correlates with worse clinical outcomes for glioblastoma (GBM) patients. GBM cells aggressively invade the brain parenchyma while encountering spatiotemporal changes in their local ECM, including HA concentration. To investigate how varying HA concentrations affect GBM invasion, patient-derived GBM cells are cultured within a soft, 3D matrix in which HA concentration is precisely varied and cell migration observed. Data demonstrate that HA concentration can determine the invasive activity of patient-derived GBM cells in a biphasic and highly sensitive manner, where the absolute concentration of HA at which cell migration peaked is specific to each patient-derived line. Furthermore, evidence that this response relies on phosphorylated ezrin, which interacts with the intracellular domain of HA-engaged CD44 to effectively link the actin cytoskeleton to the local ECM is provided. Overall, this study highlights CD44-HA binding as a major mediator of GBM cell migration that acts independently of integrins and focal adhesion complexes and suggests that targeting HA-CD44-ezrin interactions represents a promising therapeutic strategy to prevent tumor cell invasion in the brain.

Nanotopography and Microconfinement Impact on Primary Hippocampal Astrocyte Morphology, Cytoskeleton and Spontaneous Calcium Wave Signalling

Astrocytes' organisation affects the functioning and the fine morphology of the brain, both in physiological and pathological contexts. Although many aspects of their role have been characterised, their complex functions remain, to a certain extent, unclear with respect to their contribution to brain cell communication. Here, we studied the effects of nanotopography and microconfinement on primary hippocampal rat astrocytes. For this purpose, we fabricated nanostructured zirconia surfaces as homogenous substrates and as micrometric patterns, the latter produced by a combination of an additive nanofabrication and micropatterning technique. These engineered substrates reproduce both nanotopographical features and microscale geometries that astrocytes encounter in their natural environment, such as basement membrane topography, as well as blood vessels and axonal fibre topology. The impact of restrictive adhesion manifests in the modulation of several cellular properties of single cells (morphological and actin cytoskeletal changes) and the network organisation and functioning. Calcium wave signalling was observed only in astrocytes grown in confined geometries, with an activity enhancement in cells forming elongated agglomerates with dimensions typical of blood vessels or axon fibres. Our results suggest that calcium oscillation and wave propagation are closely related to astrocytic morphology and actin cytoskeleton organisation.

Biomechanics of Traumatic Head and Neck Injuries on Women: A State-of-the-Art Review and Future Directions

The biomechanics of traumatic injuries of the human body as a consequence of road crashes, falling, contact sports, and military environments have been studied for decades. In particular, traumatic brain injury (TBI), the so-called "silent epidemic", is the traumatic insult responsible for the greatest percentage of death and disability, justifying the relevance of this research topic. Despite its great importance, only recently have research groups started to seriously consider the sex differences regarding the morphology and physiology of women, which differs from men and may result in a specific outcome for a given traumatic event. This work aims to provide a summary of the contributions given in this field so far, from clinical reports to numerical models, covering not only the direct injuries from inertial loading scenarios but also the role sex plays in the conditions that precede an accident, and post-traumatic events, with an emphasis on neuroendocrine dysfunctions and chronic traumatic encephalopathy. A review on finite element head models and finite element neck models for the study of specific traumatic events is also performed, discussing whether sex was a factor in validating them. Based on the information collected, improvement perspectives and future directions are discussed.

3D Bioprinting of Induced Pluripotent Stem Cells and Disease Modeling

Patient-derived induced pluripotent stem cells (iPSCs), carrying the genetic information of the disease and capable of differentiating into multilineages in vitro, are valuable for disease modeling. 3D bioprinting enables the assembly of the cell-laden hydrogel into hierarchically three-dimensional architectures that recapitulate the natural tissues and organs. Investigation of iPSC-derived physiological and pathological models constructed by 3D bioprinting is a fast-growing field still in its infancy. Distinctly from cell lines and adult stem cells, iPSCs and iPSC-derived cells are more susceptible to external stimuli which can disturb the differentiation, maturation, and organization of iPSCs and their progeny. Here we discuss the fitness of iPSCs and 3D bioprinting from the perspective of bioinks and printing technologies. We provide a timely review of the progress of 3D bioprinting iPSC-derived physiological and pathological models by exemplifying the relatively prosperous cardiac and neurological fields. We also discuss scientific rigors and highlight the remaining issues to offer a guiding framework for bioprinting-assisted personalized medicine.

Structural Flexibility of Proteins Dramatically Alters Membrane Stability─A Novel Aspect of Lipid-Protein Interaction

Protein isoforms are structural variants with changes in the overall flexibility predominantly at the tertiary level. For membrane associated proteins, such structural flexibility or rigidity affects membrane stability by playing modulatory roles in lipid-protein interaction. Herein, we investigate the protein chain flexibility mediated changes in the mechanistic behavior of phospholipid model membranes in the presence of two well-known isoforms, erythroid (ER) and nonerythroid (NER) spectrin. We show dramatic alterations of membrane elasticity and stability induced by spectrin in the Langmuir monolayers of phosphatidylocholine (PC) and phosphatidylethanolamine (PE) by a combination of isobaric relaxation, surface pressure-area isotherm, X-ray scattering, and microscopy measurements. The NER spectrin drives all monolayers to possess an approximately equal stability, and that required 25-fold increase and 5-fold decrease of stability in PC and PE monolayers, respectively. The untilting transition of the PC membrane in the presence of NER spectrin observed in X-ray measurements can explain better membrane packing and stability.

Biomechanical forces in the aged brain: Relationship to AD

The pathogenesis of neurodegenerative disorders, including Alzheimer's disease, has been studied with a focus on biochemical mechanisms, such as the amyloid-β plaque deposition and removal. Recently, the importance of brain microenvironmental cues, which comprise the sophisticated cellular and fluid system, has been emphasized in the aged brain or in pathological conditions. Especially, substrate rigidity and biomechanical forces of the brain microenvironment determine the function of glial cells and neurons; furthermore, these microenvironmental cues change with age. However, our understanding of role of the biomechanical cues on glial cells and neurons is relatively poor. In this review, we briefly introduce an overview of biomechanical forces that present in the aged brain and its sensations, and then examine the brain in Alzheimer's disease, which constitutes a representative neurodegenerative disorder, with regard to changes in the biomechanical forces associated with disease and aging.

Long-term recovery behavior of brain tissue in hydrocephalus patients after shunting

The unpredictable complexities in hydrocephalus shunt outcomes may be related to the recovery behavior of brain tissue after shunting. The simulated cerebrospinal fluid (CSF) velocity and intracranial pressure (ICP) over 15 months after shunting were validated by experimental data. The mean strain and creep of the brain had notable changes after shunting and their trends were monotonic. The highest stiffness of the hydrocephalic brain was in the first consolidation phase (between pre-shunting to 1 month after shunting). The viscous component overcame and damped the input load in the third consolidation phase (after the fifteenth month) and changes in brain volume were stopped. The long-intracranial elastance (long-IE) changed oscillatory after shunting and there was not a linear relationship between long-IE and ICP. We showed the long-term effect of the viscous component on brain recovery behavior of hydrocephalic brain. The results shed light on the brain recovery mechanism after shunting and the mechanisms for shunt failure.

Morphological changes in glial cells arrangement under mechanical loading: A quantitative study

The mechanical properties and microstructure of brain tissue, as its two main physical parameters, could be affected by mechanical stimuli. In previous studies, microstructural alterations due to mechanical loading have received less attention than the mechanical properties of the tissue. Therefore, the current study aimed to investigate the effect of ex-vivo mechanical forces on the micro-architecture of brain tissue including axons and glial cells. A three-step loading protocol (i.e., loading-recovery-loading) including eight strain levels from 5% to 40% was applied to bovine brain samples with axons aligned in one preferred direction (each sample experienced only one level of strain). After either the first or secondary loading step, the samples were fixed, cut in planes parallel and perpendicular to the loading direction, and stained for histology. The histological images were analyzed to measure the end-to-end length of axons and glial cell-cell distances. The results showed that after both loading steps, as the strain increased, the changes in the cell nuclei arrangement in the direction parallel to axons were more significant compared to the other two perpendicular directions. Based on this evidence, we hypothesized that the spatial pattern of glial cells is highly affected by the orientation of axonal fibers. Moreover, the results revealed that in both loading steps, the maximum cell-cell distance occurred at 15% strain, and this distance decreased for higher strains. Since 15% strain is close to the previously reported brain injury threshold, this evidence could suggest that at higher strains, the axons start to rupture, causing a reduction in the displacement of glial cells. Accordingly, it was concluded that more attention to glial cells' architecture during mechanical loading may lead to introduce a new biomarker for brain injury.