More Like this

1. Impaired visual working memory and reduced connectivity in undergraduates with a history of mild traumatic brain injury

   https://doi.org/10.1038/s41598-021-80995-1  

   Arciniega, Hector; Shires, Jorja; Furlong, Sarah; Kilgore-Gomez, Alexandrea; Cerreta, Adelle; Murray, Nicholas G.; Berryhill, Marian E. (December 2021, Scientific Reports)

   null (Ed.)

   Abstract Mild traumatic brain injury (mTBI), or concussion, accounts for 85% of all TBIs. Yet survivors anticipate full cognitive recovery within several months of injury, if not sooner, dependent upon the specific outcome/measure. Recovery is variable and deficits in executive function, e.g., working memory (WM) can persist years post-mTBI. We tested whether cognitive deficits persist in otherwise healthy undergraduates, as a conservative indicator for mTBI survivors at large. We collected WM performance (change detection, n-back tasks) using various stimuli (shapes, locations, letters; aurally presented numbers and letters), and wide-ranging cognitive assessments (e.g., RBANS). We replicated the observation of a general visual WM deficit, with preserved auditory WM. Surprisingly, visual WM deficits were equivalent in participants with a history of mTBI (mean 4.3 years post-injury) and in undergraduates with recent sports-related mTBI (mean 17 days post-injury). In seeking the underlying mechanism of these behavioral deficits, we collected resting state fMRI (rsfMRI) and EEG (rsEEG). RsfMRI revealed significantly reduced connectivity within WM-relevant networks (default mode, central executive, dorsal attention, salience), whereas rsEEG identified no differences (modularity, global efficiency, local efficiency). In summary, otherwise healthy current undergraduates with a history of mTBI present behavioral deficits with evidence of persistent disconnection long after full recovery is expected. 

   more » « less
2. A theoretical framework for predicting the heterogeneous stiffness map of brain white matter tissue

   https://doi.org/10.1088/1478-3975/ad88e4  

   Chavoshnejad, Poorya; Li, Guangfa; Solhtalab, Akbar; Liu, Dehao; Razavi, Mir_Jalil (October 2024, Physical Biology)

   Abstract Finding the stiffness map of biological tissues is of great importance in evaluating their healthy or pathological conditions. However, due to the heterogeneity and anisotropy of biological fibrous tissues, this task presents challenges and significant uncertainty when characterized only by single-mode loading experiments. In this study, we propose a new theoretical framework to map the stiffness landscape of fibrous tissues, specifically focusing on brain white matter tissue. Initially, a finite element (FE) model of the fibrous tissue was subjected to six loading cases, and their corresponding stress–strain curves were characterized. By employing multiobjective optimization, the material constants of an equivalent anisotropic material model were inversely extracted to best fit all six loading modes simultaneously. Subsequently, large-scale FE simulations were conducted, incorporating various fiber volume fractions and orientations, to train a convolutional neural network capable of predicting the equivalent anisotropic material properties solely based on the fibrous architecture of any given tissue. The proposed method, leveraging brain fiber tractography, was applied to a localized volume of white matter, demonstrating its effectiveness in precisely mapping the anisotropic behavior of fibrous tissue. In the long-term, the proposed method may find applications in traumatic brain injury, brain folding studies, and neurodegenerative diseases, where accurately capturing the material behavior of the tissue is crucial for simulations and experiments. 

   more » « less
3. A Strain Rate-Dependent Constitutive Model for Göttingen Minipig Cerebral Arteries

   https://doi.org/10.1115/1.4053796  

   Pearson, Noah; Boiczyk, Gregory M.; Kote, Vivek Bhaskar; Sundaramurthy, Aravind; Subramaniam, Dhananjay Radhakrishnan; Rubio, Jose E.; Unnikrishnan, Ginu; Reifman, Jaques; Monson, Kenneth (August 2022, Journal of Biomechanical Engineering)

   Abstract Computational simulations of traumatic brain injury (TBI) are commonly used to advance understanding of the injury–pathology relationship, tissue damage thresholds, and design of protective equipment such as helmets. Both human and animal TBI models have developed substantially over recent decades, partially due to the inclusion of more detailed brain geometry and representation of tissues like cerebral blood vessels. Explicit incorporation of vessels dramatically affects local strain and enables researchers to investigate TBI-induced damage to the vasculature. While some studies have indicated that cerebral arteries are rate-dependent, no published experimentally based, rate-sensitive constitutive models of cerebral arteries exist. In this work, we characterize the mechanical properties of axially failed porcine arteries, both quasi-statically (0.01 s−1) and at high rate (>100 s−1), and propose a rate-sensitive model to fit the data. We find that the quasi-static and high-rate stress–stretch curves become significantly different (p < 0.05) above a stretch of 1.23. We additionally find a significant change in both failure stretch and stress as a result of strain rate. The stress–stretch curve is then modeled as a Holzapfel–Gasser–Ogden material, with a Prony series added to capture the effects of viscoelasticity. Ultimately, this paper demonstrates that rate dependence should be considered in the material properties of cerebral arteries undergoing high strain-rate deformations and provides a ready-to-use model for finite element implementation. 

   more » « less
4. Characterization of neural mechanotransduction response in human traumatic brain injury organoid model

   https://doi.org/10.1038/s41598-023-40431-y  

   Beltrán, Susana M; Bobo, Justin; Habib, Ahmed; Kodavali, Chowdari V; Edwards, Lincoln; Mamindla, Priyadarshini; Taylor, Rebecca E; LeDuc, Philip R; Zinn, Pascal O (December 2023, Scientific Reports)

   Abstract The ability to model physiological systems through 3D neural in-vitro systems may enable new treatments for various diseases while lowering the need for challenging animal and human testing. Creating such an environment, and even more impactful, one that mimics human brain tissue under mechanical stimulation, would be extremely useful to study a range of human-specific biological processes and conditions related to brain trauma. One approach is to use human cerebral organoids (hCOs) in-vitro models. hCOs recreate key cytoarchitectural features of the human brain, distinguishing themselves from more traditional 2D cultures and organ-on-a-chip models, as well as in-vivo animal models. Here, we propose a novel approach to emulate mild and moderate traumatic brain injury (TBI) using hCOs that undergo strain rates indicative of TBI. We subjected the hCOs to mild (2 s⁻¹) and moderate (14 s⁻¹) loading conditions, examined the mechanotransduction response, and investigated downstream genomic effects and regulatory pathways. The revealed pathways of note were cell death and metabolic and biosynthetic pathways implicating genes such as CARD9, ENO1, and FOXP3, respectively. Additionally, we show a steeper ascent in calcium signaling as we imposed higher loading conditions on the organoids. The elucidation of neural response to mechanical stimulation in reliable human cerebral organoid models gives insights into a better understanding of TBI in humans. 

   more » « less
5. Brain White Matter Model of Orthotropic Viscoelastic Properties in Frequency Domain

   https://doi.org/10.1115/IMECE2019-12182  

   Wu, Xuehai; Georgiadis, John G.; Pelegri, Assimina A. (November 2019, ASME INternational Mechanical Engineering Congress and Exposition)

   Finite element analysis is used to study brain axonal injury and develop Brain White Matter (BWM) models while accounting for both the strain magnitude and the strain rate. These models are becoming more sophisticated and complicated due to the complex nature of the BMW composite structure with different material properties for each constituent phase. State-of-the-art studies focus on employing techniques that combine information about the local axonal directionality in different areas of the brain with diagnostic tools such as Diffusion-Weighted Magnetic Resonance Imaging (Diffusion-MRI). The diffusion-MRI data offers localization and orientation information of axonal tracks which are analyzed in finite element models to simulate virtual loading scenarios. Here, a BMW biphasic material model comprised of axons and neuroglia is considered. The model’s architectural anisotropy represented by a multitude of axonal orientations, that depend on specific brain regions, adds to its complexity. During this effort, we develop a finite element method to merge micro-scale Representative Volume Elements (RVEs) with orthotropic frequency domain viscoelasticity to an integrated macro-scale BWM finite element model, which incorporates local axonal orientation. Previous studies of this group focused on building RVEs that combined different volume fractions of axons and neuroglia and simulating their anisotropic viscoelastic properties. Via the proposed model, we can assign material properties and local architecture on each element based on the information from the orientation of the axonal traces. Consecutively, a BWM finite element model is derived with fully defined both material properties and material orientation. The frequency-domain dynamic response of the BMW model is analyzed to simulate larger scale diagnostic modalities such as MRI and MRE. 

   more » « less