A Machine Learning Enhanced Mechanistic Simulation Framework for Functional Deficit Prediction in TBI

Accuracy and efficiency of finite element head models: The role of finite element formulation and material laws

Traumatic brain injury is a significant problem worldwide. In the United States of America, around 1.7 million cases are documented annually, displaying the need for a deeper understanding of the effects on the human brain. The tests required for this assessment are very complex. Tests on cadavers may raise serious ethical questions, and in vivo crash tests are not viable. In this context, there is a great need to developing finite element head models (FEHM) to study the biomechanics of the tissues when submitted to a certain impact or acceleration/deceleration scenario. An excellent compromise between accuracy and CPU efficiency is always desirable for a FEHM, For this reason, this work focuses on the improvement of an existing head model, including the study of the behavior of the brain using distinct finite element types. The finite element type and formulation is of utmost importance for the general accuracy and efficiency of the models. Several validations were performed, comparing the simulation results against experimental data. The simulations with hexahedral elements, under specific conditions, obtained more accurate results with a lower computational cost. Using hexahedrals, a comparison was also performed using two material characterizations with more than 10 years apart, using the latest finite element head model validation experiment. Overall, the newer material model displays a less stiff response, although its implementation must always depend on the overall purpose of the model it is being applied to.

Statistical and machine learning approaches to predict the necessity for computed tomography in children with mild traumatic brain injury

BACKGROUND

Minor head trauma in children is a common reason for emergency department visits, but the risk of traumatic brain injury (TBI) in those children is very low. Therefore, physicians should consider the indication for computed tomography (CT) to avoid unnecessary radiation exposure to children. The purpose of this study was to statistically assess the differences between control and mild TBI (mTBI). In addition, we also investigate the feasibility of machine learning (ML) to predict the necessity of CT scans in children with mTBI.

METHODS AND FINDINGS

The study enrolled 1100 children under the age of 2 years to assess pre-verbal children. Other inclusion and exclusion criteria were per the PECARN study. Data such as demographics, injury details, medical history, and neurological assessment were used for statistical evaluation and creation of the ML algorithm. The number of children with clinically important TBI (ciTBI), mTBI on CT, and controls was 28, 30, and 1042, respectively. Statistical significance between the control group and clinically significant TBI requiring hospitalization (csTBI: ciTBI+mTBI on CT) was demonstrated for all nonparametric predictors except severity of the injury mechanism. The comparison between the three groups also showed significance for all predictors (p<0.05). This study showed that supervised ML for predicting the need for CT scan can be generated with 95% accuracy. It also revealed the significance of each predictor in the decision tree, especially the "days of life."

CONCLUSIONS

These results confirm the role and importance of each of the predictors mentioned in the PECARN study and show that ML could discriminate between children with csTBI and the control group.

Paroxysmal Sympathetic Hyperactivity in Adult Patients with Brain Injury: A Systematic Review and Meta-Analysis

BACKGROUND

Paroxysmal sympathetic hyperactivity (PSH) is a syndrome of excessive sympathetic activity, mainly occurring in severe traumatic brain injury. However, few studies have reported the frequency of PSH and its related risk factors in adult patients with brain injury.

METHODS

We performed this systematic review and meta-analysis to estimate the combined incidence of PSH and the associated risk factors in adult patients with brain injury. This study was registered with the PROSPERO international prospective register of systematic reviews (https://www.crd.york. ac.uk/PROSPERO/Identifier: CRD 42021260493), and a systematic search was conducted of the scientific databases Embase, PubMed, Web of Science, Cochrane Library, and Google Scholar. All identified observational studies regarding the incidence and risk factors of PSH in adult patients with brain injury were included. Two authors extracted data independently; data were analyzed by STATA version 16.

RESULTS

The search yielded 9 studies involving 1643 adult patients. PSH was detected in 438 patients. The combined incidence of PSH in adult patients with brain injury was 27.4% (95% confidence interval [CI], 0.190-0.358). The risk factors include patients' age (SMD = -0.592; I² = 77.5%; 95% CI, -1.027 to -0.156; P = 0.008), traffic accident (odds ratio [OR], 1.783; I² =18.0%; 95% CI, 1.128-2.820; P = 0.013), admission Glasgow Coma Scale score (SMD = -1.097; I² =28.3%; 95% CI, -1.500 to -0.693; P = 0.000), hydrocephalus (OR, 3.936; I² =67.9%; 95% CI, 1.144-13.540; P = 0.030), and diffuse axonal injury (OR, 4.747; I² =71.1%; 95% CI, 1.221-18.463; P = 0.025) and were significantly associated with the presence of PSH after brain injury.

CONCLUSIONS

PSH occurs in nearly a quarter of adult patients with brain injury. Patient's age, traffic accident, admission Glasgow Coma Scale score, hydrocephalus, and diffuse axonal injury were risk factors for PSH in adult patients with brain injury. These findings may contribute to novel strategies for early diagnosis and interventions that aid in the rehabilitation of patients with brain injury.

Biochemical Pathways of Cellular Mechanosensing/Mechanotransduction and Their Role in Neurodegenerative Diseases Pathogenesis

In this review, we shed light on recent advances regarding the characterization of biochemical pathways of cellular mechanosensing and mechanotransduction with particular attention to their role in neurodegenerative disease pathogenesis. While the mechanistic components of these pathways are mostly uncovered today, the crosstalk between mechanical forces and soluble intracellular signaling is still not fully elucidated. Here, we recapitulate the general concepts of mechanobiology and the mechanisms that govern the mechanosensing and mechanotransduction processes, and we examine the crosstalk between mechanical stimuli and intracellular biochemical response, highlighting their effect on cellular organelles' homeostasis and dysfunction. In particular, we discuss the current knowledge about the translation of mechanosignaling into biochemical signaling, focusing on those diseases that encompass metabolic accumulation of mutant proteins and have as primary characteristics the formation of pathological intracellular aggregates, such as Alzheimer's Disease, Huntington's Disease, Amyotrophic Lateral Sclerosis and Parkinson's Disease. Overall, recent findings elucidate how mechanosensing and mechanotransduction pathways may be crucial to understand the pathogenic mechanisms underlying neurodegenerative diseases and emphasize the importance of these pathways for identifying potential therapeutic targets.

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.