Relationship between brain injury criteria and brain strain across different types of head impacts can be different

• URL: http://arxiv.org/abs/2012.10006v3
• Date: Mon, 19 Apr 2021 06:51:37 GMT
• Title: Relationship between brain injury criteria and brain strain across different types of head impacts can be different
• Authors: Xianghao Zhan, Yiheng Li, Yuzhe Liu, August G. Domel, Hossein Vahid Alizadeh, Samuel J. Raymond, Jesse Ruan, Saeed Barbat, Stephen Tiernan, Olivier Gevaert, Michael Zeineh, Gerald Grant, David B. Camarillo
• Abstract summary: Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. The accuracy of using the BIC on brain injury risk estimation across different types of head impacts has not been evaluated.
• Score: 2.231237759133542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different types of head impacts (e.g., sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across different types of head impacts has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum, and cumulative strain damage (15%) on each of 18 BIC respectively. The results show a significant difference in the relationship between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain in different head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fit on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

Related papers

• Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation [53.70131202548981]
  We present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI. Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels. The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes.
  arXiv  Detail & Related papers  (2024-07-31T04:32:43Z)
• CausalDialogue: Modeling Utterance-level Causality in Conversations [83.03604651485327]
  We have compiled and expanded upon a new dataset called CausalDialogue through crowd-sourcing. This dataset includes multiple cause-effect pairs within a directed acyclic graph (DAG) structure. We propose a causality-enhanced method called Exponential Average Treatment Effect (ExMATE) to enhance the impact of causality at the utterance level in training neural conversation models.
  arXiv  Detail & Related papers  (2022-12-20T18:31:50Z)
• Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps [75.84770193489639]
  We examine the impact of test loss, training corpus and model architecture on the prediction of functional Magnetic Resonance Imaging timecourses of participants listening to an audiobook. We find that untrained versions of each model already explain significant amount of signal in the brain by capturing similarity in brain responses across identical words. We suggest good practices for future studies aiming at explaining the human language system using neural language models.
  arXiv  Detail & Related papers  (2022-07-07T15:37:17Z)
• Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts [2.216657815393579]
  Strain and strain rate are effective traumatic brain injury predictors. Kinematics-based models estimating these metrics suffer from significant different distributions of both kinematics and the injury metrics across head impact types. We have previously shown the kinematic features vary largely across head impact types, resulting in different patterns of brain deformation.
  arXiv  Detail & Related papers  (2021-10-27T01:38:01Z)
• Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion [2.216657815393579]
  Brain strain and strain rate are effective in predicting traumatic brain injury (TBI) caused by head impacts. FEM demands considerable computational time in the computation, limiting its application in real-time TBI risk monitoring. Machine learning head models (MLHMs) were developed, and the model accuracy was found to decrease when the training/test datasets were from different head impacts types. We trained and tested the MLHMs on 13,623 head impacts from simulations, American football, mixed martial arts, car crash, and compared against the models trained on only simulations or only on-field impacts.
  arXiv  Detail & Related papers  (2021-08-31T01:45:20Z)
• Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction [2.1108097398435337]
  Traumatic brain injury can be caused by various types of head impacts. Many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain.
  arXiv  Detail & Related papers  (2021-08-07T18:31:05Z)
• The Causal Neural Connection: Expressiveness, Learnability, and Inference [125.57815987218756]
  An object called structural causal model (SCM) represents a collection of mechanisms and sources of random variation of the system under investigation. In this paper, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020) still holds for neural models. We introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences.
  arXiv  Detail & Related papers  (2021-07-02T01:55:18Z)
• Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss [75.03117866578913]
  A novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Experiments with $6586$ MRIs showed that TSAN could provide accurate brain age estimation.
  arXiv  Detail & Related papers  (2021-06-06T07:11:25Z)
• Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation [2.606682359022052]
  Brain tissue deformation resulting from head impacts is primarily caused by rotation. Various brain injury criteria based on different factors of these kinematics have been developed. To better design brain injury criteria, the predictive power of rotational kinematics factors were analyzed.
  arXiv  Detail & Related papers  (2021-02-09T18:37:20Z)
• Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion [1.707455267617432]
  We propose a deep learning head model with a five-layer deep neural network and feature engineering. We trained the model on 1803 total head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts. The proposed deep learning head model can calculate the maximum principal strain for every element in the entire brain in less than 0.001s.
  arXiv  Detail & Related papers  (2020-10-16T17:37:59Z)
• Patch-based Brain Age Estimation from MR Images [64.66978138243083]
  Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. We develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator.
  arXiv  Detail & Related papers  (2020-08-29T11:50:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.