Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Abstract

Reliable prediction and diagnosis of concussion is important for its effective clinical management. Previous model- based studies largely employ peak responses from a single element in a pre-selected anatomical region of interest (ROI) and utilize a single training dataset for injury prediction. A more systematic and rigorous approach is necessary to scrutinize the entire white matter (WM) ROIs as well as ROI-constrained neural tracts. To this end, we evaluated injury prediction performances of the 50 deep WM regions using predictor variables based on strains obtained from simulating the 58 reconstructed American National Football League (NFL) head impacts. To objectively evaluate performance, repeated random subsampling was employed to split the impacts into independent training and testing datasets (39 and 19 cases, respectively, with 100 trials). Univariate logistic regressions were conducted based on training datasets to compute the area under the receiver operating characteristic curve (AUC), while accuracy, sensitivity, and specificity were reported based on testing datasets. Two tract-wise injury susceptibilities were identified as the best overall via pair-wise permutation test. They had comparable AUC, accuracy, and sensitivity, with the highest values occurring in SLF (superior longitudinal fasciculus; 0.867–0.879, 84.4–85.2%, and 84.1–84.6%, respectively). Using metrics based on WM fiber strain, the most vulnerable ROIs included genu of corpus callosum, cerebral peduncle, and uncinate fasciculus, while genu and main body of corpus callosum, and SLF were among the most vulnerable tracts. Even for one un-concussed athlete, injury susceptibility of the cingulum (hippocampus) right was elevated. These findings highlight the unique injury discriminatory potentials of computational models, and may provide important insight into how best to incorporate WM structural anisotropy for investigation of brain injury.

Concussion classification via deep learning using whole-brain white matter fiber strains

“…Commonly used tissue response metrics include peak maximum principal strain and cumulative strain damage measure (CSDM; [10]) for the whole brain. More recently, white matter (WM) fiber strain [11][12][13][14] is also being explored as a potential improvement. There is growing interest in utilizing modelsimulated responses to benchmark the performance of other kinematic injury metrics [6,[15][16][17].…”

“…Consequently, even when using the same reconstructed American National Football League (NFL) head impacts, studies have found inconsistent "optimal" injury predictors (e.g., maximum shear stress in the brainstem [22], strain in the gray matter and CSDM 0.1 (using a strain threshold of 0.1) in the WM [18], peak axonal strain within the brainstem [13], or tract-wise injury susceptibilities in the super longitudinal fasciculus [14]). Most of these efforts are essentially "trial-and-error" in nature as they attempt to pinpoint a specific variable in a given ROI for injury prediction.…”

Performance Evaluation of a Pre-computed Brain Response Atlas in Dummy Head Impacts

“…Most biomechanical studies have so far focused on identifying the “best” injury predictor to assess the probability of the occurrence of a binary injury 8,15,17,19,27,28,30,33,40,44 . There is a large gap of knowledge on how external head impacts, through induced brain responses at the time of impact, are related to subsequent, specific neurological disorder and injury severity often observed at a later time in the clinic.…”

“…Unfortunately, most biomechanical head injury models incorporate generic regions of the brain but not yet targeted ROIs or the structural basis for brain function that is available from advanced neuroimaging (except, perhaps, for limited work incorporating whole-brain tractography 16,44,45 ). Existing response-based injury metrics such as ε p and CSDM do not consider the spatial distribution of impact-induced strains.…”

“…Details of the WHIM mesh development, material property and boundary condition assignment, as well as validation performances were reported previously 24,25 . Most recently, this model has also incorporated whole-brain tractography for injury analysis 44,45 . Importantly, the WHIM validation against relative brain–skull displacement and intracranial pressures from cadaveric impacts, as well as strain responses in a live human volunteer has achieved an overall “good” to “excellent” rating in terms of correlation scores based on Normalized Integral Square Error, as reported before 24,25 .…”