Pupillary Light Response Deficits in 4-Week-Old Piglets and Adolescent Children after Low-Velocity Head Rotations and Sports-Related Concussions

Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion

Background: Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). Objective: To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. Methods: Division I college football players had baseline pupillometry including pupillary light reflex (PLR) parameters of maximum resting diameter, minimum diameter after light stimulus, percent change in pupil diameter, latency of pupil constriction onset, mean constriction velocity, maximum constriction velocity, and mean dilation velocity using a smartphone-based app. When an SRC occurred, athletes had the smartphone pupillometry repeated as part of their concussion testing. All combinations of the seven PLR parameters were tested in machine learning binary classification models to determine the optimal combination for differentiating between non-concussed and concussed athletes. Results: 93 football athletes underwent baseline pupillometry testing. Among these athletes, 11 suffered future SRC and had pupillometry recordings repeated at the time of diagnosis. In the machine learning pupillometry analysis that used the synthetic minority oversampling technique to account for the significant class imbalance in our dataset, the best-performing model was a random forest algorithm with the combination of latency, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity PLR parameters as feature inputs. This model produced 91% overall accuracy, 98% sensitivity, 84.2% specificity, area under the curve (AUC) of 0.91, and an F1 score of 91.6% in differentiating between baseline and SRC recordings. In the machine learning analysis prior to oversampling of our imbalanced dataset, the best-performing model was k-nearest neighbors using latency, maximum diameter, maximum constriction velocity, and mean dilation velocity to produce 82% accuracy, 40% sensitivity, 87% specificity, AUC of 0.64, and F1 score of 24%. Conclusions: Smartphone pupillometry in combination with machine learning may provide fast and objective SRC diagnosis in football athletes.

Evaluation of Quantitative Pupillometry in Acute Postinjury Pediatric Concussion

BACKGROUND

Although millions of children sustain concussions each year, a rapid and objective test for concussion has remained elusive. The aim of this study was to investigate quantitative pupillometry in pediatric patients in the acute, postinjury setting.

METHODS

This was a prospective case-control study of concussed patients presenting to the emergency department within 72 hours of injury. Pupillary measurements were gathered using NeurOptics' PLR 3000; evaluation included a symptom checklist and neurocognitive assessment. Data were analyzed using descriptive statistics and regression models.

RESULTS

A total of 126 participants were enrolled. One significant difference in pupillometry between concussed and control participants was found: left minimum pupil diameter in 12- to 18 year-olds (P = 0.02). Models demonstrating odds of a concussion revealed significant associations for time to 75% recovery (T75) of the left pupil in five- to 11-year-olds and average dilation velocity of the left pupil in 12- to 18-year-olds (P = 0.03 and 0.02 respectively). Models predicting symptom improvement showed one significant association: percent change of the right pupil in five-to-11-year-olds (P = 0.02). Models predicting neurocognitive improvement in 12- to 18-year-olds demonstrated significant association in T75 in the left pupil for visual memory, visual motor processing speed, and reaction time (P = 0.002, P = 0.04, P = 0.04).

CONCLUSIONS

The limited statistically significant associations found in this study suggest that pupillometry may not be useful in pediatrics in the acute postinjury setting for either the diagnosis of concussion or to stratify risk for prolonged recovery.

Porcine Models of Neurotrauma and Neurological Disorders

The translation of therapeutics from lab to clinic has a dismal record in the fields of neurotrauma and neurological disorders [...].

Altered Auditory and Visual Evoked Potentials following Single and Repeated Low-Velocity Head Rotations in 4-Week-Old Swine

Auditory and visually evoked potentials (EP) have the ability to monitor cognitive changes after concussion. In the literature, decreases in EP are commonly reported; however, a subset of studies shows increased cortical activity after injury. We studied auditory and visual EP in 4-week-old female Yorkshire piglets (N = 35) divided into anesthetized sham, and animals subject to single (sRNR) and repeated (rRNR) rapid non-impact head rotations (RNR) in the sagittal direction. Two-tone auditory oddball tasks and a simple white-light visual stimulus were evaluated in piglets pre-injury, and at days 1, 4- and 7 post injury using a 32-electrode net. Traditional EP indices (N1, P2 amplitudes and latencies) were extracted, and a piglet model was used to source-localize the data to estimate brain regions related to auditory and visual processing. In comparison to each group's pre-injury baselines, auditory Eps and brain activity (but not visual activity) were decreased in sham. In contrast, sRNR had increases in N1 and P2 amplitudes from both stimuli. The rRNR group had decreased visual N1 amplitudes but faster visual P2 latencies. Auditory and visual EPs have different change trajectories after sRNR and rRNR, suggesting that injury biomechanics are an important factor to delineate neurofunctional deficits after concussion.