Concussion is a heterogeneous injury occurring throughout a range of impact magnitudes. Consequently, research focusing on a single or set of variables at the time of injury to understand concussive biomechanics has been thwarted by low injury prediction sensitivity. The current study examined the role of Impact Density in estimating concussive injury risk. Head impact data were collected across seven high school football seasons with the Head Impact Telemetry System (HIT System). Over the study period, 29 concussions were included for data analysis. The linear acceleration of the concussive impact was matched to a Control athlete, along with impacts in the 24 h before. Linear and rotational acceleration for the 19 impacts leading into the final event and the cumulative accelerations over time were evaluated. Analyses indicated no difference in impact counts within the final 24 h, or impact magnitudes for linear and rotational acceleration among the final 20 impacts (p > 0.05). A novel metric, Impact Density, was calculated from the final 20 impacts by summing the acceleration magnitude divided by time from the previous impact. Analyses indicated the Concussed athletes incurred a significantly higher linear (concussed: 255.4g/sec (standard error of the mean [SEM] = 40.1), controls:145.4g/sec (SEM = 23.8), p = 0.016), and rotational (Concussed:10311.3 rad/s/s/s (SEM = 1883.7), Controls: 6083.8 rad/s/s/s (SEM = 1115.9), p = 0.029) Impact Density than the Control athletes. Similar to other investigations, there was no difference in individual linear or rotational impact magnitude in the 20 impacts before and including the injury. The measure of Impact Density, however, revealed differences between the Concussed and Control athletes. These data suggest that the biomechanical threshold for concussion fluctuates downwardly with a greater impact magnitude and number with a return to pre-impact levels with time, suggesting physiological vulnerability to repeated head impacts. The current results highlight that time between impacts, not just impact magnitude, influences risk for concussion.

Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20–100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland–Altman plots and coefficient of variation (CoV). A sampling frequency of ~100–200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40–50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1–8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates.

Heart rate variability (HRV) is a popular tool to quantify autonomic function. However, this typically requires an expensive 3–12 lead electrocardiogram (ECG) and BioAmp system. This investigation sought to determine the validity and reliability of an OpenBCI cyton biosensing board (open source) for accurately quantifying HRV. A cyton board with a 3-lead ECG was employed to acquire heart rate waveform data, which was processed to obtain HRV within both time- and frequency-domains. The concurrent validity was compared to a simultaneous recording from an industry-standard 3-lead ECG (ADInstruments) (n = 15). The reliability of the cyton board was compared between three days within a 7-day timespan (n = 10). Upright quiet-stance short-term HRV metrics were quantified in time- and frequency-domains. The two devices displayed excellent limits of agreements (all log mean differences ±0.4) and very high between-device variable associations (all r2 > 0.98). Between the three time points in the same subjects, no differences were noted within time- (all p > 0.71) or frequency-domains (all p > 0.88) across testing points. Finally, all HRV metrics exhibited excellent levels of reliability through high Cronbach's Alpha (all ≥0.916) and intraclass correlation coefficients (all ≥0.930); and small standard error of the measurement (all ≤0.7) and typical error of the measurement (all ≤0.1) metrics. The cyton board with 3-lead ECG was compared with an industry-standard ADInstruments ECG during HRV assessments. There were no significant differences between devices with respect to time- and frequency-domains. The cyton board displayed high-levels of between-day reliability and provided values harmonious to previous ECG literature highlighting the applicability for longitudinal studies. With proper background knowledge regarding ECG principles and a small degree of set-up complexity, an open source cyton board can be created and employed to perform multimodal HRV assessments at a fraction of the cost (~4%) of an industry-standard ECG setup.

Functional near-infrared spectroscopy (fNIRS) is a technology that is increasingly being applied to monitor brain function in healthy, diseased, or injured brains.^{1 – 6} As it is portable and relatively accessible in terms of data collection and processing, and it may rival MRI for assessing brain function in a wide range of neurological conditions including concussion. Concussion is a mild traumatic brain injury (mTBI) that results in altered neurological states leading to symptoms of memory and perception deficits, headaches, and other behavioral changes.^{7 – 10} The functional nature of these symptoms makes fNIRS an ideal method for monitoring the injury. In this paper, we investigate fNIRS as a tool for assessing concussion.