Browsing by Author "Cecchi, Nicholas J"

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Open Access
A new open-access platform for measuring and sharing mTBI data.
(Scientific reports, 2021-04) Domel, August G; Raymond, Samuel J; Giordano, Chiara; Liu, Yuzhe; Yousefsani, Seyed Abdolmajid; Fanton, Michael; Cecchi, Nicholas J; Vovk, Olga; Pirozzi, Ileana; Kight, Ali; Avery, Brett; Boumis, Athanasia; Fetters, Tyler; Jandu, Simran; Mehring, William M; Monga, Sam; Mouchawar, Nicole; Rangel, India; Rice, Eli; Roy, Pritha; Sami, Sohrab; Singh, Heer; Wu, Lyndia; Kuo, Calvin; Zeineh, Michael; Grant, Gerald; Camarillo, David B
Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.
Open Access
Correction: Identifying Factors Associated with Head Impact Kinematics and Brain Strain in High School American Football via Instrumented Mouthguards.
(Annals of biomedical engineering, 2023-02) Cecchi, Nicholas J; Domel, August G; Liu, Yuzhe; Rice, Eli; Lu, Rong; Zhan, Xianghao; Zhou, Zhou; Raymond, Samuel J; Sami, Sohrab; Singh, Heer; Rangel, India; Watson, Landon P; Kleiven, Svein; Zeineh, Michael; Camarillo, David B; Grant, Gerald
This erratum is to correct the mean and standard deviation values of MPS95 in the first paragraph of the Results section and the Abstract. The mean ± SD values for MPS95 should be stated as 0.145 ±0.119. Further, the caption for Fig. 5 should state that the brain simulations are showing maximum principal strain, not 95% maximum principal strain. All data and images from figures including values of MPS95 are accurate as originally presented.
Open Access
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics
(Journal of Sport and Health Science, 2023-03) Zhan, Xianghao; Li, Yiheng; Liu, Yuzhe; Cecchi, Nicholas J; Raymond, Samuel J; Zhou, Zhou; Vahid Alizadeh, Hossein; Ruan, Jesse; Barbat, Saeed; Tiernan, Stephen; Gevaert, Olivier; Zeineh, Michael M; Grant, Gerald A; Camarillo, David B
Open Access
Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings.
(Annals of biomedical engineering, 2023-03) Cecchi, Nicholas J; Callan, Ashlyn A; Watson, Landon P; Liu, Yuzhe; Zhan, Xianghao; Vegesna, Ramanand V; Pang, Collin; Le Flao, Enora; Grant, Gerald A; Zeineh, Michael M; Camarillo, David B
Protective headgear effects measured in the laboratory may not always translate to the field. In this study, we evaluated the impact attenuation capabilities of a commercially available padded helmet shell cover in the laboratory and on the field. In the laboratory, we evaluated the padded helmet shell cover's efficacy in attenuating impact magnitude across six impact locations and three impact velocities when equipped to three different helmet models. In a preliminary on-field investigation, we used instrumented mouthguards to monitor head impact magnitude in collegiate linebackers during practice sessions while not wearing the padded helmet shell covers (i.e., bare helmets) for one season and whilst wearing the padded helmet shell covers for another season. The addition of the padded helmet shell cover was effective in attenuating the magnitude of angular head accelerations and two brain injury risk metrics (DAMAGE, HARM) across most laboratory impact conditions, but did not significantly attenuate linear head accelerations for all helmets. Overall, HARM values were reduced in laboratory impact tests by an average of 25% at 3.5 m/s (range: 9.7 to 39.6%), 18% at 5.5 m/s (range: - 5.5 to 40.5%), and 10% at 7.4 m/s (range: - 6.0 to 31.0%). However, on the field, no significant differences in any measure of head impact magnitude were observed between the bare helmet impacts and padded helmet impacts. Further laboratory tests were conducted to evaluate the ability of the padded helmet shell cover to maintain its performance after exposure to repeated, successive impacts and across a range of temperatures. This research provides a detailed assessment of padded helmet shell covers and supports the continuation of in vivo helmet research to validate laboratory testing results.
Open Access
Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts.
(Annals of biomedical engineering, 2022-11) Zhan, Xianghao; Li, Yiheng; Liu, Yuzhe; Cecchi, Nicholas J; Gevaert, Olivier; Zeineh, Michael M; Grant, Gerald A; Camarillo, David B
In a previous study, we found that the relationship between brain strain and kinematic features cannot be described by a generalized linear model across different types of head impacts. In this study, we investigate if such a linear relationship exists when partitioning head impacts using a data-driven approach. We applied the K-means clustering method to partition 3161 impacts from various sources including simulation, college football, mixed martial arts, and car crashes. We found piecewise multivariate linearity between the cumulative strain damage (CSDM; assessed at the threshold of 0.15) and head kinematic features. Compared with the linear regression models without partition and the partition according to the types of head impacts, K-means-based data-driven partition showed significantly higher CSDM regression accuracy, which suggested the presence of piecewise multivariate linearity across types of head impacts. Additionally, we compared the piecewise linearity with the partitions based on individual features used in clustering. We found that the partition with maximum angular acceleration magnitude at 4706 rad/s² led to the highest piecewise linearity. This study may contribute to an improved method for the rapid prediction of CSDM in the future.