Stability of an ERP-based measure of brain network activation (BNA) in athletes: A new electrophysiological assessment tool for concussion

Abstract

Primary objective: To determine test–re-test reliabilities of novel Evoked Response Potential (ERP)-based Brain Network Activation (BNA) scores in healthy athletes.

Research design: Observational, repeated-measures study.

Methods and design: Forty-two healthy male and female high school and collegiate athletes completed auditory oddball and go/no-go ERP assessments at baseline, 1 week, 6 weeks and 1 year. The BNA algorithm was applied to the ERP data, considering electrode location, frequency band, peak latency and normalized amplitude to generate seven unique BNA scores for each testing session.

Main outcomes and results: Mean BNA scores, intra-class correlation coefficient (ICC) values and reliable change (RC) values were calculated for each of the seven BNA networks. BNA scores ranged from 46.3 ± 34.9 to 69.9 ± 22.8, ICC values ranged from 0.46–0.65 and 95% RC values ranged from 38.3–68.1 across the seven networks.

Conclusions: The wide range of BNA scores observed in this population of healthy athletes suggests that a single BNA score or set of BNA scores from a single after-injury test session may be difficult to interpret in isolation without knowledge of the athlete’s own baseline BNA score(s) and/or the results of serial tests performed at additional time points. The stability of each BNA network should be considered when interpreting test–re-test BNA score changes.

Keywords:

• Athlete
• brain network activation
• concussion
• electroencephalography
• event-related potential
• reliability

Acknowledgements

The authors would like to thank Dr. Jeffrey Kutcher for his contributions to this project. They would like to thank Mr. Max Zeiger and Ms. Samantha Zetlin for their assistance with data collection. The authors would also like to thank ElMindA, LTD., for its assistance with BNA score management and interpretation, as well as its explanation of the BNA algorithm.

Declaration of interest

This research was funded by ElMindA, LTD. Dr. Eckner’s effort on this project was partially supported by career development awards from the Rehabilitation Medicine Scientist Training Program (5 K12 HD001097) and the National Institutes of Health (1 K23 HD078502). Dr. Eckner has received research support from ElMindA, LTD. His active research funding includes the National Institutes of Health (1 K23 HD078502), the National Collegiate Athletics Association, the United States Department of Defense (14132004), the University of Michigan Injury Center and the Foundation for Physical Medicine and Rehabilitation. Ms. Rettmann has received research support from ElMindA, LTD. Mr. Greer has received research support from ElMindA, LTD. Mr. Moore has received research support from ElMindA, LTD. Ms. Brimacombe has received research support from ElMindA, LTD. Dr. He has received research support from ElMindA, LTD. Dr. Broglio has received research support from ElMindA, LTD. His active research funding includes the National Institutes of Health (1R15NS081691-01, 3R15NS081691-01S1), the National Collegiate Athletic Association, the United States Department of Defense (14132004) and the University of Michigan Injury Center. Mr. Narisetty reports no conflicts of interest.