'Knock down the brain': a nonlinear analysis of electroencephalography to study the effects of sub-concussion in boxers.

Abstract

Boxing is associated with a high risk of head injuries and increases the likelihood of chronic traumatic encephalopathy. This study explores the effects of sub-concussive impacts on boxers by applying both linear and nonlinear analysis methods to electroencephalogram (EEG) data. Twenty-one boxers were selected (mean ± SD, age 28.38 ± 5.5 years; weight 67.55 ± 8.90 kg; years of activity 6.76 ± 5.45; education 14.19 ± 3.08 years) and divided into 'beginner' and 'advanced' groups. The Montreal Cognitive Assessment and the Frontal Assessment Battery were administered; EEG data were collected in both eyes-open (EO) and eyes-closed (EC) conditions during resting states. Analyses of EEG data included normalized power spectral density (nPSD), power law exponent (PLE), detrended fluctuation analysis and multiscale entropy. Statistical analyses were used to compare the groups. Significant differences in nPSD and PLE were observed between the beginner and advanced boxers, with advanced boxers showing decreased mean nPSD and PLE (nPSD 4-7 Hz, p = 0.013; 8-13 Hz, p = 0.003; PLE frontal lobe F3 EC, p = 0.010). Multiscale entropy analysis indicated increased entropy at lower frequencies and decreased entropy at higher frequencies in advanced boxers (F3 EC, p = 0.024; occipital lobe O1 EO, p = 0.029; occipital lobe O2 EO, p = 0.036). These changes are similar to those seen in Alzheimer's disease. Nonlinear analysis of EEG data shows potential as a neurophysiological biomarker for detecting the asymptomatic phase of chronic traumatic encephalopathy in boxers. This methodology could help monitor athletes' health and reduce the risk of future neurological injuries in sports.