A study was published this month in the European Journal of Neurology suggesting that EEG readings may have promise in showing early signs of CTE.
In the study, titled 'Knock down the brain': a nonlinear analysis of electroencephalography to study the effects of sub-concussion in boxers, the authors selected 21 boxers. They seperated the athletes based on experience (those with at least 25 fights and a minimum of 5 years of boxing experience vs those with less). They then used a cognitive assessment on the athletes and EEG data was also collected. The authors found that the more experienced athletes (ie those who have likely had more brain rattling in their careers) had objectively measurable differences on the EEG readings leading them to speculate that this could be a useful screening tool for early signs of CTE.
The full abstract reads as follows:
Abstract
Background and purpose: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
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