Differential effect of first versus second concussive episodes on wavelet information quality of EEG

Abstract

Objective: Recent reports have suggested that long-term residual brain dysfunctions from mild traumatic brain injury (MTBI) that are often overlooked by clinical criteria may be detected using advanced research methods. The aim of the present study was to examine the feasibility of EEG wavelet information quality measures (EEG-IQ) in monitoring alterations of brain functions as well as to determine the differential rate of recovery between the first and second concussive episodes.

Methods: Student-athletes at high risk for MTBI (n=265) were tested prior to concussive episodes as a baseline. From this subject pool, twenty one athletes who suffered from two concussive episodes within one athletic season and were tested on days 7, 14 and 21 post-first and second injuries using a within-subjects design. Specifically, EEG was recorded and processed using wavelet entropy (EEG-IQ) algorithm along with a battery of neuropsychological (NS) tests. Spatial distribution of EEG-IQ and its dynamics in conjunction with NS data were analyzed prior to and after MTBI.

Results: No neuropsychological deficits were present in concussed subjects beyond 7 days post-injury after first and second concussions. However, EEG-IQ measures were significantly reduced primarily at temporal, parietal and the occipital regions (ROIs) after first and especially after second MTBI (p<0.01) beyond 7 days post-injury. Rate of recovery of EEG-IQ measures was significantly slower after second MTBI compared to those after the first concussion (p<0.01).

Conclusions: EEG-IQ measures may reveal alterations in the brain of concussed individuals that are most often overlooked by current assessment tools. In this regard, EEG-IQ may potentially be a valuable tool for assessing and monitoring residual brain dysfunction in "asymptomatic" MTBI subjects.

Significance: The results demonstrate the potential utility of EEG-IQ measures to classify concussed individuals at various stages of recovery.

Fig.1

The n-level filter bank, at each level, the input is decomposed into high frequency component h[n] and low frequency component g[n]. g[n] is then down sampled by 2 and served as the input of the upper level decomposition, h[n] is down sampled by 2 and served as the output of current level, i.e. detailed coefficients of the current level. Thus, the coefficients of the n-level DWT are composed of the detail coefficients of all of the n levels subspace and the approximation coefficients of the level n subspace, denoted as: DWT_n (x) [d₁, d₂ …d_n, a_n]

Fig.2

Group mean values of neuropsychological test “Trails B” dynamics as a function of testing date and MTBI (1^st versus 2^nd concussion).

Fig.3

Mean absolute values (n=21) of EEG-IQ at occipital, parietal and temporal ROIs prior to MTBI obtained during baseline testing and those on day 7, 14 and 21 post-first MTBI (a); and post-second MTBI (b).

Fig.4

Linear Pearson correlation between each subject’s time period separating two concussive episodes (days) and EEG-IQ differences (i.e., % change of EEG-IQ values between baseline and 2^nd concussion