P 136 Brain network analysis of EEG data in the service of clinical assessment – utilizing big data and prior theoretical knowledge to identify a biomarker for mTBI in adolscents. Issue 10 (October 2017) - Reches, A.; Or-ly, H.; Weiss, M.; Stern, Y.; Baumeister, J.C.; Foss, K.B.; Ellis, J.; Laish, B.; Laufer, O.; Sadeh, B.; Ettinger, M.; Arthur, T.; et al.
Abstract : Mild traumatic brain injury (mTBI) in adolecents has gained increased attention in recent years amongst parents, clinicians, and researchers due to their growing rate and hazardous outcomes. Electroencephalography (EEG) and Event-Related Potential (ERP) have been encouraged as a diagnostic tool for mTBI due to its objectivity and cost-effectiveness. However, extracting clinically meaningful neuro-cognitive information from human EEG/ERP is challenging, particularly in highly variable groups like adolescents. Therefore, it is not surprising that despite them being especially susceptible to the effects of mTBI, no sensitive and specific application of EEG has yet been determined for this age group. To overcome these challenges, we have developed and applied a framework, termed Brain Network Activity (BNA) analysis, which utilizes a hybrid approach of very large datasets, novel signal processing technology, and academic knowledge (see Stern, Y., Reches, A., and Geva A. "Using Spatiotemporal Features in the Brain Network Activation Analysis for Improved Data Classification". Frontiers in computational neuroscience, 2016). For the BNA core database, spatiotemporal data from 15, 100 EEG recording files of healthy individuals performing various computerized cognitive tasks were extracted. Advanced machine learning and prior knowledge from the ERP literature was used to assemble an optimal set of brain network models for various cognitive functions, such as attention, memory, motor control, and sensory processing. This resulted in reference brain network models, to which the BNA of an individual or of a whole patient group can be compared to in terms of quantitative scores and qualitative brain map illustrations. In cases of specific sub-groups such as adolescents with mTBI, these BNA scores and maps may be applied to identify biomarkers specific to that population. In order to generate an mTBI biomarker for adolescents, BNA analysis was applied to EEG recordings of 107 healthy participants and 36 mTBI adolescent patients while they performed an auditory oddball task. The BNA features of all participants were extracted and included in repeated-measures analyses of variance (ANOVA) with EEG session gain (2nd vs 1st, 3rd vs 2nd), interval between sessions (<2 months, >2 months) and group (healthy, mTBI) as factors. Results showed a large negative evoked potential component in frontal-central regions between ∼200 and 500 ms following the standard stimulus for both groups (mTBI and healthy) during the baseline EEG recording sessions. The average negative amplitude decreased in the healthy control group's 2nd EEG recording session. It remained steady in the concussed group's 2nd (post-mTBI) session, and was significantly different from healthy controls. The pre- to post-mTBI gain in amplitude was correlated with the time interval between recording sessions ( r = −0.47, p -value < 0.05). These results indicate that the negative component's amplitude gains are associated with mTBI in adolescent, making it a potential biomarker for mTBI diagnosis and monitoring. BNA analysis may therefore not only assist clinicians as a neural assessment tool in individual patients, but may also be utilized for generating potential markers specific to certain patient groups.