Reliable prediction of traumatic brain injury (TBI) outcomes based on machine learning (ML) that is derived from quantitative electroencephalography (EEG) features has renewed interest in recent years. Nevertheless, the approach has suffered from imbalanced datasets. Hence, to get a reliable predictive model for predicting outcomes, specifically in a high proportion of moderate TBI with good outcomes, could be challenging. This work proposes an improved outcome predictive model that combines the absolute power spectral density (PSD) as input features for training random under-sampling boosting decision trees (RUSBoosted Trees) as a classifier. Resting-state, eyes-closed EEG data were obtained from 27 moderate TBI patients with follow-up visits. Patient outcome at 4-10 weeks to 12-month was dichotomized based on the Glasgow Outcome Scale as poor (GOS score ≤ 4) and good outcomes (GOS score = 5). The predictive values of absolute PSD from five frequency bands: δ (0.5-4Hz), θ (4-7Hz), α (7-13Hz), β (13-30Hz) and γ (30-100Hz) were evaluated to identify the most informative predictors for reliable prediction outcomes. RUSBoosted Trees performed best at discriminating patients into two outcomes categories (G-Mean = 92.95%, TP rate =100%, TN rate = 86.4%) of absolute PSD in δ and γ bands, which was excellent compared to the other state-of-the-art methods. The highest area under the curve (AUC) of absolute PSD in δ (AUC δ = 0.97) and γ (AUC γ = 0.95) revealed their predictive values as robust prognostic markers for prediction outcomes. The RUSBoosted Trees presents a promising result in prognosis prediction of highly imbalanced data, making it an accessible prediction tool for clinical decision-making, unlike the black-box approaches.

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for the prediction of recovery in moderate traumatic brain injury (TBI). However, the brain’s recorded electrical activity has always been contaminated with artifacts, which in turn further impede the subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features for successful model development. This work proposed the use of multiple artifact rejection algorithms (MARA), which is an independent component analysis (ICA)-based algorithm, to eliminate artifacts automatically, and explored their effects on the predictive performance of the random undersampling boosting (RUSBoost) model. Continuous EEG were acquired using 64 electrodes from 27 moderate TBI patients at four weeks to one-year post-accident. The MARA incorporates an artifact removal stage based on ICA prior to RUSBoost, SVM, DT, and k-NN classification. The area under the curve (AUC) of RUSBoost was higher in absolute power spectral density (PSD) in AUCδ = 0.75, AUC α = 0.73 and AUCθ = 0.71 bands than SVM, DT, and k-NN. The MARA has provided a good generalization performance of the RUSBoost prediction model.

BackgroundPrevious studies from animal models have shown that pre-synaptic NMDA receptors (preNMDARs) are present in the cortex, but the role of inhibition mediated by preNMDARs during epileptogenesis remains unclear. In this study, we wanted to observe the changes in GABAergic inhibition through preNMDARs in sensory-motor and visual cortical pyramidal neurons after pilocarpine-induced status epilepticus.MethodsUsing a pilocarpine-induced epileptic mouse model, sensory-motor and visual cortical slices were prepared, and the whole-cell patch clamp technique was used to record spontaneous inhibitory post-synaptic currents (sIPSCs).ResultsThe primary finding was that the mean amplitude of sIPSC from the sensory-motor cortex increased significantly in epileptic mice when the recording pipette contained MK-801 compared to control mice, whereas the mean sIPSC frequency was not significantly different, indicating that post-synaptic mechanisms are involved. However, there was no significant pre-synaptic inhibition through preNMDARs in the acute brain slices from pilocarpine-induced epileptic mice.ConclusionIn the acute case of epilepsy, a compensatory mechanism of post-synaptic inhibition, possibly from ambient GABA, was observed through changes in the amplitude without significant changes in the frequency of sIPSC compared to control mice. The role of preNMDAR-mediated inhibition in epileptogenesis during the chronic condition or in the juvenile stage warrants further investigation.