Residual Deficits Observed In Athletes Following Concussion: Combined Eeg And Cognitive Study

The neurocognitive sequelae of a sport-related concussion and its management are poorly defined. Emerging evidence suggests that the residual deficits can persist one year or more following a brain injury. Detecting and quantifying the residual deficits are vital in making a decision about the treatment plan and may prevent further damage. For example, improper return to play (RTP) decisions in sports such as football have proven to be associated with the further chance of recurring injury, long-term neurophysiological impairments, and worsening of brain functional activity. The reliability of traditional cognitive assessment tools is debatable, and thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations. In this study, we calculated neurocognitive deficits in two different datasets. One dataset contains a combination of EEG analysis with three standard post-concussive assessment tools. The data for this dataset were collected for all testing modalities from 21 adolescent athletes (seven concussive and fourteen healthy) in three different trials. Another dataset contains post-concussion eyes closed EEG signal for twenty concussed and twenty age-matched controls. For EEG assessment, along with linear frequency-based features, we introduced a set of time-frequency and nonlinear features for the first time to explore post-concussive deficits. In conjunction with traditional frequency band analysis, we also presented a new individual frequency based approach for EEG assessment. A set of linear, time-frequency and nonlinear EEG markers were found to be significantly different in the concussed group compared to their matched peers in the healthy group. Although EEG analysis exhibited discrepancies, none of the cognitive assessment resulted in significant deficits. Therefore, the evidence from the study highlight that our proposed EEG analysis and markers are more efficient at deciphering post-concussion residual neurocognitive deficits and thus has a potential clinical utility of proper concussion assessment and management. Moreover, a number of studies have clearly demonstrated the feasibility of supervised and unsupervised pattern recognition algorithms to classify patients with various health-related issues. Inspired by these studies, we hypothesized that a set of robust features would accurately differentiate concussed athletes from control athletes. To verify it, features such as power spectral, statistical, wavelet, and other nonlinear features were extracted from the EEG signal and were used as an input to various classification algorithms to classify the concussed individuals. Various techniques were applied to classify control and concussed athletes and the performance of the classifiers was compared to ensure the best accuracy. Finally, an automated approach based on meaningful feature detection and efficient classification algorithm were presented to systematically identify concussed athletes from healthy controls with a reasonable accuracy. Thus, the study provides sufficient evidence that the proposed analysis is useful in evaluating the post-concussion deficits and may be incorporated into clinical assessments for a standard evaluation of athletes after a concussion

Coupled Correlates of Attention and Consciousness

Introduction: Brain Computer Interfaces (BCIs) have been shown to restore lost motor function that occurs in stroke using electrophysiological signals. However, little evidence exists for the use of BCIs to restore non-motor stroke deficits, such as the attention deficits seen in hemineglect. Attention is a cognitive function that selects objects or ideas for further neural processing, presumably to facilitate optimal behavior. Developing BCIs for attention is different from developing motor BCIs because attention networks in the brain are more distributed and associative than motor networks. For example, hemineglect patients have reduced levels of arousal, which exacerbates their attentional deficits. More generally, attention is a state of high arousal and salient conscious experience. Current models of consciousness suggest that both slow wave sleep and Propofol-induced unconsciousness lie at one end of the consciousness spectrum, while attentive states lie at the other end. Accordingly, investigating the electrophysiology underlying attention and the extremes of consciousness will further the development of attentional BCIs. Phase amplitude coupling (PAC) of neural oscillations has been suggested as a mechanism for organizing local and global brain activity across regions. While evidence suggests that delta-high-gamma PAC, which includes very low frequencies (i.e. delta, 1-3 Hz) coupled with very high frequencies (i.e. gamma 70-150 Hz), is implicated in attention, less evidence exists for the involvement of coupled mid-range frequencies (i.e. theta, 4-7Hz, alpha: 8-15 Hz, beta: 15-30 Hz and low-gamma: 30-50 Hz, aka TABL PAC). We found that TABL PAC correlates with reaction time in an attention task. These mid-range frequencies are important because they can be used in non-invasive electroencephalography (EEG) BCI’s. Therefore, we investigated the origins of these mid-frequency interactions in both attention and consciousness. In this work, we evaluate the relationship between PAC to attention and arousal, with emphasis on developing control signals for an attentional BCI. Objective: To understand how PAC facilitates attention and arousal for building BCI’s that restore lost attentional function. More generally, our objective was to discover and understand potential control features for BCIs that enhance attention and conscious experience. Methods: We used four electrophysiological datasets in human subjects. The first dataset included six subjects with invasive ECoG recordings while subjects engaged in a Posner cued spatial attention task. The second dataset included five subjects with ECoG recordings during sleep and awake states. The third dataset included 6 subjects with invasively monitored ECoG during induction and emergence from Propofol anesthesia. We validated findings from the second dataset with an EEG dataset that included 39 subjects with EEG and sleep scoring. We developed custom, wavelet-based, signal processing algorithms designed to optimally calculate differences in mid-frequency-range (i.e. TABL) PAC and compare them to DH PAC across different attentional and conscious states. We developed non-parametric cluster-based permutation tests to infer statistical significance while minimizing the false-positive rate. In the attention experiment, we used the location of cued spatial stimuli and reaction time (RT) as markers of attention. We defined stimulus-related and behaviorally-related cortical sites and compared their relative PAC magnitudes. In the sleep dataset, we compared PAC across sleep states (e.g. Wake vs Slow Wave Sleep). In the anesthesia dataset, we compared the beginning and ending of induction and emergence (e.g. Wake vs Propofol Induced Loss of Consciousness) Results: We found different patterns of activity represented by TABL PAC and DH PAC in both attention and sleep datasets. First, during a spatial attention task TABL PAC robustly predicted whether a subject would respond quickly or slowly. TABL PAC maintained a consistent phase-preference across all cortical sites and was strongest in behaviorally-relevant cortical sites. In contrast, DH PAC represented the location of attention in spatially-relevant cortical sites. Furthermore, we discovered that sharp waves caused TABL PAC. These sharp waves appeared to be transient beta (50ms) waves that occurred at ~140 ms intervals, corresponding to a theta oscillation. In the arousal dataset DH PAC increased in both slow wave sleep (SWS) and Propofol-induced loss of consciousness (PILOC) states. However, TABL PAC increased only during PILOC and decreased during SWS, when compared to waking states. We provide evidence that TABL PAC represents “gating by inhibition” in the human brain. Conclusions: Our goal was to develop electrophysiological signals representing attention and to understand how these features explain the relationship between attention and low-arousal states. We found a novel biomarker, TABL PAC, that predicted non-spatial aspects of attention and discriminated between two states of unconsciousness. The evidence suggested that TABL PAC represents inhibitory activity that filters out irrelevant information in attention tasks. This inhibitory mechanism of was confirmed by significant increases in TABL PAC during Propofol anesthesia, when compared to SWS or waking brain activity. We conclude that TABL PAC informs the development of electrophysiological control signals for attention and the discrimination of unconscious states

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges

We have witnessed a rapid development of brain-computer interfaces (BCIs) linking the brain to external devices. BCIs can be utilized to treat neurological conditions and even to augment brain functions. BCIs offer a promising treatment for mental disorders, including disorders of attention. Here we review the current state of the art and challenges of attention-based BCIs, with a focus on visual attention. Attention-based BCIs utilize electroencephalograms (EEGs) or other recording techniques to generate neurofeedback, which patients use to improve their attention, a complex cognitive function. Although progress has been made in the studies of neural mechanisms of attention, extraction of attention-related neural signals needed for BCI operations is a difficult problem. To attain good BCI performance, it is important to select the features of neural activity that represent attentional signals. BCI decoding of attention-related activity may be hindered by the presence of different neural signals. Therefore, BCI accuracy can be improved by signal processing algorithms that dissociate signals of interest from irrelevant activities. Notwithstanding recent progress, optimal processing of attentional neural signals remains a fundamental challenge for the development of efficient therapies for disorders of attention

Deep brain stimulation for disorders of consciousness and diminished motivation:A search for awakenings

This thesis deals with patients who are amongst the most severely affected after severe brain injury: those with permanent disorders of consciousness or diminished motivation. The research in this thesis is an attempt to improve consciousness and the general behavioral performance of these patients with the use of experimental interventions, including medication (such as zolpidem), and more invasive procedures, such as deep brain stimulation (DBS). The thesis contains extensive descriptions of the role of the intralaminar thalamus in the arousal regulation system, the importance of recognizing and treating secondary complications after brain injury, such as hydrocephalus, as well as a pathophysiological elaboration on akinetic mutism: a severe disorder of diminished motivation. Moreover, it describes the neurophysiological changes that accompany the paradoxical effects of zolpidem, a sleeping pill that temporarily induces ‘awakenings’ in some patients with severe brain injury. Further, it describes the first clinical and neurophysiological results of an N=6 trial of DBS in patients with a minimally conscious state and shows the importance of recognizing pathological changes from the brain’s ‘physiological baseline’ that seem to disturb normal brain functions. The thesis concludes with a description of the use of moral case deliberation in dealing with research dilemmas in patients with loss of autonomy after severe brain injury

Exploring Effects of Background Music in A Serious Game on Attention by Means of EEG Signals in Children

Music and Serious Games are separately useful alternative therapy methods for helping people with a cognitive disorder, including Attention Deficit Hyperactivity Disorder (ADHD). The goal of this thesis is to explore the effect of background music on children with and without ADHD. In this study, a simple Tetris game is designed with Beethoven, Mozart music, and no-music. There are different brainwave techniques for recording; among others, the electroencephalography (EEG) allows for the most efficient use of BCI. We recorded the EEG brain signals of the regular and ADHD subjects who played the Tetris we designed according to our protocol that consists of three trials with three different background music. Attention related Alpha and Beta waves of EEG signals analyzed based on time and time-frequency domain features. The changes in the data over the 1-minute Tetris game sections are investigated with the Short-time Fourier Transform (STFT) method. The results showed that music has a considerable impact on attention of children. When it comes to music types, in general, Mozart music increases Beta waves while decreasing the Alpha band waves for subjects without ADHD. On the other hand, Beethoven music increased both Alpha and Beta band values for children with ADHD

Monitoring the Depth of Anaesthesia

One of the current challenges in medicine is monitoring the patients’ depth of general anaesthesia (DGA). Accurate assessment of the depth of anaesthesia contributes to tailoring drug administration to the individual patient, thus preventing awareness or excessive anaesthetic depth and improving patients’ outcomes. In the past decade, there has been a significant increase in the number of studies on the development, comparison and validation of commercial devices that estimate the DGA by analyzing electrical activity of the brain (i.e., evoked potentials or brain waves). In this paper we review the most frequently used sensors and mathematical methods for monitoring the DGA, their validation in clinical practice and discuss the central question of whether these approaches can, compared to other conventional methods, reduce the risk of patient awareness during surgical procedures