Display Enhanced Testing For Concussions And Mild Traumatic Brain Injury

Cognitive assessment systems and methods that provide an integrated solution for evaluating the presence or absence of cognitive impairment. The present invention is used to test cognitive functions of an individual including information processing speed, working memory, work list learning and recall, along with variations of these tasks. Immersive and non-immersive systems and methods are disclosed. Testing and results feedback using the present invention may be completed in real time, typically in less than 15 minutes.Emory UniversityGeorgia Tech Research Corporatio

How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: A comparative study

ObjectiveClinical assessment of consciousness relies on behavioural assessments, which have several limitations. Hence, disorder of consciousness (DOC) patients are often misdiagnosed. In this work, we aimed to compare the repetitive assessment of consciousness performed with a clinical behavioural and a Brain-Computer Interface (BCI) approach. Materials and methodsFor 7 weeks, sixteen DOC patients participated in weekly evaluations using both the Coma Recovery Scale-Revised (CRS-R) and a vibrotactile P300 BCI paradigm. To use the BCI, patients had to perform an active mental task that required detecting specific stimuli while ignoring other stimuli. We analysed the reliability and the efficacy in the detection of command following resulting from the two methodologies. ResultsOver repetitive administrations, the BCI paradigm detected command following before the CRS-R in seven patients. Four clinically unresponsive patients consistently showed command following during the BCI assessments. ConclusionBrain-Computer Interface active paradigms might contribute to the evaluation of the level of consciousness, increasing the diagnostic precision of the clinical bedside approach. SignificanceThe integration of different diagnostic methods leads to a better knowledge and care for the DOC

Electrophysiological investigations of brain function in coma, vegetative and minimally conscious patients.

Electroencephalographic activity in the context of disorders of consciousness is a swiss knife like tool that can evaluate different aspects of cognitive residual function, detect consciousness and provide a mean to communicate with the outside world without using muscular channels. Standard recordings in the neurological department offer a first global view of the electrogenesis of a patient and can spot abnormal epileptiform activity and therefore guide treatment. Although visual patterns have a prognosis value, they are not sufficient to provide a diagnosis between vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) patients. Quantitative electroencephalography (qEEG) processes the data and retrieves features, not visible on the raw traces, which can then be classified. Current results using qEEG show that MCS can be differentiated from VS/UWS patients at the group level. Event Related Potentials (ERP) are triggered by varying stimuli and reflect the time course of information processing related to the stimuli from low-level peripheral receptive structures to high-order associative cortices. It is hence possible to assess auditory, visual, or emotive pathways. Different stimuli elicit positive or negative components with different time signatures. The presence of these components when observed in passive paradigms is usually a sign of good prognosis but it cannot differentiate VS/UWS and MCS patients. Recently, researchers have developed active paradigms showing that the amplitude of the component is modulated when the subject's attention is focused on a task during stimulus presentation. Hence significant differences between ERPs of a patient in a passive compared to an active paradigm can be a proof of consciousness. An EEG-based brain-computer interface (BCI) can then be tested to provide the patient with a communication tool. BCIs have considerably improved the past two decades. However they are not easily adaptable to comatose patients as they can have visual or auditory impairments or different lesions affecting their EEG signal. Future progress will require large databases of resting state-EEG and ERPs experiment of patients of different etiologies. This will allow the identification of specific patterns related to the diagnostic of consciousness. Standardized procedures in the use of BCIs will also be needed to find the most suited technique for each individual patient.Peer reviewe

Functional Magnetic Resonance Imaging as an Assessment Tool in Critically Ill Patients

Little is known about whether residual cognitive function occurs in the earliest stages of brain injury. The overarching goal of the work presented in this dissertation was to elucidate the role of functional neuroimaging in assessing brain activity in critically ill patients. The overall objective was addressed in the following four empirical chapters: In Chapter 2, three versions of a hierarchically-designed auditory task were developed and their ability to detect various levels of auditory language processing was assessed in individual healthy participants. The same procedure was then applied in two acutely comatose patients. In Chapter 3, a hierarchical auditory task was employed in a heterogeneous cohort of acutely comatose patients. The results revealed that the level of auditory processing in coma may be predictive of subsequent functional recovery. In Chapter 4, two mental imagery paradigms were utilized to assess covert command-following in coma. The findings demonstrate, for the first time, preserved awareness in an acutely comatose patient. In Chapter 5, functional neuroimaging techniques were used for covert communication with two completely locked-in, critically ill patients. The results suggest that this methodology could be used as an augmentative communication tool to allow patients to be involved in their own medical decision-making. Taken together, the proceeding chapters of this work demonstrate that functional neuroimaging can detect preserved cognitive functions in some acutely comatose patients, which has both diagnostic and prognostic relevance. Moreover, these techniques may be extended even further to be used as a communication tool in critically ill patients

EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review

Background: Disorders of Consciousness (DoC) are clinical conditions following a severe acquired brain injury (ABI) characterized by absent or reduced awareness, known as coma, Vegetative State (VS)/Unresponsive Wakefulness Syndrome (VS/UWS), and Minimally Conscious State (MCS). Misdiagnosis rate between VS/UWS and MCS is attested around 40% due to the clinical and behavioral fluctuations of the patients during bedside consciousness assessments. Given the large body of evidence that some patients with DoC possess "covert" awareness, revealed by neuroimaging and neurophysiological techniques, they are candidates for intervention with brain-computer interfaces (BCIs). Objectives: The aims of the present work are (i) to describe the characteristics of BCI systems based on electroencephalography (EEG) performed on DoC patients, in terms of control signals adopted to control the system, characteristics of the paradigm implemented, classification algorithms and applications (ii) to evaluate the performance of DoC patients with BCI. Methods: The search was conducted on Pubmed, Web of Science, Scopus and Google Scholar. The PRISMA guidelines were followed in order to collect papers published in english, testing a BCI and including at least one DoC patient. Results: Among the 527 papers identified with the first run of the search, 27 papers were included in the systematic review. Characteristics of the sample of participants, behavioral assessment, control signals employed to control the BCI, the classification algorithms, the characteristics of the paradigm, the applications and performance of BCI were the data extracted from the study. Control signals employed to operate the BCI were: P300 (N = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, N = 4), sensorimotor rhythms (SMRs; N = 5) and brain rhythms elicited by an emotional task (N = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients. Conclusion: Despite the BCI is a promising tool in the management of DoC patients, supporting diagnosis and prognosis evaluation, results are still preliminary, and no definitive conclusions may be drawn; even though neurophysiological methods, such as BCI, are more sensitive to covert cognition, it is suggested to adopt a multimodal approach and a repeated assessment strategy

BCI performance and brain metabolism profile in severely brain-injured patients without response to command at bedside

peer reviewedDetection and interpretation of signs of "covert command following" in patients with disorders of consciousness (DOC) remains a challenge for clinicians. In this study, we used a tactile P3-based BCI in 12 patients without behavioral command following, attempting to establish "covert command following." These results were then confronted to cerebral metabolism preservation as measured with glucose PET (FDG-PET). One patient showed "covert command following" (i.e., above-threshold BCI performance) during the active tactile paradigm. This patient also showed a higher cerebral glucose metabolism within the language network (presumably required for command following) when compared with the other patients without "covert command-following" but having a cerebral glucose metabolism indicative of minimally conscious state. Our results suggest that the P3-based BCI might probe "covert command following" in patients without behavioral response to command and therefore could be a valuable addition in the clinical assessment of patients with DOC

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.

After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery

Decoding Mental States after Severe Brain Injury

Some patients with disorders of consciousness retain sensory and cognitive abilities that are not apparent from their outward behaviour. It is crucial to identify and characterise these covert abilities for diagnosis, prognosis, and medical ethics. This thesis uses neuroimaging techniques to investigate cognitive preservation and awareness in patients who are behaviourally non-responsive due to acquired brain injuries. In the first chapter, a large sample of healthy volunteers, including experienced athletes and musicians, imagined actions of varying complexity and familiarity. Motor imagery involving certain complex, familiar actions correlated with a more robust sensorimotor rhythm. In the second chapter, several patients with disorders of consciousness participated in multiple experiments based on neural responses to mental imagery, including one task featuring complex, familiar imagined actions. Although the patients did not generate enhanced sensorimotor rhythms for the complex, familiar motor imagery, the detection of covert cognition was more sensitive owing to the multi-modal nature of the assessment. In the final empirical chapter, a sample of healthy volunteers and a heterogeneous cohort of patients with disorders of consciousness completed a novel oddball task based on tactile stimulation. Critically, this task delineated an attentional hierarchy in the patient sample, and patients with the ability to follow commands were differentiated from those unable to do so by event-related potential evidence of attentional orienting. Due to the heterogeneity of aetiology and pathology in the disorders of consciousness, these patients vary in their suitability for neuroimaging, the preservation of neural structures, and the cognitive resources available to them. Assessments of several perceptual and cognitive abilities supported by spatially-distinct brain regions and indexed by multiple neural signatures are therefore required to accurately characterise a patient’s abilities and probable subjective experience

Neuro-critical multimodal Edge-AI monitoring algorithm and IoT system design and development

In recent years, with the continuous development of neurocritical medicine, the success rate of treatment of patients with traumatic brain injury (TBI) has continued to increase, and the prognosis has also improved. TBI patients' condition is usually very complicated, and after treatment, patients often need a more extended time to recover. The degree of recovery is also related to prognosis. However, as a young discipline, neurocritical medicine still has many shortcomings. Especially in most hospitals, the condition of Neuro-intensive Care Unit (NICU) is uneven, the equipment has limited functionality, and there is no unified data specification. Most of the instruments are cumbersome and expensive, and patients often need to pay high medical expenses. Recent years have seen a rapid development of big data and artificial intelligence (AI) technology, which are advancing the medical IoT field. However, further development and a wider range of applications of these technologies are needed to achieve widespread adoption. Based on the above premises, the main contributions of this thesis are the following. First, the design and development of a multi-modal brain monitoring system including 8-channel electroencephalography (EEG) signals, dual-channel NIRS signals, and intracranial pressure (ICP) signals acquisition. Furthermore, an integrated display platform for multi-modal physiological data to display and analysis signals in real-time was designed. This thesis also introduces the use of the Qt signal and slot event processing mechanism and multi-threaded to improve the real-time performance of data processing to a higher level. In addition, multi-modal electrophysiological data storage and processing was realized on cloud server. The system also includes a custom built Django cloud server which realizes real-time transmission between server and WeChat applet. Based on WebSocket protocol, the data transmission delay is less than 10ms. The analysis platform can be equipped with deep learning models to realize the monitoring of patients with epileptic seizures and assess the level of consciousness of Disorders of Consciousness (DOC) patients. This thesis combines the standard open-source data set CHB-MIT, a clinical data set provided by Huashan Hospital, and additional data collected by the system described in this thesis. These data sets are merged to build a deep learning network model and develop related applications for automatic disease diagnosis for smart medical IoT systems. It mainly includes the use of the clinical data to analyze the characteristics of the EEG signal of DOC patients and building a CNN model to evaluate the patient's level of consciousness automatically. Also, epilepsy is a common disease in neuro-intensive care. In this regard, this thesis also analyzes the differences of various deep learning model between the CHB-MIT data set and clinical data set for epilepsy monitoring, in order to select the most appropriate model for the system being designed and developed. Finally, this thesis also verifies the AI-assisted analysis model.. The results show that the accuracy of the CNN network model based on the evaluation of consciousness disorder on the clinical data set reaches 82%. The CNN+STFT network model based on epilepsy monitoring reaches 90% of the accuracy rate in clinical data. Also, the multi-modal brain monitoring system built is fully verified. The EEG signal collected by this system has a high signal-to-noise ratio, strong anti-interference ability, and is very stable. The built brain monitoring system performs well in real-time and stability. Keywords: TBI, Neurocritical care, Multi-modal, Consciousness Assessment, seizures detection, deep learning, CNN, IoT