Active clinical trials for "Consciousness Disorders"

Altered gamma activity has been observed in several neurological and psychiatric disorders, including a reduction in gamma synchronization in patients with disorders of consciousness. Modulation of gamma oscillations with rhythmic stimulation has been used as a possible therapeutic tool. Hence, we try to use acoustic and electric stimulation at gamma frequency to restore brain oscillation and thereby to improve conscious awareness in patients with disorders of consciousness.

BACKGROUND: Transient loss of consciousness (TLOC) - defined as spontaneous disruption of consciousness not due to head trauma and with complete recovery - has a lifetime prevalence of 50%. It is one of the commonest neurological complaints in primary and emergency care. Over 90% of TLOC is due to either syncope, epilepsy or dissociative seizures (DS, also known as 'Psychogenic Nonepileptic Seizures'). The rapid and accurate distinction of these diagnoses is vital to allow appropriate further management but at least 20-30% of patients are not managed optimally or misdiagnosed. We have previously demonstrated that, in patients with established diagnoses of epilepsy, syncope, or DS, an automated classifier using only information from 36 questions based on patient experience and lay witness reports (the initial Paroxysmal Event Profile, iPEP) could accurately diagnose 86.0% of patients (with 100% sensitivity and 91.7% specificity for syncope) AIMS: To calibrate the iPEP for discrimination between syncope, epilepsy, and DS in patients newly presenting with TLOC, validate its performance in an independent sample, and to explore acceptability of the use of such a tool to people with TLOC and witnesses. METHODS: Nested qualitative-quantitative prospective single-centre development and validation of the iPEP in patients presenting to Emergency Departments, syncope or epilepsy clinics with first presentations of TLOC, with semi-structured interviews conducted with a purposive sample of participants from the quantitative study. The iPEP will be calibrated using a previously-described procedure for variable selection and training of Random Forest (RF) classifiers, and validated with assessment of overall classification accuracy, alongside sensitivity, specificity, positive and negative predictive values, and area under receiver-operating curve for each of the three target diagnoses. Performance will be evaluated against a benchmark set by results from previous research in patients with established diagnoses of epilepsy, syncope, and DS. OUTPUTS: Results will be submitted for publication in academic and professional literature. If performance from feasibility can be replicated in validation, the iPEP will be suitable to begin process of registration as a medical device for implementation in clinical pathways to minimise inappropriate referrals and treatment, streamline patient pathways, and enable earlier ordering of appropriate investigations to ensure prompt and appropriate diagnosis and management. If pilot performance could be replicated in this population and proportional savings from current estimated costs of misdiagnosis achieved, this could potentially save £63.9 million of annual UK healthcare expenditure.

The purpose of this study is to determine if a weaning strategy from artificial ventilation governs by respiratory behaviour status assessed by our method is safe enough.

Electroencephalogram/event-related potentials (EEG/ERP) data will be collected from 50 participants in coma or other disorder of consciousness (DOC; i.e., Unresponsive Wakefulness Syndrome [UWS] or Minimally Conscious State [MCS]), clinically diagnosed using the Glasgow Coma Scale (GCS). For coma patients, EEG recordings will be conducted for up to 24 consecutive hours at a maximum of 5 timepoints, spanning 30 days from the date of recruitment, to track participants' clinical state. For DOC patients, there will be an initial EEG recording up to 24 hours, with possible subsequent weekly recordings up to 2 hours. An additional dataset from 40 healthy controls will be collected, each spanning up to a 12-hour recording period in order to formulate a baseline. Collected data are to form the basis for automatic analysis and detection of ERP components in DOC, using a machine learning paradigm. Salient features (i.e., biomarkers) extracted from the ERPs and resting-state EEG will be identified and combined in an optimal fashion to give an accurate indicator of prognosis.

Patients with severe brain injuries often have slow accumulating recoveries of function. In ongoing studies, we have discovered that elements of electrical activity during sleep may correlate with the level of behavioral recovery observed in patients. It is unknown whether such changes are causally linked to behavioral recovery. Sleep processes are, however, associated with several critical processes supporting the cellular integrity of neurons and neuronal mechanisms associated with learning and synaptic modifications. These known associations suggest the possibility that targeting the normalization of brain electrical activity during sleep may aid the recovery process. A well-studied mechanism organizing the pattern of electrical activity that characterizes sleep is the body's release of the substance melatonin. Melatonin is produced in the brain and released at a precise time during the day (normally around 8-10PM) to signal the brain to initiate aspects of the sleep process each day. Ongoing research by other scientists has demonstrated that providing a small dose of melatonin can improve the regular pattern of sleep and help aid sleep induction. Melatonin use has been shown to be effective in the treatment of time change effects on sleep ("jet lag") and mood disturbances associated with changes in daily light cues such as seasonal affective disorder. We propose to study the effects of melatonin administration in patients with severe structural brain injuries and disorders of consciousness. We will measure the patient's own timing of release of melatonin and provide a dose of melatonin at night to test the effects on the electrical activity of sleep over a three month period. In addition to brain electrical activity we will record sleep behavioral data and physical activity using activity monitors worn by the patients. Patient subjects in this study will be studied twice during the three month period in three day inpatient visits where they will undergo video monitoring and sampling of brain electrical activity using pasted electrodes ("EEG"), hourly saliva sampling for one day, and participation in behavioral testing.

The aim of this study is to evaluate the effect of nutraceutical supplement on depressed mood and anhedonia in volunteers after 8 weeks of consumption.

Severe brain injuries lead to disorders of consciousness after coma. During this awakening period, detection of arousal is critical to the adaptation of medical strategy, but global paralysis, including facial expression, make the clinical assessment very difficult. Emotional facial expressions are a significant part of this clinical assessment. They are both a landmark of the internal state of the patient (comfort versus discomfort) and a landmark of the relational level with his environment. Visible emotional facial expression is a large temporal phenomenon lasting a couple of seconds, while a microexpression is barely noticeable and very brief. These micro expressions are usually produced when one tried to voluntary hide emotional expressions. In this study, we hypothesize that some patients awakening from coma could still produce microexpression before being able to produce visible emotional facial expressions. This ability to produce micro-expression could be an early landmark of relational awakening in severe brain lesions.

test

By collecting multimodal metrics (e.g., clinical factors, neuroimaging, and EEG) in the early phase of severe brain injury (i.e., during the acute hospitalization when a patient has impaired consciousness), and measuring the patients' recovery of consciousness, function, and quality of life in the late phase (at 6 months following the brain injury), we aim to construct an algorithm that synthesizes the results of these metrics to help predict recovery.

Verticalization was reported to improve the level of arousal and awareness in patients with severe acquired brain injury (ABI) and to be safe in ICU. The investigators evaluated the effectiveness of a very early stepping verticalization protocol on the functional and neurological outcome of patients affected by disorder of consciousness due to ABI. Consecutive patients with Vegetative State or Minimally Conscious State were enrolled in ICU on the third day after an ABI. They were randomized to undergo conventional physiotherapy alone or associated to fifteen 30-minute sessions of verticalization, using a tilt table with robotic stepping device. Once stabilized, patients were transferred to a Neurorehabilitation unit for an individualized treatment. Outcome measures were assessed on the third day from the injury (T0), at ICU discharge (T1) and at Rehab discharge (T2).