Dataset Information

Patients with a severe prolonged Disorder of Consciousness can show classical EEG responses to their own name compared with others' names.

ABSTRACT: Patients in Vegetative State (VS), also known as Unresponsive Wakefulness State (UWS) are deemed to be unaware of themselves or their environment. This is different from patients diagnosed with Minimally Conscious state (MCS), who can have intermittent awareness. In both states, there is a severe impairment of consciousness; these disorders are referred to as disorders of consciousness (DOC) and if the state is prolonged, pDOC. There is growing evidence that some patients who are behaviourally in VS/UWS can show neural activation to environmental stimuli and that this response can be detected using functional brain imaging (fMRI/PET) and electroencephalography (EEG). Recently, it has also been suggested that a more reliable detection of brain responsiveness and hence a more reliable differentiation between VS/UWS and MCS requires person-centred and person-specific stimuli, such as the subject's own name stimulus. In this study we obtained event related potential data (ERP) from 12 healthy subjects and 16 patients in pDOC, five of whom were in the VS/UWS and 11 in the Minimally Conscious State (MCS). We used as the ERP stimuli the subjects' own name, others' names and reversed other names. We performed a sensor level analysis using Statistical Parametric Mapping (SPM) software. Using this paradigm in 4 DOC patients (3 in MCS, and 1 in VS/UWS) we detected a statistically significant difference in EEG response to their own name versus other peoples' names with ERP latencies (~300?ms and ~700?ms post stimuli). Some of these differences were similar to those found in a control group of healthy subjects. This study shows the feasibility of using self-relevant stimuli such as a subject's own name for assessment of brain function in pDOC patients. This neurophysiological test is suitable for bed-side/hospital based assessment of pDOC patients. As it does not require sophisticated scanning equipment it can feasibly be used within a hospital or care setting to help professionals tailor medical and psycho-social management for patients.

SUBMITTER: Kempny AM

PROVIDER: S-EPMC6044184 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Patients with a severe prolonged Disorder of Consciousness can show classical EEG responses to their own name compared with others' names.

Kempny Agnieszka M AM James Leon L Yelden Kudret K Duport Sophie S Farmer Simon F SF Diane Playford E E Leff Alexander P AP

NeuroImage. Clinical 20180430

Patients in Vegetative State (VS), also known as Unresponsive Wakefulness State (UWS) are deemed to be unaware of themselves or their environment. This is different from patients diagnosed with Minimally Conscious state (MCS), who can have intermittent awareness. In both states, there is a severe impairment of consciousness; these disorders are referred to as disorders of consciousness (DOC) and if the state is prolonged, pDOC. There is growing evidence that some patients who are behaviourally i ...[more]

PMID: 30013914

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Project description:BackgroundThe digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this 'names problem' has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science.ResultsThe TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets.ConclusionsWe show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ and as a RESTful web service and application programming interface. Source code is available at https://github.com/iPlantCollaborativeOpenSource/TNRS/.

Project description:Understanding the origin of the main physiological processes involved in consciousness is a major challenge of contemporary neuroscience, with crucial implications for the study of Disorders of Consciousness (DOC). The difficulties in achieving this task include the considerable quantity of experimental data in this field, along with the non-intuitive, nonlinear nature of neuronal dynamics. One possibility of integrating the main results from the experimental literature into a cohesive framework, while accounting for nonlinear brain dynamics, is the use of physiologically-inspired computational models. In this study, we present a physiologically-grounded computational model, attempting to account for the main micro-circuits identified in the human cortex, while including the specificities of each neuronal type. More specifically, the model accounts for thalamo-cortical (vertical) regulation of cortico-cortical (horizontal) connectivity, which is a central mechanism for brain information integration and processing. The distinct neuronal assemblies communicate through feedforward and feedback excitatory and inhibitory synaptic connections implemented in a template brain accounting for long-range connectome. The EEG generated by this physiologically-based simulated brain is validated through comparison with brain rhythms recorded in humans in two states of consciousness (wakefulness, sleep). Using the model, it is possible to reproduce the local disynaptic disinhibition of basket cells (fast GABAergic inhibition) and glutamatergic pyramidal neurons through long-range activation of vasoactive intestinal-peptide (VIP) interneurons that induced inhibition of somatostatin positive (SST) interneurons. The model (COALIA) predicts that the strength and dynamics of the thalamic output on the cortex control the local and long-range cortical processing of information. Furthermore, the model reproduces and explains clinical results regarding the complexity of transcranial magnetic stimulation TMS-evoked EEG responses in DOC patients and healthy volunteers, through a modulation of thalamo-cortical connectivity that governs the level of cortico-cortical communication. This new model provides a quantitative framework to accelerate the study of the physiological mechanisms involved in the emergence, maintenance and disruption (sleep, anesthesia, DOC) of consciousness.

Project description:Background/objectiveBehavioral examinations may fail to detect language function in patients with severe traumatic brain injury (TBI) due to confounds such as having an endotracheal tube. We investigated whether resting and stimulus-evoked electroencephalography (EEG) methods detect the presence of language function in patients with severe TBI.MethodsFour EEG measures were assessed: (1) resting background (applying Forgacs' criteria), (2) reactivity to speech, (3) background and reactivity (applying Synek's criteria); and (4) an automated support vector machine (classifier for speech versus rest). Cohen's kappa measured agreement between the four EEG measures and evidence of language function on a behavioral coma recovery scale-revised (CRS-R) and composite (CRS-R or functional MRI) reference standard. Sensitivity and specificity of each EEG measure were calculated against the reference standards.ResultsWe enrolled 17 adult patients with severe TBI (mean ± SD age 27.0 ± 7.0 years; median [range] 11.5 [2-1173] days post-injury) and 16 healthy subjects (age 28.5 ± 7.8 years). The classifier, followed by Forgacs' criteria for resting background, demonstrated the highest agreement with the behavioral reference standard. Only Synek's criteria for background and reactivity showed significant agreement with the composite reference standard. The classifier and resting background showed balanced sensitivity and specificity for behavioral (sensitivity = 84.6% and 80.8%; specificity = 57.1% for both) and composite reference standards (sensitivity = 79.3% and 75.9%, specificity = 50% for both).ConclusionsMethods applying an automated classifier, resting background, or resting background with reactivity may identify severe TBI patients with preserved language function. Automated classifier methods may enable unbiased and efficient assessment of larger populations or serial timepoints, while qualitative visual methods may be practical in community settings.

Dataset Information

Patients with a severe prolonged Disorder of Consciousness can show classical EEG responses to their own name compared with others' names.

Publications

Patients with a severe prolonged Disorder of Consciousness can show classical EEG responses to their own name compared with others' names.

Similar Datasets

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