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      Electrophysiological correlates of thalamocortical function in acute severe traumatic brain injury

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          Abstract

          Tools assaying the neural networks that modulate consciousness may facilitate tracking of recovery after acute severe brain injury. The ABCD framework classifies resting-state EEG into categories reflecting levels of thalamocortical network function that correlate with outcome in post-cardiac arrest coma. In this longitudinal cohort study, we applied the ABCD framework to 20 patients with acute severe traumatic brain injury requiring intensive care (12 of whom were also studied at ≥6-months post-injury) and 16 healthy controls. We tested four hypotheses: 1) EEG ABCD classifications are spatially heterogeneous and temporally variable; 2) ABCD classifications improve longitudinally, commensurate with the degree of behavioral recovery; 3) ABCD classifications correlate with behavioral level of consciousness; and 4) the Coma Recovery Scale-Revised arousal facilitation protocol yields improved ABCD classifications. Channel-level EEG power spectra were classified based on spectral peaks within pre-defined frequency bands: ‘A’ = no peaks above delta (<4 Hz) range (complete thalamocortical disruption); ‘B’ = theta (4–8 Hz) peak (severe thalamocortical disruption); ‘C’ = theta and beta (13–24 Hz) peaks (moderate thalamocortical disruption); or ‘D’ = alpha (8–13 Hz) and beta peaks (normal thalamocortical function). Acutely, 95% of patients demonstrated ‘D’ signals in at least one channel but exhibited within-session temporal variability and spatial heterogeneity in the proportion of different channel-level ABCD classifications. By contrast, healthy participants and patients at follow-up consistently demonstrated signals corresponding to intact thalamocortical network function. Patients demonstrated longitudinal improvement in ABCD classifications ( p < .05) and ABCD classification distinguished patients with and without command-following in the subacute-to-chronic phase of recovery ( p < .01). In patients studied acutely, ABCD classifications improved after the Coma Recovery Scale-Revised arousal facilitation protocol ( p < .05) but did not correspond with behavioral level of consciousness. These findings support the use of the ABCD framework to characterize channel-level EEG dynamics and track fluctuations in functional thalamocortical network integrity in spatial detail.

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            Spectrum estimation and harmonic analysis

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              The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients.

              Sedative medications are widely used in intensive care unit (ICU) patients. Structured assessment of sedation and agitation is useful to titrate sedative medications and to evaluate agitated behavior, yet existing sedation scales have limitations. We measured inter-rater reliability and validity of a new 10-level (+4 "combative" to -5 "unarousable") scale, the Richmond Agitation-Sedation Scale (RASS), in two phases. In phase 1, we demonstrated excellent (r = 0.956, lower 90% confidence limit = 0.948; kappa = 0.73, 95% confidence interval = 0.71, 0.75) inter-rater reliability among five investigators (two physicians, two nurses, and one pharmacist) in adult ICU patient encounters (n = 192). Robust inter-rater reliability (r = 0.922-0.983) (kappa = 0.64-0.82) was demonstrated for patients from medical, surgical, cardiac surgery, coronary, and neuroscience ICUs, patients with and without mechanical ventilation, and patients with and without sedative medications. In validity testing, RASS correlated highly (r = 0.93) with a visual analog scale anchored by "combative" and "unresponsive," including all patient subgroups (r = 0.84-0.98). In the second phase, after implementation of RASS in our medical ICU, inter-rater reliability between a nurse educator and 27 RASS-trained bedside nurses in 101 patient encounters was high (r = 0.964, lower 90% confidence limit = 0.950; kappa = 0.80, 95% confidence interval = 0.69, 0.90) and very good for all subgroups (r = 0.773-0.970, kappa = 0.66-0.89). Correlations between RASS and the Ramsay sedation scale (r = -0.78) and the Sedation Agitation Scale (r = 0.78) confirmed validity. Our nurses described RASS as logical, easy to administer, and readily recalled. RASS has high reliability and validity in medical and surgical, ventilated and nonventilated, and sedated and nonsedated adult ICU patients.
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                Author and article information

                Journal
                0100725
                3187
                Cortex
                Cortex
                Cortex; a journal devoted to the study of the nervous system and behavior
                0010-9452
                1973-8102
                14 November 2022
                July 2022
                15 April 2022
                18 December 2022
                : 152
                : 136-152
                Affiliations
                [a ]Harvard Medical School, Boston, MA, USA
                [b ]Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
                [c ]Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA, USA
                [d ]Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
                [e ]Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, NY, USA
                [f ]Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
                [g ]Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
                [h ]Department of Neurology, New York Presbyterian Hospital, New York, NY, USA
                [i ]Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
                Author notes
                [* ] Corresponding author. Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, 101 Merrimac Street – Suite 300, Boston, MA 02114, USA. bedlow@ 123456mgh.harvard.edu (B.L. Edlow).
                [1]

                These authors contributed equally to this work.

                Author contributions

                William H. Curley: Conceptualization, Methodology, Software, Formal Analysis, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualization, Project Administration. Yelena G. Bodien: Conceptualization, Methodology, Investigation, Data Curation, Supervision, Writing – Review & Editing. David W. Zhou: Software, Investigation, Data Curation, Writing – Review & Editing. Mary M. Conte: Writing – Review & Editing, Supervision. Andrea S. Foulkes: Methodology. Joseph T. Giacino: Writing – Review & Editing. Jonathan D. Victor: Conceptualization, Methodology, Software, Writing – Review & Editing, Supervision. Nicholas D. Schiff: Conceptualization, Methodology, Software, Writing – Review & Editing, Supervision. Brian L. Edlow: Conceptualization, Methodology, Investigation, Resources, Writing – Review & Editing, Supervision, Project Administration, Funding Acquisition.

                Article
                NIHMS1849176
                10.1016/j.cortex.2022.04.007
                9759728
                35569326
                3e88974b-aa63-4a9f-9684-edfda66622e7

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Categories
                Article

                Neurology
                eeg,traumatic brain injury,consciousness,intensive care unit
                Neurology
                eeg, traumatic brain injury, consciousness, intensive care unit

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