EEG Slow Waves in Traumatic Brain Injury: Convergent Findings in Mouse and Man

OBJECTIVE: Evidence from previous studies suggests that greater sleep pressure, in the form of EEG-based slow waves, accumulates in specific brain regions that are more active during prior waking experience. We sought to quantify the number and coherence of EEG slow waves in subjects with mild traumatic brain injury (mTBI). METHODS: We developed a method to automatically detect individual slow waves in each EEG channel, and validated this method using simulated EEG data. We then used this method to quantify EEG-based slow waves during sleep and wake states in both mouse and human subjects with mTBI. A modified coherence index that accounts for information from multiple channels was calculated as a measure of slow wave synchrony. RESULTS: Brain-injured mice showed significantly higher theta:alpha amplitude ratios and significantly more slow waves during spontaneous wakefulness and during prolonged sleep deprivation, compared to sham-injured control mice. Human subjects with mTBI showed significantly higher theta:beta amplitude ratios and significantly more EEG slow waves while awake compared to age-matched control subjects. We then quantified the global coherence index of slow waves across several EEG channels in human subjects. Individuals with mTBI showed significantly less EEG global coherence compared to control subjects while awake, but not during sleep. EEG global coherence was significantly correlated with severity of post-concussive symptoms (as assessed by the Neurobehavioral Symptom Inventory scale). CONCLUSION AND IMPLICATIONS: Taken together, our data from both mouse and human studies suggest that EEG slow wave quantity and the global coherence index of slow waves may represent a sensitive marker for the diagnosis and prognosis of mTBI and post-concussive symptoms

EEG Slow Waves in Traumatic Brain Injury: Convergent Findings in Mouse and Man

OBJECTIVE: Evidence from previous studies suggests that greater sleep pressure, in the form of EEG-based slow waves, accumulates in specific brain regions that are more active during prior waking experience. We sought to quantify the number and coherence of EEG slow waves in subjects with mild traumatic brain injury (mTBI). METHODS: We developed a method to automatically detect individual slow waves in each EEG channel, and validated this method using simulated EEG data. We then used this method to quantify EEG-based slow waves during sleep and wake states in both mouse and human subjects with mTBI. A modified coherence index that accounts for information from multiple channels was calculated as a measure of slow wave synchrony. RESULTS: Brain-injured mice showed significantly higher theta:alpha amplitude ratios and significantly more slow waves during spontaneous wakefulness and during prolonged sleep deprivation, compared to sham-injured control mice. Human subjects with mTBI showed significantly higher theta:beta amplitude ratios and significantly more EEG slow waves while awake compared to age-matched control subjects. We then quantified the global coherence index of slow waves across several EEG channels in human subjects. Individuals with mTBI showed significantly less EEG global coherence compared to control subjects while awake, but not during sleep. EEG global coherence was significantly correlated with severity of post-concussive symptoms (as assessed by the Neurobehavioral Symptom Inventory scale). CONCLUSION AND IMPLICATIONS: Taken together, our data from both mouse and human studies suggest that EEG slow wave quantity and the global coherence index of slow waves may represent a sensitive marker for the diagnosis and prognosis of mTBI and post-concussive symptoms

Using information from the electronic health record to improve measurement of unemployment in service members and veterans with mTBI and post-deployment stress.

OBJECTIVE:The purpose of this pilot study is 1) to develop an annotation schema and a training set of annotated notes to support the future development of a natural language processing (NLP) system to automatically extract employment information, and 2) to determine if information about employment status, goals and work-related challenges reported by service members and Veterans with mild traumatic brain injury (mTBI) and post-deployment stress can be identified in the Electronic Health Record (EHR). DESIGN:Retrospective cohort study using data from selected progress notes stored in the EHR. SETTING:Post-deployment Rehabilitation and Evaluation Program (PREP), an in-patient rehabilitation program for Veterans with TBI at the James A. Haley Veterans' Hospital in Tampa, Florida. PARTICIPANTS:Service members and Veterans with TBI who participated in the PREP program (N = 60). MAIN OUTCOME MEASURES:Documentation of employment status, goals, and work-related challenges reported by service members and recorded in the EHR. RESULTS:Two hundred notes were examined and unique vocational information was found indicating a variety of self-reported employment challenges. Current employment status and future vocational goals along with information about cognitive, physical, and behavioral symptoms that may affect return-to-work were extracted from the EHR. The annotation schema developed for this study provides an excellent tool upon which NLP studies can be developed. CONCLUSIONS:Information related to employment status and vocational history is stored in text notes in the EHR system. Information stored in text does not lend itself to easy extraction or summarization for research and rehabilitation planning purposes. Development of NLP systems to automatically extract text-based employment information provides data that may improve the understanding and measurement of employment in this important cohort

EHR Note Title Selection, Annotation Schema, Chart Review & Annotation.

<p>EHR Note Title Selection, Annotation Schema, Chart Review & Annotation.</p

EHR Note Title Selection, Annotation Schema, Chart Review & Annotation.

<p>EHR Note Title Selection, Annotation Schema, Chart Review & Annotation.</p

Military Rank, MOS, Work History, Employment Goals, Vocational Interests & Driving Ability (N = 60).

<p><i>Note</i>:* Veteran may report more than one previous work history. ^†Veteran many have more than one future goal. ^‡Veterans had a record of a CareerScope assessment including interest inventory clusters.</p><p>Military Rank, MOS, Work History, Employment Goals, Vocational Interests & Driving Ability (N = 60).</p

Note Titles Reviewed and Selected.

<p>*Selection based upon richness of employment information found within each note title.</p><p>Note Titles Reviewed and Selected.</p

Deployments, Injury Severity, Etiology & Current Symptoms Reported (N = 60).

<p><i>Note:</i> *Veterans may report more than one blast exposure. ^†Severity as reported in the notes reviewed. ^‡Specific information found in notes reviewed. ^§Veterans can report more than one venue of injury. ^‖Veterans can report multiple and unique causes of injury. ^¶Veterans can report multiple numbers of injuries. ^#Symptoms reported at the point in time when case notes were recorded in the EHR record and Veterans can report multiple symptoms.</p><p>Deployments, Injury Severity, Etiology & Current Symptoms Reported (N = 60).</p

Veterans with TBI Demographic and other Characteristics (N = 60).

<p><i>Note:*</i> A large percentage of Veterans indicated they took college classes but were unable to finish their degree. ^†Other  =  Education that was not otherwise specified. ^‡Status from most recent note reviewed.</p><p>Veterans with TBI Demographic and other Characteristics (N = 60).</p