Comparison of comprehensive quantitative EEG metrics between typically developing boys and girls in resting state eyes-open and eyes-closed conditions.

Introduction: A majority of published studies comparing quantitative EEG (qEEG) in typically developing (TD) children and children with neurodevelopmental or psychiatric disorders have used a control group (e.g., TD children) that combines boys and girls. This suggests a widespread supposition that typically developing boys and girls have similar brain activity at all locations and frequencies, allowing the data from TD boys and girls to be aggregated in a single group. Methods: In this study, we have rigorously challenged this assumption by performing a comprehensive qEEG analysis on EEG recoding of TD boys (n = 84) and girls (n = 62), during resting state eyes-open and eyes-closed conditions (EEG recordings from Child Mind Institute's Healthy Brain Network (HBN) initiative). Our qEEG analysis was performed over narrow-band frequencies (e.g., separating low α from high α, etc.), included sex, age, and head size as covariates in the analysis, and encompassed computation of a wide range of qEEG metrics that included both absolute and relative spectral power levels, regional hemispheric asymmetry, and inter- and intra-hemispheric magnitude coherences as well as phase coherency among cortical regions. We have also introduced a novel compact yet comprehensive visual presentation of the results that allows comparison of the qEEG metrics of boys and girls for the entire EEG locations, pairs, and frequencies in a single graph. Results: Our results show there are wide-spread EEG locations and frequencies where TD boys and girls exhibit differences in their absolute and relative spectral powers, hemispheric power asymmetry, and magnitude coherence and phase synchrony. Discussion: These findings strongly support the necessity of including sex, age, and head size as covariates in the analysis of qEEG of children, and argue against combining data from boys and girls. Our analysis also supports the utility of narrow-band frequencies, e.g., dividing α, ß, and γ band into finer sub-scales. The results of this study can serve as a comprehensive normative qEEG database for resting state studies in children containing both eyes open and eyes closed paradigms.

Biomarkers Based on Comprehensive Hierarchical EEG Coherence Analysis: Example Application to Social Competence in Autism (Preliminary Results).

Electroencephalography (EEG) coherence analysis, based on measurement of synchronous oscillations of neuronal clusters, has been used extensively to evaluate functional connectivity in brain networks. EEG coherence studies have used a variety of analysis variables (e.g., time and frequency resolutions corresponding to the analysis time period and frequency bandwidth), regions of the brain (e.g., connectivity within and between various cortical lobes and hemispheres) and experimental paradigms (e.g., resting state with eyes open or closed; performance of cognitive tasks). This variability in study designs has resulted in difficulties in comparing the findings from different studies and assimilating a comprehensive understanding of the underlying brain activity and regions with abnormal functional connectivity in a particular disorder. In order to address the variability in methods across studies and to facilitate the comparison of research findings between studies, this paper presents the structure and utilization of a comprehensive hierarchical electroencephalography (EEG) coherence analysis that allows for formal inclusion of analysis duration, EEG frequency band, cortical region, and experimental test condition in the computation of the EEG coherences. It further describes the method by which this EEG coherence analysis can be utilized to derive biomarkers related to brain (dys)function and abnormalities. In order to document the utility of this approach, the paper describes the results of the application of this method to EEG and behavioral data from a social synchrony paradigm in a small cohort of adolescents with and without Autism Spectral Disorder.

Four methods of brain pattern analyses of fMRI signals associated with wrist extension versus wrist flexion studied for potential use in future motor learning BCI.

OBJECTIVE: In stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults. APPROACH: We studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) 'Winner Take All'; and 4) Relative Dominance. RESULTS: With these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects. SIGNIFICANCE: We encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data.

Validation of Visually Identified Muscle Potentials during Human Sleep Using High Frequency/Low Frequency Spectral Power Ratios.

Surface electromyography (EMG), typically recorded from muscle groups such as the mentalis (chin/mentum) and anterior tibialis (lower leg/crus), is often performed in human subjects undergoing overnight polysomnography. Such signals have great importance, not only in aiding in the definitions of normal sleep stages, but also in defining certain disease states with abnormal EMG activity during rapid eye movement (REM) sleep, e.g., REM sleep behavior disorder and parkinsonism. Gold standard approaches to evaluation of such EMG signals in the clinical realm are typically qualitative, and therefore burdensome and subject to individual interpretation. We originally developed a digitized, signal processing method using the ratio of high frequency to low frequency spectral power and validated this method against expert human scorer interpretation of transient muscle activation of the EMG signal. Herein, we further refine and validate our initial approach, applying this to EMG activity across 1,618,842 s of polysomnography recorded REM sleep acquired from 461 human participants. These data demonstrate a significant association between visual interpretation and the spectrally processed signals, indicating a highly accurate approach to detecting and quantifying abnormally high levels of EMG activity during REM sleep. Accordingly, our automated approach to EMG quantification during human sleep recording is practical, feasible, and may provide a much-needed clinical tool for the screening of REM sleep behavior disorder and parkinsonism.

Strong correlation of novel sleep electroencephalography coherence markers with diagnosis and severity of posttraumatic stress disorder.

Objective biomarkers of the presence and severity of posttraumatic stress disorder (PTSD) are elusive, yet badly needed. Electroencephalographic (EEG) coherence represents a promising approach to identifying and understanding brain biomarker activity in PTSD. Overnight polysomnography data containing EEG across sleep and wake states was collected in n = 76 Veterans with and without PTSD from a single site under IRB approval. Brain coherence markers (BCM) were calculated from EEG signals using a novel approach to produce one index for PTSD diagnosis (PTSDdx), and another index for PTSD severity (PTSDsev). PTSDdx showed strong sensitivity to the presence of PTSD in the awake state, during non-rapid eye movement (NREM) stage N2 sleep, and in a hybrid BCM incorporating both awake and NREM sleep states. PTSDsev showed a strong correlation with PTSD symptom severity (using the PTSD Checklist 5, or PCL5 survey) in the awake state, during N2 sleep, and in a hybrid BCM incorporating both awake and NREM sleep states. Thus, sleep EEG-based brain coherence markers can be utilized as an objective means for determining the presence and severity of PTSD. This portable, inexpensive, and non-invasive tool holds promise for better understanding the physiological mechanisms underlying PTSD and for tracking objective responses to treatment.

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.

Concordance of Actigraphy With Polysomnography in Traumatic Brain Injury Neurorehabilitation Admissions.

OBJECTIVE: To examine concordance of accelerometer-based actigraphy (ACG) with polysomnography (PSG) in the determination of sleep states in inpatients with traumatic brain injury (TBI), and examine the impact of injury severity and comorbid conditions (spasticity, apnea) on concordance. PARTICIPANTS: This was a convenience sample of 50 participants with primarily severe TBI. DESIGN: This was a retrospective chart review of concurrent administration of PSG with ACG in nonconsecutive rehabilitation admissions with TBI. MAIN MEASURES: Total sleep time and sleep efficiency were measured by PSG and ACG. RESULTS: Moderate to strong correlations between ACG and PSG were observed for total sleep time (r = 0.78, P < .01) and sleep efficiency (r = 0.66, P < .01). PSG and ACG estimates of total sleep time (316 minutes vs 325 minutes, respectively) and sleep efficiency (78% vs 77%, respectively) were statistically indistinguishable. CONCLUSIONS: Actigraphy is a valid proxy for monitoring of sleep in this population across injury severity and common comorbidity groups. However, further research with larger sample sizes to examine concordance in patients with TBI with disorder of consciousness and spasticity is recommended.

Prospective evaluation of the nature, course, and impact of acute sleep abnormality after traumatic brain injury.

OBJECTIVE: To prospectively characterize the prevalence, course, and impact of acute sleep abnormality among traumatic brain injury (TBI) neurorehabilitation admissions. DESIGN: Prospective observational study. SETTING: Freestanding rehabilitation hospital. PARTICIPANTS: Primarily severe TBI (median emergency department Glasgow Coma Scale [GCS] score=7; N=205) patients who were mostly men (71%) and white (68%) were evaluated during acute neurorehabilitation. INTERVENTIONS: None. MAIN OUTCOME MEASURE: Delirium Rating Scale-Revised-98 (DelRS-R98) was administered weekly throughout rehabilitation hospitalization. DelRS-R98 item 1 was used to classify severity of sleep-wake cycle disturbance (SWCD) as none, mild, moderate, or severe. SWCD ratings were analyzed both serially and at 1 month postinjury. RESULTS: For the entire sample, 66% (mild to severe) had SWCD at 1 month postinjury. The course of the SWCD using a subset (n=152) revealed that 84% had SWCD on rehabilitation admission, with 63% having moderate to severe ratings (median, 24d postinjury). By the third serial exam (median, 35d postinjury), 59% remained with SWCD, and 28% had moderate to severe ratings. Using general linear modeling and adjusting for age, emergency department GCS score, and days postinjury, presence of moderate to severe SWCD at 1 month postinjury made significant contributions in predicting duration of posttraumatic amnesia (P<.01) and rehabilitation hospital length of stay (P<.01). CONCLUSIONS: Results suggest that sleep abnormalities after TBI are prevalent and decrease over time. However, a high percent remained with SWCD throughout the course of rehabilitation intervention. Given the brevity of inpatient neurorehabilitation, future studies may explore targeting SWCD to improve early outcomes, such as cognitive functioning and economic impact, after TBI.

An electroencephalographic recording platform for real-time seizure detection.

There are currently no clinical devices that can be worn by epilepsy patients who suffer from intractable seizures to warn them of seizure onset. Here we summarize state-of-the-art therapies and devices, and present a second-generation hardware platform in which seizure detection algorithms may be programmed into the device. Bi-polar electrographic data is presented for a prototype device and future implementations are discussed.