Identification of Concussion Subtypes Based on Intrinsic Brain Activity

Importance

The identification of brain activity-based concussion subtypes at time of injury has the potential to advance the understanding of concussion pathophysiology and to optimize treatment planning and outcomes.

Objective

To investigate the presence of intrinsic brain activity-based concussion subtypes, defined as distinct resting state quantitative electroencephalography (qEEG) profiles, at the time of injury.

Design, Setting, and Participants

In this retrospective, multicenter (9 US universities and high schools and 4 US clinical sites) cohort study, participants aged 13 to 70 years with mild head injuries were included in longitudinal cohort studies from 2017 to 2022. Patients had a clinical diagnosis of concussion and were restrained from activity by site guidelines for more than 5 days, with an initial Glasgow Coma Scale score of 14 to 15. Participants were excluded for known neurological disease or history of traumatic brain injury within the last year. Patients were assessed with 2 minutes of artifact-free EEG acquired from frontal and frontotemporal regions within 120 hours of head injury. Data analysis was performed from July 2021 to June 2023.

Main Outcomes and Measures

Quantitative features characterizing the EEG signal were extracted from a 1- to 2-minute artifact-free EEG data for each participant, within 120 hours of injury. Symptom inventories and days to return to activity were also acquired.

Results

From the 771 participants (mean [SD] age, 20.16 [5.75] years; 432 male [56.03%]), 600 were randomly selected for cluster analysis according to 471 qEEG features. Participants and features were simultaneously grouped into 5 disjoint subtypes by a bootstrapped coclustering algorithm with an overall agreement of 98.87% over 100 restarts. Subtypes were characterized by distinctive profiles of qEEG measure sets, including power, connectivity, and complexity, and were validated in the independent test set. Subtype membership showed a statistically significant association with time to return to activity.

Conclusions and Relevance

In this cohort study, distinct subtypes based on resting state qEEG activity were identified within the concussed population at the time of injury. The existence of such physiological subtypes supports different underlying pathophysiology and could aid in personalized prognosis and optimization of care path.

Long-Term Changes in Brain Connectivity Reflected in Quantitative Electrophysiology of Symptomatic Former National Football League Players

Exposure to repetitive head impacts (RHI) has been associated with long-term disturbances in cognition, mood, and neurobehavioral dysregulation, and reflected in neuroimaging. Distinct patterns of changes in quantitative features of the brain electrical activity (quantitative electroencephalogram [qEEG]) have been demonstrated to be sensitive to brain changes seen in neurodegenerative disorders and in traumatic brain injuries (TBI). While these qEEG biomarkers are highly sensitive at time of injury, the long-term effects of exposure to RHI on brain electrical activity are relatively unexplored. Ten minutes of eyes closed resting EEG data were collected from a frontal and frontotemporal electrode montage (BrainScope Food and Drug Administration-cleared EEG acquisition device), as well as assessments of neuropsychiatric function and age of first exposure (AFE) to American football. A machine learning methodology was used to derive a qEEG-based algorithm to discriminate former National Football League (NFL) players (n = 87, 55.40 ± 7.98 years old) from same-age men without history of RHI (n = 68, 54.94 ± 7.63 years old), and a second algorithm to discriminate former players with AFE <12 years (n = 33) from AFE ≥12 years (n = 54). The algorithm separating NFL retirees from controls had a specificity = 80%, a sensitivity = 60%, and an area under curve (AUC) = 0.75. Within the NFL population, the algorithm separating AFE <12 from AFE ≥12 resulted in a sensitivity = 76%, a specificity = 52%, and an AUC = 0.72. The presence of a profile of EEG abnormalities in the NFL retirees and in those with younger AFE includes features associated with neurodegeneration and the disruption of neuronal transmission between regions. These results support the long-term consequences of RHI and the potential of EEG as a biomarker of persistent changes in brain function.

Harmonized-Multinational qEEG norms (HarMNqEEG)

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

A Proposed Brain-, Spine-, and Mental- Health Screening Methodology (NEUROSCREEN) for Healthcare Systems: Position of the Society for Brain Mapping and Therapeutics

The COVID-19 pandemic has accelerated neurological, mental health disorders, and neurocognitive issues. However, there is a lack of inexpensive and efficient brain evaluation and screening systems. As a result, a considerable fraction of patients with neurocognitive or psychobehavioral predicaments either do not get timely diagnosed or fail to receive personalized treatment plans. This is especially true in the elderly populations, wherein only 16% of seniors say they receive regular cognitive evaluations. Therefore, there is a great need for development of an optimized clinical brain screening workflow methodology like what is already in existence for prostate and breast exams. Such a methodology should be designed to facilitate objective early detection and cost-effective treatment of such disorders. In this paper we have reviewed the existing clinical protocols, recent technological advances and suggested reliable clinical workflows for brain screening. Such protocols range from questionnaires and smartphone apps to multi-modality brain mapping and advanced imaging where applicable. To that end, the Society for Brain Mapping and Therapeutics (SBMT) proposes the Brain, Spine and Mental Health Screening (NEUROSCREEN) as a multi-faceted approach. Beside other assessment tools, NEUROSCREEN employs smartphone guided cognitive assessments and quantitative electroencephalography (qEEG) as well as potential genetic testing for cognitive decline risk as inexpensive and effective screening tools to facilitate objective diagnosis, monitor disease progression, and guide personalized treatment interventions. Operationalizing NEUROSCREEN is expected to result in reduced healthcare costs and improving quality of life at national and later, global scales.

Validation of a Multimodal EEG-Based Index to Aid in Diagnosing and Tracking Concussion Among Athletes

OBJECTIVE

The goal of this study was to validate an EEG based multimodal index to aid in the assessment of concussion at time of injury, severity of concussion, and aid in evaluating readiness to return to play/activity.

BACKGROUND

The absence of a gold standard for diagnosis of concussion results in reliance on subjective self-report of symptoms. EEG has been demonstrated to be sensitive to changes in brain function following head injury, especially in connectivity. Using machine learning with inputs primarily from EEG measures, and including multimodal inputs, an objective marker of the likelihood of concussion (Concussion Index, CI) was derived.

DESIGN/METHODS

Male and female concussed athletes and controls ages of 13-25 years, represented a convenience sample (n = 580), enrolled from US High School, Colleges, and Concussion Clinics. Concussed subjects had a witnessed head impact and were removed from play by site guidelines. Assessments were performed within 72 hours of injury, at clinically determined return to play (RTP), 45 days following RTP, and included EEG (frontal and frontotemporal regions), neurocognitive performance, and standard concussion assessments.

RESULTS

Sensitivity = 85.99%, Specificity = 70.78%, NPV = 90.10% and PPV = 62.02, were obtained. Results demonstrated significance: (1) between CI at injury compared to RTP (p < 0.0001); (2) between CI in patients with rapid (<14 days) compared with those with prolonged recovery (=14 days), (p = 0.0038); (3) stability over time in controls (p < 0.0001); and (4) between CI and total symptom burden (correlation coefficient 0.8031, p < 0.0001).

CONCLUSIONS

This study independently validated a multimodal, EEG-based, objective index of concussion (CI). The neurotechnology platform incorporating this capability is handheld, rapid to use, and lends itself to incorporation into the standard assessment of concussion to aid in clinical diagnosis and assessment of readiness to RTP. This data supported the FDA clearance for the Concussion Index (embedded in the BrainScope medical device).

Concussion assessment potentially aided by use of an objective multimodal concussion index

Objective

Prompt, accurate, objective assessment of concussion is crucial as delays can lead to increased short and long-term consequences. The purpose of this study was to derive an objective multimodal concussion index (CI) using EEG at its core, to identify concussion, and to assess change over time throughout recovery.

Methods

Male and female concussed ( N = 232) and control ( N = 206) subjects 13–25 years were enrolled at 12 US colleges and high schools. Evaluations occurred within 72 h of injury, 5 days post-injury, at return-to-play (RTP), 45 days after RTP (RTP + 45); and included EEG, neurocognitive performance, and standard concussion assessments. Concussed subjects had a witnessed head impact, were removed from play for ≥ 5 days using site guidelines, and were divided into those with RTP < 14 or ≥14 days. Part 1 describes the derivation and efficacy of the machine learning derived classifier as a marker of concussion. Part 2 describes significance of differences in CI between groups at each time point and within each group across time points.

Results

Sensitivity = 84.9%, specificity = 76.0%, and AUC = 0.89 were obtained on a test Hold-Out group representing 20% of the total dataset. EEG features reflecting connectivity between brain regions contributed most to the CI. CI was stable over time in controls. Significant differences in CI between controls and concussed subjects were found at time of injury, with no significant differences at RTP and RTP + 45. Within the concussed, differences in rate of recovery were seen.

Conclusions

The CI was shown to have high accuracy as a marker of likelihood of concussion. Stability of CI in controls supports reliable interpretation of CI change in concussed subjects. Objective identification of the presence of concussion and assessment of readiness to return to normal activity can be aided by use of the CI, a rapidly obtained, point of care assessment tool.

Quantitative EEG Tomography of Early Childhood Malnutrition

The goal of this study is to identify the quantitative electroencephalographic (qEEG) signature of early childhood malnutrition [protein-energy malnutrition (PEM)]. To this end, archival digital EEG recordings of 108 participants in the Barbados Nutrition Study (BNS) were recovered and cleaned of artifacts (46 children who suffered an episode of PEM limited to the first year of life) and 62 healthy controls). The participants of the still ongoing BNS were initially enrolled in 1973, and EEGs for both groups were recorded in 1977-1978 (at 5-11 years). Scalp and source EEG Z-spectra (to correct for age effects) were obtained by comparison with the normative Cuban Human Brain Mapping database. Differences between both groups in the z spectra (for all electrode locations and frequency bins) were assessed by t-tests with thresholds corrected for multiple comparisons by permutation tests. Four clusters of differences were found: (a) increased theta activity (3.91-5.86 Hz) in electrodes T4, O2, Pz and in the sources of the supplementary motor area (SMA); b) decreased alpha1 (8.59-8.98 Hz) in Fronto-central electrodes and sources of widespread bilateral prefrontal are; (c) increased alpha2 (11.33-12.50 Hz) in Temporo-parietal electrodes as well as in sources in Central-parietal areas of the right hemisphere; and (d) increased beta (13.67-18.36 Hz), in T4, T5 and P4 electrodes and decreased in the sources of bilateral occipital-temporal areas. Multivariate Item Response Theory of EEGs scored visually by experts revealed a neurophysiological latent variable which indicated excessive paroxysmal and focal abnormality activity in the PEM group. A robust biomarker construction procedure based on elastic-net regressions and 1000-cross-validations was used to: (i) select stable variables and (ii) calculate the area under ROC curves (AUC). Thus, qEEG differentiate between the two nutrition groups (PEM vs Control) performing as well as visual inspection of the EEG scored by experts (AUC = 0.83). Since PEM is a global public health problem with lifelong neurodevelopmental consequences, our finding of consistent differences between PEM and controls, both in qualitative and quantitative EEG analysis, suggest that this technology may be a source of scalable and affordable biomarkers for assessing the long-term brain impact of early PEM.

Exploration of the Pathophysiology of Chronic Pain Using Quantitative EEG Source Localization

Chronic pain affects more than 35% of the US adult population representing a major public health imperative. Currently, there are no objective means for identifying the presence of pain, nor for quantifying pain severity. Through a better understanding of the pathophysiology of pain, objective indicators of pain might be forthcoming. Brain mechanisms mediating the painful state were imaged in this study, using source localization of the EEG. In a population of 77 chronic pain patients, significant overactivation of the "Pain Matrix" or pain network, was found in brain regions including, the anterior cingulate, anterior and posterior insula, parietal lobule, thalamus, S1, and dorsolateral prefrontal cortex (DLPFC), consistent with those reported with conventional functional imaging, and extended to include the mid and posterior cingulate, suggesting that the increased temporal resolution of electrophysiological measures may allow a more precise identification of the pain network. Significant differences between those who self-report high and low pain were reported for some of the regions of interest (ROIs), maximally on left hemisphere in the DLPFC, suggesting encoding of pain intensity occurs in a subset of pain network ROIs. Furthermore, a preliminary multivariate logistic regression analysis was used to select quantitative-EEG features which demonstrated a highly significant predictive relationship of self-reported pain scores. Findings support the potential to derive a quantitative measure of the severity of pain using information extracted from a multivariate descriptor of the abnormal overactivation. Furthermore, the frequency specific (theta/low alpha band) overactivation in the regions reported, while not providing direct evidence, are consistent with a model of thalamocortical dysrhythmia as the potential mechanism of the neuropathic painful condition.

A Brain Electrical Activity Electroencephalographic-Based Biomarker of Functional Impairment in Traumatic Brain Injury: A Multi-Site Validation Trial

The potential clinical utility of a novel quantitative electroencephalographic (EEG)-based Brain Function Index (BFI) as a measure of the presence and severity of functional brain injury was studied as part of an independent prospective validation trial. The BFI was derived using quantitative EEG (QEEG) features associated with functional brain impairment reflecting current consensus on the physiology of concussive injury. Seven hundred and twenty adult patients (18-85 years of age) evaluated within 72 h of sustaining a closed head injury were enrolled at 11 U.S. emergency departments (EDs). Glasgow Coma Scale (GCS) score was 15 in 97%. Standard clinical evaluations were conducted and 5 to 10 min of EEG acquired from frontal locations. Clinical utility of the BFI was assessed for raw scores and percentile values. A multinomial logistic regression analysis demonstrated that the odds ratios (computed against controls) of the mild and moderate functionally impaired groups were significantly different from the odds ratio of the computed tomography (CT) postive (CT+, structural injury visible on CT) group (p = 0.0009 and p = 0.0026, respectively). However, no significant differences were observed between the odds ratios of the mild and moderately functionally impaired groups. Analysis of variance (ANOVA) demonstrated significant differences in BFI among normal (16.8%), mild TBI (mTBI)/concussed with mild or moderate functional impairment, (61.3%), and CT+ (21.9%) patients (p < 0.0001). Regression slopes of the odds ratios for likelihood of group membership suggest a relationship between the BFI and severity of impairment. Findings support the BFI as a quantitative marker of brain function impairment, which scaled with severity of functional impairment in mTBI patients. When integrated into the clinical assessment, the BFI has the potential to aid in early diagnosis and thereby potential to impact the sequelae of TBI by providing an objective marker that is available at the point of care, hand-held, non-invasive, and rapid to obtain.

Emergency Department Triage of Traumatic Head Injury Using a Brain Electrical Activity Biomarker: A Multisite Prospective Observational Validation Trial

OBJECTIVES

A brain electrical activity biomarker for identifying traumatic brain injury (TBI) in emergency department (ED) patients presenting with high Glasgow Coma Scale (GCS) after sustaining a head injury has shown promise for objective, rapid triage. The main objective of this study was to prospectively evaluate the efficacy of an automated classification algorithm to determine the likelihood of being computed tomography (CT) positive, in high-functioning TBI patients in the acute state.

METHODS

Adult patients admitted to the ED for evaluation within 72 hours of sustaining a closed head injury with GCS 12 to 15 were candidates for study. A total of 720 patients (18-85 years) meeting inclusion/exclusion criteria were enrolled in this observational, prospective validation trial, at 11 U.S. EDs. GCS was 15 in 97%, with the first and third quartiles being 15 (interquartile range = 0) in the study population at the time of the evaluation. Standard clinical evaluations were conducted and 5 to 10 minutes of electroencephalogram (EEG) was acquired from frontal and frontal-temporal scalp locations. Using an a priori derived EEG-based classification algorithm developed on an independent population and applied to this validation population prospectively, the likelihood of each subject being CT+ was determined, and performance metrics were computed relative to adjudicated CT findings.

RESULTS

Sensitivity of the binary classifier (likely CT+ or CT-) was 92.3% (95% confidence interval [CI] = 87.8%-95.5%) for detection of any intracranial injury visible on CT (CT+), with specificity of 51.6% (95% CI = 48.1%-55.1%) and negative predictive value (NPV) of 96.0% (95% CI = 93.2%-97.9%). Using ternary classification (likely CT+, equivocal, likely CT-) demonstrated enhanced sensitivity to traumatic hematomas (≥1 mL of blood), 98.6% (95% CI = 92.6%-100.0%), and NPV of 98.2% (95% CI = 95.5%-99.5%).

CONCLUSION

Using an EEG-based biomarker high accuracy of predicting the likelihood of being CT+ was obtained, with high NPV and sensitivity to any traumatic bleeding and to hematomas. Specificity was significantly higher than standard CT decision rules. The short time to acquire results and the ease of use in the ED environment suggests that EEG-based classifier algorithms have potential to impact triage and clinical management of head-injured patients.

Localization of Deep White Matter Lymphoma Using VARETA: A Case Study

Methods have recently been proposed for localization of multiple brain sources of particular EEG frequencies recorded from the scalp, to identify their most probable neuroanatomical generators. This paper reports the accurate localization of a deep white matter lymphoma, using Variable Resolution Electromagnetic Tomography (VARETA). The accuracy of this localization was confirmed by MRI studies. The patient was referred for a quantitative EEG evaluation, two weeks following an automobile accident, with no known loss of consciousness. There was marked excess and asymmetry of frontal slow wave activity, with highly significant hypocoherence. Significant gradient shifts within the left hemisphere were also seen. Visual inspection of the EEG tracings revealed theta paroxysms in left dorsolateral and mesial frontal regions. The MRI revealed a large space-occupying lesion deep within the white matter of the left frontal lobe, with evidence of subependymal spread and significant surrounding vasogenic edema. Localization of the sources of the maximal QEEG abnormalities using VARETA was consistent with the lesion location seen in the MRI images. This case demonstrates that VARETA can achieve highly sensitive and accurate localization of sources of QEEG abnormalities which lie in the deepest brain regions.

The quantified EEG characteristics of responders and non-responders to long-term treatment with atomoxetine in children with attention deficit hyperactivity disorders

OBJECTIVE

The aim of our study is to examine quantitative Electroencephalogram (QEEG) differences between ADHD patients that are responders and non-responders to long-term treatment with Atomoxetine at baseline and after 6 and 12months of treatment. Patients with attention deficit hyperactivity disorder (ADHD) received atomoxetine titrated, over 7days, from 0.5 to 1.2mg/kg/day. QEEG and Swanson, Nolan, and Pelham-IV Questionnaire (SNAP-IV) scores were recorded before treatment and after therapy.

METHODS

Twenty minutes of eyes closed resting EEG was recorded from 19 electrodes referenced to linked earlobes. Full frequency and narrow band spectra of two minutes of artifact-free EEG were computed as well as source localization using Variable Resolution Electrical Tomography (VARETA). Abnormalities were identified using Z-spectra relative to normative values.

RESULTS

Patients were classified as responders, non-responders and partial responders based upon the SNAP-IV findings. At baseline, the responders showed increased absolute power in alpha and delta in frontal and temporal regions, whereas, non-responders showed increased absolute power in all frequency bands that was widely distributed. With treatment responders' absolute power values moved toward normal values, whereas, non-responders remained at baseline values.

CONCLUSIONS

Patients with increased power in the alpha band with no evidence of alterations in the beta or theta range, might be responders to treatment with atomoxetine. Increased power in the beta band coupled with increased alpha seems to be related to non-responders and one should consider atomoxetine withdrawal, especially if there is persistence of increased alpha and beta accompanied by an increase of theta.

Identification of hematomas in mild traumatic brain injury using an index of quantitative brain electrical activity

Rapid identification of traumatic intracranial hematomas following closed head injury represents a significant health care need because of the potentially life-threatening risk they present. This study demonstrates the clinical utility of an index of brain electrical activity used to identify intracranial hematomas in traumatic brain injury (TBI) presenting to the emergency department (ED). Brain electrical activity was recorded from a limited montage located on the forehead of 394 closed head injured patients who were referred for CT scans as part of their standard ED assessment. A total of 116 of these patients were found to be CT positive (CT+), of which 46 patients with traumatic intracranial hematomas (CT+) were identified for study. A total of 278 patients were found to be CT negative (CT-) and were used as controls. CT scans were subjected to quantitative measurements of volume of blood and distance of bleed from recording electrodes by blinded independent experts, implementing a validated method for hematoma measurement. Using an algorithm based on brain electrical activity developed on a large independent cohort of TBI patients and controls (TBI-Index), patients were classified as either positive or negative for structural brain injury. Sensitivity to hematomas was found to be 95.7% (95% CI = 85.2, 99.5), specificity was 43.9% (95% CI = 38.0, 49.9). There was no significant relationship between the TBI-Index and distance of the bleed from recording sites (F = 0.044, p = 0.833), or volume of blood measured F = 0.179, p = 0.674). Results of this study are a validation and extension of previously published retrospective findings in an independent population, and provide evidence that a TBI-Index for structural brain injury is a highly sensitive measure for the detection of potentially life-threatening traumatic intracranial hematomas, and could contribute to the rapid, quantitative evaluation and treatment of such patients.

Identification of acute stroke using quantified brain electrical activity

OBJECTIVES

Acute stroke is a leading cause of brain injury and death and requires rapid and accurate diagnosis. Noncontrast head computed tomography (CT) is the first line for diagnosis in the emergency department (ED). Complicating rapid triage are presenting conditions that clinically mimic stroke. There is an extensive literature reporting clinical utility of brain electrical activity in early diagnosis and management of acute stroke. However, existing technologies do not lend themselves to easily acquired rapid evaluation. This investigation used an independently derived classifier algorithm for the identification of traumatic structural brain injury based on brain electrical activity recorded from a reduced frontal montage to explore the potential clinical utility of such an approach in acute stroke assessment.

METHODS

Adult patients (age 18 to 95 years) presenting with stroke-like and/or altered mental status symptoms were recruited from urban academic EDs as part of a large research study evaluating the clinical utility of quantitative brain electrical activity in acutely brain-injured patients. All patients from the parent study who had confirmed strokes, and a control group of stroke mimics (those with final ED diagnoses of migraine or syncope), were selected for this study. All stroke patients underwent head CT scans. Some patients with negative CTs had further imaging with magnetic resonance imaging (MRI). Ten minutes of electroencephalographic data were acquired on a hand-held device in development, from five frontal electrodes. Data analyses were done offline. A Structural Brain Injury Index (SBII) was derived using an independently developed binary discriminant classification algorithm whose input was specified features of brain electrical activity. The SBII was previously found to have high accuracy in the identification of traumatic brain-injured patients who were found to have brain injury on CT (CT+). This algorithm was applied to patients in this study and used to classify patients as CT+ or not CT+. Performance was assessed using sensitivity, specificity, and negative and positive predictive values (NPV, PPV).

RESULTS

Forty-eight stroke patients (31 ischemic and 17 hemorrhagic) and 135 stroke mimic controls were included. Within the ischemic population, approximately half were CT- but later confirmed for stroke with MRI (CT-/MRI+). Sensitivity to stroke was 91.7%, specificity 50.4% (to stroke mimic), NPV 94.4%, and PPV 39.6%. Eighty percent of the CT-/MRI+ ischemic strokes were correctly identified at the time of the CT- scan.

CONCLUSIONS

Despite a small population and the use of a classifier without the benefit of training on a stroke population, these data suggest that a rapidly acquired, easy-to-use system to assess brain electrical activity at the time of evaluation of acute stroke could be a valuable adjunct to current clinical practice.

Use of brain electrical activity for the identification of hematomas in mild traumatic brain injury

This study investigates the potential clinical utility in the emergency department (ED) of an index of brain electrical activity to identify intracranial hematomas. The relationship between this index and depth, size, and type of hematoma was explored. Ten minutes of brain electrical activity was recorded from a limited montage in 38 adult patients with traumatic hematomas (CT scan positive) and 38 mild head injured controls (CT scan negative) in the ED. The volume of blood and distance from recording electrodes were measured by blinded independent experts. Brain electrical activity data were submitted to a classification algorithm independently developed traumatic brain injury (TBI) index to identify the probability of a CT+traumatic event. There was no significant relationship between the TBI-Index and type of hematoma, or distance of the bleed from recording sites. A significant correlation was found between TBI-Index and blood volume. The sensitivity to hematomas was 100%, positive predictive value was 74.5%, and positive likelihood ratio was 2.92. The TBI-Index, derived from brain electrical activity, demonstrates high accuracy for identification of traumatic hematomas. Further, this was not influenced by distance of the bleed from the recording electrodes, blood volume, or type of hematoma. Distance and volume limitations noted with other methods, (such as that based on near-infrared spectroscopy) were not found, thus suggesting the TBI-Index to be a potentially important adjunct to acute assessment of head injury. Because of the life-threatening risk of undetected hematomas (false negatives), specificity was permitted to be lower, 66%, in exchange for extremely high sensitivity.

Childhood abuse and EEG source localization in crack cocaine dependence

Fourteen subjects with histories of sexual and/or physical abuse in childhood and 13 matched control subjects were selected from a consecutive series of clients in residential treatment for crack cocaine dependence. Standardized low-resolution electromagnetic brain tomography (sLORETA) was used to estimate the source generators of the EEG in a cortical mask with voxel z-scores referenced to normative data at frequency intervals of 039 Hz, with nonparametric permutation to correct by randomization for the number of comparisons and the intercorrelations and variance of distribution of voxel values. Subjects with histories of abuse in childhood had significantly greater EEG power than controls in the theta frequency range (3.51-7.41 Hz), with greatest differences in the 3.90-Hz band distributed mainly in the parahippocampal, fusiform, lingual, posterior cingulate, and insular gyri. The groups did not differ significantly with regard to delta (1.56-3.12 Hz), alpha (7.81-12.48 Hz), beta (12.87-19.89 Hz), and gamma (20.28-35.10 Hz) frequency power. In excess, theta EEG power, a bandwidth of transactions among hippocampus and amygdala and paralimbic and visual association cortex, may be a correlate of childhood exposure to abuse.

Guidelines for the recording and evaluation of pharmaco-EEG data in man: the International Pharmaco-EEG Society (IPEG)

The International Pharmaco-EEG Society (IPEG) presents updated guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-EEG data in man. Since the publication of the first pharmaco-EEG guidelines in 1982, technical and data processing methods have advanced steadily, thus enhancing data quality and expanding the palette of tools available to investigate the action of drugs on the central nervous system (CNS), determine the pharmacokinetic and pharmacodynamic properties of novel therapeutics and evaluate the CNS penetration or toxicity of compounds. However, a review of the literature reveals inconsistent operating procedures from one study to another. While this fact does not invalidate results per se, the lack of standardisation constitutes a regrettable shortcoming, especially in the context of drug development programmes. Moreover, this shortcoming hampers reliable comparisons between outcomes of studies from different laboratories and hence also prevents pooling of data which is a requirement for sufficiently powering the validation of novel analytical algorithms and EEG-based biomarkers. The present updated guidelines reflect the consensus of a global panel of EEG experts and are intended to assist investigators using pharmaco-EEG in clinical research, by providing clear and concise recommendations and thereby enabling standardisation of methodology and facilitating comparability of data across laboratories.

Quantitative brain electrical activity in the initial screening of mild traumatic brain injuries

Introduction:

The incidence of emergency department (ED) visits for Traumatic Brain Injury (TBI) in the United States exceeds 1,000,000 cases/year with the vast majority classified as mild (mTBI). Using existing computed tomography (CT) decision rules for selecting patients to be referred for CT, such as the New Orleans Criteria (NOC), approximately 70% of those scanned are found to have a negative CT. This study investigates the use of quantified brain electrical activity to assess its possible role in the initial screening of ED mTBI patients as compared to NOC.

Methods:

We studied 119 patients who reported to the ED with mTBI and received a CT. Using a hand-held electroencephalogram (EEG) acquisition device, we collected data from frontal leads to determine the likelihood of a positive CT. The brain electrical activity was processed off-line to generate an index (TBI-Index, biomarker). This index was previously derived using an independent population, and the value found to be sensitive for significant brain dysfunction in TBI patients. We compared this performance of the TBI-Index to the NOC for accuracy in prediction of positive CT findings.

Results:

Both the brain electrical activity TBI-Index and the NOC had sensitivities, at 94.7% and 92.1% respectively. The specificity of the TBI-Index was more than twice that of NOC, 49.4% and 23.5% respectively. The positive predictive value, negative predictive value and the positive likelihood ratio were better with the TBI-Index. When either the TBI-Index or the NOC are positive (combining both indices) the sensitivity to detect a positive CT increases to 97%.

Conclusion:

The hand-held EEG device with a limited frontal montage is applicable to the ED environment and its performance was superior to that obtained using the New Orleans criteria. This study suggests a possible role for an index of brain function based on EEG to aid in the acute assessment of mTBI patients.

Measuring brain electrical activity to track recovery from sport-related concussion

PRIMARY OBJECTIVE

To follow recovery from concussion in a sample of athletes using an electroencephalographic (EEG) index of quantitative brain activity developed previously on an independent Emergency Department (ED) sample of head-injured subjects with traumatic brain injury.

METHODS AND PROCEDURES

EEG recordings from five frontal electrode sites were obtained on 59 injured athletes and 31 controls at the time of injury and at 8 and 45 days afterward. All subjects also completed standardized clinical assessment of post-concussion symptoms, postural stability and cognitive functioning at injury and 8 and 45 days post-injury.

RESULTS

Abnormalities in clinical assessment measures were observed in injured subjects only at time of injury. Statistical analysis of brain electrical activity measures with the ED-based algorithm revealed significant differences between injured athletes vs controls at the time of injury and at day 8. Measures from the two groups did not differ on day 45.

CONCLUSIONS

This study demonstrated that an algorithm of brain electrical activity developed on an independent sample of ED subjects with head injury is sensitive to the effects of sport-related concussion. Using this algorithm, abnormal features of brain electrical activity were detected in athletes with concussion at the time of injury and persisted beyond the point of recovery on clinical measures.

Evaluation of the pain matrix using EEG source localization: a feasibility study

OBJECTIVES

An extensive neuroimaging literature on chronic pain demonstrates increased cerebral blood flow and metabolism consistent with increased neuronal activity in the structures comprising the "pain matrix"; furthermore, some of these regions have been shown to encode pain intensity. It is the objective of this study to demonstrate the feasibility of using quantitative electroencephalography (EEG) source localization to reflect and to quantify activity in the pain matrix.

METHODS

Eyes closed resting EEG was recorded from 19 standardized scalp locations, in a pilot sample of five patients with chronic neuropathic pain, before and after pain reduction. Quantitative electro encephalography (QEEG) source localization was computed estimating the mathematically most probable source generators of EEG surface potentials in each state. Sources identified in this way have been demonstrated to coregister with those identified by neuroimaging methods.

RESULTS

QEEG sources demonstrated frequency specific increased neuronal activity in the baseline high pain state in structures including the thalamus, somatosensory cortex, anterior and posterior insula, medial and lateral prefrontal cortex and cingulate. Significant reduction of activation in these regions was seen when pain was reduced (≥50% on subjective ratings).

CONCLUSION

The areas that were activated in the high pain state localized to the same regions reported by other neuroimaging methods and with frequency specificity. The frequency and regionally specific activation may indicate distinctive patterns of pathophysiology underlying the pain matrix. Although in a small number of patients, this work suggests that QEEG may be a useful tool in the exploration and quantification of the pain matrix in a clinical setting.

Source imaging of QEEG as a method to detect awareness in a person in vegetative state

BACKGROUND

Assessment of awareness in patients with severe brain injury remains subjective, although patients with even limited awareness (e.g. minimal conscious state, MCS) have different prognoses and treatment than those in vegetative state (VS). Recently, task appropriate differential regional activation in VS has been reported using fMRI during mental imagery.

PRIMARY OBJECTIVE

Demonstration of conscious awareness in reproducible differential EEG source localization images in a VS patient reflecting requested mental imagery was performed.

METHODS

A VS patient (with re-test) and a normal control were requested to imagine singing and to mentally perform serial subtraction, while EEG was recorded. QEEG source localization was performed to identify regions of brain activation in response to tasks.

RESULTS

Replicable distinctive activation of brain areas appropriate for each task was seen in the VS patient and control. Frequency spectra shifted to beta, with significant source activation in regions including the bilateral anterior cingulate, insula, left caudate and dorsolateral pre-frontal cortex to singing and the putamen, insula, left pre-frontal cortex and right temporal gyrus to subtraction by 7's.

CONCLUSIONS

Results from this single case suggests the potential utility of QEEG source localization images to detect awareness in patients clinically diagnosed as being in VS. This indicates the possibility that EEG may serve as an important adjunct to the assessment of awareness in patients with disorders of consciousness in the clinical setting.

Cocaine cue versus cocaine dosing in humans: evidence for distinct neurophysiological response profiles

Subjective, physiological and electroencephalographic (EEG) profiles were studied in cocaine dependent study participants in response to cocaine cue exposure or a dose of smoked cocaine. Both stimuli increased subjective ratings of cocaine high and craving, enhanced negative affect, and boosted plasma ACTH and skin conductance levels. However, cocaine dose produced a greater increase in high and a more prolonged increase in plasma ACTH, while cocaine cue produced a decline in skin temperature. Both stimuli produced increases in absolute theta, alpha and beta EEG power over the prefrontal cortex. However, interhemispheric EEG coherence over the prefrontal cortex decreased during cocaine cue exposure but increased following cocaine dose. Moreover, correlation analysis of subjective, physiological and EEG responding to cocaine cue and dose revealed distinct profiles. Delta and theta activity were associated with negative affect during cocaine cue exposure, but were associated with cocaine craving and reward following cocaine dosing. In both conditions, alpha activity was marker for anxiousness but not high. These data demonstrate similar subjective, physiological responding in clinical laboratory states of cocaine craving and reward. However, differences in EEG response profiles, and their relationship to function, indicate distinct neurophysiological mediators of cocaine craving and reward within the prefrontal cortex.

Optimal denoising of brainstem Auditory Evoked Response (BAER) for automatic peak identification and brainstem assessment

Brainstem auditory evoked responses (BAER) are transient signals embedded in the EEG recorded from scalp electrodes, when a subject is presented with a series of acoustic clicks. These signals typically have a signal-to-noise ratio (SNR) well below -10 dB. The extraction of BAER signals from the EEG for the purpose of automatically computing features of interest from the BAER waveform(s) is described in this paper. These features are: 1) Presence of an actual BAER response (at least peak I), 2) Presence of peak V, 3) Inter-peak latency I-V. We propose to combine a signal-adaptive denoising technique based on complex wavelets with a signal quality metric referred to as the FSP variance ratio for quantitative evaluation of signal quality in order to optimally denoise BAER signals and perform reliable waveform analysis.

Electrophysiological subtypes of psychotic states

OBJECTIVE

This research sought neurobiological features common to psychotic states displayed by patients with different clinical diagnoses.

METHOD

Cluster analysis with quantitative electroencephalographic (QEEG) variables was used to subtype drug-naïve, non-medicated, and medicated schizophrenic, depressed and alcoholic patients with psychotic symptoms, from the USA and Germany. QEEG source localization brain images were computed for each cluster.

RESULTS

Psychotic patients with schizophrenia, depression and alcoholism, and drug- naïve schizophrenic patients, were distributed among six clusters. QEEG images revealed one set of brain regions differentially upregulated in each cluster and another group of structures downregulated in the same way in every cluster.

CONCLUSION

Subtypes previously found among 94 schizophrenic patients were replicated in a sample of 390 non-schizophrenic as well as schizophrenic psychotics, and displayed common neurobiological abnormalities. Collaborative longitudinal studies using these economical methods might improve differential understanding and treatment of patients based upon these features rather than clinical symptoms.

Toward a better understanding of the pathophysiology of OCD SSRI responders: QEEG source localization

OBJECTIVE

To demonstrate the utility of three-dimensional source localization of the scalp-recorded electroencephalogram (EEG) for the identification of the most probable underlying brain dysfunction in patients with obsessive-compulsive disorder (OCD).

METHOD

Eyes-closed resting EEG data was recorded from the scalp locations of the International 10/20 System. Variable resolution electromagnetic tomography (VARETA) was applied to artifact-free EEG data. This mathematical algorithm estimates the source generators of EEG recorded from the scalp.

RESULTS

An excess in the alpha range was found with sources in the corpus striatum, in the orbito-frontal and temporo-frontal regions in untreated OCD patients. This abnormality was seen to decrease following successful treatment with paroxetine.

CONCLUSION

The VARETA findings of an activation/deactivation pattern in cortical and subcortical structures in paroxetine-responsive patients are in good accordance with data obtained in previously published positron emission tomography studies related to current hypotheses of a thalamo-striatal-frontal feedback loop being relevant for understanding the pathophysiology of OCD.

Quantitative EEG and Electromagnetic Brain Imaging in Aging and in the Evolution of Dementia

Electroencephalographic (EEG) changes with normal aging have long been reported. Departures from age-expected changes have been observed in mild cognitive impairment and dementia, the magnitude of which correlates with the degree of cognitive impairment. Such abnormalities include increased delta and theta activity, decreased mean frequency, and changes in coherence. Similar findings have been reported using magnetoencephalography (MEG) at rest and during performance of mental tasks. Electrophysiological features have also been shown to be predictive of future decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). We have recently reported results from initial quantitative electroencephalography (QEEG) evaluations of normal elderly subjects (with only subjective reports of memory loss), predicting future cognitive decline or conversion to dementia, with high prediction accuracy (approximately 95%). In this report, source localization algorithms were used to identify the mathematically most probable underlying generators of abnormal features of the scalp-recorded EEG from these patients with differential outcomes. Using this QEEG method, abnormalities in brain regions identified in studies of AD using MEG, MRI, and positron emission tomography (PET) imaging were found in the premorbid recordings of those subjects who go on to decline or convert to dementia.

Correlation of PET and qEEG in normal subjects

Positron emission tomography (PET) and quantitative electroencephalography (qEEG) were obtained in 15 normal male subjects with eyes closed at rest. Correlations between qEEG variables and regional metabolism were examined as an approach to investigating the metabolic and neuroanatomical basis of the generation of the EEG. Analogous to the neurometric approach to qEEG, a normative 2-fluoro-deoxyglucose voxel data base was developed for the PET image. The PET image was transformed to an idealized cylindrical set of coordinates to allow registration with the Talairach stereotactic atlas. PET regions of interest for the thalamus, the left and right temporal lobes, the medial frontal cortex and the dorsolateral prefrontal cortex were defined using Talairach coordinates and correlated to the QEEG. Salient findings included a negative correlation of thalamic metabolism to alpha power and a positive correlation of medial frontal cortical metabolism to delta EEG power. The significance of these findings is discussed with reference to the existing literature on the physiology of the generation of the EEG.

Topographic imaging of quantitative EEG in response to smoked cocaine self-administration in humans

Quantitative electroencephalographic (qEEG) profiles were studied in cocaine-dependent patients in response to an acute, single-blind, self-administered dose of smoked cocaine base (50 mg) vs placebo. qEEG data were averaged using neurometric analytical methods and the spectral power of each primary bandwidth was computed and topographically imaged. Additional measures included cocaine-induced high, craving, and related subjective ratings, heart rate, blood pressure, and plasma cortisol and homovanillic acid levels. In all, 13 crack cocaine-dependent subjects were tested. Cocaine produced a rapid increase in subjective ratings of cocaine high and good drug effect, and a more persistent increase in cocaine craving and nervousness. Cocaine also produced a rapid rise in heart rate and a prolonged increase in plasma cortisol. Placebo, administered in the context of cocaine cues and dosing expectations, had no cocaine-like subjective or physiological effects. Cocaine produced a rapid increase in absolute theta, alpha, and beta power over the prefrontal cortex (FP1, FP2), lasting up to 25 min after dosing. The increase in theta power was correlated with good drug effect, and the increase in alpha power was correlated with nervousness. Cocaine also produced a similar increase in delta coherence over the prefrontal cortex, which was positively correlated with plasma cortisol, and negatively correlated with nervousness. Placebo resulted in an increase in alpha power over the prefrontal cortex. These data demonstrate the involvement of prefrontal cortex in the qEEG response to acute cocaine. Evidence indicates slow wave qEEG, delta and theta activity, involvement in the rewarding properties of cocaine.

The relevance of QEEG to the evaluation of behavioral disorders and pharmacological interventions

It has become apparent that the electrical signals recorded from the scalp of healthy individuals under standardized conditions are predictable, and that patients with a wide variety of brain disorders display activity with unusual features. It also early became apparent that centrally active medications produced striking changes in this activity. The application of computerized signal analysis to EEG recordings collected using standardized procedures has made it possible to obtain quantitative descriptions of brain electrical activity (QEEG) in normal individuals and patients with disorders of brain function or structure, as well as quantitative description of the ways in which centrally active medications alter this activity (Pharmaco-EEG or "PEEG"). With the emergence of three-dimensional EEG source localization techniques, it has recently become possible to visualize the mathematically most probable generators of QEEG abnormalities within the brain as well as the neuroanatomical regions where abnormal activity is most altered by efficacious medication. As QEEG and PEEG have evolved, a vast body of facts has been accumulated, describing changes in the EEG or event-related potentials (ERPs). observed in a variety of brain disorders or after administration of a variety of medications. With some notable exceptions, these studies have tended to be phenomenological rather than analytic. There has not been a systematic attempt to integrate these phenomena in order to build better understanding of how the abnormal behaviors of a particular psychiatric patient might be related to the specific pattern of the deviant electrical activity, nor just how pharmacological reduction of that deviant activity may have resulted in more normal behavior. This article is an endeavor to provide a more specific theoretical framework for understanding the relationships between the neuroanatomy and neurochemistry of the homeostatic system underlying the regulation of the QEEG, and the mechanisms revealed by Pharmaco-EEG that aid in correcting these illnesses.

Electroencephalographic Mapping During Routine Clinical Practice: Cortical Arousal During Tracheal Intubation?

We used quantitative analysis of the electroencephalogram (EEG) in 42 patients to assess the effect of tracheal intubation after induction of anesthesia with etomidate and sufentanil using standard clinical practice. The EEG was recorded from eight bipolar electrode derivations and Z-transformed relative to age expected normative data for relative power in the delta, theta, alpha, and beta frequency bands. Tracheal intubation resulted in classical cortical arousal, as indicated by acceleration of the EEG frequencies. Significant effects were seen in all frequency bands, most pronounced in the alpha frequency band, with the largest increase bilaterally in the fronto-temporal regions (F-values: Delta - 9.592, P < 0.001; theta - 1.691, P < 0.001; alpha - 18.439, P < 0.001; beta - 4.504, P < 0.001). Changes in alpha and delta power during induction of anesthesia were correlated with the dose of etomidate (P < 0.05). Changes in alpha after tracheal intubation were correlated at the parietooccipital brain regions to the dose of sufentanil (P < 0.05). Individual titration of the dose of etomidate and sufentanil, as during routine clinical practice, is not sufficient to block the strong noxious stimulation of tracheal intubation and results in cortical arousal. The clinical impact of this cortical wake-up phenomenon is undetermined.

Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging

An extensive literature reports changes in quantitative electroencephalogram (QEEG) with aging and a relationship between magnitude of changes and degree of clinical deterioration in progressive dementia. Longitudinal studies have demonstrated QEEG differences between mild cognitively impaired (MCI) elderly who go on to decline and those who do not. This study focuses on normal elderly with subjective cognitive complaints to assess the utility of QEEG in predicting future decline within 7 years. Forty-four normal elderly received extensive clinical, neurocognitive and QEEG examinations at baseline. All study subjects (N = 44) had only subjective complaints but no objective evidence of cognitive deficit (evaluated using the Global Deterioration Scale [GDS] score, GDS stage = 2) at baseline and were re-evaluated during 7-9 year follow-up. Baseline QEEGs of Decliners differed significantly (p < 0.0001, by MANOVA) from Non-Decliners, characterized by increases in theta power, slowing of mean frequency, and changes in covariance among regions, especially on the right hemisphere. Using logistic regression, an R2 of 0.93 (p < 0.001) was obtained between baseline QEEG features and probability of future decline, with an overall predictive accuracy of 90%. These data indicate high sensitivity and specificity for baseline QEEG as a differential predictor of future cognitive state in normal, subjectively impaired elderly.

Use of normative databases and statistical methods in demonstrating clinical utility of QEEG: importance and cautions

The clinical utility of the EEG, especially in psychiatric, learning and cognitive disorders, has been greatly enhanced by the use of quantitative analysis (QEEG) and comparisons to a normative database. Of primary importance in the use of such a reference database are the following considerations and cautions: adequate sampling across a broad age range; consideration of inclusion/exclusion criteria; adequate sample of artifact-free data to demonstrate reliability and replicability of norms; demonstration of specificity and sensitivity. A normative database meeting these criteria allows the multivariate description of patterns of QEEG abnormalities in patients as compared to age appropriate normative values, and the exploration of neurophysiological heterogeneity within populations. Demonstrations of the clinical significance of this approach exist in the scientific literature and demonstrate that QEEG provides high sensitivity and specificity to abnormalities in brain function seen in psychiatric populations.

The Patient State Index as an indicator of the level of hypnosis under general anaesthesia

BACKGROUND

This retrospective study describes the performance of the Patient State Index (PSI), under standard clinical practice conditions. The PSI is comprised of quantitative features of the EEG (QEEG) that display clear differences between hypnotic states, but consistency across anaesthetic agents within the state.

METHODS

The PSI was constructed from a systematic investigation of a database containing QEEG extracted from the analyses of continuous 19 channel EEG recordings obtained in 176 surgical patients. Induction was accomplished with etomidate, propofol, or thiopental. Anaesthesia was maintained by isoflurane, desflurane, or sevoflurane, total i.v. anaesthesia using propofol, or nitrous oxide/narcotics. It was hypothesized that a multivariate algorithm based on such measures of brain state, would vary significantly with changes in hypnotic state.

RESULTS

Highly significant differences were found between mean PSI values obtained during the different anaesthetic states selected for study. The relationship between level of awareness and PSI value at different stages of anaesthetic delivery was also evaluated. Regression analysis for prediction of arousal level using PSI was found to be highly significant for the combination of all anaesthetics, and for the individual anaesthetics.

CONCLUSIONS

The PSI, based upon derived features of brain electrical activity in the anterior/posterior dimension, significantly co-varies with changes in state under general anaesthesia and can significantly predict the level of arousal in varying stages of anaesthetic delivery.

Changes in cortical electrical activity during induction of anaesthesia with thiopental/fentanyl and tracheal intubation: a quantitative electroencephalographic analysis

BACKGROUND

There are regional differences in the effects of anaesthetics agents and perioperative stimuli on the EEG. We studied the topography of the EEG during induction of anaesthesia and intubation in patients receiving thiopental and fentanyl to document regional electrical brain activity.

METHODS

EEG was recorded in 25 patients in the awake state, after pre-medication, during induction, at loss of consciousness and after intubation. Eight bipolar recordings were made and the relative power of the frequency bands delta, theta, alpha, and beta were used (after z-score transformation for age) to measure changes in regional EEG activity.

RESULTS

Noxious stimulation during tracheal intubation partially reversed the slowing of the EEG caused by anaesthesia. During induction of anaesthesia alpha activity was most reduced in temporal and occipital regions. The most prominent EEG changes after intubation were an increase in alpha and a decrease in delta power (P<0.001). The largest changes were in the frontal and temporal leads for alpha and in the frontal and central leads for delta. Heart rate and arterial pressure remained constant during intubation.

CONCLUSIONS

Changes in alpha and delta power were identified as the most sensitive EEG measures of regional changes in electrical brain activity during anaesthesia and noxious stimulation.

Quantitative Electroencephalographic Studies of Cue-Induced Cocaine Craving

Quantitative electroencephalographic (qEEG) profiles were studied in cocaine dependent patients in response to cocaine cue exposure. Using neurometric analytical methods, the spectral power of each primary bandwidth was computed and topographically mapped. Additional measures of cue-reactivity included cocaine craving, anxiety and related subjective ratings, and physiological measures of skin conductance, skin temperature, heart rate, and plasma Cortisol and HVA levels. Twenty-four crack cocaine-dependent subjects were tested for their response to tactile, visual and audio cues related to crack cocaine or neutral items. All measures were analyzed for significant difference by comparing cocaine versus neutral cue conditions. An increase in cocaine craving, anxiety and related subjective ratings, elevated plasma Cortisol levels, and a decrease in skin temperature, were induced by cocaine cue exposure.

Distinct qEEG profiles were found during the paraphernalia handling and video viewing (eyes-open), and guided imagery (eyes-closed), phases of cocaine cue exposure. During paraphernalia handling and video viewing, there was an increase in beta activity accompanied by a drop in delta power in the frontal cortex, and an increase in beta mean frequency in the occipital cortex. In contrast, during guided imagery there was an increase in theta and delta power in the frontal cortex, and an increase in beta power in the occipital cortex. Correlation analyses revealed that cue-induced anxiety during paraphernalia handling and video viewing was associated with reduced high frequency and enhanced low frequency EEG activity. These findings demonstrated that EEG activation during cue-induced cocaine craving may be topographically mapped and subsequently analyzed for functional relevance.

Quantitative electroencephalography in OCD patients treated with paroxetine

The effectiveness of drugs that have a specific effect on the activity of the serotonergic neurotransmitter systemhas changed the outlook for patients suffering from obsessive-compulsive disorder (OCD). With a response rate of about 70% to such compounds and the great amount of brain imaging studies conducted over the past decades, an understanding of the biochemical nature and origins of OCD is beginning to unfold. Convergent data including ethological and experimental observations, clinico-pathological findings and different imaging methods have implicated the basal ganglia along with the cortical and related thalamic structures to be involved in the pathophysiology of OCD. In a previous study using the quantitative electroencephalographic (QEEG) method known as neurometrics, in which QEEG data from OCD patients were compared statistically with those from an age-appropriate normative population, two subtypes within a clinically homogeneous patient group were classified. Patients with relative excess theta activity, especially in the frontal regions, were nonresponders to treatment with serotonin reuptake inhibitors (SSRI), while those with increased relative power in alpha activity were responders to pharmacological treatment. These findings suggested at least two subgroups in a patient population with similar symptoms but differential responses to treatment. In the present study we used neurometric QEEG to subtype a group of 20 non-depressed OCD patients, fulfilling DSM-R-III criteria, treated with paroxetine, of whom 18 were responders to treatment. Of the treatment responders, 94.4% were predicted by subtype membership to be SSRI responsers. In these subjects there was a strong relative alpha baseline activity; after successful treatment through at least 3 months this activity decreased, looking more normal. The group average topographic maps showed none of the characteristics seen in the nonresponder cluster (no excess relative power in theta). As in the previous investigation, baseline QEEG profile membership points to a predictive value with regard to therapeutic response.

Patient State Index: titration of delivery and recovery from propofol, alfentanil, and nitrous oxide anesthesia

BACKGROUND

The Patient State Index (PSI) uses derived quantitative electroencephalogram features in a multivariate algorithm that varies as a function of hypnotic state. Data are recorded from two anterior, one midline central, and one midline posterior scalp locations. PSI has been demonstrated to have a significant relation to level of hypnosis during intravenous propofol, inhalation, and nitrous oxide-narcotic anesthesia. This multisite study evaluated the utility of PSI monitoring as an adjunct to standard anesthetic practice for guiding the delivery of propofol and alfentanil to accelerate emergence from anesthesia.

METHODS

Three hundred six patients were enrolled in this multicenter prospective randomized clinical study. Using continuous monitoring throughout the period of propofol-alfentanil-nitrous oxide anesthesia delivery, PSI guidance was compared with use of standard practice guidelines (both before [historic controls] and after exposure to the PSA 4000 monitor [Physiometrix, Inc., N. Billerica, MA; standard practice controls]). Anesthesia was always administered with the aim of providing hemodynamic stability, with rapid recovery.

RESULTS

No significant differences were found for demographic variables or for site. The PSI group received significantly less propofol than the standard practice control group (11.9 microg x kg(-1) x min(-1); P < 0.01) and historic control group (18.2 microg x kg(-1) x min(-1); P < 0.001). Verbal response time, emergence time, extubation time, and eligibility for operating room discharge time were all significantly shorter for the PSI group compared with the historic control (3.3-3.8 min; P < 0.001) and standard practice control (1.4-1.5 min; P < 0.05 or P < 0.01) groups. No significant differences in the number of unwanted somatic events or hemodynamic instability and no incidences of reported awareness were found.

CONCLUSIONS

Patient State Index-directed titration of propofol delivery resulted in faster emergence and recovery from propofol-alfentanil-nitrous oxide anesthesia, with modest decrease in the amount of propofol delivered, without increasing the number of unwanted events.

Outcome related electrophysiological subtypes of cocaine dependence

We previously described the existence of two quantitative EEG (QEEG) subtypes of cocaine dependent males, identified at baseline, displaying differential proneness to relapse. The current study expands the population to include females and enhances the measure set to include both QEEG and somatosensory EP (SEP) features. Fifty-seven cocaine dependent adults (16 F, 41 M) were evaluated 5-14 days after last cocaine use, while in residence at a drug-free therapeutic community. The median length of stay in treatment (continued abstinence) was 25 weeks. Using a small subset of QEEG and SEP baseline features, three subtypes (CLUS) were identified. CLUS 2 (n = 25) and CLUS 3 (n = 23) replicated the published subtypes, while CLUS 1 (n = 9) was previously undescribed. Cluster membership was significantly associated with length of stay in treatment (chi 2 = 13.789, P < 0.001), but not with length of exposure to crack cocaine or to any demographic or clinical features. Seventy-eight percent of CLUS 1 and 65% of CLUS 3 left treatment < or = 25 weeks, whereas 80% of CLUS 2 remained in treatment > 25 weeks. The existence of outcome related subtypes may reflect: [1] differential neurophysiological vulnerability, "traits," predisposing individuals to cocaine addiction; or [2] differential neurosensitivity, "states," due to the effects of chronic cocaine exposure, and associated differences in treatment outcome. Using Variable Resolution Electrical Tomographic Analysis (VARETA), the mathematically most probable neuroanatomical source of the scalp recorded EEG data was localized. Computation of VARETA on the baseline Cluster profiles suggest significant differences in the underlying pathophysiology of these subtypes.

Quantitative EEG changes associated with loss and return of consciousness in healthy adult volunteers anaesthetized with propofol or sevoflurane

Significant changes in topographic quantitative EEG (QEEG) features were documented during induction and emergence from anaesthesia induced by the systematic administration of sevoflurane and propofol in combination with remifentanil. The goal was to identify those changes that were sensitive to alterations in the state of consciousness but independent of anaesthetic protocol. Healthy paid volunteers were anaesthetized and reawakened using propofol/remifentanil and sevoflurane/remifentanil, administered in graded steps while the level of arousal was measured. Alterations in the level of arousal were accompanied by significant QEEG changes, many of which were consistent across anaesthetic protocols. Light sedation was accompanied by decreased posterior alpha and increased frontal/central beta power. Frontal power predominance increased with deeper sedation, involving alpha and, to a lesser extent, delta and theta power. With loss of consciousness, delta and theta power increased further in anterior regions and also spread to posterior regions. These changes reversed with return to consciousness.

Invariant reversible QEEG effects of anesthetics

Continuous recordings of brain electrical activity were obtained from a group of 176 patients throughout surgical procedures using general anesthesia. Artifact-free data from the 19 electrodes of the International 10/20 System were subjected to quantitative analysis of the electroencephalogram (QEEG). Induction was variously accomplished with etomidate, propofol or thiopental. Anesthesia was maintained throughout the procedures by isoflurane, desflurane or sevoflurane (N = 68), total intravenous anesthesia using propofol (N = 49), or nitrous oxide plus narcotics (N = 59). A set of QEEG measures were found which reversibly displayed high heterogeneity of variance between four states as follows: (1) during induction; (2) just after loss of consciousness (LOC); (3) just before return of consciousness (ROC); (4) just after ROC. Homogeneity of variance across all agents within states was found. Topographic statistical probability images were compared between states. At LOC, power increased in all frequency bands in the power spectrum with the exception of a decrease in gamma activity, and there was a marked anteriorization of power. Additionally, a significant change occurred in hemispheric relationships, with prefrontal and frontal regions of each hemisphere becoming more closely coupled, and anterior and posterior regions on each hemisphere, as well as homologous regions between the two hemispheres, uncoupling. All of these changes reversed upon ROC. Variable resolution electromagnetic tomography (VARETA) was performed to localize salient features of power anteriorization in three dimensions. A common set of neuroanatomical regions appeared to be the locus of the most probable generators of the observed EEG changes.

Correlation of qEEG with PET in schizophrenia

PET relative metabolism was correlated with quantitative EEG in 9 schizophrenic patients. The PET metabolic regions of interest were the frontal lobes, thalamus and basal ganglia, and right and left temporal lobes. Significant positive correlations were seen for the frontal lobes and delta EEG power, and alpha power with subcortical metabolism. The physiologic plausibility of those correlations is discussed with reference to the possible effect of neuroleptic medication.

Prediction of treatment outcome in cocaine dependent males using quantitative EEG

This study investigates the existence of outcome related neurophysiological subtypes within a population of abstinent cocaine dependent adults. We have previously reported and replicated the existence of a distinctive quantitative EEG (QEEG) profile in such a population, and demonstrated the persistence of this pattern at one and six month follow-up evaluations. This profile is characterized by significant deficits of absolute and relative delta and theta power, and excess of relative alpha power, as compared with age expected normal values. Abnormalities were greater in anterior than posterior regions, and disturbances in interhemispheric relationships were also observed. In the current study, 35 adult males with DSM-III-R cocaine dependence, were evaluated while residents of a drug-free residential therapeutic community, 5-15 days after last use of crack cocaine. Using multivariate cluster analysis, two neurophysiological subtypes were identified from the baseline QEEGs; Cluster 1 characterized by significant deficits of delta and theta activity, significant excess of alpha activity and more normal amounts of beta activity (alpha CLUS) and Cluster 2 characterized by deficits of delta, more normal amounts of theta and anterior excess of alpha and beta activity beta CLUS). No significant relationships were found between QEEG subtype membership and length of exposure to cocaine, time since last use of cocaine or any demographic characteristics. Further, no significant relationships were found between the commonly reported comorbid clinical features of depression and anxiety and subtype membership. However, a significant relationship was found between QEEG subtype membership and length of stay in treatment, with members of the alpha CLUS retained in treatment significantly longer than members of the beta CLUS.