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.

The Use of an Electrophysiological Brain Function Index in the Evaluation of Concussed Athletes.

OBJECTIVE: To evaluate the effectiveness of the electroencephalographic (EEG) Brain Function Index (BFI) for characterizing sports-related concussive injury and recovery. PARTICIPANTS: Three hundred fifty-four (354) male contact sport high school and college athletes were prospectively recruited from multiple locations over 6 academic years of play (244 control baseline athletes and 110 athletes with a concussion). METHODS: Using 5 to 10 minutes of eyes closed resting EEG collected from frontal and frontotemporal regions, a BFI was computed for all subjects and sessions. Group comparisons were performed to test for the significance of the difference in the BFI score between the controls at baseline and athletes with a concussion at several time points. RESULTS: There was no significant difference in BFI between athletes with a concussion at baseline (ie, prior to injury) and controls at baseline (P = .4634). Athletes with a concussion, tested within 72 hours of injury, exhibited significant differences in BFI compared with controls (P = .0036). The significant differences in BFI were no longer observed at 45 days following injury (P = .19). CONCLUSION: Controls and athletes with a concussion exhibited equivalent BFI scores at preseason baseline. The concussive injury (measured within 72 hours) significantly affected brain function reflected in the BFI in the athletes with a concussion. The BFI of the athletes with a concussion returned to levels seen in controls by day 45, suggesting recovery. The BFI may provide an important objective marker of concussive injury and recovery.

Increased prognostic accuracy of TBI when a brain electrical activity biomarker is added to loss of consciousness (LOC).

BACKGROUND: Extremely high accuracy for predicting CT+ traumatic brain injury (TBI) using a quantitative EEG (QEEG) based multivariate classification algorithm was demonstrated in an independent validation trial, in Emergency Department (ED) patients, using an easy to use handheld device. This study compares the predictive power using that algorithm (which includes LOC and amnesia), to the predictive power of LOC alone or LOC plus traumatic amnesia. PARTICIPANTS: ED patients 18-85years presenting within 72h of closed head injury, with GSC 12-15, were study candidates. 680 patients with known absence or presence of LOC were enrolled (145 CT+ and 535 CT- patients). METHODS: 5-10min of eyes closed EEG was acquired using the Ahead 300 handheld device, from frontal and frontotemporal regions. The same classification algorithm methodology was used for both the EEG based and the LOC based algorithms. Predictive power was evaluated using area under the ROC curve (AUC) and odds ratios. RESULTS: The QEEG based classification algorithm demonstrated significant improvement in predictive power compared with LOC alone, both in improved AUC (83% improvement) and odds ratio (increase from 4.65 to 16.22). Adding RGA and/or PTA to LOC was not improved over LOC alone. CONCLUSIONS: Rapid triage of TBI relies on strong initial predictors. Addition of an electrophysiological based marker was shown to outperform report of LOC alone or LOC plus amnesia, in determining risk of an intracranial bleed. In addition, ease of use at point-of-care, non-invasive, and rapid result using such technology suggests significant value added to standard clinical prediction.

Comparison of quantitative EEG to current clinical decision rules for head CT use in acute mild traumatic brain injury in the ED.

STUDY OBJECTIVE: We compared the performance of a handheld quantitative electroencephalogram (QEEG) acquisition device to New Orleans Criteria (NOC), Canadian CT Head Rule (CCHR), and National Emergency X-Radiography Utilization Study II (NEXUS II) Rule in predicting intracranial lesions on head computed tomography (CT) in acute mild traumatic brain injury in the emergency department (ED). METHODS: Patients between 18 and 80 years of age who presented to the ED with acute blunt head trauma were enrolled in this prospective observational study at 2 urban academic EDs in Detroit, MI. Data were collected for 10 minutes from frontal leads to determine a QEEG discriminant score that could maximally classify intracranial lesions on head CT. RESULTS: One hundred fifty-two patients were enrolled from July 2012 to February 2013. A total 17.1% had acute traumatic intracranial lesions on head CT. Quantitative electroencephalogram discriminant score of greater than or equal to 31 was found to be a good cutoff (area under receiver operating characteristic curve = 0.84; 95% confidence interval [CI], 0.76-0.93) to classify patients with positive head CT. The sensitivity of QEEG discriminant score was 92.3 (95% CI, 73.4-98.6), whereas the specificity was 57.1 (95% CI, 48.0-65.8). The sensitivity and specificity of the decision rules were as follows: NOC 96.1 (95% CI, 78.4-99.7) and 15.8 (95% CI, 10.1-23.6); CCHR 46.1 (95% CI, 27.1-66.2) and 86.5 (95% CI, 78.9-91.7); NEXUS II 96.1 (95% CI, 78.4-99.7) and 31.7 (95% CI, 23.9-40.7). CONCLUSION: At a sensitivity of greater than 90%, QEEG discriminant score had better specificity than NOC and NEXUS II. Only CCHR had better specificity than QEEG discriminant score but at the cost of low (<50%) sensitivity.

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.

Time course of clinical and electrophysiological recovery after sport-related concussion.

BACKGROUND AND PURPOSE: Recent neuroimaging studies suggest that abnormalities in brain function after concussion exist beyond the point of observed clinical recovery. This study investigated the relationship between an index of brain dysfunction (traumatic brain injury [TBI] Index), concussion severity, and outcome. METHODS: EEG was collected from forehead locations in 65 male athletes with concussion within 24 hours of concussion, with follow-up at 8 and 45 days postinjury. Neurocognitive and symptom assessments were also performed and used to classify subjects in mild or moderate concussion categories. Time to return to play was recorded. RESULTS: The TBI Index was higher in the moderate than mild concussion group at injury, day 8, and day 45. The moderate group had increased symptoms and decreased cognitive performance only at the time of injury. At the time of injury, only the TBI Index was significantly associated with the length of time to return to play. CONCLUSIONS: Recovery of brain function after sport-related concussion may extend well beyond the time course of clinical recovery and be related to clinical severity. An index of brain dysfunction may be an objective indicator of injury, recovery, and readiness to return to play. The relatively small sample indicates the need for further study on the time course of physiological recovery.

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.

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.

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.

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.

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.

A multimodal biomarker for concussion identification, prognosis and management.

BACKGROUND: Prompt, accurate, objective assessment of concussion is crucial, particularly for children/adolescents and young adults. While there is currently no gold standard for the diagnosis of concussion, the importance of multidimensional/multimodal assessments has recently been emphasized. METHODS: Concussed subjects (N = 177), matched controls (N = 187) and healthy volunteers (N = 204) represented a convenience sample of male and female subjects between the ages of 13 and 25 years, enrolled at 29 Colleges and 19 High Schools in the US. Subjects were tested at time of injury and at multiple time points during recovery. Assessments included EEG, neurocognitive tests and standard concussion assessment tools. Multimodal classifiers to maximally separate controls from concussed subjects with prolonged recovery (≥14 days) were derived using quantitative EEG, neurocognitive and vestibular measures, informed feature reduction and a Genetic Algorithm methodology for classifier derivation. The methodology protected against overtraining using an internal cross-validation framework. An enhanced multimodal Brain Function Index (eBFI) was derived from the classifier output and mapped to a percentile scale which expressed the index relative to non-injured controls. RESULTS: At time of injury eBFIs were significantly different between controls and concussed subjects with prolonged recovery, showing return to non-concussed levels at return-to-play plus 45 days. For the combined concussed population, and for the short recovery subjects, a more rapid recovery was seen. CONCLUSIONS: This multivariate, multimodal, objective index of brain function impairment can potentially be used, along with other tools, to aid in diagnosis, assessment, and tracking of recovery from concussion.

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.

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.

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.

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.

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.

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.