M. Westover

Emergence of Stable Functional Networks in Long-Term Human Electroencephalography

Functional connectivity networks have become a central focus in neuroscience because they reveal key higher-dimensional features of normal and abnormal nervous system physiology. Functional networks reflect activity-based coupling between brain regions that may be constrained by relatively static anatomical connections, yet these networks appear to support tremendously dynamic behaviors. Within this growing field, the stability and temporal characteristics of functional connectivity brain networks have not been well characterized. We evaluated the temporal stability of spontaneous functional connectivity networks derived from multi-day scalp encephalogram (EEG) recordings in five healthy human subjects. Topological stability and graph characteristics of networks derived from averaged data epochs ranging from 1 s to multiple hours across different states of consciousness were compared. We show that, although functional networks are highly variable on the order of seconds, stable network templates emerge after as little as ∼100 s of recording and persist across different states and frequency bands (albeit with slightly different characteristics in different states and frequencies). Within these network templates, the most common edges are markedly consistent, constituting a network “core.” Although average network topologies persist across time, measures of global network connectivity, density and clustering coefficient, are state and frequency specific, with sparsest but most highly clustered networks seen during sleep and in the gamma frequency band. These findings support the notion that a core functional organization underlies spontaneous cortical processing and may provide a reference template on which unstable, transient, and rapidly adaptive long-range assemblies are overlaid in a frequency-dependent manner.

High-redshift galaxies and the Ly alpha forest in a cold dark matter universe

We use a cosmological hydrodynamic simulation of a cold dark matter universe to investigate theoretically the relationship between high redshift galaxies and the Lyman=alpha forest at redshift z=3. Galaxies in the simulation are surrounded by halos of hot gas, which nevertheless contain enough neutral hydrogen to cause a Ly-alpha flux decrement, its strength increasing with galaxy mass. A comparison with recent observational data by Adelberger et. al on the Ly-alpha forest around galaxies reveals that actual galaxies may have systematically less Ly-alpha absorption within 1 Mpc of them than our simulated galaxies. In order to investigate this possibility, we add several simple prescriptions for galaxy feedback on the IGM to the evolved simulation. These include the effect of photoionizing background radiation coming from galactic sources, galactic winds whose only effect is to deposit thermal energy into the IGM, and another, kinetic model for winds, which are assumed to evacuate cavities in the IGM around galaxies. We find that only the latter is able to produce a large effect, enough to match the tentative observational data, given the energy available from star formation in the simulated galaxies. Another intriguing possibility is that a selection effect is responsible, so that galaxies with low Ly-alpha absorption are preferentially included in the sample. This is also viable, but predicts very different galaxy properties (including clustering) than the other scenarios.We use a cosmological hydrodynamic simulation of a cold dark matter universe to investigate theoretically the relationship between high-redshift galaxies and the Lyα forest at redshift z = 3. Galaxies in the simulation are surrounded by halos of hot gas, which nevertheless contain enough neutral hydrogen to cause a Lyα flux decrement, its strength increasing with galaxy mass. A comparison with recent observational data by Adelberger et al. on the Lyα forest around galaxies reveals that actual galaxies may have systematically less Lyα absorption within 1 Mpc of them than our simulated galaxies. In order to investigate this possibility, we add several simple prescriptions for galaxy feedback on the intergalactic medium (IGM) to the evolved simulation. These include the effect of photoionizing background radiation coming from galactic sources, galactic winds whose only effect is to deposit thermal energy into the IGM, and another, kinetic model for winds, which are assumed to evacuate cavities in the IGM around galaxies. We find that only the latter is able to produce a large effect, enough to match the tentative observational data, given the energy available from star formation in the simulated galaxies. Another intriguing possibility is that a selection effect is responsible, so that galaxies with low Lyα absorption are preferentially included in the sample. This is also viable but predicts galaxy properties (including clustering) that are very different from the other scenarios.

Spectrogram screening of adult EEGs is sensitive and efficient

Objective: Quantitatively evaluate whether screening with compressed spectral arrays (CSAs) is a practical and time-effective protocol for assisting expert review of continuous EEG (cEEG) studies in hospitalized adults. Methods: Three neurophysiologists reviewed the reported findings of the first 30 minutes of 118 cEEGs, then used CSA to guide subsequent review (“CSA-guided review” protocol). Reviewers viewed 120 seconds of raw EEG data surrounding suspicious CSA segments. The same neurophysiologists performed independent page-by-page visual interpretation (“conventional review”) of all cEEGs. Independent conventional review by 2 additional, more experienced neurophysiologists served as a gold standard. We compared review times and detection rates for seizures and other pathologic patterns relative to conventional review. Results: A total of 2,092 hours of cEEG data were reviewed. Average times to review 24 hours of cEEG data were 8 (±4) minutes for CSA-guided review vs 38 (±17) minutes for conventional review (p < 0.005). Studies containing seizures required longer review: 10 (±4) minutes for CSA-guided review vs 44 (±20) minutes for conventional review (p < 0.005). CSA-guided review was sensitive for seizures (87.3%), periodic epileptiform discharges (100%), rhythmic delta activity (97.1%), focal slowing (98.7%), generalized slowing (100%), and epileptiform discharges (88.5%). Conclusions: CSA-guided review reduces cEEG review time by 78% with minimal loss of sensitivity compared with conventional review. Classification of evidence: This study provides Class IV evidence that screening of cEEG with CSAs efficiently and accurately identifies seizures and other EEG abnormalities as compared with standard cEEG visual interpretation.

Development and Feasibility Testing of a Critical Care EEG Monitoring Database for Standardized Clinical Reporting and Multicenter Collaborative Research.

Purpose: The rapid expansion of the use of continuous critical care electroencephalogram (cEEG) monitoring and resulting multicenter research studies through the Critical Care EEG Monitoring Research Consortium has created the need for a collaborative data sharing mechanism and repository. The authors describe the development of a research database incorporating the American Clinical Neurophysiology Society standardized terminology for critical care EEG monitoring. The database includes flexible report generation tools that allow for daily clinical use. Methods: Key clinical and research variables were incorporated into a Microsoft Access database. To assess its utility for multicenter research data collection, the authors performed a 21-center feasibility study in which each center entered data from 12 consecutive intensive care unit monitoring patients. To assess its utility as a clinical report generating tool, three large volume centers used it to generate daily clinical critical care EEG reports. Results: A total of 280 subjects were enrolled in the multicenter feasibility study. The duration of recording (median, 25.5 hours) varied significantly between the centers. The incidence of seizure (17.6%), periodic/rhythmic discharges (35.7%), and interictal epileptiform discharges (11.8%) was similar to previous studies. The database was used as a clinical reporting tool by 3 centers that entered a total of 3,144 unique patients covering 6,665 recording days. Conclusions: The Critical Care EEG Monitoring Research Consortium database has been successfully developed and implemented with a dual role as a collaborative research platform and a clinical reporting tool. It is now available for public download to be used as a clinical data repository and report generating tool.

Automatic Classification of Sedation Levels in Icu Patients Using Heart Rate Variability.

Objective: To explore the potential value of heart rate variability features for automated monitoring of sedation levels in mechanically ventilated ICU patients. Design: Multicenter, pilot study. Setting: Several ICUs at Massachusetts General Hospital, Boston, MA. Patients: Electrocardiogram recordings from 40 mechanically ventilated adult patients receiving sedatives in an ICU setting were used to develop and test the proposed automated system. Measurements and Main Results: Richmond Agitation-Sedation Scale scores were acquired prospectively to assess patient sedation levels and were used as ground truth. Richmond Agitation-Sedation Scale scores were grouped into four levels, denoted “unarousable” (Richmond Agitation- Sedation Scale = –5, –4), “sedated” (–3, –2, –1), “awake” (0), “agitated” (+1, +2, +3, +4). A multiclass support vector machine algorithm was used for classification. Classifier training and performance evaluations were carried out using leave-oneout cross validation. An overall accuracy of 69% was achieved for discriminating between the four levels of sedation. The proposed system was able to reliably discriminate (accuracy = 79%) between sedated (Richmond Agitation-Sedation Scale < 0) and nonsedated states (Richmond Agitation-Sedation Scale > 0). Conclusions: With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and undersedation.

Epileptiform abnormalities predict delayed cerebral ischemia in subarachnoid hemorrhage

OBJECTIVE To identify whether abnormal neural activity, in the form of epileptiform discharges and rhythmic or periodic activity, which we term here ictal-interictal continuum abnormalities (IICAs), are associated with delayed cerebral ischemia (DCI). METHODS Retrospective analysis of continuous electroencephalography (cEEG) reports and medical records from 124 patients with moderate to severe grade subarachnoid hemorrhage (SAH). We identified daily occurrence of seizures and IICAs. Using survival analysis methods, we estimated the cumulative probability of IICA onset time for patients with and without delayed cerebral ischemia (DCI). RESULTS Our data suggest the presence of IICAs indeed increases the risk of developing DCI, especially when they begin several days after the onset of SAH. We found that all IICA types except generalized rhythmic delta activity occur more commonly in patients who develop DCI. In particular, IICAs that begin later in hospitalization correlate with increased risk of DCI. CONCLUSIONS IICAs represent a new marker for identifying early patients at increased risk for DCI. Moreover, IICAs might contribute mechanistically to DCI and therefore represent a new potential target for intervention to prevent secondary cerebral injury following SAH. SIGNIFICANCE These findings imply that IICAs may be a novel marker for predicting those at higher risk for DCI development.

Clinical Development and Implementation of an Institutional Guideline for Prospective EEG Monitoring and Reporting of Delayed Cerebral Ischemia.

Summary: Delayed cerebral ischemia (DCI) is the most common and disabling complication among patients admitted to the hospital for subarachnoid hemorrhage (SAH). Clinical and radiographic methods often fail to detect DCI early enough to avert irreversible injury. We assessed the clinical feasibility of implementing a continuous EEG (cEEG) ischemia monitoring service for early DCI detection as part of an institutional guideline. An institutional neuromonitoring guideline was designed by an interdisciplinary team of neurocritical care, clinical neurophysiology, and neurosurgery physicians and nursing staff and cEEG technologists. The interdisciplinary team focused on (1) selection criteria of high-risk patients, (2) minimization of safety concerns related to prolonged monitoring, (3) technical selection of quantitative and qualitative neurophysiologic parameters based on expert consensus and review of the literature, (4) a structured interpretation and reporting methodology, prompting direct patient evaluation and iterative neurocritical care, and (5) a two-layered quality assurance process including structured clinician interviews assessing events of neurologic worsening and an adjudicated consensus review of neuroimaging and medical records. The resulting guidelines clinical feasibility was then prospectively evaluated. The institutional SAH monitoring guideline used transcranial Doppler ultrasound and cEEG monitoring for vasospasm and ischemia monitoring in patients with either Fisher group 3 or Hunt–Hess grade IV or V SAH. Safety criteria focused on prevention of skin breakdown and agitation. Technical components included monitoring of transcranial Doppler ultrasound velocities and cEEG features, including quantitative alpha:delta ratio and percent alpha variability, qualitative evidence of new focal slowing, late-onset epileptiform activity, or overall worsening of background. Structured cEEG reports were introduced including verbal communication for findings concerning neurologic decline. The guideline was successfully implemented over 27 months, during which neurocritical care physicians referred 71 SAH patients for combined transcranial Doppler ultrasound and cEEG monitoring. The quality assurance process determined a DCI rate of 48% among the monitored population, more than 90% of which occurred during the duration of cEEG monitoring (mean 6.9 days) beginning 2.7 days after symptom onset. An institutional guideline implementing cEEG for SAH ischemia monitoring and reporting is feasible to implement and efficiently identify patients at high baseline risk of DCI during the period of monitoring.

Propagation of Uncertainty in Bayesian Diagnostic Test Interpretation

Objectives Bayesian interpretation of diagnostic test results usually involves point estimates of the pretest probability and the likelihood ratio corresponding to the test result; however, it may be more appropriate in clinical situations to consider instead a range of possible values to express uncertainty in the estimates of these parameters. We thus sought to demonstrate how uncertainty in sensitivity, specificity, and disease pretest probability can be accommodated in Bayesian interpretation of diagnostic testing. Methods We investigated three questions: How does uncertainty in the likelihood ratio propagate to the posttest probability range, assuming a point estimate of pretest probability? How does uncertainty in the sensitivity and specificity of a test affect uncertainty in the likelihood ratio? How does uncertainty propagate when present in both the pretest probability and the likelihood ratio? Results Propagation of likelihood ratio uncertainty depends on the pretest probability and is more prominent for unexpected test results. Uncertainty in sensitivity and specificity propagates into the calculation of likelihood ratio prominently as these parameters approach 100%; even modest errors of ±10% caused dramatic propagation. Combining errors of ±20% in the pretest probability and in the likelihood ratio exhibited modest propagation to posttest probability, suggesting a realistic target range for clinical estimations. Conclusions The results provide a framework for incorporating ranges of uncertainty into Bayesian reasoning. Although point estimates simplify the implementation of Bayesian reasoning, it is important to recognize the implications of error propagation when ranges are considered in this multistep process.

Interrater agreement for consensus definitions of delayed ischemic events after aneurysmal subarachnoid hemorrhage

Background: Thirty percent of patients with subarachnoid hemorrhage experience delayed cerebral ischemia or delayed ischemic neurologic decline (DIND). Variability in the definitions of delayed ischemia makes outcome studies difficult to compare. A recent consensus statement advocates standardized definitions for delayed ischemia in clinical trials of subarachnoid hemorrhage. We sought to evaluate the interrater agreement of these definitions. Methods: Based on consensus definitions, we assessed for: (1) delayed cerebral infarction, defined as radiographic cerebral infarction; (2) DIND type 1 (DIND1), defined as focal neurologic decline; and (3) DIND2, defined as a global decline in arousal. Five neurologists retrospectively reviewed electronic records of 58 patients with subarachnoid hemorrhage. Three reviewers had access to and reviewed neuroradiology imaging. We assessed interrater agreement using the Gwet kappa statistic. Results: Interrater agreement statistics were excellent (95.83%) for overall agreement on the presence or absence of any delayed ischemic event (DIND1, DIND2, or delayed cerebral infarction). Agreement was “moderate” for specifically identifying DIND1 (56.58%) and DIND2 (48.66%) events. We observed greater agreement for DIND1 when there was a significant focal motor decline of at least 1 point in the motor score. There was fair agreement (39.20%) for identifying delayed cerebral infarction; CT imaging was the predominant modality. Conclusions: Consensus definitions for delayed cerebral ischemia yielded near-perfect overall agreement and can thus be applied in future large-scale studies. However, a strict process of adjudication, explicit thresholds for determining focal neurologic decline, and MRI techniques that better discriminate edema from infarction seem critical for reproducibility of determination of specific outcome phenotypes, and will be important for successful clinical trials.

Automation of Classical QEEG Trending Methods for Early Detection of Delayed Cerebral Ischemia: More Work to Do.

Summary: The purpose of this study is to evaluate automated implementations of continuous EEG monitoring-based detection of delayed cerebral ischemia based on methods used in classical retrospective studies. We studied 95 patients with either Fisher 3 or Hunt Hess 4 to 5 aneurysmal subarachnoid hemorrhage who were admitted to the Neurosciences ICU and underwent continuous EEG monitoring. We implemented several variations of two classical algorithms for automated detection of delayed cerebral ischemia based on decreases in alpha-delta ratio and relative alpha variability. Of 95 patients, 43 (45%) developed delayed cerebral ischemia. Our automated implementation of the classical alpha-delta ratio-based trending method resulted in a sensitivity and specificity (Se,Sp) of (80,27)%, compared with the values of (100,76)% reported in the classic study using similar methods in a nonautomated fashion. Our automated implementation of the classical relative alpha variability-based trending method yielded (Se,Sp) values of (65,43)%, compared with (100,46)% reported in the classic study using nonautomated analysis. Our findings suggest that improved methods to detect decreases in alpha-delta ratio and relative alpha variability are needed before an automated EEG-based early delayed cerebral ischemia detection system is ready for clinical use.