Brexanolone Treatment in a Real-World Patient Population: A Case Series and Pilot Feasibility Study of Precision Neuroimaging.

PURPOSE/BACKGROUND: Brexanolone is approved for postpartum depression (PPD) by the United States Food and Drug Administration. Brexanolone has outperformed placebo in clinical trials, but less is known about the efficacy in real-world patients with complex social and medical histories. Furthermore, the impact of brexanolone on large-scale brain systems such as changes in functional connectivity (FC) is unknown. METHODS/PROCEDURES: We tracked changes in depressive symptoms across a diverse group of patients who received brexanolone at a large medical center. Edinburgh Postnatal Depression Scale (EPDS) scores were collected through chart review for 17 patients immediately prior to infusion through approximately 1 year postinfusion. In 2 participants, we performed precision functional neuroimaging (pfMRI), including before and after treatment in 1 patient. pfMRI collects many hours of data in individuals for precision medicine applications and was performed to assess the feasibility of investigating changes in FC with brexanolone. FINDINGS/RESULTS: The mean EPDS score immediately postinfusion was significantly lower than the mean preinfusion score (mean change [95% CI]: 10.76 [7.11-14.40], t (15) = 6.29, P < 0.0001). The mean EPDS score stayed significantly lower at 1 week (mean difference [95% CI]: 9.50 [5.23-13.76], t (11) = 4.90, P = 0.0005) and 3 months (mean difference [95% CI]: 9.99 [4.71-15.27], t (6) = 4.63, P = 0.0036) postinfusion. Widespread changes in FC followed infusion, which correlated with EPDS scores. IMPLICATIONS/CONCLUSIONS: Brexanolone is a successful treatment for PPD in the clinical setting. In conjunction with routine clinical care, brexanolone was linked to a reduction in symptoms lasting at least 3 months. pfMRI is feasible in postpartum patients receiving brexanolone and has the potential to elucidate individual-specific mechanisms of action.

Functional parcellation of the neonatal cortical surface.

The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. We next derive a set of 283 cortical surface parcels from a sample of n = 261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.

Multi-echo Acquisition and Thermal Denoising Advances Infant Precision Functional Imaging.

The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development, but precise characterization of systems organization during periods of high plasticity might be most influential towards discoveries promoting lifelong health. Collecting and analyzing precision fMRI data during early development has unique challenges and emphasizes the importance of novel methods to improve data acquisition, processing, and analysis strategies in infant samples. Here, we investigate the applicability of two such methods from adult MRI research, multi-echo (ME) data acquisition and thermal noise removal with Noise reduction with distribution corrected principal component analysis (NORDIC), in precision fMRI data from three newborn infants. Compared to an adult example subject, T2* relaxation times calculated from ME data in infants were longer and more variable across the brain, pointing towards ME acquisition being a promising tool for optimizing developmental fMRI. The application of thermal denoising via NORDIC increased tSNR and the overall strength of functional connections as well as the split-half reliability of functional connectivity matrices in infant ME data. While our findings related to NORDIC denoising are coherent with the adult literature and ME data acquisition showed high promise, its application in developmental samples needs further investigation. The present work reveals gaps in our understanding of the best techniques for developmental brain imaging and highlights the need for further developmentally-specific methodological advances and optimizations, towards precision functional imaging in infants.

Functional parcellation of the neonatal brain.

The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that adult- and older infant-derived parcels are a poor fit with neonatal data, emphasizing the need for neonatal-specific parcels. We next derive a set of 283 cortical surface parcels from a sample of n=261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.

The posterior dominant rhythm: an electroencephalographic biomarker for cognitive recovery after general anaesthesia.

BACKGROUND: The posterior dominant rhythm (PDR) was the first oscillatory pattern noted in the EEG. Evoked by wakeful eyelid closure, these oscillations dissipate over seconds during loss of arousal. The peak frequency of the PDR maintains stability over years, suggesting utility as a state biomarker in the surveillance of acute cognitive impairments. This EEG signature has not been systematically investigated for tracking cognitive dysfunction after anaesthetic-induced loss of consciousness. METHODS: This substudy of Reconstructing Consciousness and Cognition (NCT01911195) investigated the PDR and cognitive function in 60 adult volunteers randomised to either 3 h of isoflurane general anaesthesia or resting wakefulness. Serial measurements of EEG power and cognitive task performance were assessed relative to pre-intervention baseline. Mixed-effects models allowed quantification of PDR and neurocognitive trajectories after return of responsiveness (ROR). RESULTS: Individuals in the control group showed stability in the PDR peak frequency over several hours (median difference/inter-quartile range [IQR] of 0.02/0.20 Hz, P=0.39). After isoflurane general anaesthesia, the PDR peak frequency was initially reduced at ROR (median difference/IQR of 0.88/0.65 Hz, P<0.001). PDR peak frequency recovered at a rate of 0.20 Hz h-1. After ROR, the PDR peak frequency correlated with reaction time and accuracy on multiple cognitive tasks (P<0.001). CONCLUSION: The temporal trajectory of the PDR peak frequency could be a useful perioperative marker for tracking cognitive dysfunction on the order of hours after surgery, particularly for cognitive domains of working memory, visuomotor speed, and executive function. CLINICAL TRIAL REGISTRATION: NCT01911195.

Correlating electroconvulsive therapy response to electroencephalographic markers: Study protocol.

Introduction: Electroconvulsive therapy (ECT) is an effective intervention for patients with major depressive disorder (MDD). Despite longstanding use, the underlying mechanisms of ECT are unknown, and there are no objective prognostic biomarkers that are routinely used for ECT response. Two electroencephalographic (EEG) markers, sleep slow waves and sleep spindles, could address these needs. Both sleep microstructure EEG markers are associated with synaptic plasticity, implicated in memory consolidation, and have reduced expression in depressed individuals. We hypothesize that ECT alleviates depression through enhanced expression of sleep slow waves and sleep spindles, thereby facilitating synaptic reconfiguration in pathologic neural circuits. Methods: Correlating ECT Response to EEG Markers (CET-REM) is a single-center, prospective, observational investigation. Wireless wearable headbands with dry EEG electrodes will be utilized for at-home unattended sleep studies to allow calculation of quantitative measures of sleep slow waves (EEG SWA, 0.5-4 Hz power) and sleep spindles (density in number/minute). High-density EEG data will be acquired during ECT to quantify seizure markers. Discussion: This innovative study focuses on the longitudinal relationships of sleep microstructure and ECT seizure markers over the treatment course. We anticipate that the results from this study will improve our understanding of ECT.

Quiescence during burst suppression and postictal generalized EEG suppression are distinct patterns of activity.

OBJECTIVE: Periods of low-amplitude electroencephalographic (EEG) signal (quiescence) are present during both anesthetic-induced burst suppression (BS) and postictal generalized electroencephalographic suppression (PGES). PGES following generalized seizures induced by electroconvulsive therapy (ECT) has been previously linked to antidepressant response. The commonality of quiescence during both BS and PGES motivated trials to recapitulate the antidepressant effects of ECT using high doses of anesthetics. However, there have been no direct electrographic comparisons of these quiescent periods to address whether these are distinct entities. METHODS: We compared periods of EEG quiescence recorded from two human studies: BS induced in 29 healthy adult volunteers by isoflurane general anesthesia and PGES in 11 patients undergoing right unilateral ECT for treatment-resistant depression. An automated algorithm allowed detection of EEG quiescence based on a 10-microvolt amplitude threshold. Spatial, spectral, and temporal analyses compared quiescent epochs during BS and PGES. RESULTS: The median (interquartile range) voltage for quiescent periods during PGES was greater than during BS (1.81 (0.22) microvolts vs 1.22 (0.33) microvolts, p < 0.001). Relative power was greater for quiescence during PGES than BS for the 1-4 Hz delta band (p < 0.001), at the expense of power in the theta (4-8 Hz, p < 0.001), beta (13-30 Hz, p = 0.04) and gamma (30-70 Hz, p = 0.006) frequency bands. Topographic analyses revealed that amplitude across the scalp was consistently higher for quiescent periods during PGES than BS, whose voltage was within the noise floor. CONCLUSIONS: Quiescent epochs during PGES and BS have distinct patterns of EEG signals across voltage, frequency, and spatial domains. SIGNIFICANCE: Quiescent epochs during PGES and BS, important neurophysiological markers for clinical outcomes, are shown to have distinct voltage and frequency characteristics.

Postictal generalized electroencephalographic suppression following electroconvulsive therapy: Temporal characteristics and impact of anesthetic regimen.

OBJECTIVE: Postictal generalized electroencephalographic suppression (PGES) has been defined as electroencephalographic (EEG) activity of less than 10 microvolts following a generalized seizure. PGES is associated with an increased risk of sudden unexplained death in epilepsy, as well as treatment efficacy of electroconvulsive therapy (ECT). We investigated the impact of anesthetic on PGES expression and temporal characteristics. METHODS: We recorded postictal EEG in 50 ECT sessions in 11 patients with treatment resistant depression (ClinicalTrials.gov NCT02761330). For each participant, repeated sessions included either ketamine or etomidate general anesthesia during ECT. An automated algorithm was employed to detect PGES within 5 minutes after seizure termination. RESULTS: PGES was detected in 31/50 recordings, with intermittent epochs recurring up to five minutes after seizure termination. PGES total duration was greater following ketamine than etomidate anesthesia (p = 0.04). PGES expression declined loglinearly as a function of time (r = -0.89, p < 10-4). EEG amplitude during PGES did not vary linearly with time. CONCLUSIONS: PGES can occur intermittently for several minutes following seizure termination. Anesthetic effects should be considered when correlating PGES duration to clinical outcomes. SIGNIFICANCE: Prolonged EEG monitoring several minutes following seizure termination may be necessary to fully evaluate the presence and total duration of PGES.

Closed-Loop Acoustic Stimulation During Sedation with Dexmedetomidine (CLASS-D): Protocol for a Within-Subject, Crossover, Controlled, Interventional Trial with Healthy Volunteers.

INTRODUCTION: The relative power of slow-delta oscillations in the electroencephalogram (EEG), termed slow-wave activity (SWA), correlates with level of unconsciousness. Acoustic enhancement of SWA has been reported for sleep states, but it remains unknown if pharmacologically induced SWA can be enhanced using sound. Dexmedetomidine is a sedative whose EEG oscillations resemble those of natural sleep. This pilot study was designed to investigate whether SWA can be enhanced using closed-loop acoustic stimulation during sedation (CLASS) with dexmedetomidine. METHODS: Closed-Loop Acoustic Stimulation during Sedation with Dexmedetomidine (CLASS-D) is a within-subject, crossover, controlled, interventional trial with healthy volunteers. Each participant will be sedated with a dexmedetomidine target-controlled infusion (TCI). Participants will undergo three CLASS conditions in a multiple crossover design: in-phase (phase-locked to slow-wave upslopes), anti-phase (phase-locked to slow-wave downslopes) and sham (silence). High-density EEG recordings will assess the effects of CLASS across the scalp. A volitional behavioral task and sequential thermal arousals will assess the anesthetic effects of CLASS. Ambulatory sleep studies will be performed on nights immediately preceding and following the sedation session. EEG effects of CLASS will be assessed using linear mixed-effects models. The impacts of CLASS on behavior and arousal thresholds will be assessed using logistic regression modeling. Parametric modeling will determine differences in sleepiness and measures of sleep homeostasis before and after sedation. RESULTS: The primary outcome of this pilot study is the effect of CLASS on EEG slow waves. Secondary outcomes include the effects of CLASS on the following: performance of a volitional task, arousal thresholds, and subsequent sleep. DISCUSSION: This investigation will elucidate 1) the potential of exogenous sensory stimulation to potentiate SWA during sedation; 2) the physiologic significance of this intervention; and 3) the connection between EEG slow-waves observed during sleep and sedation.

Protocol for the Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography (P-DROWS-E) study: a prospective observational study of delirium in elderly cardiac surgical patients.

INTRODUCTION: Delirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome. METHODS AND ANALYSIS: P-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1-2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time. ETHICS AND DISSEMINATION: P-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media. TRIAL REGISTRATION NUMBER: NCT03291626.

Voltage-based automated detection of postictal generalized electroencephalographic suppression: Algorithm development and validation.

OBJECTIVE: Postictal generalized electroencephalographic suppression (PGES) is a pattern of low-voltage scalp electroencephalographic (EEG) activity following termination of generalized seizures. PGES has been associated with both sudden unexplained death in patients with epilepsy and therapeutic efficacy of electroconvulsive therapy (ECT). Automated detection of PGES epochs may aid in reliable quantification of this phenomenon. METHODS: We developed a voltage-based algorithm for detecting PGES. This algorithm applies existing criteria to simulate expert epileptologist readings. Validation relied on postictal EEG recording from patients undergoing ECT (NCT02761330), assessing concordance among the algorithm and four clinical epileptologists. RESULTS: We observed low-to-moderate concordance among epileptologist ratings of PGES. Despite this, the algorithm displayed high discriminability in comparison to individual epileptologists (C-statistic range: 0.86-0.92). The algorithm displayed high discrimination (C-statistic: 0.91) and substantial peak agreement (Cohen's Kappa: 0.65) in comparison to a consensus of clinical ratings. Interrater agreement between the algorithm and individual epileptologists was on par with that among expert epileptologists. CONCLUSIONS: An automated voltage-based algorithm can be used to detect PGES following ECT, with discriminability nearing that of experts. SIGNIFICANCE: Algorithmic detection may support clinical readings of PGES and improve precision when correlating this marker with clinical outcomes following generalized seizures.