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Abstract The development of neural circuits has long-lasting effects on brain function, yet our understanding of early circuit development in humans remains limited. Here, periodic EEG power features and aperiodic components were examined from longitudinal EEGs collected from 592 healthy 2–44 month-old infants, revealing age-dependent nonlinear changes suggestive of distinct milestones in early brain maturation. Developmental changes in periodic peaks include (1) the presence and then absence of a 9-10 Hz alpha peak between 2-6 months, (2) nonlinear changes in high beta peaks (20-30 Hz) between 4-18 months, and (3) the emergence of a low beta peak (12-20 Hz) in some infants after six months of age. We hypothesized that the emergence of the low beta peak may reflect maturation of thalamocortical network development. Infant anesthesia studies observe that GABA-modulating anesthetics do not induce thalamocortical mediated frontal alpha coherence until 10-12 months of age. Using a small cohort of infants (n = 23) with EEG before and during GABA-modulating anesthesia, we provide preliminary evidence that infants with a low beta peak have higher anesthesia-induced alpha coherence compared to those without a low beta peak.

Abstract Preoperative knowledge of expected postoperative pain can help guide perioperative pain management and focus interventions on patients with the greatest risk of acute pain. However, current methods for predicting postoperative pain require patient and clinician input or laborious manual chart review and often do not achieve sufficient performance. We use routinely collected electronic health record data from a multicenter dataset of 234,274 adult non-cardiac surgical patients to develop a machine learning method which predicts maximum pain scores on the day of surgery and four subsequent days and validate this method in a prospective cohort. Our method, POPS, is fully automated and relies only on data available prior to surgery, allowing application in all patients scheduled for or considering surgery. Here we report that POPS achieves state-of-the-art performance and outperforms clinician predictions on all postoperative days when predicting maximum pain on the 0–10 NRS in prospective validation, though with degraded calibration. POPS is interpretable, identifying comorbidities that significantly contribute to postoperative pain based on patient-specific context, which can assist clinicians in mitigating cases of acute pain.

The neural mechanisms underpinning ketamine’s dissociative and antidepressant effects remain poorly understood. Here, the authors analyzed ketamine-induced brain dynamics with intracranial recordings in humans and found that ketamine engages different brain areas in distinct frequency-dependent patterns that may relate to its dissociative and antidepressant effects.

Abstract Phase amplitude coupling (PAC) is thought to play a fundamental role in the dynamic coordination of brain circuits and systems. There are however growing concerns that existing methods for PAC analysis are prone to error and misinterpretation. Improper frequency band selection can render true PAC undetectable, while non-linearities or abrupt changes in the signal can produce spurious PAC. Current methods require large amounts of data and lack formal statistical inference tools. We describe here a novel approach for PAC analysis that substantially addresses these problems. We use a state space model to estimate the component oscillations, avoiding problems with frequency band selection, nonlinearities, and sharp signal transitions. We represent cross-frequency coupling in parametric and time-varying forms to further improve statistical efficiency and estimate the posterior distribution of the coupling parameters to derive their credible intervals. We demonstrate the method using simulated data, rat local field potentials (LFP) data, and human EEG data.

Electroencephalogram (EEG) power reductions in the aging brain have been described by numerous previous studies. However, the underlying mechanism for the observed brain signal power reduction remains unclear. One possible cause for reduced EEG signals in elderly subjects might be the increased distance from the primary neural electrical currents on the cortex to the scalp electrodes as the result of cortical atrophies. While brain shrinkage itself reflects age-related neurological changes, the effects of changes in the distribution of electrical conductivity are often not distinguished from altered neural activity when interpreting EEG power reductions. To address this ambiguity, we employed EEG forward models to investigate whether brain shrinkage is a major factor for the signal attenuation in the aging brain. We simulated brain shrinkage in spherical and realistic brain models and found that changes in the conductor geometry cannot fully account for the EEG power reductions even when the brain was shrunk to unrealistic sizes. Our results quantify the extent of power reductions from brain shrinkage and pave the way for more accurate inferences about deficient neural activity and circuit integrity based on EEG power reductions in the aging population.

During general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

Patients with autism spectrum disorder (ASD) often require sedation or general anesthesia. ASD is thought to arise from deficits in GABAergic signaling leading to abnormal neurodevelopment. We sought to investigate differences in how ASD patients respond to the GABAergic drug propofol by comparing the propofol-induced electroencephalogram (EEG) of ASD and neurotypical (NT) patients. This investigation was a prospective observational study. Continuous 4-channel frontal EEG was recorded during routine anesthetic care of patients undergoing endoscopic procedures between July 1, 2014 and May 1, 2016. Study patients were defined as those with previously diagnosed ASD by DSM-V criteria, aged 2–30 years old. NT patients were defined as those lacking neurological or psychiatric abnormalities, aged 2–30 years old. The primary outcome was changes in propofol-induced alpha (8–13 Hz) and slow (0.1–1 Hz) oscillation power by age. A post hoc analysis was performed to characterize incidence of burst suppression during propofol anesthesia. The primary risk factor of interest was a prior diagnosis of ASD. Outcomes were compared between ASD and NT patients using Bayesian methods. Compared to NT patients, slow oscillation power was initially higher in ASD patients (17.05 vs. 14.20 dB at 2.33 years), but progressively declined with age (11.56 vs. 13.95 dB at 22.5 years). Frontal alpha power was initially lower in ASD patients (17.65 vs. 18.86 dB at 5.42 years) and continued to decline with age (6.37 vs. 11.89 dB at 22.5 years). The incidence of burst suppression was significantly higher in ASD vs. NT patients (23.0% vs. 12.2%, p < 0.01) despite reduced total propofol dosing in ASD patients. Ultimately, we found that ASD patients respond differently to propofol compared to NT patients. A similar pattern of decreased alpha power and increased sensitivity to burst suppression develops in older NT adults; one interpretation of our data could be that ASD patients undergo a form of accelerated neuronal aging in adolescence. Our results suggest that investigations of the propofol-induced EEG in ASD patients may enable insights into the underlying differences in neural circuitry of ASD and yield safer practices for managing patients with ASD.

Anesthetic drugs are typically administered to induce altered states of arousal that range from sedation to general anesthesia (GA). Systems neuroscience studies are currently being used to investigate the neural circuit mechanisms of anesthesia-induced altered arousal states. These studies suggest that by disrupting the oscillatory dynamics that are associated with arousal states, anesthesia-induced oscillations are a putative mechanism through which anesthetic drugs produce altered states of arousal. However, an empirical clinical observation is that even at relatively stable anesthetic doses, patients are sometimes intermittently responsive to verbal commands during states of light sedation. During these periods, prominent anesthesia-induced neural oscillations such as slow-delta (0.1–4 Hz) oscillations are notably absent. Neural correlates of intermittent responsiveness during light sedation have been insufficiently investigated. A principled understanding of the neural correlates of intermittent responsiveness may fundamentally advance our understanding of neural dynamics that are essential for maintaining arousal states, and how they are disrupted by anesthetics. Therefore, we performed a high-density (128 channels) electroencephalogram (EEG) study (n = 8) of sevoflurane-induced altered arousal in healthy volunteers. We administered temporally precise behavioral stimuli every 5 s to assess responsiveness. Here, we show that decreased eyes-closed, awake-alpha (8–12 Hz) oscillation power is associated with lack of responsiveness during sevoflurane effect-onset and -offset. We also show that anteriorization—the transition from occipitally dominant awake-alpha oscillations to frontally dominant anesthesia induced-alpha oscillations—is not a binary phenomenon. Rather, we suggest that periods, which were defined by lack of responsiveness, represent an intermediate brain state. We conclude that awake-alpha oscillation, previously thought to be an idling rhythm, is associated with responsiveness to behavioral stimuli.

During awake consciousness, the brain intrinsically maintains a dynamical state in which it can coordinate complex responses to sensory input. How the brain reaches this state spontaneously is not known. General anesthesia provides a unique opportunity to examine how the human brain recovers its functional capabilities after profound unconsciousness. We used intracranial electrocorticography and scalp EEG in humans to track neural dynamics during emergence from propofol general anesthesia. We identify a distinct transient brain state that occurs immediately prior to recovery of behavioral responsiveness. This state is characterized by large, spatially distributed, slow sensory-evoked potentials that resemble the K-complexes that are hallmarks of stage two sleep. However, the ongoing spontaneous dynamics in this transitional state differ from sleep. These results identify an asymmetry in the neurophysiology of induction and emergence, as the emerging brain can enter a state with a sleep-like sensory blockade before regaining responsivity to arousing stimuli.