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IntroductionDelirium 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 analysisP-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 disseminationP-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 numberNCT03291626.

IntroductionElectroconvulsive therapy (ECT) is a highly effective treatment for refractory depression, but it may also cause cognitive side effects. Despite decades of use, the mechanisms by which ECT exerts both its antidepressant and cognitive effects are still poorly understood, with the latter substantially limiting referral and adherence to therapy. ECT induces changes in correlated neural activity—functional connectivity—across various brain networks, which may underlie both its clinical efficacy and associated cognitive side effects. Electroencephalography (EEG) could address these knowledge gaps by identifying biomarkers that predict therapeutic outcomes or cognitive side effects. Such developments could ultimately improve patient selection and adherence. Such markers likely span large-scale functional brain networks or temporal dynamics of brain activity during sleep. We hypothesise that enhancement in slow wave sleep mediates the relationship between antidepressant effects and changes in functional connectivity throughout the course of ECT.Methods and analysisDisruptions of Brain Networks and Sleep by Electroconvulsive Therapy (DNS-ECT) is an ongoing observational study investigating the impact of ECT on large-scale brain functional networks and their relationships to sleep slow waves, an EEG marker linked to synaptic plasticity. The novelty of this study stems from our focus on the assessment of EEG markers during sleep, wakefulness and ECT-induced seizures over the course of therapy. Graph-based network analyses of high-density EEG signals allow characterisation of functional networks locally in specific subnetworks and globally over large-scale functional networks. Longitudinal assessments of EEG alongside clinical and cognitive outcomes provide a unique opportunity to improve our understanding of the circuit mechanisms underlying the development of cognitive impairments and antidepressant effects incurred during ECT.Ethics and disseminationRecruitment for this 5-year study started in March 2023. Dissemination plans include presentations at scientific conferences and peer-reviewed publications. This study has been registered with ClinicalTrials.gov registry under identifier.Trial registration numberNCT05905705.

IntroductionLate-life treatment-resistant depression (LL-TRD) is common and increases risk for accelerated ageing and cognitive decline. Impaired sleep is common in LL-TRD and is a risk factor for cognitive decline. Slow wave sleep (SWS) has been implicated in key processes including synaptic plasticity and memory. A deficiency in SWS may be a core component of depression pathophysiology. The anaesthetic propofol can induce electroencephalographic (EEG) slow waves that resemble SWS. Propofol may enhance SWS and oral antidepressant therapy, but relationships are unclear. We hypothesise that propofol infusions will enhance SWS and improve depression in older adults with LL-TRD. This hypothesis has been supported by a recent small case series.Methods and analysisSWIPED (Slow Wave Induction by Propofol to Eliminate Depression) phase I is an ongoing open-label, single-arm trial that assesses the safety and feasibility of using propofol to enhance SWS in older adults with LL-TRD. The study is enrolling 15 English-speaking adults over age 60 with LL-TRD. Participants will receive two propofol infusions 2–6 days apart. Propofol infusions are individually titrated to maximise the expression of EEG slow waves. Preinfusion and postinfusion sleep architecture are evaluated through at-home overnight EEG recordings acquired using a wireless headband equipped with dry electrodes. Sleep EEG recordings are scored manually. Key EEG measures include sleep slow wave activity, SWS duration and delta sleep ratio. Longitudinal changes in depression, suicidality and anhedonia are assessed. Assessments are performed prior to the first infusion and up to 10 weeks after the second infusion. Cognitive ability is assessed at enrolment and approximately 3 weeks after the second infusion.Ethics and disseminationThe study was approved by the Washington University Human Research Protection Office. Recruitment began in November 2022. Dissemination plans include presentations at scientific conferences, peer-reviewed publications and mass media. Positive results will lead to a larger phase II randomised placebo-controlled trial.Trial registration numberNCT04680910.

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. 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. 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. 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.

Cortical areas are a fundamental organizational property of the brain, but their development in humans is not well understood. Key unanswered questions include whether cortical areas are fully established near birth, the extent of individual variation in the arrangement of cortical areas, and whether any such individual variation in cortical area location is greater in later-developing association areas as compared to earlier-developing sensorimotor areas. To address these questions, we used functional MRI to collect precision functional mapping (PFM) data in eight individual neonates (mean 42.7 weeks postmenstrual age) over 2-5 days (mean 77.9 minutes of low motion data per subject [framewise displacement <0.1]). Each subject's dataset was split into two roughly equal halves of data from different days of data collection to measure within-subject reliability and across-subject similarity. Whole-brain patterns of functional connectivity (FC) reached a mean within-subject, across-day reliability of r=0.78 with 41.9 minutes of data. Across subject similarity of whole-brain FC was r=0.62 on average and significantly lower than within-subject similarity (t=5.9, p<0.001). Using established methods to identify transitions in FC across the cortical surface, we identified sets of cortical areas for each individual that were subject-specific and highly reliable across split-halves (mean z=4.4, SD=1.4). The arrangement of cortical areas was thus individually specific across the entire cortical surface, and this individual specificity did not vary as a function of the sensorimotor-association axis. This study establishes the feasibility of neonatal PFM and suggests that cortical area arrangement is individually specific and largely established shortly following birth.

Adaptively responding to salient stimuli in the environment is a fundamental feature of cognitive development in early life, which is enabled by the developing brain. Understanding individual variability in how the brain supports this fundamental process is essential for uncovering neurodevelopmental trajectories and potential neurodevelopmental risks. In the present study, we used a precision functional imaging approach to probe activation in response to salient auditory stimuli and its relation to brain functional networks in individual infants. A minimum of 60 minutes of fMRI BOLD data with an auditory oddball paradigm were collected in ten infants with a mean postmenstrual age of 48 weeks. Results demonstrate the feasibility of performing a precision functional imaging study to investigate individual specific responses to salient stimuli in infants. While responses to the auditory oddball were consistent between individuals in auditory processing areas, responses across the rest of the brain differed across individuals in their magnitude and shape. Individual specific response patterns appeared to be relatively stable and differed from other participant's response patterns, despite fluctuations across runs. Commonalities and differences between individuals demonstrated in this sample contribute to our understanding of how the developing brain instantiates processing of salient stimuli. Our findings suggest that during early development, early unimodal processing is well conserved across individuals, however subsequent perceptual processing is still being personally defined. In this context, individual specific response patterns could be a promising target for biomarkers of normative brain and cognitive development.

The cerebral cortex consists of distinct areas that develop through intrinsic embryonic patterning and postnatal experiences. Accurate parcellation of these areas in neuroimaging studies improves statistical power and cross-study comparability. Given significant brain changes in volume, microstructure, and connectivity during early life, we hypothesized that cortical areas in 1- to 3-year-olds would differ markedly from neonates and increasingly resemble adult patterns as development progresses. Here, we parcellated the cerebral cortex into putative areas using local functional connectivity gradients in 92 toddlers at 2 years old. We demonstrate high reproducibility of these cortical regions across 1- to 3-year-olds in two independent datasets. The area boundaries in 1- to 3-year-olds were more similar to those in adults than those in neonates. While the age-specific group area parcellation better fit the underlying functional connectivity in individuals during the first 3 years, adult area parcellations might still have some utility in developmental studies, especially in children older than 6 years. Additionally, we provide connectivity-based community assignments of the parcels, showing fragmented anterior and posterior components based on the strongest connectivity, yet alignment with adult systems when weaker connectivity was included.

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. Precise characterization of systems organization during periods of high plasticity is likely to be essential for discoveries promoting lifelong health. Obtaining precision fMRI data during development has unique challenges that highlight the importance of establishing new methods to improve data acquisition, processing, and analysis. Here, we investigate two methods that can facilitate attaining this goal: multi-echo (ME) data acquisition and thermal noise removal with Noise Reduction with Distribution Corrected (NORDIC) principal component analysis. We applied these methods to precision fMRI data from adults, children, and newborn infants. In adults, both ME acquisitions and NORDIC increased temporal signal to noise ratio (tSNR) as well as the split-half reliability of functional connectivity matrices, with the combination helping more than either technique alone. The benefits of NORDIC denoising replicated in both our developmental samples. ME acquisitions revealed longer and more variable T2* relaxation times across the brain in infants relative to older children and adults, leading to major differences in the echo weighting for optimally combining ME data. This result suggests ME acquisitions may be a promising tool for optimizing developmental fMRI, albeit application in infants needs further investigation. The present work showcases methodological advances that improve Precision Functional Mapping in adults and developmental populations and, at the same time, highlights the need for further improvements in infant specific fMRI.

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.Competing Interest StatementDamien A. Fair is a patent holder on the Framewise Integrated Real-Time Motion Monitoring (FIRMM) software. He is also a co-founder of Turing Medical Inc. that licenses this software. The nature of this financial interest and the design of the study have been reviewed by the University of Minnesota, and a plan has been established to ensure that this research study is not affected by the financial interest. The other authors declare no competing interests.

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 mixedeffects 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. Keywords: sleep, anesthesia, consciousness, dexmedetomidine, electroencephalography, acoustic stimulation