Suvorexant alters dynamics of the sleep-electroencephalography-power spectrum and depressive-symptom trajectories during inpatient opioid withdrawal.

STUDY OBJECTIVES: Opioid withdrawal is an aversive experience that often exacerbates depressive symptoms and poor sleep. The aims of the present study were to examine the effects of suvorexant on oscillatory sleep-electroencephalography (EEG) band power during medically managed opioid withdrawal, and to examine their association with withdrawal severity and depressive symptoms. METHODS: Participants with opioid use disorder (N = 38: age-range:21-63, 87% male, 45% white) underwent an 11-day buprenorphine taper, in which they were randomly assigned to suvorexant (20 mg [n = 14] or 40 mg [n = 12]), or placebo [n = 12], while ambulatory sleep-EEG data was collected. Linear mixed-effect models were used to explore: (1) main and interactive effects of drug group, and time on sleep-EEG band power, and (2) associations between sleep-EEG band power change, depressive symptoms, and withdrawal severity. RESULTS: Oscillatory spectral power tended to be greater in the suvorexant groups. Over the course of the study, decreases in delta power were observed in all study groups (ß = -189.082, d = -0.522, p = <0.005), increases in beta power (20 mg: ß = 2.579, d = 0.413, p = 0.009 | 40 mg ß = 5.265, d = 0.847, p < 0.001) alpha power (20 mg: ß = 158.304, d = 0.397, p = 0.009 | 40 mg: ß = 250.212, d = 0.601, p = 0.001) and sigma power (20 mg: ß = 48.97, d = 0.410, p < 0.001 | 40 mg: ß = 71.54, d = 0.568, p < 0.001) were observed in the two suvorexant groups. During the four-night taper, decreases in delta power were associated with decreases in depressive symptoms (20 mg: ß = 190.90, d = 0.308, p = 0.99 | 40 mg: ß = 433.33, d = 0.889 p = <0.001), and withdrawal severity (20 mg: ß = 215.55, d = 0.034, p = 0.006 | 40 mg: ß = 192.64, d = -0.854, p = <0.001), in both suvorexant groups and increases in sigma power were associated with decreases in withdrawal severity (20 mg: ß = -357.84, d = -0.659, p = 0.004 | 40 mg: ß = -906.35, d = -1.053, p = <0.001). Post-taper decreases in delta (20 mg: ß = 740.58, d = 0.964 p = <0.001 | 40 mg: ß = 662.23, d = 0.882, p = <0.001) and sigma power (20 mg only: ß = 335.54, d = 0.560, p = 0.023) were associated with reduced depressive symptoms in the placebo group. CONCLUSIONS: Results highlight a complex and nuanced relationship between sleep-EEG power and symptoms of depression and withdrawal. Changes in delta power may represent a mechanism influencing depressive symptoms and withdrawal.

Self-Supervised Transformer Model Training for a Sleep-EEG Foundation Model.

The American Academy of Sleep Medicine (AASM) recognizes five sleep/wake states (Wake, N1, N2, N3, REM), yet this classification schema provides only a high-level summary of sleep and likely overlooks important neurological or health information. New, data-driven approaches are needed to more deeply probe the information content of sleep signals. Here we present a self-supervised approach that learns the structure embedded in large quantities of neurophysiological sleep data. This masked transformer training procedure is inspired by high performing self-supervised methods developed for speech transcription. We show that self-supervised pre-training matches or outperforms supervised sleep stage classification, especially when labeled data or compute-power is limited. Perhaps more importantly, we also show that our pretrained model is flexible and can be fine-tuned to perform well on new tasks including distinguishing individuals and quantifying "brain age" (a potential health biomarker). This suggests that modern methods can automatically learn information that is potentially overlooked by the 5-class sleep staging schema, laying the groundwork for new schemas and further data-driven exploration of sleep.

Laying the Foundation: Modern Transformers for Gold-Standard Sleep Analysis and Beyond.

Accurate sleep assessment is critical to the practice of sleep medicine and sleep research. The recent availability of large quantities of publicly available sleep data, alongside recent breakthroughs in AI like transformer architectures, present novel opportunities for data-driven discovery efforts. Transformers are flexible neural networks that not only excel at classification tasks, but also can enable data-driven discovery through un- or self-supervised learning, which requires no human annotations to the input data. While transformers have been extensively used in supervised learning scenarios for sleep stage classification, they have not been fully explored or optimized in forms designed from the ground up for use in un- or self-supervised learning tasks in sleep. A necessary first step will be to study these models on a canonical benchmark supervised learning task (5-class sleep stage classification). Hence, to lay the groundwork for future data-driven discovery efforts, we evaluated optimizations of a transformer-based architecture that has already demonstrated substantial success in self-supervised learning in another domain (audio speech recognition), and trained it to perform the canonical 5-class sleep stage classification task, to establish foundational baselines in the sleep domain. We found that small transformer models designed from the start for (later) self-supervised learning can match other state-of-the-art automated sleep scoring techniques, while also providing the basis for future data-driven discovery efforts using large sleep data sets.

Within-subject reaction time variability: Role of cortical networks and underlying neurophysiological mechanisms.

Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject's task is the same from trial to trial.

Working memory load-dependent changes in cortical network connectivity estimated by machine learning.

Working memory engages multiple distributed brain networks to support goal-directed behavior and higher order cognition. Dysfunction in working memory has been associated with cognitive impairment in neuropsychiatric disorders. It is important to characterize the interactions among cortical networks that are sensitive to working memory load since such interactions can also hint at the impaired dynamics in patients with poor working memory performance. Functional connectivity is a powerful tool used to investigate coordinated activity among local and distant brain regions. Here, we identified connectivity footprints that differentiate task states representing distinct working memory load levels. We employed linear support vector machines to decode working memory load from task-based functional connectivity matrices in 177 healthy adults. Using neighborhood component analysis, we also identified the most important connectivity pairs in classifying high and low working memory loads. We found that between-network coupling among frontoparietal, ventral attention and default mode networks, and within-network connectivity in ventral attention network are the most important factors in classifying low vs. high working memory load. Task-based within-network connectivity profiles at high working memory load in ventral attention and default mode networks were the most predictive of load-related increases in response times. Our findings reveal the large-scale impact of working memory load on the cerebral cortex and highlight the complex dynamics of intrinsic brain networks during active task states.

Naps reliably estimate nocturnal sleep spindle density in health and schizophrenia.

Sleep spindles, defining oscillations of non-rapid eye movement stage 2 sleep (N2), mediate memory consolidation. Spindle density (spindles/minute) is a stable, heritable feature of the sleep electroencephalogram. In schizophrenia, reduced spindle density correlates with impaired sleep-dependent memory consolidation and is a promising treatment target. Measuring sleep spindles is also important for basic studies of memory. However, overnight sleep studies are expensive, time consuming and require considerable infrastructure. Here we investigated whether afternoon naps can reliably and accurately estimate nocturnal spindle density in health and schizophrenia. Fourteen schizophrenia patients and eight healthy controls had polysomnography during two overnights and three afternoon naps. Although spindle density was lower during naps than nights, the two measures were highly correlated. For both groups, naps and nights provided highly reliable estimates of spindle density. We conclude that naps provide an accurate, reliable and more scalable alternative to measuring spindle density overnight.

Instantaneous voltage as an alternative to power- and phase-based interpretation of oscillatory brain activity.

For decades, oscillatory brain activity has been characterized primarily by measurements of power and phase. While many studies have linked those measurements to cortical excitability, their relationship to each other and to the physiological underpinnings of excitability is unclear. The recently proposed Function-through-Biased-Oscillations (FBO) hypothesis (Schalk, 2015) addressed these issues by suggesting that the voltage potential at the cortical surface directly reflects the excitability of cortical populations, that this voltage is rhythmically driven away from a low resting potential (associated with depolarized cortical populations) towards positivity (associated with hyperpolarized cortical populations). This view explains how oscillatory power and phase together influence the instantaneous voltage potential that directly regulates cortical excitability. This implies that the alternative measurement of instantaneous voltage of oscillatory activity should better predict cortical excitability compared to either of the more traditional measurements of power or phase. Using electrocorticographic (ECoG) data from 28 human subjects, the results of our study confirm this prediction: compared to oscillatory power and phase, the instantaneous voltage explained 20% and 31% more of the variance in broadband gamma, respectively, and power and phase together did not produce better predictions than the instantaneous voltage. These results synthesize the previously separate power- and phase-based interpretations and associate oscillatory activity directly with a physiological interpretation of cortical excitability. This alternative view has implications for the interpretation of studies of oscillatory activity and for current theories of cortical information transmission.

Stage 2 Sleep EEG Sigma Activity and Motor Learning in Childhood ADHD: A Pilot Study.

Attention deficit hyperactivity disorder (ADHD) is associated with deficits in motor learning and sleep. In healthy adults, overnight improvements in motor skills are associated with sleep spindle activity in the sleep electroencephalogram (EEG). This association is poorly characterized in children, particularly in pediatric ADHD. Polysomnographic sleep was monitored in 7 children with ADHD and 14 typically developing controls. All children were trained on a validated motor sequence task (MST) in the evening with retesting the following morning. Analyses focused on MST precision (speed-accuracy trade-off). NREM Stage 2 sleep EEG power spectral analyses focused on spindle-frequency EEG activity in the sigma (12-15 Hz) band. The ADHD group demonstrated a selective decrease in power within the sigma band. Evening MST precision was lower in ADHD, yet no difference in performance was observed following sleep. Moreover, ADHD status moderated the association between slow sleep spindle activity (12-13.5 Hz) and overnight improvement; spindle-frequency EEG activity was positively associated with performance improvements in children with ADHD but not in controls. These data highlight the importance of sleep in supporting next-day behavior in ADHD while indicating that differences in sleep neurophysiology may contribute to deficits in this population.

Neural correlate of the construction of sentence meaning.

The neural processes that underlie your ability to read and understand this sentence are unknown. Sentence comprehension occurs very rapidly, and can only be understood at a mechanistic level by discovering the precise sequence of underlying computational and neural events. However, we have no continuous and online neural measure of sentence processing with high spatial and temporal resolution. Here we report just such a measure: intracranial recordings from the surface of the human brain show that neural activity, indexed by γ-power, increases monotonically over the course of a sentence as people read it. This steady increase in activity is absent when people read and remember nonword-lists, despite the higher cognitive demand entailed, ruling out accounts in terms of generic attention, working memory, and cognitive load. Response increases are lower for sentence structure without meaning ("Jabberwocky" sentences) and word meaning without sentence structure (word-lists), showing that this effect is not explained by responses to syntax or word meaning alone. Instead, the full effect is found only for sentences, implicating compositional processes of sentence understanding, a striking and unique feature of human language not shared with animal communication systems. This work opens up new avenues for investigating the sequence of neural events that underlie the construction of linguistic meaning.

Odor identification in mild cognitive impairment subtypes.

The current study examined odor identification using the Brief Smell Identification Test (BSIT) in mild cognitive impairment (MCI) subtypes (17 "amnestic MCI", 46 "amnestic-plus MCI", and 25 "MCI other"). Performance in participants with MCI was compared to that of participants with Alzheimer's disease (AD, n=44) and healthy elderly (n=21). MCI participants performed worse than controls, but better than those with AD. MCI subtypes did not differ. The magnitude of difference between MCI participants and controls was modest, raising some question of the clinical utility of the BSIT in early detection of MCI and early differential diagnosis.