Abstract Importance Anxiety of fluctuating severity is common in many psychiatric disorders. Few studies addressed factors determining individual differences in trajectories of the recovery phase, while their identification could inspire treatment innovation. Given the role of REM sleep in overnight alleviation of emotional distress, we here investigate whether individual differences in this overnight regulatory process matter for anxiety recovery. Objective To investigate whether individual differences in overnight alleviation of distress by REM sleep predict anxiety recovery rate. Design People tend to volunteer for intervention trials when fluctuating symptoms peak. This results in a significant recovery even in waitlist or control conditions. Leveraging this opportunity to recruit people prior to the recovery phase, in this cohort study, we utilized data from people who volunteered for optional sleep EEG and overnight distress assessment prior to their participation in an intervention trial (2021-2025). Anxiety severity was assessed at baseline and two months later. Setting Home-based assessment in the Netherlands. Participants Adults with insomnia alongside cross-threshold symptom severities of generalized anxiety disorder, social anxiety disorder, panic disorder, posttraumatic stress disorder, or borderline personality disorder (N = 223, 157 female [70.4%]; mean [SD] age, 45.7 [14.5] years; clinical diagnoses confirmed in 165 [74.0%]). Exposures Cognitive behavioral therapy for insomnia (CBT-I) or waitlist control. Main Outcomes and Measures Predicting 2-month anxiety improvement by individual differences in the strength of the effect of REM sleep on overnight distress alleviation at baseline. Results Within-subject mixed model analysis showed stronger overnight distress alleviation across nights with longer REM sleep ( b = -0.011; 95% CI, -0.016 to -0.007; P < .001). Individual differences in the strength of REM–related distress alleviation predicted anxiety improvement after two months ( b = −0.521; 95% CI, −0.854 to −0.188; P = .002), irrespective of treatment or waitlist control (interaction b = 0.011; 95% CI, −0.656 to 0.678; P = .97). Conclusions and Relevance Individual differences in the degree to which REM sleep drives overnight alleviation of distress predict the trajectory of anxiety recovery in people with clinically relevant psychiatric complaints. These findings suggest REM-related emotion regulation as a mechanism linking sleep physiology to anxiety recovery. Key Points Question Do individual differences in the degree to which sleep alleviates distress overnight predict the subsequent trajectory of anxiety severity? Findings In this cohort study, EEG recordings and measures of overnight distress alleviation were acquired across 662 nights in 223 adults experiencing anxiety related to different types of psychiatric conditions. Individual differences in the role of REM sleep in overnight distress alleviation predicted anxiety symptom improvement over 2 months. Meaning REM sleep–dependent overnight distress alleviation may represent a biomarker of anxiety recovery potential, highlighting REM-related emotional regulation as a candidate mechanism linking sleep physiology to clinical improvement.

Insomnia-specific features of sleep EEG activity have remained elusive, with existing findings being inconsistent and often weak in effect. Using machine learning, we analyzed two independent electroencephalogram (EEG) datasets spanning two nights (Nsubjects/nights=198/396), comprising individuals with insomnia disorder (ID) (mild to moderate/severe) and good sleeper controls (GSCs). The findings demonstrated that sleep EEG spectral features differentiated ID from GSC only when using identical participants for training and testing, indicating that model performance was driven by individual EEG signatures instead of ID-related patterns. Analyses with unsupervised learning, similarity matrices, and periodicity assessments further confirmed that brain activity during sleep is characterized by robust, individual-specific EEG signatures with trait-like stability over two nights. We also show that the individual sleep EEG signatures are driven by high frequency cortical activity, previously associated with cortical arousal during sleep. The results then demonstrate that high frequency cortical activity is not specific to ID, but the key to characterizing individual sleep EEG signatures. While ID may be characterized by EEG features beyond spectral power, our findings underscore the importance of a precision brain health framework that prioritizes deviations from an individual’s own neural baseline rather than relying solely on group-level comparisons.

STUDY OBJECTIVES:Event-related potential (ERP) studies on attentional brain processes in insomnia disorder (ID) have yielded inconsistent findings. Such inconsistencies may relate to small sample sizes, limited corrections for multiple comparisons, and the possibility of heterogeneity within the clinical population. We aimed to overcome these limitations by studying ERP responses both across and within subtypes in a larger sample of ID. METHODS:ERPs were recorded in 201 participants with ID and 70 normal sleeper controls (NS) with an auditory oddball task. Participants with ID were subtyped using a validated multivariate trait profile. Analyses evaluated subtype-specific and nonspecific deviations using both conventional ERP components as well as cluster-based permutation tests. RESULTS:All five subtypes were well-represented in the ID sample (subtypes 1-5 respectively N = 31, 83, 28, 29 and 19). ERP component analyses with false discovery rate corrections revealed no evidence for differences between the heterogeneous ID group and NS. However, subtype-specific analyses revealed that ERPs were significantly altered, but in different ways for different subtypes. Specifically, ERP component analyses revealed stronger N100 amplitudes for standards and deviants both in subtypes 2 and 3, and a lower P300 amplitude and longer P300 latency for deviants in subtype 3. Cluster-based permutation tests on ERPs corroborated the P300 amplitude effect for deviants in subtype 3, with subtype 3 and 4 additionally showing a smaller difference between deviant and standard P300 amplitudes. CONCLUSION:Our findings indicate that ID is a heterogeneous disorder. Ignoring subtype identity dilutes ERP alterations occurring only in specific insomnia subtypes.

Sleep supports memory consolidation, but the specific roles of different sleep stages in this process remain unclear. While rapid eye movement sleep (REM) has traditionally been linked to the processing of emotionally charged material, recent evidence suggests that slow wave sleep (SWS) also plays a role in strengthening emotional memories. Here, we use targeted memory reactivation (TMR) during REM and SWS in a daytime nap to directly examine which sleep stage is primarily involved in consolidating emotional declarative memories. Contrary to our hypothesis, reactivating emotional stimuli during REM impairs memory. Meanwhile, TMR benefit in SWS is strongly correlated with the product of time spent in REM and SWS. The emotional valence of cued items modulates both delta/theta power and sleep spindles. Furthermore, emotional memories benefit more from TMR than neutral ones. Our findings suggest that SWS and REM have complementary roles in consolidating emotional memories, with REM potentially involved in forgetting them. These results also expand on recent evidence highlighting a connection between sleep spindles and emotional processing.

Human imaging studies suggest that visually presented words are processed by distributed networks beyond classical language areas, reflecting the properties related to their meanings. Based on human single-neuron recordings, we investigated whether and how the odor aspect of words is processed in mediotemporal lobe regions involved in olfactory perception. We analyzed ensemble activity in response to odor-related versus control words in the piriform cortex, amygdala, hippocampus, entorhinal cortex, and parahippocampal cortex and identified stimulus-responsive and odor-associated neurons. We detected converging evidence for odor-associated responses to words in the amygdala, indicated by increased ensemble activity, and a significant proportion of odor-associated neurons. These findings support and extend the notion that the amygdala integrates information across sensory modalities, allowing for the evaluation of its emotional and social significance.

Insomnia-specific sleep EEG features have remained elusive. Using machine learning, we analyzed two independent electroencephalogram (EEG) datasets spanning two nights (N subjects/nights =198/396), including individuals with insomnia disorder (ID) (ranging from mild to moderate/severe) and good sleeper controls (GSCs). Sleep EEG spectral features can differentiate ID from GSC only when using the same participants for both training and testing data. This shows that performance depends on recognizing individual EEG profiles instead of generalizable ID patterns. We also demonstrate that epoch-averaged EEG spectral features exhibit highly individual-specific signatures, identified using unsupervised learning, similarity matrix analyses and periodicity assessments. Our results further indicate that signatures are primarily driven by high frequency cortical activity, possibly reflecting cortical arousal during sleep. While ID may be characterized by EEG features beyond spectral power, our findings underscore the importance of a precision brain health framework that focuses on deviations from an individual’s own neural baseline rather than relying solely on group-level comparisons.

While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insight into the relevant brain circuits requires high-density electroencephalography (EEG). However, identifying and handling sleep EEG artifacts becomes increasingly challenging with higher channel counts and/or volume of recordings. Whereas manual cleaning is time-consuming, subjective, and often yields data loss (e.g., complete removal of channels or epochs), automated approaches suitable and practical for overnight sleep EEG remain limited, especially when control over detection and repair behavior is desired. Here, we introduce a flexible approach for automated cleaning of multichannel sleep recordings, as part of the free Matlab-based toolbox SleepTrip. Key functionality includes 1) channel-wise detection of various artifact types encountered in sleep EEG, 2) channel- and time-resolved marking of data segments for repair through interpolation, and 3) visualization options to review and monitor performance. Functionality for Independent Component Analysis is also included. Extensive customization options allow tailoring cleaning behavior to data properties and analysis goals. By enabling computationally efficient and flexible automated data cleaning, this tool helps to facilitate fundamental and clinical sleep EEG research.

In the past decades, actigraphy has emerged as a promising, cost-effective, and easy-to-use tool for ambulatory sleep recording. Polysomnography (PSG) validation studies showed that actigraphic sleep estimates fare relatively well in healthy sleepers. Additionally, round-the-clock actigraphy recording has been used to study circadian rhythms in various populations. To this date, however, there is little evidence that the diagnosis, monitoring, or treatment of insomnia can significantly benefit from actigraphy recordings. Using a case-control design, we therefore critically examined whether mean or within-subject variability of actigraphy sleep estimates or circadian patterns add to the understanding of sleep complaints in insomnia. We acquired actigraphy recordings and sleep diaries of 37 controls and 167 patients with varying degrees of insomnia severity for up to 9 consecutive days in their home environment. Additionally, the participants spent one night in the laboratory, where actigraphy was recorded alongside PSG to check whether sleep, in principle, is well estimated. Despite moderate to strong agreement between actigraphy and PSG sleep scoring in the laboratory, ambulatory actigraphic estimates of average sleep and circadian rhythm variables failed to successfully differentiate patients with insomnia from controls in the home environment. Only total sleep time differed between the groups. Additionally, within-subject variability of sleep efficiency and wake after sleep onset was higher in patients. Insomnia research may therefore benefit from shifting attention from average sleep variables to day-to-day variability or from the development of non-motor home-assessed indicators of sleep quality.

Sleep supports memory consolidation. However, it is not completely clear how different sleep stages contribute to this process. While rapid eye movement sleep (REM) has traditionally been implicated in the processing of emotionally charged material, recent studies indicate a role for slow wave sleep (SWS) in strengthening emotional memories. Here, to directly examine which sleep stage is primarily involved in emotional memory consolidation, we used targeted memory reactivation (TMR) in REM and SWS during a daytime nap. Contrary to our hypothesis, reactivation of emotional stimuli during REM led to impaired memory. Consistent with this, REM% was correlated with worse recall in the group that took a nap without TMR. Meanwhile, cueing benefit in SWS was strongly correlated with the product of times spent in REM and SWS (SWS-REM product), and reactivation significantly enhanced memory in those with high SWS-REM product. Surprisingly, SWS-REM product was associated with better memory for reactivated items and poorer memory for non-reactivated items, suggesting that sleep both preserved and eliminated emotional memories, depending on whether they were reactivated. Notably, the emotional valence of cued items modulated both sleep spindles and delta/theta power. Finally, we found that emotional memories benefited from TMR more than did neutral ones. Our results suggest that emotional memories decay during REM, unless they are reactivated during prior SWS. Furthermore, we show that active forgetting complements memory consolidation, and both take place across SWS and REM. In addition, our findings expand upon recent evidence indicating a link between sleep spindles and emotional processing.

The nature and degree of objective sleep impairments in insomnia disorder remain unclear. This issue is complicated further by potential changes in sleep architecture on the first compared with subsequent nights in the laboratory. Evidence regarding differential first-night effects in people with insomnia disorder and controls is mixed. Here, we aimed to further characterize insomnia- and night-related differences in sleep architecture. A comprehensive set of 26 sleep variables was derived from two consecutive nights of polysomnography in 61 age-matched patients with insomnia and 61 good sleeper controls. People with insomnia expressed consistently poorer sleep than controls on several variables during both nights. While poorer sleep during the first night was observed in both groups, there were qualitative differences regarding the specific sleep variables expressing a first-night effect. Short sleep (total sleep time < 6 hr) was more likely during the first night and in insomnia, although approximately 40% of patients with insomnia presenting with short sleep on night 1 no longer met this criterion on night 2, which is important given the notion of short-sleeping insomnia as a robust subtype.

STUDY OBJECTIVES:Converging evidence from neuroimaging, sleep, and genetic studies suggest that dysregulation of thalamocortical interactions mediated by the thalamic reticular nucleus (TRN) contribute to autism spectrum disorder (ASD). Sleep spindles assay TRN function, and their coordination with cortical slow oscillations (SOs) indexes thalamocortical communication. These oscillations mediate memory consolidation during sleep. In the present study, we comprehensively characterized spindles and their coordination with SOs in relation to memory and age in children with ASD.METHODS:Nineteen children and adolescents with ASD, without intellectual disability, and 18 typically developing (TD) peers, aged 9-17, completed a home polysomnography study with testing on a spatial memory task before and after sleep. Spindles, SOs, and their coordination were characterized during stages 2 (N2) and 3 (N3) non-rapid eye movement sleep.RESULTS:ASD participants showed disrupted SO-spindle coordination during N2 sleep. Spindles peaked later in SO upstates and their timing was less consistent. They also showed a spindle density (#/min) deficit during N3 sleep. Both groups showed significant sleep-dependent memory consolidation, but their relations with spindle density differed. While TD participants showed the expected positive correlations, ASD participants showed the opposite.CONCLUSIONS:The disrupted SO-spindle coordination and spindle deficit provide further evidence of abnormal thalamocortical interactions and TRN dysfunction in ASD. The inverse relations of spindle density with memory suggest a different function for spindles in ASD than TD. We propose that abnormal sleep oscillations reflect genetically mediated disruptions of TRN-dependent thalamocortical circuit development that contribute to the manifestations of ASD and are potentially treatable.

Extracting shared structure across our experiences allows us to generalize our knowledge to novel contexts. How do different brain states influence this ability to generalize? Using a novel category learning paradigm, we assess the effect of both sleep and time of day on generalization that depends on the flexible integration of recent information. Counter to our expectations, we found no evidence that this form of generalization is better after a night of sleep relative to a day awake. Instead, we observed an effect of time of day, with better generalization in the morning than the evening. This effect also manifested as increased false memory for generalized information. In a nap experiment, we found that generalization did not benefit from having slept recently, suggesting a role for time of day apart from sleep. In follow-up experiments, we were unable to replicate the time of day effect for reasons that may relate to changes in category structure and task engagement. Despite this lack of consistency, we found a morning benefit for generalization when analyzing all the data from experiments with matched protocols (n = 136). We suggest that a state of lowered inhibition in the morning may facilitate spreading activation between otherwise separate memories, promoting this form of generalization.

Patients with schizophrenia have sleep spindle deficits that correlate with impaired sleep-dependent memory consolidation. In a previous pilot study and in this clinical trial, eszopiclone, a non-benzodiazepine sedative hypnotic, despite increasing spindles, failed to improve memory. Here, we investigated the basis of this failure.

Extracting shared structure across our experiences allows us to generalize our knowledge to novel contexts. How do different brain states influence this ability to generalize? Using a novel category learning paradigm, we assess the effect of both sleep and time of day on generalization that depends on the flexible integration of recent information. Counter to our expectations, we found no evidence that this form of generalization is better after a night of sleep relative to a day awake. Instead, we observed an effect of time of day, with better generalization in the morning than the evening. This effect also manifested as increased false memory for generalized information. In a nap experiment, we found that generalization did not benefit from having slept recently, suggesting a role for circadian rhythms apart from sleep. We found, however, that this time of day effect appears to be sensitive to category structure and to task engagement more generally. We propose that a state of lowered inhibition in the morning may facilitate spreading activation between otherwise separate memories, promoting this form of generalization.

Cooperative interactions between the amygdala and hippocampus are widely regarded as critical for overnight emotional processing of waking experiences, but direct support from the human brain for such a dialog is absent. Using intracranial recordings in four pre-surgical epilepsy patients (two male, two female), we discovered ripples within human amygdala during non-rapid eye movement (NREM) sleep. Like hippocampal ripples, amygdala ripples are strongly associated with sharp waves, are linked to sleep spindles, and tend to co-occur with their hippocampal counterparts. Moreover, sharp waves and ripples are temporally linked across the two brain structures, with amygdala ripples occurring during hippocampal sharp waves and vice versa . Combined with further evidence of interregional sharp wave and spindle synchronization, these findings offer a potential physiological substrate for the NREM-sleep-dependent consolidation and regulation of emotional experiences.

During sleep, new memories undergo a gradual transfer from hippocampal (HPC) to neocortical (NC) sites. Precisely timed neural oscillations are thought to mediate this sleep-dependent memory consolidation, but exactly how sleep oscillations instantiate the HPC-NC dialog remains elusive. Employing overnight invasive electroencephalography in ten neurosurgical patients, we identified three broad classes of phase-based communication between HPC and lateral temporal NC. First, we observed interregional phase synchrony for non-rapid eye movement (NREM) spindles, and N2 and rapid eye movement (REM) theta activity. Second, we found asymmetrical N3 cross-frequency phase-amplitude coupling between HPC slow oscillations (SOs) and NC activity spanning the delta to high-gamma/ripple bands, but not in the opposite direction. Lastly, N2 theta and NREM spindle synchrony were themselves modulated by HPC SOs. These forms of interregional communication emphasize the role of HPC SOs in the HPC-NC dialog, and may offer a physiological basis for the sleep-dependent reorganization of mnemonic content.

Sleep spindles, defining oscillations of stage 2 non-rapid eye movement sleep (N2), mediate memory consolidation. Schizophrenia is characterized by reduced spindle activity that correlates with impaired sleep-dependent memory consolidation. In a small, randomized, placebo-controlled pilot study of schizophrenia, eszopiclone (Lunesta®), a nonbenzodiazepine sedative hypnotic, increased N2 spindle density (number/minute) but did not significantly improve memory. This larger double-blind crossover study that included healthy controls investigated whether eszopiclone could both increase N2 spindle density and improve memory. Twenty-six medicated schizophrenia outpatients and 29 healthy controls were randomly assigned to have a placebo or eszopiclone (3 mg) sleep visit first. Each visit involved two consecutive nights of high density polysomnography with training on the Motor Sequence Task (MST) on the second night and testing the following morning. Patients showed a widespread reduction of spindle density and, in both groups, eszopiclone increased spindle density but failed to enhance sleep-dependent procedural memory consolidation. Follow-up analyses revealed that eszopiclone also affected cortical slow oscillations: it decreased their amplitude, increased their duration, and rendered their phase locking with spindles more variable. Regardless of group or visit, the density of coupled spindle-slow oscillation events predicted memory consolidation significantly better than spindle density alone, suggesting that they are a better biomarker of memory consolidation. In conclusion, sleep oscillations are promising targets for improving memory consolidation in schizophrenia, but enhancing spindles is not enough. Effective therapies also need to preserve or enhance cortical slow oscillations and their coordination with thalamic spindles, an interregional dialog that is necessary for sleep-dependent memory consolidation.

Recent years have witnessed a surge in human sleep electroencephalography (EEG) studies, employing increasingly sophisticated analysis strategies to relate electrophysiological activity to cognition and disease. However, properly calculating and interpreting metrics used in contemporary sleep EEG requires attention to numerous theoretical and practical signal-processing details that are not always obvious. Moreover, the vast number of outcome measures that can be derived from a single dataset inflates the risk of false positives and threatens replicability. We review several methodological issues related to 1) spectral analysis, 2) montage choice, 3) extraction of phase and amplitude information, 4) surrogate construction, and 5) minimizing false positives, illustrating both the impact of methodological choices on downstream results, and the importance of checking processing steps through visualization and simplified examples. By presenting these issues in non-mathematical form, with sleep-specific examples, and with code implementation, this paper aims to instill a deeper appreciation of methodological considerations in novice and non-technical audiences, and thereby help improve the quality of future sleep EEG studies.