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Spike-and-wave discharges (SWDs) and sleep spindles are characteristic electroencephalographic (EEG) hallmarks of absence seizures and nonrapid eye movement sleep, respectively. They are commonly generated by the cortico-thalamo-cortical network including the thalamic reticular nucleus (TRN). It has been reported that SWD development is accompanied by a decrease in sleep spindle density in absence seizure patients and animal models. However, whether the decrease in sleep spindle density precedes, coincides with, or follows, the SWD development remains unknown. To clarify this, we exploited Pvalb-tetracycline transactivator (tTA)::tetO-ArchT (PV-ArchT) double-transgenic mouse, which can induce an absence seizure phenotype in a time-controllable manner by expressing ArchT in PV neurons of the TRN. In these mice, EEG recordings demonstrated that a decrease in sleep spindle density occurred 1 week before the onset of typical SWDs, with the expression of ArchT. To confirm such temporal relationship observed in these genetic model mice, we used a gamma-butyrolactone (GBL) pharmacological model of SWDs. Prior to GBL administration, we administered caffeine to wild-type mice for 3 consecutive days to induce a decrease in sleep spindle density. We then administered low-dose GBL, which cannot induce SWDs in normally conditioned mice but led to the occurrence of SWDs in caffeine-conditioned mice. These findings indicate a temporal relationship in which the decrease in sleep spindle density consistently precedes SWD development. Furthermore, the decrease in sleep spindle activity may have a role in facilitating the development of SWDs. Our findings suggest that sleep spindle reductions could serve as early indicators of seizure susceptibility.

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Purpose of Review: Neurodevelopmental disorders are a group of conditions that affect the development and function of the nervous system, typically arising early in life. These disorders can have various genetic, environmental, and/or neural underpinnings, which can impact the thalamocortical system. Sleep spindles, brief bursts of oscillatory activity that occur during NREM sleep, provide a unique in vivo measure of the thalamocortical system. In this manuscript, we review the development of the thalamocortical system and sleep spindles in rodent models and humans. We then utilize this as a foundation to discuss alterations in sleep spindle activity in four of the most pervasive neurodevelopmental disorders-intellectual disability, attention deficit hyperactivity disorder, autism, and schizophrenia. Recent Findings: Recent work in humans has shown alterations in sleep spindles across several neurodevelopmental disorders. Simultaneously, rodent models have elucidated the mechanisms which may underlie these deficits in spindle activity. This review merges recent findings from these two separate lines of research to draw conclusions about the pathogenesis of neurodevelopmental disorders. Summary: We speculate that deficits in the thalamocortical system associated with neurodevelopmental disorders are exquisitely reflected in sleep spindle activity. We propose that sleep spindles may represent a promising biomarker for drug discovery, risk stratification, and treatment monitoring.

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OBJECTIVES: To evaluate the evolution of interhemispheric coherences (ICo) in background and spindle frequency bands during childhood and use it to identify individuals with corpus callosum dysgenesis (CCd). METHODS: A monocentric cohort of children aged from 0.25 to 15 years old, consisting of 13 children with CCd and 164 without, was analyzed. The ICo of background activity (ICOBckgrdA), sleep spindles (ICOspindles), and their sum (sICO) were calculated. The impact of age, gender, and CC status on the ICo was evaluated, and the sICO was used to discriminate children with or without CCd. RESULTS: ICOBckgrdA, ICOspindles and sICO increased significantly with age without any effect of gender (p < 10-4), in both groups. The regression equations of the different ICo were stronger, with adjusted R2 values of 0.54, 0.35, and 0.57, respectively. The ICo was lower in children with CCd compared to those without CCd (p < 10-4 for all comparisons). The area under the precision recall curves for predicting CCd using sICO was 0.992 with 98.9 % sensitivity and 87.5 % specificity. DISCUSSION: ICo of spindles and background activity evolve in parallel to brain maturation and depends on the integrity of the corpus callosum. sICO could be an effective diagnostic biomarker for screening children with interhemispheric dysfunction.

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Emotionally salient components of memory are preferentially remembered at the expense of accompanying neutral information. This emotional memory trade-off is enhanced over time, and possibly sleep, through a process of memory consolidation. Sleep is believed to benefit memory through a process of reactivation during nonrapid eye movement sleep (NREM). Here, targeted memory reactivation (TMR) was used to manipulate the reactivation of negative and neutral memories during NREM sleep. Thirty-one male and female participants encoded composite scenes containing either a negative or neutral object superimposed on an always neutral background. During NREM sleep, sounds associated with the scene object were replayed, and memory for object and background components was tested the following morning. We found that TMR during NREM sleep improved memory for neutral, but not negative scene objects. This effect was associated with sleep spindle activity, with a larger spindle response following TMR cues predicting TMR effectiveness for neutral items only. These findings therefore do not suggest a role of NREM memory reactivation in enhancing the emotional memory trade-off across a 12 h period but do align with growing evidence of spindle-mediated memory reactivation in service of neutral declarative memory.

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Background: Sleep plays a critical role in human physiological and psychological health, and electroencephalography (EEG), an effective sleep-monitoring method, is of great importance in revealing sleep characteristics and aiding the diagnosis of sleep disorders. Sleep spindles, which are a typical phenomenon in EEG, hold importance in sleep science. Methods: This paper proposes a novel convolutional neural network (CNN) model to classify sleep spindles. Transfer learning is employed to apply the model trained on the sleep spindles of healthy subjects to those of subjects with insomnia for classification. To analyze the effect of transfer learning, we discuss the classification results of both partially and fully transferred convolutional layers. Results: The classification accuracy for the healthy and insomnia subjects' spindles were 93.68% and 92.77%, respectively. During transfer learning, when transferring all convolutional layers, the classification accuracy for the insomnia subjects' spindles was 91.41% and transferring only the first four convolutional layers achieved a classification result of 92.80%. The experimental results demonstrate that the proposed CNN model can effectively classify sleep spindles. Furthermore, the features learned from the data of the normal subjects can be effectively applied to the data for subjects with insomnia, yielding desirable outcomes. Discussion: These outcomes underscore the efficacy of both the collected dataset and the proposed CNN model. The proposed model exhibits potential as a rapid and effective means to diagnose and treat sleep disorders, thereby improving the speed and quality of patient care.

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STUDY OBJECTIVES: Post-hoc analysis to evaluate the effect of daridorexant on sleep architecture in people with insomnia, focusing on features associated with hyperarousal. METHODS: We studied sleep architecture in adults with chronic insomnia disorder from two randomized Phase 3 clinical studies (Clinicaltrials.gov: NCT03545191 and NCT03575104) investigating 3 months of daridorexant treatment (placebo, daridorexant 25 mg, daridorexant 50 mg). We analyzed sleep-wake transition probabilities, EEG spectra and sleep spindle properties including density, dispersion, and slow oscillation phase coupling. The Wake EEG Similarity Index (WESI) was determined using a machine learning algorithm analyzing the spectral profile of the EEG. RESULTS: At Month 3, daridorexant 50 mg decreased Wake-to-Wake transition probabilities (P<0.05) and increased the probability of transitions from Wake-to-N1 (P<0.05), N2 (P<0.05), and REM sleep (P<0.05), as well as from N1-to-N2 (P<0.05) compared to baseline and placebo. Daridorexant 50 mg decreased relative beta power during Wake (P=0.011) and N1 (P<0.001) compared to baseline and placebo. During Wake, relative alpha power decreased (P<0.001) and relative delta power increased (P<0.001) compared to placebo. Daridorexant did not alter EEG spectra bands in N2, N3, and REM stages or in sleep spindle activity. Daridorexant decreased the WESI score during Wake compared to baseline (P=0.004). Effects with 50 mg were consistent between Month 1 and Month 3 and less pronounced with 25 mg. CONCLUSION: Daridorexant reduced EEG features associated with hyperarousal as indicated by reduced Wake-to-Wake transition probabilities and enhanced spectral features associated with drowsiness and sleep during Wake and N1.

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INTRODUCTION: Sleep spindles play a key role in sleep-mediated cognitive processes. Cognitive functions are well-known to be affected in obstructive sleep apnea (OSA). Here, we analyzed attention and executive functions in patients with OSA and investigated the relationship between sleep spindles and cognitive abilities. METHODS: Sixty patients with OSA (18-65 years, 19 females and 41 males) and a control group (n = 41) including age-and sex-matched healthy individuals were consecutively and prospectively enrolled. All participants had a full-night polysomnography, and sleep spindles were analyzed using a semi-automated program. For the evaluation of short-term memory, attention and executive functions, Stroop test, forward and backward digit span tests were applied to all participants upon awakening following polysomnography. RESULTS: Scores of forward and backward digit span and Stroop tests were worse in OSA patients in compared to those in controls. Mean density of sleep spindles was decreased in OSA patients than those in controls (p = 0.044). A positive correlation was found between fast sleep spindle frequency and forward digit span (r = 2.222; p = 0.038) and backward digit span test scores (r = 2,157; p = 0.042) in OSA patients. In patients with moderate to severe OSA, sleep spindle density was positively correlated with forward (r = 2.323, p = 0.029) and backward (r = 2.500, p = 0.016) DSTs, and the duration of sleep spindles had positive correlation with backward DST (r = 2.452, p = 0.010). CONCLUSION: Our findings demonstrated that the disturbances in sleep spindle characteristics in OSA are associated with the cognitive impairments in attention, short-term memory, and executive functions, especially in patients with moderate to severe OSA.

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The influence of brain atrophy on sleep microstructure in Spinocerebellar Ataxias (SCAs) has not been extensively explored limiting the use of these sleep traits as surrogate biomarkers of neurodegeneration and clinical phenotype. The objective of the study is to explore the relationship between sleep microstructure and brain atrophy in SCA2 and its role in the clinical phenotype. Fourteen SCA2 mutation carriers (7 pre-manifest and 7 manifest subjects) underwent polysomnographic, structural MRI, and clinical assessments. Particularly, markers of REM and non-REM sleep microstructure, measures of cerebellar and brainstem atrophy, and clinical scores were analyzed through correlation and mediation analyses. The sleep spindle activity exhibited a negative correlation with the number of trials required to complete the verbal memory test (VMT), and a positive correlation with the cerebellar volume, but the significance of the latter correlation did not survive multiple testing corrections. However, the causal mediation analyses unveiled that sleep spindle activity significantly mediates the association between cerebellar atrophy and VMT performance. Regarding REM sleep, both phasic EMG activity and REM sleep without atonia exhibited significant associations with pontine atrophy and disease severity measures. However, they did not demonstrate a causal mediation effect between the atrophy measures and disease severity. Our study provides evidence about the association of the pontocerebellar atrophy with sleep microstructure in SCA2 offering insights into the cerebellar involvement in cognition via the control of the sleep spindle activity. Therefore, our findings may help to understand the disease pathogenesis and to better characterize sleep microstructure parameters as disease biomarkers.Clinical trial registration number (TRN): No applicable.

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STUDY OBJECTIVES: Sleep is required for successful memory consolidation. Sleep spindles, bursts of oscillatory activity occurring during non-rapid eye movement sleep, are known to be crucial for this process and, recently, it has been proposed that the temporal organization of spindles into clusters might additionally play a role in memory consolidation. In Parkinson's disease, spindle activity is reduced, and this reduction has been found to be predictive of cognitive decline. However, it remains unknown whether alterations in sleep spindles in Parkinson's disease are predictive of sleep-dependent cognitive processes such as memory consolidation, leaving open questions about the possible mechanisms linking sleep and a more general cognitive state in Parkinson's patients. METHODS: The current study sought to fill this gap by recording overnight polysomnography and measuring overnight declarative memory consolidation in a sample of 35 patients with Parkinson's. Memory consolidation was measured using a verbal paired-associates task administered before and after the night of recorded sleep. RESULTS: We found that lower sleep spindle density at frontal leads during non-rapid eye movement stage 3 was associated with worse overnight declarative memory consolidation. We also found that patients who showed less temporal clustering of spindles exhibited worse declarative memory consolidation. CONCLUSIONS: These results suggest alterations to sleep spindles, which are known to be a consequence of Parkinson's disease, might represent a mechanism by which poor sleep leads to worse cognitive function in Parkinson's patients. CITATION: Lahlou S, Kaminska M, Doyon J, Carrier J, Sharp M. Sleep spindle density and temporal clustering are associated with sleep-dependent memory consolidation in Parkinson's disease. J Clin Sleep Med. 2024;20(7):1153-1162.

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Brain oscillations of non-rapid eye movement sleep, including slow oscillations (SO, 0.5-1.5 Hz) and spindles (10-16 Hz), mirror underlying brain maturation across development and are associated with cognition. Hence, age-associated emergence and changes in the electrophysiological properties of these rhythms can lend insight into cortical development, specifically in comparisons between pediatric populations and typically developing peers. We previously evaluated age-associated changes in SOs in male patients with Duchenne muscular dystrophy (DMD), finding a significant age-related decline between 4 and 18 years. While primarily a muscle disorder, male patients with DMD can also have sleep, cognitive, and cortical abnormalities, thought to be driven by altered dystrophin expression in the brain. In this follow-up study, we characterized the age-associated changes in sleep spindles. We found that age-dependent spindle characteristics in patients with DMD, including density, frequency, amplitude, and duration, were consistent with age-associated trends reported in the literature for typically developing controls. Combined with our prior finding of age-associated decline in SOs, our results suggest that SOs, but not spindles, are a candidate intervention target to enhance sleep in patients with DMD.

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We propose a two-step procedure for atomic decomposition of multichannel EEGs, based upon multivariate matching pursuit and dipolar inverse solution, from which atoms representing relevant EEG structures are selected according to prior knowledge. We detect sleep spindles in 147 polysomnographic recordings from the Montreal Archive of Sleep Studies. Detection is compared with human scorers and two state-of-the-art algorithms, which find only about a third of the structures conforming to the definition of sleep spindles and detected by the proposed method. We provide arguments supporting the thesis that the previously undetectable sleep spindles share the same properties as those marked by human experts and previously applied methods, and were previously omitted only because of unfavorable local signal-to-noise ratios, obscuring their visibility to both human experts and algorithms replicating their markings. All detected EEG structures are automatically parametrized by their time and frequency centers, width duration, phase, and spatial location of an equivalent dipolar source within the brain. It allowed us, for the first time, to estimate the spatial gradient of sleep spindles frequencies, which not only confirmed quantitatively the well-known prevalence of higher frequencies in posterior regions, but also revealed a significant gradient in the sagittal plane. The software used in this study is freely available.

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The post-COVID-19 condition is defined by the World Health Organization as the persistence of symptoms or development of new symptoms three months after the initial SARS-CoV-2 infection, lasting for at least two months without a clear explanation. Neuropsychiatric disorders associated with this condition include asthenia, memory and concentration problems, and sleep disturbances. Our study aims to investigate sleep patterns following SARS-CoV-2 infection using EEG findings and a sleep quality questionnaire completed by parents (Sleep Disturbance Scale for Children-SDSC). Notably, our investigation is based on a convenience sample. The patients in our sample, aged 1 to 14 years, are not currently taking any medications; rather, they are undergoing follow-up assessments at the Child Neuropsychiatry department of the University Hospital of Messina for neurodevelopmental evaluations. Specifically, we are analyzing amplitude and power spectrum data in the first five minutes of NREM2 sleep, calculated from EEG recordings obtained via bipolar leads within three months after the onset of the disease. These results will be compared with controls performed on the same subjects in the six months preceding the infection. The focus of the study was sleep spindles, which are generated by the thalamocortical systems and play a role in sleep modulation, memory, and learning. Preliminary analysis suggests a predominant increase in the slow component of the spindles in the right-frontal lead.

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Sleep plays a crucial role in brain development, sensory information processing, and consolidation. Sleep spindles are markers of these mechanisms as they mirror the activity of the thalamocortical circuits. Spindles can be subdivided into two groups, slow (10-13 Hz) and fast (13-16 Hz), which are each associated with different functions. Specifically, fast spindles oscillate in the high-sigma band and are associated with sensorimotor processing, which is affected by visual deprivation. However, how blindness influences spindle development has not yet been investigated. We recorded nap video-EEG of 50 blind/severely visually impaired (BSI) and 64 sighted children aged 5 months to 6 years old. We considered aspects of both macro- and micro-structural spindles. The BSI children lacked the evolution of developmental spindles within the central area. Specifically, young BSI children presented low central high-sigma and high-beta (25-30 Hz) event-related spectral perturbation and showed no signs of maturational decrease. High-sigma and high-beta activity in the BSI group correlated with clinical indices predicting perceptual and motor disorders. Our findings suggest that fast spindles are pivotal biomarkers for identifying an early developmental deviation in BSI children. These findings are critical for initial therapeutic intervention.

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Closed-loop auditory stimulation (CLAS) is a brain modulation technique in which sounds are timed to enhance or disrupt endogenous neurophysiological events. CLAS of slow oscillation up-states in sleep is becoming a popular tool to study and enhance sleep's functions, as it increases slow oscillations, evokes sleep spindles and enhances memory consolidation of certain tasks. However, few studies have examined the specific neurophysiological mechanisms involved in CLAS, in part because of practical limitations to available tools. To evaluate evidence for possible models of how sound stimulation during brain up-states alters brain activity, we simultaneously recorded electro- and magnetoencephalography in human participants who received auditory stimulation across sleep stages. We conducted a series of analyses that test different models of pathways through which CLAS of slow oscillations may affect widespread neural activity that have been suggested in literature, using spatial information, timing and phase relationships in the source-localized magnetoencephalography data. The results suggest that auditory information reaches ventral frontal lobe areas via non-lemniscal pathways. From there, a slow oscillation is created and propagated. We demonstrate that while the state of excitability of tissue in auditory cortex and frontal ventral regions shows some synchrony with the electroencephalography (EEG)-recorded up-states that are commonly used for CLAS, it is the state of ventral frontal regions that is most critical for slow oscillation generation. Our findings advance models of how CLAS leads to enhancement of slow oscillations, sleep spindles and associated cognitive benefits and offer insight into how the effectiveness of brain stimulation techniques can be improved.

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INTRODUCTION: Literature lacks studies investigating the cortical generation of sleep spindles in drug-resistant epilepsy (DRE) and how they evolve after resection of the epileptogenic zone (EZ). Here, we examined sleep EEGs of children with focal DRE who became seizure-free after focal epilepsy surgery, and aimed to investigate the changes in the spindle generation before and after the surgery using low-density scalp EEG and electrical source imaging (ESI). METHODS: We analyzed N2-sleep EEGs from 19 children with DRE before and after surgery. We identified slow (8-12 Hz) and fast spindles (13-16 Hz), computed their spectral features and cortical generators through ESI and computed their distance from the EZ and irritative zone (IZ). We performed two-way ANOVA testing the effect of spindle type (slow vs. fast) and surgical phase (pre-surgery vs. post-surgery) on each feature. RESULTS: Power, frequency and cortical activation of slow spindles increased after surgery (p < 0.005), while this was not seen for fast spindles. Before surgery, the cortical generators of slow spindles were closer to the EZ (57.3 vs. 66.2 mm, p = 0.007) and IZ (41.3 vs. 55.5 mm, p = 0.02) than fast spindle generators. CONCLUSIONS: Our data indicate alterations in the EEG slow spindles after resective epilepsy surgery. Fast spindle generation on the contrary did not change after surgery. Although the study is limited by its retrospective nature, lack of healthy controls, and reduced cortical spatial sampling, our findings suggest a spatial relationship between the slow spindles and the epileptogenic generators.

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Objective Sleep spindles play an important role in promoting cognition.This paper discusses the influencing factors of sleep spindles and provides objective evidence for clinical intervention and regulation of spindles to improve sleep and cognition.Methods Fifty patients with poor sleep quality were monitored overnight by sleep monitoring system,and physiological parameters of sleep structure,electroencephalography power spectrum,cardiovascular and respiratory function were obtained.The correlations between the parameters,age,sex and the spindle index and characteristics(frequency,duration and amplitude)of non rapid-eye-movement sleep(NREM)Ⅱphase were calculated.In Poincare diagram,SD1 represents the positive index of parasympathetic nerve activity,and SD2 represents the negative index of sympathetic nerve activity.Pulse transit time(PTT)decline index represents vascular sympathetic stability.Results SD1(β =-0.512,P＜0.05)and PTT decline index(β =-0.271,P＜0.05)were negatively correlated with spindle index respectively,while SD2 was positively correlated with spindle index(β =0.474,P＜0.05).The sleep change index,NREMⅠ phase proportion and cortical EEG microarousal index were negatively correlated with spindle index(r =-0.316,r =-0.359,r =-0.326;all P＜0.05).Age was negatively correlated with spindle index(β =-0.422,P＜0.05).δ power of deep sleep was negatively correlated with Spindle wave amplitude(β = 0.65,P＜0.001).No correlation was found between sex and sleep spindles.Conclusions The production of sleep spindles depends on good sleep and stable autonomic nerves.It is related to cognition and reflects the strength of synaptic connections,which provides evidence for clinical intervention and regulation of sleep spindles,and also provides a new physiological indicator for evaluating cognitive and brain function.

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Sleep spindles are isolated transient surges of oscillatory neural activity present during sleep stages 2 and 3 in the nonrapid eye movement (NREM). They can indicate the mechanisms of memory consolidation and plasticity in the brain. Spindles can be identified across cortical areas and classified as either slow or fast. There are spindle transients across different frequencies and power, yet most of their functions remain a mystery. Using several electroencephalogram (EEG) databases, this study presents a new method, called the "spindles across multiple channels" (SAMC) method, for identifying and categorizing sleep spindles in EEGs during the NREM sleep. The SAMC method uses a multitapers and convolution (MT&C) approach to extract the spectral estimation of different frequencies present in sleep EEGs and graphically identify spindles across multiple channels. The characteristics of spindles, such as duration, power, and event areas, are also extracted by the SAMC method. Comparison with other state-of-the-art spindle identification methods demonstrated the superiority of the proposed method with an agreement rate, average positive predictive value, and sensitivity of over 90% for spindle classification across the three databases used in this paper. The computing cost was found to be, on average, 0.004 seconds per epoch. The proposed method can potentially improve the understanding of the behavior of spindles across the scalp and accurately identify and categories sleep spindles.

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AIM: Many biophysical and non-biophysical models have been able to reproduce the corticothalamic activities underlying different EEG sleep rhythms but none of them included the known ability of neocortical networks and single thalamic neurons to generate some of these waves intrinsically. METHODS: We built a large-scale corticothalamic model with a high fidelity in anatomical connectivity consisting of a single cortical column and first- and higher-order thalamic nuclei. The model is constrained by different neocortical excitatory and inhibitory neuronal populations eliciting slow (<1 Hz) oscillations and by thalamic neurons generating sleep waves when isolated from the neocortex. RESULTS: Our model faithfully reproduces all EEG sleep waves and the transition from a desynchronized EEG to spindles, slow (<1 Hz) oscillations, and delta waves by progressively increasing neuronal membrane hyperpolarization as it occurs in the intact brain. Moreover, our model shows that slow (<1 Hz) waves most often start in a small assembly of thalamocortical neurons though they can also originate in cortical layer 5. Moreover, the input of thalamocortical neurons increases the frequency of EEG slow (<1 Hz) waves compared to those generated by isolated cortical networks. CONCLUSION: Our simulations challenge current mechanistic understanding of the temporal dynamics of sleep wave generation and suggest testable predictions.

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Infantile Epileptic Spasms Syndrome (IESS) is an epileptic encephalopathy in childhood that affects infants under the age of two years. When spasm series occur, prognosis for cognitive outcome is poor in the majority of cases. The encephalopathy in IESS includes delayed maturation of normal sleep phenomena in the EEG, such as sleep spindles. Children with intellectual disabilities often have abnormal sleep, and children with sleep problems have difficulties learning at school. We examined whether there is evidence of prognostic value of detection of sleep spindles in the EEG of children with IESS on their future cognitive development. A systematic literature search yielded five studies touching this question. They were evaluated by two scorers independently. The lack of normal sleep patterns including lack of sleep spindles was used as a biomarker of poor cognitive outcome. Positive (PPV) and Negative (NPV) prognostic values were calculated. A summary of all five studies indicates a PPV of 82% and an NPV of 45%. Given the small amount of data, the retrospective quality of most studies, and the differences in the outcome parameters reported, it is prudent to say that currently available data do not allow us to conclude whether spindles have a specific and independent role in the cognitive prognosis of affected children. Since sleep spindles are needed for memory consolidation and demonstrate the active role of sleep for learning and memory, the hypothesis remains that their absence in the EEG may indicate an increased risk of cognitive delay, but more supporting data are needed to reach such a firm conclusion.

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Acute exercise has been shown to affect long-term memory and sleep. However, it is unclear whether exercise-induced changes in sleep architecture are associated with enhanced memory. Recently, it has been shown that exercise followed by a nap improved declarative memory. Whether these effects transfer to night sleep and other memory domains has not yet been studied. Here, we investigate the influence of exercise on nocturnal sleep architecture and associations with sleep-dependent procedural and declarative memory consolidation. Nineteen subjects (23.68 ± 3.97 years) were tested in a balanced cross-over design. In two evening sessions, participants either exercised (high-intensity interval training) or rested immediately after encoding two memory tasks: (1) a finger tapping task and (2) a paired-associate learning task. Subsequent nocturnal sleep was recorded by polysomnography. Retrieval was conducted the following morning. High-intensity interval training lead to an increased declarative memory retention (p = 0.047, d = 0.40) along with a decrease in REM sleep (p = 0.012, d = 0.75). Neither procedural memory nor NREM sleep were significantly affected. Exercise-induced changes in N2 showed a positive correlation with procedural memory retention which did not withstand multiple comparison correction. Exploratory analyses on sleep spindles and slow wave activity did not reveal significant effects. The present findings suggest an exercise-induced enhancement of declarative memory which aligns with changes in nocturnal sleep architecture. This gives additional support for the idea of a potential link between exercise-induced sleep modifications and memory formation which requires further investigation in larger scaled studies.

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