Brain Activation Time-Locked to Sleep Spindles Associated With Human Cognitive Abilities.
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ABSTRACT:
SUBMITTER: Fang Z
PROVIDER: S-EPMC6372948 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Sleep spindles are related to sleep-dependent memory consolidation and general cognitive abilities. However, they undergo drastic maturational changes during adolescence. Here we used a longitudinal approach (across 7 years) to explore whether developmental changes in sleep spindle density can explain individual differences in sleep-dependent memory consolidation and general cognitive abilities. Ambulatory polysomnography was recorded during four nights in 34 healthy subjects (24 female) with two nights (baseline and experimental) at initial recording (age range 8-11 years) and two nights at follow-up recording (age range 14-18 years). For declarative learning, participants encoded word pairs with a subsequent recall before and after sleep. General cognitive abilities were measured by the Wechsler Intelligence Scale. Higher slow (11-13 Hz) than fast (13-15 Hz) spindle density at frontal, central, and parietal sites during initial recordings, followed by a shift to higher fast than slow spindle density at central and parietal sites during follow-up recordings, suggest that mature spindle topography develops throughout adolescence. Fast spindle density increases from baseline to experimental night were positively related to sleep-dependent memory consolidation. In addition, we found that the development of fast spindles predicted the improvement in memory consolidation across the two longitudinal measurements, a finding that underlines a crucial role for mature fast spindles for sleep-dependent memory consolidation. Furthermore, slow spindle changes across adolescence were related to general cognitive abilities, a relationship that could indicate the maturation of frontal networks relevant for efficient cognitive processing. A video abstract of this article can be viewed at: https://www.youtube.com/watch?v=7NXJzm8HbIw and https://www.youtube.com/watch?v=iuMQY1OIJ0s.
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Project description:Current theories of human neural development emphasize the posterior-to-anterior pattern of brain maturation. However, this scenario leaves out significant brain areas not directly involved with sensory input and behavioral control. Suggesting the relevance of cortical activity unrelated to sensory stimulation, such as sleep, we investigated adolescent transformations in the topography of sleep spindles. Sleep spindles are known to be involved in neural plasticity and in adults have a bimodal topography: slow spindles are frontally dominant, while fast spindles have a parietal/precuneal origin. The late functional segregation of the precuneus from the frontoparietal network during adolescence suggests that spindle topography might approach the adult state relatively late in development, and it may not be a result of the posterior-to-anterior maturational pattern. We analyzed the topographical distribution of spindle parameters in HD-EEG polysomnographic sleep recordings of adolescents and found that slow spindle duration maxima traveled from central to anterior brain regions, while fast spindle density, amplitude and frequency peaks traveled from central to more posterior brain regions. These results provide evidence for the gradual posteriorization of the anatomical localization of fast sleep spindles during adolescence and indicate the existence of an anterior-to-posterior pattern of human brain maturation.
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Project description:The most prominent EEG events in sleep are slow waves, reflecting a slow (<1 Hz) oscillation between up and down states in cortical neurons. It is unknown whether slow oscillations are synchronous across the majority or the minority of brain regions--are they a global or local phenomenon? To examine this, we recorded simultaneously scalp EEG, intracerebral EEG, and unit firing in multiple brain regions of neurosurgical patients. We find that most sleep slow waves and the underlying active and inactive neuronal states occur locally. Thus, especially in late sleep, some regions can be active while others are silent. We also find that slow waves can propagate, usually from medial prefrontal cortex to the medial temporal lobe and hippocampus. Sleep spindles, the other hallmark of NREM sleep EEG, are likewise predominantly local. Thus, intracerebral communication during sleep is constrained because slow and spindle oscillations often occur out-of-phase in different brain regions.
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Project description:This study investigated how proactive and reactive cognitive control processing in the brain was associated with habitual sleep health. BOLD fMRI data were acquired from 81 healthy adults with normal sleep (41 females, age 20.96-39.58 years) during a test of cognitive control (Not-X-CPT). Sleep health was assessed in the week before MRI scanning, using both objective (actigraphy) and self-report measures. Multiple measures indicating poorer sleep health-including later/more variable sleep timing, later chronotype preference, more insomnia symptoms, and lower sleep efficiency-were associated with stronger and more widespread BOLD activations in fronto-parietal and subcortical brain regions during cognitive control processing (adjusted for age, sex, education, and fMRI task performance). Most associations were found for reactive cognitive control activation, indicating that poorer sleep health is linked to a "hyper-reactive" brain state. Analysis of time-on-task effects showed that, with longer time on task, poorer sleep health was predominantly associated with increased proactive cognitive control activation, indicating recruitment of additional neural resources over time. Finally, shorter objective sleep duration was associated with lower BOLD activation with time on task and poorer task performance. In conclusion, even in "normal sleepers," relatively poorer sleep health is associated with altered cognitive control processing, possibly reflecting compensatory mechanisms and/or inefficient neural processing.
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Project description:Sleep spindles are approximately 1 s bursts of 10-16 Hz activity that occur during stage 2 sleep. Spindles are highly synchronous across the cortex and thalamus in animals, and across the scalp in humans, implying correspondingly widespread and synchronized cortical generators. However, prior studies have noted occasional dissociations of the magnetoencephalogram (MEG) from the EEG during spindles, although detailed studies of this phenomenon have been lacking. We systematically compared high-density MEG and EEG recordings during naturally occurring spindles in healthy humans. As expected, EEG was highly coherent across the scalp, with consistent topography across spindles. In contrast, the simultaneously recorded MEG was not synchronous, but varied strongly in amplitude and phase across locations and spindles. Overall, average coherence between pairs of EEG sensors was approximately 0.7, whereas MEG coherence was approximately 0.3 during spindles. Whereas 2 principle components explained approximately 50% of EEG spindle variance, >15 were required for MEG. Each PCA component for MEG typically involved several widely distributed locations, which were relatively coherent with each other. These results show that, in contrast to current models based on animal experiments, multiple asynchronous neural generators are active during normal human sleep spindles and are visible to MEG. It is possible that these multiple sources may overlap sufficiently in different EEG sensors to appear synchronous. Alternatively, EEG recordings may reflect diffusely distributed synchronous generators that are less visible to MEG. An intriguing possibility is that MEG preferentially records from the focal core thalamocortical system during spindles, and EEG from the distributed matrix system.
| S-EPMC2904206 | biostudies-literature
Project description:EEG sleep spindles are short (0.5-2.0 s) bursts of activity in the 11-16 Hz band occurring during non-rapid eye movement (NREM) sleep. This sporadic activity is thought to play a role in memory consolidation, brain plasticity, and protection of sleep integrity. Many automatic detectors have been proposed to assist or replace experts for sleep spindle scoring. However, these algorithms usually detect too many events making it difficult to achieve a good tradeoff between sensitivity (Se) and false detection rate (FDr). In this work, we propose a semi-automatic detector comprising a sensitivity phase based on well-established criteria followed by a specificity phase using spatial and spectral criteria. In the sensitivity phase, selected events are those which amplitude in the 10-16 Hz band and spectral ratio characteristics both reject a null hypothesis (p < 0.1) stating that the considered event is not a spindle. This null hypothesis is constructed from events occurring during rapid eye movement (REM) sleep epochs. In the specificity phase, a hierarchical clustering of the selected candidates is done based on events' frequency and spatial position along the anterior-posterior axis. Only events from the classes grouping most (at least 80%) spindles scored by an expert are kept. We obtain Se = 93.2% and FDr = 93.0% in the first phase and Se = 85.4% and FDr = 86.2% in the second phase. For these two phases, Matthew's correlation coefficients are respectively 0.228 and 0.324. Results suggest that spindles are defined by specific spatio-spectral properties and that automatic detection methods can be improved by considering these features.
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Project description:The brain is the body's largest energy consumer, even in the absence of demanding tasks. Electrophysiologists report on-going neuronal firing during stimulation or task in regions beyond those of primary relationship to the perturbation. Although the biological origin of consciousness remains elusive, it is argued that it emerges from complex, continuous whole-brain neuronal collaboration. Despite converging evidence suggesting the whole brain is continuously working and adapting to anticipate and actuate in response to the environment, over the last 20 y, task-based functional MRI (fMRI) have emphasized a localizationist view of brain function, with fMRI showing only a handful of activated regions in response to task/stimulation. Here, we challenge that view with evidence that under optimal noise conditions, fMRI activations extend well beyond areas of primary relationship to the task; and blood-oxygen level-dependent signal changes correlated with task-timing appear in over 95% of the brain for a simple visual stimulation plus attention control task. Moreover, we show that response shape varies substantially across regions, and that whole-brain parcellations based on those differences produce distributed clusters that are anatomically and functionally meaningful, symmetrical across hemispheres, and reproducible across subjects. These findings highlight the exquisite detail lying in fMRI signals beyond what is normally examined, and emphasize both the pervasiveness of false negatives, and how the sparseness of fMRI maps is not a result of localized brain function, but a consequence of high noise and overly strict predictive response models.
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Project description:The learning of a motor task is known to be improved by sleep, and sleep spindles are thought to facilitate this learning by enabling synaptic plasticity. In this study subjects implanted with electrocorticography (ECoG) arrays for long-term epilepsy monitoring were trained to control a cursor on a computer screen by modulating either the high-gamma or mu/beta power at a single electrode located over the motor or premotor area. In all trained subjects, spindle density in posttraining sleep was increased with respect to pretraining sleep in a remarkably spatially specific manner. The pattern of increased spindle activity reflects the functionally specific regions that were involved in learning of a highly novel and salient task during wakefulness, supporting the idea that sleep spindles are involved in learning to use a motor-based brain-computer interface device.
| S-EPMC3494921 | biostudies-literature
Project description:In humans, some evidence suggests that there are two different types of spindles during sleep, which differ by their scalp topography and possibly some aspects of their regulation. To test for the existence of two different spindle types, we characterized the activity associated with slow (11-13 Hz) and fast (13-15 Hz) spindles, identified as discrete events during non-rapid eye movement sleep, in non-sleep-deprived human volunteers, using simultaneous electroencephalography and functional MRI. An activation pattern common to both spindle types involved the thalami, paralimbic areas (anterior cingulate and insular cortices), and superior temporal gyri. No thalamic difference was detected in the direct comparison between slow and fast spindles although some thalamic areas were preferentially activated in relation to either spindle type. Beyond the common activation pattern, the increases in cortical activity differed significantly between the two spindle types. Slow spindles were associated with increased activity in the superior frontal gyrus. In contrast, fast spindles recruited a set of cortical regions involved in sensorimotor processing, as well as the mesial frontal cortex and hippocampus. The recruitment of partially segregated cortical networks for slow and fast spindles further supports the existence of two spindle types during human non-rapid eye movement sleep, with potentially different functional significance.
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Project description:ObjectiveCognitive comorbidities are common in epilepsy; however, symptomatic treatment is currently the only available effective therapy. Sleep, cognition, and epilepsy are closely associated. Therefore, many studies on epilepsy and cognition have focused on sleep structures, such as sleep spindles, which are considered windows to understanding the sleeping brain. This study aimed to investigate the relationship between sleep spindles and the severity of cognitive impairment in adult epilepsy.MethodsFifty-seven adults with epilepsy underwent overnight sleep electroencephalogram recordings and cognitive testing. Slow (9-12 Hz) and fast (12-15 Hz) spindle characteristics during N2 sleep were calculated using a convolutional neural network-based sleep staging system and automatic spindle detection algorithm. Repeated-measures analysis of variance was used to analyze differences in fast and slow spindle densities among subgroups of patients based on cognitive impairment severity.ResultsA significant between-group effect was observed for both slow and fast spindle densities. Multiple comparisons showed that slow and fast spindle densities of the severe cognitive impairment subgroup were lower than those of the noncognitive impairment subgroup (P < 0.05). Simple-effect analysis revealed differences in slow spindle density distributed among the EEG channels Fp1, Fp2, F3, C3, P4, O1, O2, F8, T4, T5, T6, Fz, and Cz (P < 0.05). Differences in fast spindle density were distributed among the channels Fp1, Fp2, F3, C3, O1, O2, F7, F8, T4, T5, T6, and Fz (P < 0.05).SignificanceSignificant differences in topographical distribution of fast and slow spindle densities were observed at the scalp level among patients with different cognitive statuses. Compared with patients with no cognitive impairment, those with severe cognitive impairment had lower slow and fast spindle densities over multiple scalp regions during N2 sleep. This study provides a reference for objective assessment of cognitive dysfunction in epilepsy patients.
| S-EPMC10472368 | biostudies-literature