Masako Tamaki

Neural Decoding of Visual Imagery During Sleep

Reading Dreams How specific visual dream contents are represented by brain activity is unclear. Machine-learning–based analyses can decode the stimulus- and task-induced brain activity patterns that represent specific visual contents. Horikawa et al. (p. 639, published online 4 April) examined patterns of brain activity during dreaming and compared these to waking responses to visual stimuli. The findings suggest that the visual content of dreams is represented by the same neural substrate as observed during awake perception. Machine-learning models can predict specific visual dream contents from brain activity measurement alone. Visual imagery during sleep has long been a topic of persistent speculation, but its private nature has hampered objective analysis. Here we present a neural decoding approach in which machine-learning models predict the contents of visual imagery during the sleep-onset period, given measured brain activity, by discovering links between human functional magnetic resonance imaging patterns and verbal reports with the assistance of lexical and image databases. Decoding models trained on stimulus-induced brain activity in visual cortical areas showed accurate classification, detection, and identification of contents. Our findings demonstrate that specific visual experience during sleep is represented by brain activity patterns shared by stimulus perception, providing a means to uncover subjective contents of dreaming using objective neural measurement.

Activation of fast sleep spindles at the premotor cortex and parietal areas contributes to motor learning: A study using sLORETA

OBJECTIVE The present study examined whether slow and/or fast sleep spindles are related to visuomotor learning, by examining the densities of current sleep spindle activities. METHODS Participants completed a visuomotor task before and after sleep on the learning night. This task was not performed on the non-learning night. Standard polysomnographic recordings were made. After the amplitudes of slow and fast spindles were calculated, sLORETA was used to localize the source of slow and fast spindles and to investigate the relationship between spindle activity and motor learning. RESULTS Fast spindle amplitude was significantly larger on the learning than on the non-learning nights, particularly at the left frontal area. sLORETA revealed that fast spindle activities in the left frontal and left parietal areas were enhanced when a new visuomotor skill was learned. There were no significant learning-dependent changes in slow spindle activity. CONCLUSIONS Fast spindle activity increases in cortical areas that are involved in learning a new visuomotor skill. The thalamocortical network that underlies the generation of fast spindles may contribute to the synaptic plasticity that occurs during sleep. SIGNIFICANCE Activity of fast sleep spindles is a possible biomarker of memory deficits.

Night Watch in One Brain Hemisphere during Sleep Associated with the First-Night Effect in Humans

We often experience troubled sleep in a novel environment [1]. This is called the first-night effect (FNE) in human sleep research and has been regarded as a typical sleep disturbance [2-4]. Here, we show that the FNE is a manifestation of one hemisphere being more vigilant than the other as a night watch to monitor unfamiliar surroundings during sleep [5, 6]. Using advanced neuroimaging techniques [7, 8] as well as polysomnography, we found that the temporary sleep disturbance in the first sleep experimental session involves regional interhemispheric asymmetry of sleep depth [9]. The interhemispheric asymmetry of sleep depth associated with the FNE was found in the default-mode network (DMN) involved with spontaneous internal thoughts during wakeful rest [10, 11]. The degree of asymmetry was significantly correlated with the sleep-onset latency, which reflects the degree of difficulty of falling asleep and is a critical measure for the FNE. Furthermore, the hemisphere with reduced sleep depth showed enhanced evoked brain response to deviant external stimuli. Deviant external stimuli detected by the less-sleeping hemisphere caused more arousals and faster behavioral responses than those detected by the other hemisphere. None of these asymmetries were evident during subsequent sleep sessions. These lines of evidence are in accord with the hypothesis that troubled sleep in an unfamiliar environment is an act for survival over an unfamiliar and potentially dangerous environment by keeping one hemisphere partially more vigilant than the other hemisphere as a night watch, which wakes the sleeper up when unfamiliar external signals are detected.

Enhanced Spontaneous Oscillations in the Supplementary Motor Area Are Associated with Sleep-Dependent Offline Learning of Finger-Tapping Motor-Sequence Task

Sleep is beneficial for various types of learning and memory, including a finger-tapping motor-sequence task. However, methodological issues hinder clarification of the crucial cortical regions for sleep-dependent consolidation in motor-sequence learning. Here, to investigate the core cortical region for sleep-dependent consolidation of finger-tapping motor-sequence learning, while human subjects were asleep, we measured spontaneous cortical oscillations by magnetoencephalography together with polysomnography, and source-localized the origins of oscillations using individual anatomical brain information from MRI. First, we confirmed that performance of the task at a retest session after sleep significantly increased compared with performance at the training session before sleep. Second, spontaneous δ and fast-σ oscillations significantly increased in the supplementary motor area (SMA) during post-training compared with pretraining sleep, showing significant and high correlation with the performance increase. Third, the increased spontaneous oscillations in the SMA correlated with performance improvement were specific to slow-wave sleep. We also found that correlations of δ oscillation between the SMA and the prefrontal and between the SMA and the parietal regions tended to decrease after training. These results suggest that a core brain region for sleep-dependent consolidation of the finger-tapping motor-sequence learning resides in the SMA contralateral to the trained hand and is mediated by spontaneous δ and fast-σ oscillations, especially during slow-wave sleep. The consolidation may arise along with possible reorganization of a larger-scale cortical network that involves the SMA and cortical regions outside the motor regions, including prefrontal and parietal regions.

Overlearning hyperstabilizes a skill by rapidly making neurochemical processing inhibitory-dominant

Overlearning refers to the continued training of a skill after performance improvement has plateaued. Whether overlearning is beneficial is a question in our daily lives that has never been clearly answered. Here we report a new important role: overlearning in humans abruptly changes neurochemical processing, to hyperstabilize and protect trained perceptual learning from subsequent new learning. Usually, learning immediately after training is so unstable that it can be disrupted by subsequent new learning until after passive stabilization occurs hours later. However, overlearning so rapidly and strongly stabilizes the learning state that it not only becomes resilient against, but also disrupts, subsequent new learning. Such hyperstabilization is associated with an abrupt shift from glutamate-dominant excitatory to GABA-dominant inhibitory processing in early visual areas. Hyperstabilization contrasts with passive and slower stabilization, which is associated with a mere reduction of excitatory dominance to baseline levels. Using hyperstabilization may lead to efficient learning paradigms.

NREM Sleep Stage Transitions Control Ultradian REM Sleep Rhythm

STUDY OBJECTIVES The cyclic sequence of NREM and REM sleep, the so-called ultradian rhythm, is a highly characteristic feature of sleep. However, the mechanisms responsible for the ultradian REM sleep rhythm, particularly in humans, have not to date been fully elucidated. We hypothesize that a stage transition mechanism is involved in the determination of the ultradian REM sleep rhythm. PARTICIPANTS Ten healthy young male volunteers (AGE: 22 ± 4 years, range 19-31 years) spent 3 nights in a sleep laboratory. The first was the adaptation night, and the second was the baseline night. On the third night, the subjects received risperidone (1 mg tablet), a central serotonergic and dopaminergic antagonist, 30 min before the polysomnography recording. MEASUREMENTS AND RESULTS We measured and investigated transition probabilities between waking, REM, and NREM sleep stages (N1, N2, and N3) within the REM-onset intervals, defined as the intervals between the onset of one REM period and the beginning of the next, altered by risperidone. We also calculated the transition intensity (i.e., instantaneous transition rate) and examined the temporal pattern of transitions within the altered REM-onset intervals. We found that when the REM-onset interval was prolonged by risperidone, the probability of transitions from N2 to N3 was significantly increased within the same prolonged interval, with a significant delay and/or recurrences of the peak intensity of transitions from N2 to N3. CONCLUSIONS These results suggest that the mechanism governing NREM sleep stage transitions (from light to deep sleep) plays an important role in determining ultradian REM sleep rhythms.

The first-night effect suppresses the strength of slow-wave activity originating in the visual areas during sleep

Our visual system is plastic and adaptive in response to the stimuli and environments we experience. Although visual adaptation and plasticity have been extensively studied while participants are awake, little is known about what happens while they are asleep. It has been documented that sleep structure as measured by sleep stages using polysomnography is altered specifically in the first sleep session due to exposure to a new sleep environment, known as the first-night effect (FNE). However, the impact of the FNE on spontaneous oscillations in the visual system is poorly understood. How does the FNE affect the visual system during sleep? To address this question, the present study examined whether the FNE modifies the strength of slow-wave activity (SWA, 1-4Hz)-the dominant spontaneous brain oscillation in slow-wave sleep-in the visual areas. We measured the strength of SWA originating in the visual areas during the first and the second sleep sessions. Magnetoencephalography, polysomnography, and magnetic resonance imaging were used to localize the source of SWA to the visual areas. The visual areas were objectively defined using retinotopic mapping and an automated anatomical parcellation technique. The results showed that the strength of SWA was reduced in the first sleep session in comparison to the second sleep session, especially during slow-wave sleep, in the ventral part of the visual areas. These results suggest that environmental novelty may affect the visual system through suppression of SWA. The impact of the FNE may not be negligible in vision research.

Corrigendum: Overlearning hyperstabilizes a skill by rapidly making neurochemical processing inhibitory-dominant

Nat. Neurosci. 20, 470–475 (2017); published online 30 January 2017; corrected after print 18 September 2017 In the version of this article initially published, NIH grant R01EY019466 was missing from grants to T.W. in the Acknowledgments. The error has been corrected in the HTML and PDF versions of the article.

Trained-feature specific offline learning in an orientation detection task

It has been suggested that sleep provides additional enhancement of visual perceptual learning (VPL) acquired before sleep, termed offline performance gains. A majority of the studies that found offline performance gains of VPL used discrimination tasks including the texture discrimination task (TDT). This makes it questionable whether offline performance gains on VPL are generalized to other visual tasks. The present study examined whether a Gabor orientation detection task, which is a standard task in VPL, shows offline performance gains. In Experiment 1, we investigated whether sleep leads to offline performance gains on the task. Subjects were trained with the Gabor orientation detection task, and re-tested it after a 12-hr interval that included either nightly sleep or only wakefulness. We found that performance on the task improved to a significantly greater degree after the interval that included sleep and wakefulness than the interval including wakefulness alone. In addition, offline performance gains were specific to the trained orientation. In Experiment 2, we tested whether offline performance gains occur by a nap. Also, we tested whether spontaneous sigma activity in early visual areas during non-rapid eye movement (NREM) sleep, previously implicated in offline performance gains of TDT, was associated with offline performance gains of the task. A different group of subjects had a nap with polysomnography. The subjects were trained with the task before the nap and re-tested after the nap. The performance of the task improved significantly after the nap only on the trained orientation. Sigma activity in the trained region of early visual areas during NREM sleep was significantly larger than in the untrained region, in correlation with offline performance gains. These aspects were also found with VPL of TDT. The results of the present study demonstrate that offline performance gains are not specific to a discrimination task such as TDT, and can be generalized to other forms of VPL tasks, along with trained-feature specificity. Moreover, the present results also suggest that sigma activity in the trained region of early visual areas plays an important role in offline performance gains of VPL of detection as well as discrimination tasks.

Sigma activity originated in the early visual cortex during sleep associated with visual perceptual learning

A growing body of evidence indicates that sleep facilitates visual perceptual learning (VPL). However, how this facilitation occurs is controversial: researchers in the field are divided into two groups with two completely different views: use-dependent and learning-consolidation models. The use-dependent model assumes that sleep downscales overly increased synapses in the networks used during wakefulness, irrespective of whether learning is involved in the networks or not, and leads to the survival of only significant synapses. Downscaling is assumed to be involved in long-term depression related to slow-wave activity (SWA). The learning-consolidation model assumes that sleep affects the networks specifically related to learning by replaying activity patterns during training. The replay is assumed to be involved in long-term potentiation related to sigma activity. Although usage and learning components were not separated in previous studies, here we successfully dissociated the usage from learning components using an interference effect and tested which model is correct. There were two conditions. In a learning condition (n=12), participants were trained on one texture discrimination task (TDT), the learning of which is associated with response changes in the region of the early visual cortex that retinotopically corresponds to the trained visual field (trained region). In an interference condition (n=12), two different TDTs were serially trained so that learning should not occur due to mutual interference effects. The use-dependent model predicts increased SWA in the trained region during the post-training sleep in both conditions, whereas learning-consolidation model predicts sigma increase in the trained region during the post-training sleep only in the learning condition. Participants were trained with TDT before sleep and retested with TDT after sleep. Learning occurred only in the learning condition. EEG-source localization analysis indicated significant increase in sigma activity in the trained region during the post-training sleep. These results supports learning-consolidation model in VPL. Meeting abstract presented at VSS 2015.