Caroline Kussé

Local modulation of human brain responses by circadian rhythmicity and sleep debt

Circadian rhythms and sleep deprivation Sleep deprivation, such as that experienced because of shift work, jet lag, sleep disorders, and aging, leads to deterioration of many aspects of health. Cognition deteriorates rapidly and substantially when we stay awake through the night. To investigate the time course of brain responses during sleep loss, Muto et al. scanned volunteers repeatedly during an extended period of wakefulness (see the Perspective by Czeisler) in which circadian and homeostatic drives differentially affected local brain regions. Science, this issue p. 687; see also p. 648 Activity in different brain regions varies according to circadian rhythm and homeostatic sleep pressure. Human performance is modulated by circadian rhythmicity and homeostatic sleep pressure. Whether and how this interaction is represented at the regional brain level has not been established. We quantified changes in brain responses to a sustained-attention task during 13 functional magnetic resonance imaging sessions scheduled across the circadian cycle, during 42 hours of wakefulness and after recovery sleep, in 33 healthy participants. Cortical responses showed significant circadian rhythmicity, the phase of which varied across brain regions. Cortical responses also significantly decreased with accrued sleep debt. Subcortical areas exhibited primarily a circadian modulation that closely followed the melatonin profile. These findings expand our understanding of the mechanisms involved in maintaining cognition during the day and its deterioration during sleep deprivation and circadian misalignment.

Seasonality in human cognitive brain responses

Significance Evidence for seasonality in humans is limited. Mood probably stands as the aspect of human brain function most acknowledged as being affected by season. Yet, the present study provides compelling evidence for previously unappreciated annual variations in the cerebral activity required to sustain ongoing cognitive processes in healthy volunteers. The data further show that this annual rhythmicity is cognitive-process-specific (i.e., the phase of the rhythm changes between cognitive tasks), speaking for a complex impact of season on human brain function. Annual variations in cognitive brain function may contribute to explain intraindividual cognitive changes that could emerge at specific times of year. Daily variations in the environment have shaped life on Earth, with circadian cycles identified in most living organisms. Likewise, seasons correspond to annual environmental fluctuations to which organisms have adapted. However, little is known about seasonal variations in human brain physiology. We investigated annual rhythms of brain activity in a cross-sectional study of healthy young participants. They were maintained in an environment free of seasonal cues for 4.5 d, after which brain responses were assessed using functional magnetic resonance imaging (fMRI) while they performed two different cognitive tasks. Brain responses to both tasks varied significantly across seasons, but the phase of these annual rhythms was strikingly different, speaking for a complex impact of season on human brain function. For the sustained attention task, the maximum and minimum responses were located around summer and winter solstices, respectively, whereas for the working memory task, maximum and minimum responses were observed around autumn and spring equinoxes. These findings reveal previously unappreciated process-specific seasonality in human cognitive brain function that could contribute to intraindividual cognitive changes at specific times of year and changes in affective control in vulnerable populations.

Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.

Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experimental design. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. This study tests the classification of brain activity, acquired on 16 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. To deal with these issues, two classification techniques were considered (Support Vector Machines, SVM, and Gaussian Processes, GP), as well as different feature extraction methods (General Linear Model, GLM and SVM). These techniques were combined in order to identify the procedures leading to the highest accuracy measures. Our results showed that 12 data sets out of 16 could be significantly modeled by either SVM or GP. Model accuracies tended to be related to the degree of imbalance between classes and to task performance of the volunteers. We also conclude that the GP technique tends to be more robust than SVM to model unbalanced data sets.

Experience-dependent induction of hypnagogic images during daytime naps: a combined behavioural and EEG study.

This study characterizes hypnagogic hallucinations reported during a polygraphically recorded 90‐min daytime nap following or preceding practice of the computer game Tetris. In the experimental group (N = 16), participants played Tetris in the morning for 2 h during three consecutive days, while in a first control group (N = 13, controlling the effect of experience) participants did not play any game, and in a second control group (N = 14, controlling the effect of anticipation) participants played Tetris after the nap. During afternoon naps, participants were repetitively awakened 15, 45, 75, 120 or 180 s after the onset of S1, and were asked to report their mental content. Reports content was scored by three judges (inter‐rater reliability 85%). In the experimental group, 48 out of 485 (10%) sleep‐onset reports were Tetris‐related. They mostly consisted of images and sounds with very little emotional content. They exactly reproduced Tetris elements or mixed them with other mnemonic components. By contrast, in the first control group, only one report out of 107 was scored as Tetris‐related (1%), and in the second control group only three reports out of 112 were scored as Tetris‐related (3%; between‐groups comparison; P = 0.006). Hypnagogic hallucinations were more consistently induced by experience than by anticipation (P = 0.039), and they were predominantly observed during the transition of wakefulness to sleep. The observed attributes of experience‐related hypnagogic hallucinations are consistent with the particular organization of regional brain activity at sleep onset, characterized by high activity in sensory cortices and in the default‐mode network.

Spontaneous neural activity during human non-rapid eye movement sleep.

Recent neuroimaging studies characterized the neural correlates of slow waves and spindles during human non-rapid eye movement (NREM) sleep. They showed that significant activity was consistently associated with slow (> 140 μV) and delta waves (75-140 μV) during NREM sleep in several cortical areas including inferior frontal, medial prefrontal, precuneus, and posterior cingulate cortices. Unexpectedly, slow waves were also associated with transient responses in the pontine tegmentum and in the cerebellum. On the other hand, spindles were associated with a transient activity in the thalami, paralimbic areas (anterior cingulate and insular cortices), and superior temporal gyri. Moreover, slow spindles (11-13 Hz) were associated with increased activity in the superior frontal gyrus. In contrast, fast spindles (13-15 Hz) recruited a set of cortical regions involved in sensorimotor processing, as well as the mesial frontal cortex and hippocampus. These findings indicate that human NREM sleep is an active state during which brain activity is temporally organized by spontaneous oscillations (spindles and slow oscillation) in a regionally specific manner. The functional significance of these NREM sleep oscillations is currently interpreted in terms of synaptic homeostasis and memory consolidation.

Neuroimaging of dreaming: state of the art and limitations.

During the last two decades, functional neuroimaging has been used to characterize the regional brain function during sleep in humans, at the macroscopic systems level. In addition, the topography of brain activity, especially during rapid eye movement sleep, was thought to be compatible with the general features of dreams. In contrast, the neural correlates of dreams remain largely unexplored. This review examines the difficulties associated with the characterization of dream correlates. ἓν οἶδα ὅτι οὐδὲν οἶδα Σωκράτης (The only thing I know is that I know nothing) Socrates.

Decoding Spontaneous Brain Activity from fMRI Using Gaussian Processes: Tracking Brain Reactivation

While Multi-Variate Pattern Analysis techniques based on machine learning have now been regularly applied to neuroimaging data, decoding brain activity is usually performed in highly controlled experimental paradigms. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. Moreover, in the case of spontaneous brain activity, the mental states can not be linked to any external or internal stimulation, which makes it a highly difficult condition to decode. This study tests the classification of brain activity, acquired on 14 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. Application of the obtained model on rest sessions allowed classifying spontaneous brain activity linked to the task which, overall, correlated with their behavioural performance to the task.

Functional neuroimaging of the reciprocal influences between sleep and wakefulness.

The activity patterns adopted by brain neuronal populations differ dramatically between wakefulness and sleep. However, these vigilance states are not independent and they reciprocally interact. Here, we provide evidence that in humans, regional brain activity during wakefulness is influenced by sleep regulation, namely by the interaction between sleep homeostasis and circadian signals. We also show that, by contrast, regional brain activity during sleep is influenced by the experience acquired during the preceding waking period. These data reveal the dynamic interactions by which the succession of vigilance states support normal brain function and human cognition.

Reciprocal interactions between wakefulness and sleep influence global and regional brain activity.

Reciprocal interactions between wakefulness and sleep substantially influence human brain function in both states of vigilance. On the one hand, there is evidence that regionally-specialized brain activity during wakefulness is modulated by the interaction between a local use-dependent buildup of homeostatic sleep pressure and circadian signals. On the other hand, brain activity during sleep, although mainly constrained by genuine sleep oscillations, shows wake-dependent regionally-specific modulations, which are involved in the dissipation of local homeostatic sleep pressure and memory consolidation.

Neural Correlates of Human Sleep and Sleep-Dependent Memory Processing

Wakefulness and sleep are associated with distinct patterns of neural activity and neuromodulation. In humans, functional neuroimaging was used to characterize the related changes in regional brain metabolism and hemodynamics. Recent data combining EEG and fMRI described the transient responses associated with spindles and slow waves, as well as the changes in functional integration during NREM sleep. It was also shown that regional brain activity during sleep is influenced by the experience acquired during the preceding waking period. These data are currently interpreted in the framework of two theories. First, the use-dependent increase in slow oscillation during NREM sleep is associated with local synaptic homeostasis. Second, reactivations of memory traces during NREM sleep would reorganize declarative memories in hippocampal-neocortical networks, a systems-level memory consolidation that can be hindered by sleep deprivation. Collectively, these data reveal the dynamic changes in brain activity during sleep that support normal human cognition.