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1. Spontaneous slow oscillation—slow spindle features predict induced overnight memory retention

   https://doi.org/10.1093/sleep/zsab127  

   Dehnavi, Fereshteh; Koo-Poeggel, Ping Chai; Ghorbani, Maryam; Marshall, Lisa (May 2021, Sleep)

   Abstract Study Objectives Synchronization of neural activity within local networks and between brain regions is a major contributor to rhythmic field potentials such as the EEG. On the other hand, dynamic changes in microstructure and activity are reflected in the EEG, for instance slow oscillation (SO) slope can reflect synaptic strength. SO-spindle coupling is a measure for neural communication. It was previously associated with memory consolidation, but also shown to reveal strong interindividual differences. In studies, weak electric current stimulation has modulated brain rhythms and memory retention. Here, we investigate whether SO-spindle coupling and SO slope during baseline sleep are associated with (predictive of) stimulation efficacy on retention performance. Methods Twenty-five healthy subjects participated in three experimental sessions. Sleep-associated memory consolidation was measured in two sessions, in one anodal transcranial direct current stimulation oscillating at subjects individual SO frequency (so-tDCS) was applied during nocturnal sleep. The third session was without a learning task (baseline sleep). The dependence on SO-spindle coupling and SO-slope during baseline sleep of so-tDCS efficacy on retention performance were investigated. Results Stimulation efficacy on overnight retention of declarative memories was associated with nesting of slow spindles to SO trough in deep nonrapid eye movement baseline sleep. Steepness and direction of SO slope in baseline sleep were features indicative for stimulation efficacy. Conclusions Findings underscore a functional relevance of activity during the SO up-to-down state transition for memory consolidation and provide support for distinct consolidation mechanisms for types of declarative memories. 

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2. Closed‐loop acoustic stimulation during an afternoon nap to modulate subsequent encoding

   https://doi.org/10.1111/jsr.13734  

   Koo‐Poeggel, Ping; Neuwerk, Soé; Petersen, Eike; Grasshoff, Jan; Mölle, Matthias; Martinetz, Thomas; Marshall, Lisa (December 2022, Journal of Sleep Research)

   Summary Sleep is able to contribute not only to memory consolidation, but also to post‐sleep learning. The notion exists that either synaptic downscaling or another process during sleep increase post‐sleep learning capacity. A correlation between augmentation of the sleep slow oscillation and hippocampal activation at encoding support the contribution of sleep to encoding of declarative memories. In the present study, the effect of closed‐loop acoustic stimulation during an afternoon nap on post‐sleep encoding of two verbal (word pairs, verbal learning and memory test) and non‐verbal (figural pairs) tasks and on electroencephalogram during sleep and learning were investigated in young healthy adults (N = 16). Closed‐loop acoustic stimulation enhanced slow oscillatory and spindle activity, but did not affect encoding at the group level. Subgroup analyses and comparisons with similar studies lead us to the tentative conclusion that further parameters such as time of day and subjects' cognitive ability influenced responses to closed‐loop acoustic stimulation. 

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3. Memory ability and retention performance relate differentially to sleep depth and spindle type

   https://doi.org/10.1016/j.isci.2023.108154  

   Dehnavi, Fereshteh; Koo-Poeggel, Ping Chai; Ghorbani, Maryam; Marshall, Lisa (November 2023, iScience)

   Temporal interactions between non-rapid eye movement (NREM) sleep rhythms especially the coupling between cortical slow oscillations (SO, ∼1 Hz) and thalamic spindles (∼12 Hz) have been proposed to contribute to multi-regional interactions crucial for memory processing and cognitive ability. We investigated relationships between NREM sleep depth, sleep spindles and SO-spindle coupling regarding memory ability and memory consolidation in healthy humans. Findings underscore the functional relevance of spindle dynamics (slow versus fast), SO-phase, and most importantly NREM sleep depth for cognitive processing. Cross-frequency coupling analyses demonstrated stronger precise temporal coordination of slow spindles to SO down-state in N2 for subjects with higher general memory ability. A GLM model underscored this relationship, and furthermore that fast spindle properties were predictive of overnight memory consolidation. Our results suggest cognitive fingerprints dependent on conjoint fine-tuned SO-spindle temporal coupling, spindle properties, and brain sleep state. 

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4. Differential thalamocortical interactions in slow and fast spindle generation: A computational model

   https://doi.org/10.1371/journal.pone.0277772  

   Mushtaq, Muhammad; Marshall, Lisa; Bazhenov, Maxim; Mölle, Matthias; Martinetz, Thomas (December 2022, PLOS ONE)

   Cymbalyuk, Gennady S. (Ed.)

   Cortical slow oscillations (SOs) and thalamocortical sleep spindles are two prominent EEG rhythms of slow wave sleep. These EEG rhythms play an essential role in memory consolidation. In humans, sleep spindles are categorized into slow spindles (8–12 Hz) and fast spindles (12–16 Hz), with different properties. Slow spindles that couple with the up-to-down phase of the SO require more experimental and computational investigation to disclose their origin, functional relevance and most importantly their relation with SOs regarding memory consolidation. To examine slow spindles, we propose a biophysical thalamocortical model with two independent thalamic networks (one for slow and the other for fast spindles). Our modeling results show that fast spindles lead to faster cortical cell firing, and subsequently increase the amplitude of the cortical local field potential (LFP) during the SO down-to-up phase. Slow spindles also facilitate cortical cell firing, but the response is slower, thereby increasing the cortical LFP amplitude later, at the SO up-to-down phase of the SO cycle. Neither the SO rhythm nor the duration of the SO down state is affected by slow spindle activity. Furthermore, at a more hyperpolarized membrane potential level of fast thalamic subnetwork cells, the activity of fast spindles decreases, while the slow spindles activity increases. Together, our model results suggest that slow spindles may facilitate the initiation of the following SO cycle, without however affecting expression of the SO Up and Down states. 

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5. Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

   https://doi.org/10.7554/eLife.75769  

   Mofrad, Maryam H; Gilmore, Greydon; Koller, Dominik; Mirsattari, Seyed M; Burneo, Jorge G; Steven, David A; Khan, Ali R; Suller Marti, Ana; Muller, Lyle (June 2022, eLife)

   The brain processes memories as we sleep, generating rhythms of electrical activity called ‘sleep spindles’. Sleep spindles were long thought to be a state where the entire brain was fully synchronized by this rhythm. This was based on EEG recordings, short for electroencephalogram, a technique that uses electrodes on the scalp to measure electrical activity in the outermost layer of the brain, the cortex. But more recent intracranial recordings of people undergoing brain surgery have challenged this idea and suggested that sleep spindles may not be a state of global brain synchronization, but rather localised to specific areas. Mofrad et al. sought to clarify the extent to which spindles co-occur at multiple sites in the brain, which could shed light on how networks of neurons coordinate memory storage during sleep. To analyse highly variable brain wave recordings, Mofrad et al. adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves. The resulting algorithm, designed to more sensitively detect spindles amongst other brain activity, was then applied to a range of sleep recordings from humans and macaque monkeys. The analyses revealed that widespread and complex patterns of spindle rhythms, spanning multiple areas in the cortex of the brain, actually appear much more frequently than previously thought. This finding was consistent across all the recordings analysed, even recordings under the skull, which provide the clearest window into brain circuits. Further analyses found that these multi-area spindles occurred more often in sleep after people had completed tasks that required holding many visual scenes in memory, as opposed to control conditions with fewer visual scenes. In summary, Mofrad et al. show that neuroscientists had previously not appreciated the complex and dynamic patterns in this sleep rhythm. These patterns in sleep spindles may be able to adapt based on the demands needed for memory storage, and this will be the subject of future work. Moreover, the findings support the idea that sleep spindles help coordinate the consolidation of memories in brain circuits that stretch across the cortex. Understanding this mechanism may provide insights into how memory falters in aging and sleep-related diseases, such as Alzheimer’s disease. Lastly, the algorithm developed by Mofrad et al. stands to be a useful tool for analysing other rhythmic waveforms in noisy recordings. 

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