Attachment--and loss.

Theta Oscillations in the Hippocampus

Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of "sleep and memory" research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.

https://www.zora.uzh.ch/id/eprint/77770/1/physiol_rev_93.pdf

About sleep's role in memory

The problem of which cues, internal or external, permit a person to label and identify his own emotional state has been with us since the days that James (1890) first tendered his doctrine that \"the bodily changes follow directly the perception of the exciting fact, and that our feeling of the same changes as they occur is the emotion\" (p. 449). Since we are aware of a variety of feeling and emotion states, it should follow from James' proposition that the various emotions will be accompanied by a variety of differentiable bodily states. Following James' pronouncement, a formidable number of studies were undertaken in search of the physiological differentiators of the emotions. The results, in these early days, were almost uniformly negative. All of the emotional states experi-

"Cognitive, social, and physiological determinants of emotional state": Erratum

Is there a biology of intelligence which is characteristic of the normal human nervous system?" Here we review 37 modern neuroimaging studies in an attempt to address this question posed by Halstead (1947) as he and other icons of the last century endeavored to understand how brain and behavior are linked through the expression of intelligence and reason. Reviewing studies from functional (i.e., functional magnetic resonance imaging, positron emission tomography) and structural (i.e., magnetic resonance spectroscopy, diffusion tensor imaging, voxel-based morphometry) neuroimaging paradigms, we report a striking consensus suggesting that variations in a distributed network predict individual differences found on intelligence and reasoning tasks. We describe this network as the Parieto-Frontal Integration Theory (P-FIT). The P-FIT model includes, by Brodmann areas (BAs): the dorsolateral prefrontal cortex (BAs 6, 9, 10, 45, 46, 47), the inferior (BAs 39, 40) and superior (BA 7) parietal lobule, the anterior cingulate (BA 32), and regions within the temporal (BAs 21, 37) and occipital (BAs 18, 19) lobes. White matter regions (i.e., arcuate fasciculus) are also implicated. The P-FIT is examined in light of findings from human lesion studies, including missile wounds, frontal lobotomy/leukotomy, temporal lobectomy, and lesions resulting in damage to the language network (e.g., aphasia), as well as findings from imaging research identifying brain regions under significant genetic control. Overall, we conclude that modern neuroimaging techniques are beginning to articulate a biology of intelligence. We propose that the P-FIT provides a parsimonious account for many of the empirical observations, to date, which relate individual differences in intelligence test scores to variations in brain structure and function. Moreover, the model provides a framework for testing new hypotheses in future experimental designs.

/pdf/the-parieto-frontal-integration-theory-p-fit-of-intelligence-2kp9bfy1c3.pdf

The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence.

The role of arousals in sleep is gaining interest among both basic researchers and clinicians. In the last 20 years increasing evidence shows that arousals are deeply involved in the pathophysiology of sleep disorders. The nature of arousals in sleep is still a matter of debate. According to the conceptual framework of the American Sleep Disorders Association criteria, arousals are a marker of sleep disruption representing a detrimental and harmful feature for sleep. In contrast, our view indicates arousals as elements weaved into the texture of sleep taking part in the regulation of the sleep process. In addition, the concept of micro-arousal (MA) has been extended, incorporating, besides the classical low-voltage fast-rhythm electroencephalographic (EEG) arousals, high-amplitude EEG bursts, be they like delta-like or K-complexes, which reflects a special kind of arousal process, mobilizing parallely antiarousal swings. In physiologic conditions, the slow and fast MA are not randomly scattered but appear structurally distributed within sleep representing state-specific arousal responses. MA preceded by slow waves occurs more frequently across the descending part of sleep cycles and in the first cycles, while the traditional fast type of arousals across the ascending slope of cycles prevails during the last third of sleep. The uniform arousal characteristics of these two types of MAs is supported by the finding that different MAs are associated with an increasing magnitude of vegetative activation ranging hierarchically from the weaker slow EEG types (coupled with mild autonomic activation) to the stronger rapid EEG types (coupled with a vigorous autonomic activation). Finally, it has been ascertained that MA are not isolated events but are basically endowed with a periodic nature expressed in non-rapid eye movement (NREM) sleep by the cyclic alternating pattern (CAP). Understanding the role of arousals and CAP and the relationship between physiologic and pathologic MA can shed light on the adaptive properties of the sleeping brain and provide insight into the pathomechanisms of sleep disturbances. Functional significance of arousal in sleep, and particularly in NREM sleep, is to ensure the reversibility of sleep, without which it would be identical to coma. Arousals may connect the sleeper with the surrounding world maintaining the selection of relevant incoming information and adapting the organism to the dangers and demands of the outer world. In this dynamic perspective, ongoing phasic events carry on the one hand arousal influences and on the other elements of information processing. The other function of arousals is tailoring the more or less stereotyped endogenously determined sleep process driven by chemical influences according to internal and external demands. In this perspective, arousals shape the individual course of night sleep as a variation of the sleep program.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2869.2004.00388.x

The nature of arousal in sleep.

The usual assessment of general mental ability (or intelligence) is based on performance attained in reasoning and problem-solving tasks. Differences in general mental ability have been associated with event-related neural activity patterns of the wakeful working brain or physical, chemical and electrical brain features measured during wakeful resting conditions. Recent evidences suggest that specific sleep electroencephalogram oscillations are related to wakeful cognitive performances. Our aim is to reveal the relationship between non-rapid eye movement sleep-specific oscillations (the slow oscillation, delta activity, slow and fast sleep spindle density, the grouping of slow and fast sleep spindles) and general mental ability assessed by the Raven Progressive Matrices Test (RPMT). The grouping of fast sleep spindles by the cortical slow oscillation in the left frontopolar derivation (Fp1) as well as the density of fast sleep spindles over the right frontal area (Fp2, F4), correlated positively with general mental ability. Data from those selected electrodes that showed the high correlations with general mental ability explained almost 70% of interindividual variance in RPMT scores. Results suggest that individual differences in general mental ability are reflected in fast sleep spindle-related oscillatory activity measured over the frontal cortex.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2869.2005.00472.x

Prediction of general mental ability based on neural oscillation measures of sleep.

Title. Music improves sleep quality in students.

Aim.  This paper is a report of a study to investigate the effects of music on sleep quality in young participants with poor sleep.

Background.  Sleep disorders may result in fatigue, tiredness, depression and problems in daytime functioning. Music can reduce sympathetic nervous system activity, decrease anxiety, blood pressure, heart and respiratory rate and may have positive effects on sleep via muscle relaxation and distraction from thoughts. Control groups have not been used in most previous studies.

Methods.  We used a three-group repeated measures design. Ninety-four students (aged between 19 and 28 years) with sleep complaints were studied in 2006. Participants listened for 45 minutes either to relaxing classical music (Group 1) or an audiobook (Group 2) at bedtime for 3 weeks. The control group (Group 3) received no intervention. Sleep quality was measured using the Pittsburg Sleep Quality Index before the study and weekly during the intervention. Depressive symptoms in experimental group participants were measured using the Beck Depression Inventory.

Results.  Repeated measures anova revealed a main effect of TIME (P < 0·0001) and an interaction between TIME and GROUPS (P < 0·0001). Post hoc tests with Bonferroni correction showed that music statistically significantly improved sleep quality (P < 0·0001). Sleep quality did not improve statistically significantly in the audiobook and the control group. Depressive symptoms decreased statistically significantly in the music group (P < 0·0001), but not in the group listening to audiobooks.

Conclusion.  Relaxing classical music is an effective intervention in reducing sleeping problems. Nurses could use this safe, cheap and easy to learn method to treat insomnia.

Music Improves Sleep Quality in Students

The individual adjustment method of sleep spindle analysis: Methodological improvements and roots in the fingerprint paradigm

Hippocampal rhythmic slow activity (RSA) is a well-known electrophysiological feature of exploratory behavior, spatial cognition, and rapid eye movement (REM) sleep in several mammalian species. Recently, RSA in humans during spatial navigation was reported, but systematic data regarding human REM sleep are lacking. Using mesio-temporal corticography with foramen ovale electrodes in epileptic patients, we report the presence of a 1.5-3-Hz synchronous rhythmic hippocampal oscillation seemingly specific to REM sleep. This oscillation is continuous during whole REM periods, is clearly observable by visual inspection, and appears in tonic and phasic REM sleep episodes equally. Quantitative analysis proved that this 1.5-3-Hz frequency band significantly differentiates REM sleep from waking and slow-wake sleep (SWS). No other frequency band proved to be significant or showed this high rhythmicity. Even in temporo-lateral surface recordings, although visually much less striking, the relative power of the 1.5-3-Hz frequency band differentiates REM sleep from other states with statistical significance. This could mean that the 1.5-3-Hz hippocampal RSA spreads over other cortical areas in humans as in other mammals. We suggest that this oscillation is the counterpart of the hippocampal theta of mammalian REM sleep, and that the 1.5-3-Hz delta EEG activity is a basic neurophysiological feature of human REM sleep.

Róbert Bódizs

Papers

The nature of arousal in sleep.

Prediction of general mental ability based on neural oscillation measures of sleep.

Music Improves Sleep Quality in Students

The individual adjustment method of sleep spindle analysis: Methodological improvements and roots in the fingerprint paradigm

Rhythmic hippocampal slow oscillation characterizes REM sleep in humans.