Motor sequence learning is known to rely on more than a single process. As the skill develops with practice, two different representations of the sequence are formed: a goal representation built under spatial allocentric coordinates and a movement representation mediated through egocentric motor coordinates. This study aimed to explore the influence of daytime sleep (nap) on consolidation of these two representations. Through the manipulation of an explicit finger sequence learning task and a transfer protocol, we show that both allocentric (spatial) and egocentric (motor) representations of the sequence can be isolated after initial training. Our results also demonstrate that nap favors the emergence of offline gains in performance for the allocentric, but not the egocentric representation, even after accounting for fatigue effects. Furthermore, sleep-dependent gains in performance observed for the allocentric representation are correlated with spindle density during non-rapid eye movement (NREM) sleep of the post-training nap. In contrast, performance on the egocentric representation is only maintained, but not improved, regardless of the sleep/wake condition. These results suggest that motor sequence memory acquisition and consolidation involve distinct mechanisms that rely on sleep (and specifically, spindle) or simple passage of time, depending respectively on whether the sequence is performed under allocentric or egocentric coordinates.

SUMMAR Y The purpose of this study was to compare the changes that occur in sleep architecture following the acquisition of a simple motor learning task in young and older adults. Subjects included 14 young (range = 17-24 years) and 14 older (range = 62-79 years) adults, all of whom were in good health. Using in-home recording systems, sleep architecture (sleep stages and the density of Stage 2 sleep spindles) was examined before and after learning the pursuit rotor. To control for possible age differences in baseline motor performance and spindle density, both absolute and relative (percent change) measures were examined. Both groups improved significantly on the pursuit rotor task at Retest (1 week later); however, the magnitude of absolute improvement was larger in the young group than in the older group. There was no group difference when a relative measure of improvement (percent increase across sessions) was used. The density of Stage 2 sleep spindles increased significantly following task Acquisition in the young group but not in the older group. These age differences failed to reach significance when change was measured as a percentage of baseline level of spindle density. The increase in spindle density was correlated with performance level during acquisition in the young group but not the older group. The results of the present study are largely consistent with previous studies on sleep and memory in young adults and suggest that more detailed examination of this relationship in older adults is warranted.k e y w o r d s aging, consolidation, motor learning, non-rapid eye movement sleep, procedural learning, stage 2 sleep spindle

Abstract■ Sleep spindles-short, phasic, oscillatory bursts of activity that characterize non-rapid eye movement sleep-are one of the only electrophysiological oscillations identified as a biological marker of human intelligence (e.g., cognitive abilities commonly assessed using intelligence quotient tests). However, spindles are also important for sleep maintenance and are modulated by circadian factors. Thus, the possibility remains that the relationship between spindles and intelligence quotient may be an epiphenomenon of a putative relationship between good quality sleep and cognitive ability or perhaps modulated by circadian factors such as morningness-eveningness tendencies. We sought to ascertain whether spindles are directly or indirectly related to cognitive abilities using mediation analysis. Here, we show that fast (13.5-16 Hz) parietal but not slow (11-13.5 Hz) frontal spindles in both non-rapid eye movement stage 2 sleep and slow wave sleep are directly related to reasoning abilities (i.e., cognitive abilities that support "fluid intelligence," such as the capacity to identify complex patterns and relationships and the use of logic to solve novel problems) but not verbal abilities (i.e., cognitive abilities that support "crystalized intelligence"; accumulated knowledge and experience) or cognitive abilities that support STM (i.e., the capacity to briefly maintain information in an available state). The relationship between fast spindles and reasoning abilities is independent of the indicators of sleep maintenance and circadian chronotype, thus suggesting that spindles are indeed a biological marker of cognitive abilities and can serve as a window to further explore the physiological and biological substrates that give rise to human intelligence. ■

A spindle detection method was developed that: (1) extracts the signal of interest (i.e., spindle-related phasic changes in sigma) relative to ongoing “background” sigma activity using complex demodulation, (2) accounts for variations of spindle characteristics across the night, scalp derivations and between individuals, and (3) employs a minimum number of sometimes arbitrary, user-defined parameters. Complex demodulation was used to extract instantaneous power in the spindle band. To account for intra- and inter-individual differences, the signal was z-score transformed using a 60 s sliding window, per channel, over the course of the recording. Spindle events were detected with a z-score threshold corresponding to a low probability (e.g., 99th percentile). Spindle characteristics, such as amplitude, duration and oscillatory frequency, were derived for each individual spindle following detection, which permits spindles to be subsequently and flexibly categorized as slow or fast spindles from a single detection pass. Spindles were automatically detected in 15 young healthy subjects. Two experts manually identified spindles from C3 during Stage 2 sleep, from each recording; one employing conventional guidelines, and the other, identifying spindles with the aid of a sigma (11–16 Hz) filtered channel. These spindles were then compared between raters and to the automated detection to identify the presence of true positives, true negatives, false positives and false negatives. This method of automated spindle detection resolves or avoids many of the limitations that complicate automated spindle detection, and performs well compared to a group of non-experts, and importantly, has good external validity with respect to the extant literature in terms of the characteristics of automatically detected spindles.

SUMMARYThe goal of the current investigation was to develop a systematic method to validate the accuracy of an automated method of sleep spindle detection that takes into consideration individual differences in spindle amplitude. The benchmarking approach used here could be employed more generally to validate automated spindle scoring from other detection algorithms. In a sample of Stage 2 sleep from 10 healthy young subjects, spindles were identified both manually and automatically. The minimum amplitude threshold used by the prana Ò (PhiTools, Strasbourg, France) software spindle detection algorithm to identify a spindle was subject-specific and determined based upon each subjectÕs mean peak spindle amplitude. Overall sensitivity and specificity values were 98.96 and 88.49%, respectively, when compared to manual scoring. Selecting individual amplitude thresholds for spindle detection based on systematic benchmarking data may validate automated spindle detection methods and improve reproducibility of experimental results. Given that interindividual differences are accounted for, we feel that automatic spindle detection provides an accurate and efficient alternative approach for detecting sleep spindles. k e y w o r d s automated detection, individual differences, sleep, sleep spindles

Simultaneous electroencephalography and functional magnetic resonance imaging (EEG–fMRI) studies have revealed brain activations time-locked to spindles. Yet, the functional significance of these spindle-related brain activations is not understood. EEG studies have shown that inter-individual differences in the electrophysiological characteristics of spindles (e.g., density, amplitude, duration) are highly correlated with “Reasoning” abilities (i.e., “fluid intelligence”; problem solving skills, the ability to employ logic, identify complex patterns), but not short-term memory (STM) or verbal abilities. Spindle-dependent reactivation of brain areas recruited during new learning suggests night-to-night variations reflect offline memory processing. However, the functional significance of stable, trait-like inter-individual differences in brain activations recruited during spindle events is unknown. Using EEG–fMRI sleep recordings, we found that a subset of brain activations time-locked to spindles were specifically related to Reasoning abilities but were unrelated to STM or verbal abilities. Thus, suggesting that individuals with higher fluid intelligence have greater activation of brain regions recruited during spontaneous spindle events. This may serve as a first step to further understand the function of sleep spindles and the brain activity which supports the capacity for Reasoning.

Electroencephalographic and event-related potentials data illustrate impairment in information-processing capabilities associated with reduced arousal elicited by experimental sleep fragmentation. This subtle degree of sleep disruption, where total sleep time is not reduced, leads to sustained impairment in alertness and attention.

I f someone were to analyze current notions and fashionable catchwords, he would find 'systems' high on the list. The concept has pervaded all fields of science and penetrated into popular thinking, jargon, and mass media." In keeping with this fashion, Science presents this week a special issue with emphasis on systems biology. And we note that this is a trend with remarkable staying power, for the words quoted above were written not for today's issue of Science, but rather in Ludwig von Bertalanffy's 1967 introduction to his book, General System Theory,* a compilation of his writings, some of which date back to 1940! Bertalanffy applied general systems theory not only to biology, but to psychology, economics, and social science as well. In his view, old-fashioned science "tried to explain observable phenomena by reducing them to an interplay of elementary units investigatable independently of each other." Contemporary science, on the other hand, recognized the importance of "wholeness," defined as "problems of organization, phenomena not resolvable into local events, dynamic interactions manifest in the difference of behavior of parts when isolated or in higher configuration, etc.; in short, 'systems' of various orders not understandable by investigation of their respective parts in isolation." And this remains an effective definition of systems biology as practiced today with the integration and application of mathematics, engineering, physics, and computer science to understanding a range of complex biological regulatory systems. The delay between the early pronouncement of the theory and the work presently assembled was necessary, primarily to accumulate sufficient descriptions of the parts to enable a reasonable reassembly of the whole. Kitano's Review (p. 1662) provides an overview of the systems biology field and identifies areas where attempts to derive systems-level understanding are still thwarted by the absence of required extensive and precise information. Csete and Doyle (p. 1664) bring an engineer's perspective to their Review on biological complexity. Biologists can learn much from a discipline whose practitioners design control systems (such as that of the modern passenger jet) that generate, every minute, a volume of information similar to that in the entire human genome. They provide insights into system design from a more manageable set of parts: the modular plastic pieces that make up the "Legome." In a Review of modeling the function of cardiac cells and the heart, Noble (p. 1678) reminds us that we are just beginning to write the "user's guide" that must accompany the huge "parts catalog" emerging from efforts in genomics and proteomics. Davidson et al. (p. 1669) demonstrate that a genomic regulatory network can explain the early development of the sea urchin embryo. These regulatory circuits prescribe the ordered expression of genes that determine the fates of developing cells and move those cells together down a one-way path to yield a functional organism. Bertalanffy would be satisfied, ...