Delta power, a measure of EEG activity in the 1-4 Hz range, in slow-wave sleep (SWS) is in a quantitative and predictive relationship with prior wakefulness. Thus, sleep loss evokes a proportional increase in delta power, and excess sleep a decrease. Therefore, delta power is thought to reflect SWS need and its underlying homeostatically regulated recovery process. The neurophysiological substrate of this process is unknown and forward genetics might help elucidate the nature of what is depleted during wakefulness and recovered during SWS. We applied a mathematical method that quantifies the relationship between the sleep-wake distribution and delta power to sleep data of six inbred mouse strains. The results demonstrated that the rate at which SWS need accumulated varied greatly with genotype. This conclusion was confirmed in a "dose-response" study of sleep loss and changes in delta power; delta power strongly depended on both the duration of prior wakefulness and genotype. We followed the segregation of the rebound of delta power after sleep deprivation in 25 BXD recombinant inbred strains by quantitative trait loci (QTL) analysis. One "significant" QTL was identified on chromosome 13 that accounted for 49% of the genetic variance in this trait. Interestingly, the rate at which SWS need decreases did not vary with genotype in any of the 31 inbred strains studied. These results demonstrate, for the first time, that the increase of SWS need is under a strong genetic control, and they provide a basis for identifying genes underlying SWS homeostasis. Key words: EEG delta power; slow-wave activity; sleep deprivation; homeostatic regulation of non-REM sleep; simulation of Process S; BXD recombinant-inbred mouse strains; QTL;Dps1; Dps2; Dps3; forward genetics Slow oscillations in the delta frequency range (1-4 Hz) are characteristic of the EEG during slow-wave sleep (SWS) (i.e., non-REM sleep in humans). Delta oscillations reflect synchronized burst-pause firing patterns of hyperpolarized thalamocortical and corticothalamic neurons (Steriade et al., 1993;McCormick and Bal, 1997;Steriade, 1999). Activity in the delta frequency range can be quantified as delta power by Fourier analysis. Delta power is negatively correlated with the response to arousing stimuli (Neckelmann and Ursin, 1993) and SWS fragmentation (Franken et al., 1991a) and thus can be seen as a measure of SWS intensity. Delta power is also in a quantitative and predictive relationship with prior sleep and wakefulness in mammals, including humans. Sleep loss evokes an increase in delta power during subsequent SWS that is proportional to the loss (Tobler and Borbély, 1986;Dijk et al., 1987), excess sleep results in an attenuation of delta power (Werth et al., 1996), and delta power decreases over the course of a sleep period, independent of the circadian phase at which sleep is initiated (Dijk and Czeisler, 1995). These and other observations have been interpreted as evidence that SWS is a restorative and homeostatically regulated behavior and that delta ...

This study analyzes stroke phases and arm and leg coordination during front crawl swimming as a function of swim velocity and performance level. Forty-three swimmers constituted three groups based on performance level. All swam at three different swim velocities, corresponding to the paces appropriate for the 800 m, 100 m, and 50 m. The different stroke phases and the arm and leg coordination were identified by video analysis. Arm coordination was quantified using a new index of coordination (IdC), which expresses the three major modalities opposition, catch-up and superposition. Opposition, where one arm begins the pull phase when the other is finishing the push phase; catch up, which has a lag time (LT) between propulsive phases of the two arms; and superposition, which describes an overlap in the propulsive phases. The IdC is an index which characterizes coordination patterns by measure of LT between propulsive phases of each arm. The most important results showed that duration of the propulsive phases (B + C) increased significantly with increasing velocity: 43.1 +/- 3.3% for V800; 46.5 +/- 3% for V100 and 49 +/- 3% for V50. The arm and leg synchronization was modified in the sense of an increase in six-beat kick. The IdC increased significantly with velocity: IdCV800 = -7.6 +/- 6.4%; IdCV100 = -3.2 +/- 5.1% and IdCV50 = -0.9 +/- 5.6%. IdC increased also significantly with performance level: IdCG3 = -6.07 +/- 5.3%; IdCG2 = -3.9 +/- 4.2% and IdCG1 = -1.76 +/- 5.6% for the mean of the 3 velocity. The two extreme IdC were IdCG3V800 = -9.4 +/- 5.4% and IdCG1V50 = +2.53 +/- 4.4%.

In this study, we compared the reliability of short-term resting heart rate (HR) variability (HRV) and postexercise parasympathetic reactivation (i.e., HR recovery (HRR) and HRV) indices following either submaximal or supramaximal exercise. On 4 different occasions, beat-to-beat HR was recorded in 15 healthy males (21.5 ± 1.4 yr) during 5 min of seated rest, followed by submaximal (Sub) and supramaximal (Supra) exercise bouts; both exercise bouts were followed by 5 min of seated recovery. Reliability of all HR-derived indices was assessed by the typical error of measurement expressed as a coefficient of variation (CV,%). CV for HRV indices ranged from 4 to 17%, 7 to 27% and 41 to 82% for time domain, spectral and ratio indices, respectively. The CV for HRR ranged from 15 to 32%. Spectral CVs for HRV were lower at rest compared with Supra (e.g., natural logarithm of the high frequency range (LnHF); 12.6 vs. 26.2%; P=0.02). HRR reliability was not different between Sub and Supra (25 vs. 14%; P=0.10). The present study found discrepancy in the CVs of vagal-related heart rate indices; a finding that should be appreciated when assessing changes in these variables. Further, Supra exercise was shown to worsen the reliability of HRV-spectral indices.

We examined the preferred mode of arm coordination in 14 elite male front-crawl swimmers. Each swimmer performed eight successive swim trials in which target velocity increased from the swimmer's usual 3000-m velocity to his maximal velocity. Actual swim velocity, stroke rate, stroke length and the different arm stroke phases were then calculated from video analysis. Arm coordination was quantified by an index of coordination based on the lag time between the propulsive phases of each arm. The index expressed the three coordination modes in the front crawl: opposition, catch-up and superposition. First, in line with the dynamic approach to movement coordination, the index of coordination could be considered as an order parameter that qualitatively captured arm coordination. Second, two coordination modes were observed: a catch-up pattern (index of coordination= -8.43%) consisting of a lag time between the propulsive phases of each arm, and a relative opposition pattern (index of coordination= 0.89%) in which the propulsive phase of one arm ended when the propulsive phase of the other arm began. An abrupt change in the coordination pattern occurred at the critical velocity of 1.8 m. s(-1), which corresponded to the 100-m pace: the swimmers switched from catch-up to relative opposition. This change in coordination resulted in a reorganization of the arm phases: the duration of the entry and catch phase decreased, while the duration of the pull and push phases increased in relation to the whole stroke. Third, these changes were coupled to increased stroke rate and decreased stroke length, indicating that stroke rate, stroke length, the stroke rate/stroke length ratio, as well as velocity, could be considered as control parameters. The control parameters can be manipulated to facilitate the emergence of specific coordination modes, which is highly relevant to training and learning. By adjusting the control and order parameters within the context of a specific race distance, both coach and swimmer will be able to detect the best adapted pattern for a given race pace and follow how arm coordination changes over the course of training.

This study determined the concurrent validity and reliability of force, velocity and power measurements provided by accelerometry, linear position transducer and Samozino's methods, during loaded squat jumps. 17 subjects performed squat jumps on 2 separate occasions in 7 loading conditions (0-60% of the maximal concentric load). Force, velocity and power patterns were averaged over the push-off phase using accelerometry, linear position transducer and a method based on key positions measurements during squat jump, and compared to force plate measurements. Concurrent validity analyses indicated very good agreement with the reference method (CV=6.4-14.5%). Force, velocity and power patterns comparison confirmed the agreement with slight differences for high-velocity movements. The validity of measurements was equivalent for all tested methods (r=0.87-0.98). Bland-Altman plots showed a lower agreement for velocity and power compared to force. Mean force, velocity and power were reliable for all methods (ICC=0.84-0.99), especially for Samozino's method (CV=2.7-8.6%). Our findings showed that present methods are valid and reliable in different loading conditions and permit between-session comparisons and characterization of training-induced effects. While linear position transducer and accelerometer allow for examining the whole time-course of kinetic patterns, Samozino's method benefits from a better reliability and ease of processing.

This study proposes a new method to evaluate arm-leg coordination in flat breaststroke. Five arm and leg stroke phases were defined with a velocity-video system. Five time gaps quantified the time between arm and leg actions during three paces of a race (200 m, 100 m and 50 m) in 16 top level swimmers. Based on these time gaps, effective glide, effective propulsion, effective leg insweep and effective recovery were used to identify the different stroke phases of the body. A faster pace corresponded to increased stroke rate, decreased stroke length, increased propulsive phases, shorter glide phases, and a shorter T1 time gap, which measured the effective body glide. The top level swimmers showed short time gaps (T2, T3, T4, measuring the timing of arm-leg recoveries), which reflected the continuity in arm and leg actions. The measurement of these time gaps thus provides a pertinent evaluation of swimmers' skill in adapting their arm-leg coordination to biomechanical constraints.

No abstract

In rodents, the electroencephalogram (EEG) during paradoxical sleep and exploratory behavior is characterized by theta oscillations. Here we show that a deficiency in short-chain acyl-coenzyme A dehydrogenase (encoded by Acads) in mice causes a marked slowing in theta frequency during paradoxical sleep only. We found Acads expression in brain regions involved in theta generation, notably the hippocampus. Microarray analysis of gene expression in mice with mutations in Acads indicates overexpression of Glo1 (encoding glyoxylase 1), a gene involved in the detoxification of metabolic by-products. Administration of acetyl-L-carnitine (ALCAR) to mutant mice significantly recovers slow theta and Glo1 overexpression. Thus, an underappreciated metabolic pathway involving fatty acid beta-oxidation also regulates theta oscillations during sleep.