How do crawfish sleep

On one occasion, a crayfish with electrodes implanted onto the brain placed itself in that recumbent position; at that moment we thought it would be adequate to record its brain activity. Surprisingly, the oscilloscope records showed slow waves with a frequency of approximately 10 Hz. Shortly after, the animal regained its vertical position at the bottom of the aquarium and we observed the multitude of characteristic spikes of normal brain record. In view of this finding, we decided to wait for the crayfish to put itself on its side to obtain new records.

We performed the corresponding tests on an animal while sleeping, to see if the pattern corresponded to that cerebral state Fig. Figure 2 Records of a crayfish lying on the water surface. The black bar indicates the nocturnal period. A: Time during which the crayfish moves around the aquarium.

Most movement corresponds to the hours around the light-dark and dark-light transitions; B: Intensity-duration curves obtained by applying vibratory stimuli when the animal is moving curves Curves correspond to stimuli applied to animals standing still and curve 12, to an animal lying on water; C: Records of the period during which the crayfish is lying on the water.

A and C show an example among 12 recorded cases. Initially, the records were analyzed using the fast Fourier transform to obtain frequencies, a strategy turned out not being the best for studying the encephalographic records, and thus using the wavelet transform was decided.

It should be remembered that the Fourier transform allows the analysis of a series of periodic signals, but only delivers a space of frequency and intensity. Conversely, the wavelet transform provides a much more complete space, where intensity, frequency and time coincide, and hence it is more useful for identifying where and when frequency changes occur in the brain records. Figure 3 A: Photograph and electrophysiological record of a crayfish standing at the bottom of an aquarium.

In a subsequent stage, the crayfish brain was mapped to locate the region that produces the slow waves. The crayfish was placed in a tank with water where it was fixed to a stem leaving all its appendages free.

We made a hole in the cephalothorax to expose the dorsal and anterior surface of the brain, in order to enable choosing the region to be recorded Fig. Figure 4 Crayfish suspended in the water in a position that allowed electrophysiological record of the brain, while the animal was free to move the appendices.

The recorded cerebral regions are indicated in the brain diagram with symmetric dark dots from 1 to 8 : E1 and E3, lateral protocerebrum; E2, protocerebral bridge; E4, central brain complex; E5 and E6, deutocerebrum accessory lobe; E7 and E8, tritocerebrum image taken from Mendoza-Angeles et al. Our results show that independently of the brain state, active or passive, the central complex generates continuous oscillations that are not seen at other brain regions.

Slow waves in the accessory lobe of deutocerebrum are seen while the animal is limp, and disappear when it becomes active. Lateral protocerebrum is not initially involved in these waves and a few milliseconds are required for them to invade it.

However, occasionally a communication between the central complex and the rest of the brain opens up, and slow waves similar to those of the central complex E4 appear at the deutocerebrum E5 and E6 , while the animal also shows all other signs of sleep. The blockade by cooling the central complex area stops spreading of the slow waves to the rest of the brain and other behavioral signs of sleep, indicating that this is the area where the slow waves originate.

A comparison between records in Fig. Slow waves recorded from the central complex E4 and the accessory lobe of deutocerebrum E6 have a mean time difference of 32 ms, while between the accessory lobe of deutocerebrum and protocerebrum the difference is only of 2.

Assuming that fibers at the border region between the central complex and deutocerebrum of the crayfish brain are coupled by electrical synapses, a decrease in coupling would result in such propagation delay. We speculate that during sleep the slow waves reach the brain through neuron—glia interactions or by electrotonical coupling Coles and Abbott, ; Traub et al. Cross-correlation shows that records from the accessory lobe of deutocerebrum are always better correlated to the central complex than those from protocerebrum, corroborating that in passive or active animals slow waves move from the central complex to deuto- and then to protocerebrum Fig.

Results from the clustering analysis Fig. Thus, the central complex seems to act as an oscillator that synchronizes the deutocerebrum during sleep. Slow waves in response to appropriate stimulus have been recorded in many preparations from invertebrates to vertebrates and receive different interpretations Gelperin and Tank, ; Delaney et al. However, spontaneous, self-sustained and continuous oscillations seem present only in the hippocampus of vertebrates Fischer, , where an intrinsic oscillator with synaptic interactions and facilitating gap junction mediate axo-axonic interactions.

Models of spontaneous and self-sustained oscillations through axo-axonal gap junctions have been developed to explain such behavior Lewis and Rinzel, ; Maex and De Schutter, Sleep in invertebrates has been difficult to demonstrate and to be accepted by vertebrate physiologists. Since the early proposal from Kaiser and Steiner-Kaiser Kaiser and Steiner-Kaiser, , and from Tobler Tobler, and Tobler and Stalder Tobler and Stalder, , for almost two decades there were no references to sleep in invertebrates despite efforts made on the issue Kaiser, Perhaps one of the main reasons resides in the absence, in those works, of brain electrical recordings from unrestrained animals, which was difficult to obtain because of technical constraints.

The multiple methods used by Hendricks et al. Hendricks et al. Shaw et al. Notwithstanding brain recordings from tethered mutant flies performed by Nitz et al. Nitz et al. The slow waves recorded in crayfish do not have the same frequency as those recorded from vertebrate brains, and the brain activity from alert invertebrates is also different from similar records in vertebrates.

Nevertheless, there are common features in both groups of animals. During sleep, the threshold for sensory stimuli increases, cognitive processes are reduced and there is a homeostatic regulation and changes in the brain electrical activity Volgushev et al.

However, because sleep is a function of, by, and for the brain Hobson, , this is the organ whose changes define the function. However, it is possible to speculate that a similar central oscillator is at the core of the synchronization producing the slower frequency waves in both groups of animals.

A puzzling finding during this work is the continuous rhythmic activity in the central complex of the brain, although persistent oscillatory electrical activity is not uncommon. In some cases the oscillations change during the sleep—wake cycle and persist even under sedation Hunt et al. However, there are examples of neuronal spontaneous electrical oscillations whose function is known, such as neurons from the isolated suprachiasmatic nucleus, which become coupled to perform two main functions, i.

Although we do not know the functions of the oscillations recorded from neurons in the central complex of the crayfish brain, the following speculation seems reasonable. In crayfish the central complex of the brain, with its multiple synaptic arborizations Sandeman et al.

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Close mobile search navigation Article navigation. Volume , Issue Previous Article Next Article. Article contents. Article Navigation. Slow waves during sleep in crayfish. This site. Google Scholar. Author and article information. Karina Mendoza-Angeles. Accepted: 28 Jan Online Issn: J Exp Biol 12 : — Article history Accepted:. Cite Icon Cite. View large Download slide. Search ADS. Synchronization-induced rhythmicity of circadian oscillators in the suprachiasmatic nucleus.

Comparison of ongoing compound field potentials in the brains of invertebrates and vertebrates. Timing in the cerebellum: oscillations and resonance in the granular layer. Waves and stimulus-modulated dynamics in an oscillating olfactory network. Odor-modulated collective network oscillations of olfactory interneurons in a terrestrial mollusc.

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Not Helpful 5 Helpful Include your email address to get a message when this question is answered. By using this service, some information may be shared with YouTube.

You cannot really tame a crayfish, but it will get a little less wild if you are gentle and consistent. Helpful 5 Not Helpful 0. When a crayfish is scared it tends to tuck its tail and run backward.

If this happens, return the crayfish to his tank and try playing another time. Helpful 3 Not Helpful 0. Play with your crayfish at least once a day or you won't form a strong bond with it.

Helpful 2 Not Helpful 0. Submit a Tip All tip submissions are carefully reviewed before being published. Do not drop your crayfish! Its outer skeleton is rather thin and brittle. Don't play with your crayfish after it has just shed as it is too delicate. Wait until its shell hardens again.

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By signing up you are agreeing to receive emails according to our privacy policy. Follow Us. Note how the second cluster connects clusters 1 and 3, and its correlation pattern appears to be comparable to that of the third cluster.

In general, the correlation pattern presented at the beginning of sleep changes over time; it suggests that the characteristics and patterns of sleep derived from brain and cardiorespiratory electrical activity total average power change over time, too.

Once we found that a temporal interrelation exists during sleep, we analyzed each complete sleeping episode by k- means clustering to get a clear measure about the number of the clusters present. For this purpose, only the data corresponding to sleep episode were considered.

To avoid any bias, we computed 30 indices for determining the number of clusters see Data Analysis. Results for clustering analysis corresponding to the same complete sleeping episode are presented in Figure 12B. A Pearson correlation matrix derived from brain and cardiorespiratory electrical activity total average power. This displays the temporal evolution of sleep over a complete sleeping episode of about 20 min. Each segment shows the coefficient correlation of total average power into 30 s windows.

The figure also includes a minute before and after sleeping episode white square. The correlation is coded in color positive correlations—dark red and negative correlations—dark blue.

Each qualitatively identified cluster is framed with colors square: cluster 1 green square , cluster 2 red square , cluster 3 blue square. B k- means clustering corresponding to the same sleeping episode shown in A; each cluster represents a different pattern during sleep.

The observations of data are represented by points using principal components, PCA1 explains A concentration ellipse was drawn around each cluster. Green square—cluster 1, red diamonds—cluster 2, and blue triangles—cluster 3. The observations see Data Analysis section are represented by points using principal components, and we drew a concentration ellipse around each cluster.

The first cluster is composed of a few segments green ellipse , these values corresponding with the beginning of the sleeping episode around 3 min , and it is the smallest cluster. The second cluster is represented with a red ellipse; this is mainly related to probability distributions broader with large tails. The last one blue ellipse concentrates most observations. These results are consistent with the pattern shown in the correlation matrix compare Figures 12A,B.

This strongly suggests that the second group may share characteristics with the other two groups, and it maybe a phase of transition in which patterns from group 1 and 3 coexist. For each sleep episode see Supplementary Material to review more sleep episodes , we estimate the same measurements, and the results are consistent. Until now, we have demonstrated that in each complete sleeping episode a correlation pattern exits; at least three different patterns were identified each one is represented by a cluster.

We have presented detailed results for a sleeping episode. It remains to be shown whether these patterns reflect a crayfish-specific characteristic of brain and cardiorespiratory electrical activity or whether these represent more general features or a generic pattern across crayfish. For this purpose, we realized a k -means clustering at the group level, which included all corresponding sleeping episodes per crayfish. Results of this analysis are shown in Figure Each panel represents the global clustering per crayfish; each one includes all corresponding sleeping episodes.

For each cluster, the median for the corresponding variable is represented. Cluster 1 green line , cluster 2 red line , and cluster 3 blue line. The analysis at the group level derived from brain Hz , cardiac all frequency bands , and respiratory all frequency bands electrical activity per crayfish shows similarities between crayfish sleep. Note how each crayfish presents three different clusters during sleep into each cluster the data median corresponding with each variable is represented.

Cluster 1 green line has no clear trend, although it seemingly presents higher values than the other groups. Clusters 2 and 3 red and blue line show a pattern seemingly anti-correlated between them, although they appear to share some characteristics.

Each cluster presents specific characteristics derived from brain and cardiorespiratory activity. Note how these three groups identified during sleep are presented in the three crayfish studied. However, as is observed in Figure 13 , in the case of crayfish 1, the differences between cluster 1 and cluster 2 are marginal; this makes sense because k -means shows that cluster 2 preserves characteristics common to the rest of the groups.

However, we found that this large decrease in power within the Hz band extends up to 60 Hz Figures 2 , 3. These results suggest that it remains to be explored if the pronounced decrease in signal power extends to frequencies above 60 Hz. Furthermore, the physiological relevance for such behavior is completely unclear but worth investigating.

According with our behavioral analysis Figure 1C , crayfish shows a third position in which it remains motionless for a considerable amount of time with both chelae resting on the bottom of the aquarium and sometimes with antennae and antennulae lowered and motionless resting. During this time, we found via WT analysis that the brain electrical activity shows characteristics in between that lying on one side and that waking Figures 2 , 3.

In many cases, we found the same decrease in power at frequencies Hz; in others, values were closer to those from awake animals. Therefore, these results possibly also indicate another sleeping state in crayfish. The depth of sleep, measured as the power of EEG activity, changes over time, and power decreases while sleep deepens see Figure 5 ; this strongly suggests that crayfish has sleep phases.

The study from the brain in conjunction with the cardiorespiratory activity allows us to determine that sleep in crayfish is comprised by phases of various durations that do not seem to have a cyclic pattern Figures 5 , 11 , Our results suggest that crayfish has at least three different sleep phases: phase 1 drowsy period: at the beginning of the sleep period, the EEG and cardiorespiratory power show high values, and crayfish does not fall asleep immediately after lying on one side; like vertebrates, it requires some time to fall asleep.

Generally, this pattern changes after about 3 min Figures 5 , 11 , 12 , segment 6 and eventually, it fades out to phase 2 phase transition: this represents moderate deep sleep. After-sleep-onset wave power decreases with time segments 7 to 14, Figures 5 , 11 , Phase 3 is the deepest level of sleep: it is characterized by a further decrease in EEG and cardiorespiratory activity power, together with short, intermittent bursts of high-power waves segments 9 to 37, Figures 5 , We found that each one of these phases is conserved across crayfish Figure Although these results suggest that crayfish present sleep phases as in vertebrates, it is important to emphasize that they are quite different, and we do not intend to equate both and do not suggest similar generating mechanisms.

A possible reason for the difference between sleep and sleep phases in crayfish and vertebrate animals is because the brain structure and the architecture are completely different. In this invertebrate, there are no cortices or cortex-like structures, nuclei, or an organization that resembles the vertebrate brain.

Our study demonstrates, behaviorally and electrophysiologically, that sleep in crayfish, as in mammals and birds, is a dynamic and heterogeneous state. This suggests that sleep is a conserved function and the different sleep phases are a fundamental characteristic of sleep, maybe in any animal.

In mammals, sleep stages are well characterized, and it is known that a variety of additional physiological changes take place during the different sleep stages as compared to wakefulness.

In vertebrates, these changes are mediated by the autonomic nervous system ANS , whose actions are mediated by the sympathetic nervous system and the parasympathetic nervous system. During REM sleep, HR increases again showing a high variability which may exceed that observed during quiet wakefulness Zemaityte et al. The panorama seems quite different for invertebrates, particularly crustaceans.

In this group of animals, there are no anatomical structures resembling an ANS, but there are behavioral and cardiorespiratory responses indicative of an autonomic-like regulation. Recently, we reported for the first time that changes in these variables occur during sleep, too Osorio-Palacios et al. Here, we studied if these physiological changes take place during the different sleep stages.

Our results show that in crayfish, the heart rate and respiratory frequency are regulated during wakefulness Figures 6 , 7 and during the different sleep phases Figure 11 , as it occurs in vertebrates. By using the Pearson correlation matrix and k- means clustering, we found that brain and cardiorespiratory activity are related during sleep.

The last one confirmed that data can be arranged in three clearly separated groups Figures 12B , 13 , which gives support to the idea of three completely different sleep phases determined for the brain in conjunction with cardiorespiratory activity.

According to these, these phases of sleep are accompanied by changes in autonomic variables. Electrocardiogram ECG frequency variations have been studied extensively in vertebrates. Three main oscillatory components are present in HR vertebrates variability, very low frequency VLF , marker of hormonal and circadian oscillations, low-frequency component LF , marker of sympathetic modulation, and high-frequency component HF , marker of vagal modulation and synchronous with respiration Montano et al.

Electrocardiogram analysis has been widely used for the assessment of cardiovascular autonomic control during sleep, showing a progressive decrease of the LF component, marker of sympathetic modulation, and a predominant vagal control, as sleep becomes deeper from wakefulness to deep NREM sleep. Rapid eye movement sleep is characterized by a predominant sympathetic modulation with surges of sympathetic activity at levels even higher than in wake conditions Trinder et al.

In the case of crayfish, so far we identified three main oscillatory components from ECG signals VLF around 2 Hz, LF 3 to 12 Hz, and HF between 13 and 45 Hz Figure 10 , and these results were enough to achieve our main goal, but they suggest that a detailed study from cardiac variability in crayfish would provide more evidence about the existence of a functional ANS as it happens in vertebrates.

As we previously mentioned, in crayfish there are no descriptions of an ANS, and we ignore the mechanisms and pathways mediating this regulation of cardiorespiratory activity during wakefulness and sleep.

One possibility is that excitatory sympathetic-like and parasympathetic-like circuits would be allocated in a region of brain named tritocerebrum. Another possibility relays in the suboesophageal ganglion, where command neurons were reported a long time ago Wiersma and Novitski, ; Maynard, ; Taylor, ; Field and Larimer, a , b. We do not have evidence of an equivalent mechanism for respiratory regulation.

In this study, we analyzed physiological time series from crayfish brain and cardiorespiratory electrical activity by WT, Pearson correlation matrix, and unsupervised learning techniques k- means analysis to search for sleep phases and determine the relationship between these activities during sleep.

These techniques allow us to state that 1 in crayfish there are at least three different sleep phases and 2 changes in physiological variables like HR and RF take place during different sleep phases. Sleep phase 1 drowsy period : EEG has high power and is accompanied by cardiorespiratory electrical activity also of high power, as high as those found in wakefulness. This phase is present at the beginning of sleep. Sleep phase 2 transition phase presents a further decrease in power of EEG waves and cardiorespiratory activity; this phase can present characteristics from phases 1 and 3.

Sleep phase 3 deepest level of sleep : the predominant EEG power consists of low power, with some burst of high-power activity; cardiac frequency and respiratory frequency are also reduced to their lowest values during this phase. The main purpose in future studies will be to characterize the different sleep phases.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. All authors participated in the discussion of the results, reviewed, and approved the final version of the manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

We thank Markus F. Abdi, H. Principal component analysis. Wiley Interdiscip. Addison, P. Wavelet transforms and the ECG: a review. Arellano-Tirado, S.

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