J Sleep Res. 2020;00:e13170. wileyonlinelibrary.com/journal/jsr
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1 of 11 https://doi.org/10.1111/jsr.13170© 2020 European Sleep Research Society Received: 18 May 2020
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Revised: 27 July 2020|
Accepted: 28 July 2020DOI: 10.1111/jsr.13170
R E G U L A R R E S E A R C H P A P E R
Disruption of neocortical synchronisation during slow-wave
sleep in the rotenone model of Parkinson’s disease
Gustavo Zampier dos Santos Lima
1,2| Adriano D. S. Targa
3,4,5,6|
Samantha de Freitas Cavalcante
1| Lais S. Rodrigues
3,4| John Fontenele-Araújo
7|
Pablo Torterolo
8| Monica L. Andersen
9| Marcelo M. S. Lima
3,4Gustavo Zampier dos Santos Lima and Adriano D. S. Targa contributed equally to the manuscript.
1Science and Technology School, Federal
University of Rio Grande do Norte, Natal, Brazil
2Department of Biophysics and
Pharmacology, Federal University of Rio Grande do Norte, Natal, Brazil 3Department of Physiology, Federal University of Paraná, Curitiba, Brazil 4Department of Pharmacology, Federal University of Paraná, Curitiba, Brazil 5 Hospital Universitari Arnau de Vilanova-Santa Maria, IRBLleida, Translational Research in Respiratory Medicine, Lleida, Spain 6Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain 7Department of Physiology and Behavior, Federal University of Rio Grande do Norte, Natal, Brazil 8Department of Physiology, University of the Republic, Montevideo, Uruguay 9Department of Psychobiology, Federal University of São Paulo, São Paulo, Brazil Correspondence Marcelo M. S. Lima, Av. Francisco H. dos Santos, 100, Centro Politécnico, Jardim das Américas, 81531-980, Curitiba, Paraná, Brazil.
Email: [email protected]; marcelomslima. [email protected] Funding information This study was supported by Associação Fundo de Incentivo à Pesquisa (AFIP), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance code 001, Fundação Araucária (Programa de Apoio a Núcleos de Excelência—PRONEX, research grant 116/2018), and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). MLA, JFA and MMSL are recipients of CNPq fellowship.
Abstract
Parkinson’s disease motor dysfunctions are associated with improperly organised neural oscillatory activity. The presence of such disruption at the early stages of the disease in which altered sleep is one of the main features could be a relevant predic-tive feature. Based on this, we aimed to investigate the neocortical synchronisation dynamics during slow-wave sleep (SWS) in the rotenone model of Parkinson’s dis-ease. After rotenone administration within the substantia nigra pars compacta, one group of male Wistar rats underwent sleep–wake recording. Considering the asso-ciation between SWS oscillatory activity and memory consolidation, another group of rats underwent a memory test. The fine temporal structure of synchronisation dynamics was evaluated by a recently developed technique called first return map. We observed that rotenone administration decreased the time spent in SWS and altered the power spectrum within different frequency bands, whilst it increased the transition rate from a synchronised to desynchronised state. This neurotoxin also increased the probability of longer and decreased the probability of shorter desyn-chronisation events. At the same time, we observed impairment in object recognition memory. These findings depict an electrophysiological fingerprint represented by a disruption in the typical oscillatory activity within the neocortex at the early stages of Parkinson’s disease, concomitant with a decrease in the time spent in SWS and impairment in recognition memory.
K E Y W O R D S
1 | INTRODUCTION
Parkinson’s disease is characterised by the loss of dopaminergic neu-rons within the substantia nigra pars compacta (SNpc), leading to motor symptoms such as bradykinesia, tremors, and gait disturbances, amongst others (Braak et al., 2003; Goedert, Spillantini, Del Tredici, & Braak, 2013). In addition, non-motor signs such as sleep distur- bances can precede the motor symptoms by years or decades, dras-tically affecting the quality of life of patients with Parkinson’s disease (Lima, 2013). Sleep studies have reported a significant decrease in slow-wave sleep (SWS) duration in patients with Parkinson’s disease and in animal models of the disease; whereas alterations in the time spent in rapid eye movement (REM) sleep are less clear (Targa et al., 2016). Recent polysomnographic evaluations suggested an important role of brain synchronisation among different areas, demonstrating that exces-sively increased, decreased or improperly organised oscillatory activity may lead to symptoms of different diseases (Helfrich, Mander, Jagust, Knight, & Walker, 2018; Nimmrich, Draguhn, & Axmacher, 2015). Accordingly, increased oscillatory activity in the subthalamic nucleus (STN) and in both external and internal segments of the globus pallidus (GPe and GPi, respectively) are associated with impairments in the ex-ecution of movements and with the incidence of tremors in Parkinson’s disease (Ahn, Zauber, Worth, Witt, & Rubchinsky, 2015). Also, lesions or high-frequency stimulation in either GPi or STN improve Parkinson’s disease symptoms due to a decrease in the abnormal synchronisation within the basal ganglia (Schnitzler & Gross, 2005).
Despite the evidence related to motor symptoms, the synchroni-sation dynamics at the early stages of Parkinson’s disease remain to be investigated. In fact, the presence of oscillatory disruption before these symptoms could be a relevant predictive feature. Also, an alter-ation in synchronisthese symptoms could be a relevant predictive feature. Also, an alter-ation dynamics may affect not only motor func-tion, but sleep and memory as well (Maingret, Girardeau, Todorova, Goutierre, & Zugaro, 2016; Miyamoto, Hirai, & Murayama, 2017). To further investigate these aspects, we used the rotenone animal model of Parkinson’s disease. This model mimics an early stage of the disease with different features such as anxiety (Noseda, Targa, Rodrigues, Aurich, & Lima, 2016), depressive-like behaviour (Noseda et al., 2014), olfactory dysfunction (Ilkiw et al., 2018; Rodrigues et al., 2014), cognitive impairment (Targa, Noseda, Rodrigues, Aurich, & Lima, 2018), and sleep disturbances (Targa et al., 2016). In addition, we employed a recently developed technique to characterise the fine temporal structure of intermittent phase-locking in a wide range of oscillatory systems (Ahn, Park, & Rubchinsky, 2011; dos Santos Lima et al., 2019; Park, Worth, & Rubchinsky, 2010). We hypothesised that rotenone administration would lead to a disruption in the oscillatory activity, in parallel with a decrease in SWS duration and an impair-ment in recognition memory consolidation.
2 | METHODS
This study was approved by the Ethics Committee of Federal University of Paraná (memory experiment; ID #857) and Federal
University of São Paulo (sleep experiment; ID #9022050417). The experiments were carried out in accordance with the Guidelines of Ethics and Experimental Care and Use of Laboratory Animals (SBCAL). Male Wistar rats, weighing approximately 280–320 g were kept in a temperature-controlled room (22 ± 2°C), with a 12:12 hr light–dark cycle (lights on at 07:00 hours). During the sleep experi-ment, the rats were maintained in groups of five within polypro-pylene cages up to the day of surgery, when they were moved to individual cages to avoid the destruction of sleep connectors. In the memory experiment, the rats were maintained in groups during the entire study. Bottles of water and food pellets were available throughout the entire experiment.
2.1 | Experimental design
The first set of rats (n = 10) underwent the sleep experiment (Figure 1a). The rats underwent stereotaxic surgery for rotenone or dimethylsulphoxide (DMSO) administration within the SNpc and implantation of cortical electrodes for sleep–wake recording. After 7 days to recover from surgery, animals were submitted to the sleep-wake recording procedure at 9 a.m. for 6 hr. On the next day, they were sedated with intraperitoneal (IP) xylazine (10 mg/ kg; Syntec do Brasil Ltda, Brazil) and euthanised with IP ketamine (270 mg/kg; Syntec do Brasil Ltda, Brazil). To assure the euthana-sia procedure, we used a guillotine as the physical method. The second set of rats (n = 16) underwent the memory experi-ment (Figure 1b). On day 0, the rats underwent stereotaxic surgery for rotenone or DMSO administration within the SNpc. The habitu-ation phase of the object recognition test (ORT) took place on days 3, 5 and 7. After the last habituation (day 7), the rats performed the training for the ORT and then, on the next day (day 8), they per-formed the test phase of the ORT. Finally, the rats were sedated with IP xylazine (10 mg/kg; Syntec do Brasil Ltda, Brazil), anaesthetised with IP ketamine (270 mg/kg; Syntec do Brasil Ltda, Brazil) and then perfused for the immunohistochemistry procedure.
2.2 | Stereotaxic surgery
The rats were sedated with IP xylazine (10 mg/kg; Syntec do Brasil Ltda, Brazil) and anaesthetised with IP ketamine (90 mg/ kg; Syntec do Brasil Ltda, Brazil). For rotenone infusion within the SNpc, we used bregma as a reference for the following co-ordinates: anterior–posterior (AP) = −5.0 mm, medial–lateral (ML) = 2.1 mm, and dorsal–ventral (DV) = −8.0 mm (Paxinos & Watson, 2004). Rotenone (12 μg/μl; Sigma-Aldrich) or DMSO 10% v/v (Sigma-Aldrich, USA) bilateral infusions were per-formed using an electronic infusion pump (Insight Instruments) at a rate of 0.33 μl/min for 3 min (Targa et al., 2016). For the sleep recording procedure, stainless steel screws (Ø 0.9 mm) electrodes were implanted in the skull using the following co-ordinates: AP = −1.8 mm, ML = −2.0 mm (primary motor cortex,
first electrode) and AP = 3.0 mm, ML = 1.0 mm (secondary motor cortex, second electrode) (with bregma as a reference); AP = 1.0 mm, ML = −4.0 mm (primary visual cortex, third elec-trode) and AP = 4.0 mm, ML = 1.0 mm (retrosplenial dysgranular cortex, fourth electrode) (with lambda as a reference) (Paxinos & Watson, 2004). Two nickel-chromium flexible wires were inserted into the neck muscles to record the electromyogram (EMG) (Lima, Andersen, & Reksidler, 2008; Targa et al., 2016).
2.3 | Sleep recording procedure and classification
Electrophysiological signals were recorded with a bipolar de-sign (electrodes 1–3 [channel A] and 2–4 [channel B]) on a digital polygraph (Neurofax QP 223A Nihon Kohden) (Figure 1a). Afterconventional amplification, the signals were conditioned through analogical filters, using cut-off frequencies of 1.0 and 35.0 Hz (only for sleep staging), and were then sampled at 200 Hz using a 16-bit A/D converter. The recordings were divided into epochs of 10-s in-tervals and classified as wakefulness, SWS or REM sleep. In addition, the number and duration of SWS episodes were evaluated. After this, we selected all the SWS fragments with appropriate quality and ≥80 s in duration.
2.4 | Procedures for EEG power spectrum analysis
To perform the EEG power spectrum analysis and investigate the inter-hemispheric synchronisation dynamics, the raw (unfil-tered) EEG signals from the selected SWS fragments were used F I G U R E 1 Experimental design. Panel (a) represents the sleep experiment. Electrophysiological signals were recorded with a bipolar design (electrodes 1–3 [channel A] and 2–4 [channel B]). Panel (b) represents the memory experiment. Memory consolidation was evaluated by the ORT. The SNpc representation was adapted from the Paxinos and Watson Stereotaxic atlas (Paxinos & Watson, 2004). ORT, object recognition test; SNpc, substantia nigra pars compacta(Figure 2a–c). We first selected and filtered the frequency ranges of interest: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), low beta (13–20 Hz), high beta (21–30 Hz) and gamma (40–80 Hz). As an example, Figure 2d shows a 1-s zoom in the time window of the EEG reconstructed in the 4–8 Hz frequency band. After this, we reconstructed the signals using two functions: “butter” and “filtfilt” from MatLab (Mathworks). The first one consists of a digital filter designed to have a flat frequency response (no ripples) in the pass-band and zero roll-off response in the stop-band. Thus, we had the corresponding time-series reconstructed for each chosen band. To obtain better results in terms of accu- racy, values of distortion were close to zero and the total mag-nitude was modified by the square of the magracy, values of distortion were close to zero and the total mag-nitude from the previous filter. From this, the MatLab function “filtfilt” was used strategically to move the “signals” back and forward, minimising the beginnings and the endings of transitions by the initial match-ing (Figure 2e).
2.5 | Phase-locking analysis
According to previous studies, the recommended way to analyse neu-ral fluctuations between weakly coupled brain regions is by investigat-ing their synchronisation dynamics using the phase domain (Lachaux, Rodriguez, Martinerie, & Varela, 1999; Le Van Quyen & Bragin, 2007; Varela, Lachaux, Rodriguez, & Martinerie, 2001). Phase-locking implies temporal coordination between signals. We can observe synchronisa-tion dynamics when the coupling between phases increases from zero to low values, while the amplitudes of oscillations remain uncorrelated (Pikovsky, Rosenblum, Kurths, & Hilborn, 2002). Thus, the phase is an appropriate measure to study weakly synchronised systems.
In this study, we observed the time-series reconstructed during SWS into one of six frequency bands mentioned above. Then, we followed the steps as previously described (Hurtado, Rubchinsky, & Sigvardt, 2004; Park et al., 2010). A digital filter was used and sam-pled at 200 Hz. In the sequence, we used Butterworth filter and
F I G U R E 2 The fine temporal method to identify synchronisation/desynchronisation episodes between neocortical areas during SWS. In the first panels (a, b), the mean (±SEM) spectra of EEG signals from the channel A and the channel B for the Sham (a) and Rotenone (b) groups are represented. The small boxes inside of each of these panels are a magnified representation of the EEG power spectrum of frequency bands <12 Hz (the dotted lines in red indicate the threshold of significance). In the panel (c), there are 10-s raw signals from channels A and B of the sham group. In (d), there is a magnified representation of a short time window (1 s) from EEG signals raw data (observed in C). Panel (e) represents the filtered EEG signals observed in (d) in the 4–8 Hz frequency band (1-s time window). Panel (f) shows the sines of both EEG filtered signals in the 4–8 Hz frequency band. There is no amplitude information, and both signals have a variation between 1 and −1, but the phase information is preserved. The black circles indicate the phases from channel A in which the phases from channel B cross from negative to positive values. The grey dotted box represents a desynchronisation event with the duration of 1 cycle (in synch– out of synch–in synch). In panel (g) there is the diagram (representation) of the first return map of phases (θx, i, θx, i + 1) for all possible transitions (ri), which are indicated by arrows. i = 1, 2, 3, 4 represents the corresponding transition rates. Panel (h) displays an example of the desynchronised event observed in panel (f). The example is showing the 1 cycle (c1) path (the shortest) of desynchronisation before returning to the synchronisation state (region I). Other desynchronization paths may happen with a duration of c2, c3, and >c4 cycles, within the desynchronisation areas (II, III, IV), respecting the rule shown by the arrows
zero-phase filtering was used to avoid phase distortions displayed in Figure 2e (theta frequency band was chosen to exemplify this). The phase was extracted via Hilbert transform resulting in the sine time-series of phases (Figure 2f). For each pair of these EEG time-series, we computed the phase-locking index for two phases θ1(t) and θ2(t): 𝛾=1∕N∑Nj = 1ei𝜃�tj
�2, in which
𝜃(tj) = 𝜃1(tj) − 𝜃2(tj
) was the phase difference (when constant, both time-series were synchronised with each other), and N was the number of data points (Hurtado et al., 2004; Pikovsky et al., 2002). The index fluctuated from zero (complete lack of constancy in phase differences) to 1 (perfect phase-locking). Nevertheless, this index is not designed to describe the fine temporal structure of the dynamics, rather it provides an overall index of phase synchrony (Ahn, Rubchinsky, & Lapish, 2014; Park et al., 2010); thus, it cannot be used to inspect whether the oscillations are at the preferred phase lag or not at each cycle, even if it is evaluated on a short time window.
2.6 | First return map and the fine
temporal structure
To evaluate the fine temporal structure of the dynamics of syn-chronisation, we used a method called the first return map (Ahn & Rubchinsky, 2013; Ahn, Solfest, & Rubchinsky, 2014; Park et al., 2010; Rubchinsky, Ahn, & Park, 2014). In general, whenever the phase of the reference signal (e.g. the EEG from channel A) crossed the fixed checkpoint chosen, we recorded the phase of the other signal (channel B). This generated a set of consecutive phase values (θchannel B,i), i = 1,..., N, where N was the number crossing in the data (Figure 2f; black dots over the signal), representing the phase difference between these two signals. Then, the θ(i + 1) versus θ(i) was plotted for i = 1,..., N−1 (Figure 2g,h).
Synchronisation dynamics will appear as a cluster of points with the centre at the diagonal i + 1 = i of the phase-space (Ahn et al., 2011; Ahn, Rubchinsky, et al., 2014; Park et al., 2010). After de-termining the centre of the cluster (predominantly locked), all values of the phases are shifted to a position with the centre of the quad-rant I. Thus, this approach is not concerned with the value of phase, but considers how the system leaves the synchronisation (quadrant I to II) and its vicinity, and how it returns back to synchronisation (quadrant IV to I), by quantifying transitions between different quad-rants of the phase-space (Figure 2g,h).
2.7 | Transition rates and
desynchronisation duration
The transition rates, i.e. transitions among the four quadrants of the first return map (represented by the green arrows in Figure 2g), were defined by the number of points in the quadrant of interest divided by the total number of points in the previous quadrant. Thus: (a) r1 was the ratio between the number of paths that escaped from quadrant I to quadrant II divided by the number of all points in the previous quadrant I (the system left the synchronised state). The case (1–r1) indicated how much the system preferred to keep in quadrant I (keep synchronised). In this sense, the dynamics outside quadrant I were classified as a desynchronised event; (b) r2 was the ratio of the number of paths that escaped from quadrant II to quad-rant IV divided by the number of all points in quadrant II; (c) r3 was the ratio of the number of paths that escaped from quadrant III to quadrant IV divided by the number of all points in quadrant III; iv) r4 was the ratio of the number of paths that escaped from quadrant IV to quadrant I divided by the number of all points in quadrant IV (the system returned to the synchronised state). As the phase-space represents the current phase versus the future phase, the transition in this space is not arbitrary, therefore, assuming strict transitions shown by the green arrows in Figure 2g. Furthermore, knowing that the values of these rates vary according to the probabil- ity principle (between 0 and 1), we have: (a) r1 as how often the syn-chronisation is lost; (b) r2 as the number of times the system goes to quadrant IV, instead of going to quadrant III; (c) r3 as the number of times the system goes to quadrant IV, instead of staying in quadrant III; (d) r4 as the number of times that the system returns to quadrant I (returning to the synchronised state). Thus, low values of r4 indicate that the system frequently returns to quadrant II instead of quadrant I, meaning that desynchronisation events can be long (high [1–r4]). The duration of a desynchronisation event was characterised by the number of steps that the system spent away from the first quad- rant minus 1 (Park et al., 2010). The shortest duration of the desyn-chronisation event (desynrant minus 1 (Park et al., 2010). The shortest duration of the desyn-chronisation of one cycle) corresponded to the shortest path, passing in quadrants II–IV–I. The desynchroni-sation of two cycles corresponded to the path through quadrants II– III–IV–I. Longer desynchronisation events had many different paths over quadrants II, III, and IV before returning to quadrant I.
2.8 | Object recognition test (ORT)
To investigate the memory consolidation process, we used the ORT. The apparatus used consisted of an open box (width × length × height = 60 × 60 × 50 cm) made of wood and cov-ered with a black opaque plastic film. The objects to be discriminated were available in triplicate copies and were made of biologically neu-tral material such as glass, plastic or metal. This test is based on the tendency of the animals to explore new things instead of familiar things. Thus, when an animal remembers a familiar object and does not know a new object, the tendency is that this animal explores the new object for a longer time when compared to the familiar object. The ORT in this study consisted of three phases: the habituation phase, the sample/training phase, and the choice/test phase. In the habituation phase, the rats had 3 min on days 3, 5 and 7 to explore the arena without the objects. During the training phase (15 min after habituation on day 7), two identical objects were placed in the back corners of the open box, 10 cm away from the sidewall. The rat was placed in the open box facing away from the objects and after 3 min of exploration the rat was removed from the open box and returned
to its cage. Then 24 hr later (test phase, 3 min of duration), two ob-jects were presented in the same locations that were occupied by the previous sample objects. One of the objects was identical to the object seen in the training phase and the other one was different. The apparatus was cleaned with ethanol solution (70%) between each session to prevent the influence of the previous rats’ odour. The tests were video recorded and analysed by an experimenter blind to the treatments. It was considered as exploration only when the rat touched the object with its nose or when the rat’s nose was directed toward an object at a distance of ≤2 cm. The Index was obtained using the following formula: Index = [(time spent exploring the new object – time spent exploring the familiar object)/(time spent explor-ing the new object + time spent explorobject – time spent exploring the familiar object)/(time spent explor-ing the familiar object)] × 100.
2.9 | Tyrosine hydroxylase (TH)
immunohistochemistry
For TH-immunoreactive neurons quantification, the rats were se-dated with IP xylazine (10 mg/kg; Syntec do Brasil Ltda, Brazil) and anaesthetised with ketamine (90 mg/kg). After this, each rat was intracardially perfused with a saline solution, followed by a fixative solution of 4%formaldehyde in 0.1 M phosphate buffer (pH 7.4). Brains were removed from skulls and were immersed in a fixative solution at 4°C. Then 48 hr later, the brain was immersed in a 30% sucrose solution for 3 days and finally stored in a −80°C freezer be-fore sectioning. At this stage, we collected six slices per rat of 40 μm each, corresponding to the SNpc (Paxinos & Watson, 2004). The SNpc slices were incubated with primary mouse anti-TH antibody (1:500; Chemicon). Biotin-conjugated secondary antibody (1:200 anti-mouse; Vector Laboratories), was localised using the ABC sys-tem (Vectastain ABC Elite kit, Vector Laboratories), followed by 3,3'-diaminobenzidine reaction with nickel enhancement. Neuronal density was determined by the software Image J (National Institutes of Health, USA). Considering that each slice presented the left and right hemisphere, we quantified the TH-immunoreactive neuronal density 12 times for each rat, from which we obtained a mean value. After this, a mean value was obtained for each of the groups (sham and rotenone). Finally, the mean value of the rotenone group was converted to a percentage relative to the mean value of the sham group. The images were obtained using a motorised Axio Imager Z2 microscope (Carl Zeiss), equipped with an automated scanning VSlide (Metasystems).
2.10 | Statistical analysis
Comparisons between groups were performed by the unpaired t test (parametric data) and Kolmogorov–Smirnov test (non-para- metric data). The analyses were performed in GraphPad Prism ver-sion 8 software (GraphPad Software) and MatLab (Mathworks). The p values were corrected for multiple testing using the Bonferroni
correction test. Corrected p ≤ .05 were considered statistically significant.
3 | RESULTS
3.1 | SNpc TH-immunoreactive neurons
The immunohistochemistry was performed to evaluate the exten-sion of the lesion promoted by rotenone administration (Figure S1). We observed a decrease of 55.1% in TH-immunoreactive neuronal density within the SNpc after the administration of this neuro-toxin (p ≤ .05) (for detailed descriptive statistics of the results, see Table S1).3.2 | Sleep macrostructure
Rotenone administration decreased the time spent in SWS and in-creased the time spent awake (p ≤ .01, for both). There was no sig-nificant difference in relation to the time spent in REM sleep (Figure S2a–c). Similarly, the number and duration of SWS episodes were not different between the groups (Figure S2d, e).
3.3 | EEG power spectrum analysis
We observed that the rotenone administration altered the power spectrum in both channels (Figure S3). In channel A, there was a sig-nificant decrease in the power within the 2–3 Hz frequency bands and an increase within the 5–8 Hz range (Figure S3a, c). Differently, rotenone decreased the power in channel B within the 2, 3, 14, 16–18 Hz frequency bands (Figure S3b, d).
3.4 | Synchronisation dynamics: transition rates
The transition rates for synchronised/desynchronised states dur-ing SWS were represented by r1–r4, in which r1 represented the transition rate from the synchronised to the desynchronised state, r2–r3 represented transitions within a desynchronised state and r4 represented the return to synchronisation (Figure 2g). We observed low transition rates from the synchronised to the desynchronised state in the sham group, demonstrated by a low value of r1 (~0.2) (Figure 3). In contrast, the transition rate r4 was high (~0.8). The tran-sition rates of r2 and r3 showed intermediate values between r1 and r4. In addition, r2 and r3 were close to r4 values in low-frequency bands (1–8 Hz) (Figure 3a,b) and lower than r4 in high-frequency bands (8–80 Hz) (Figure 3c–f). In the rotenone group, we observed a similar pattern to that observed in the sham group, with low values of r1 and high values of r4. The transition rates r2 and r3 also dis-played intermediate values, with values closer to r4 in low-frequencybands (1–8 Hz) and a decrease in those values as the frequency of the bands increased.
Comparing the transition rates between the groups, we observed that r1 was increased in the rotenone group compared to the sham group in all frequency bands analysed (Figure 3). In contrast, the transition rate from desynchronisation to synchronisation states (r4) was decreased in the rotenone group when compared to the sham group in all frequency bands except that of 4–8 Hz (Figure 3b) and 21–30 Hz (Figure 3e). In relation to the transition rates r2 and r3, we observed that the rotenone group values were lower than the sham group ones, except in 1–4 Hz (Figure 3a) and 13–20 Hz (Figure 3d) (for r3) and in 4–8 Hz (Figure 3b) and 40–80 Hz (Figure 3f) (for r2) frequency bands.
3.5 | Synchronisation dynamics: duration of events
The duration of desynchronisation events during SWS was repre-sented by c1–c4, in which c1 represented the probability of a cycle of short duration, c2–c3 represented the probability of cycles of intermediate duration and c4 represented the probability of cy-cles of long duration. We observed that the probability of a cycle with short duration (c1) was high, whereas it decreased as the du-ration of the cycles increased, for all frequency bands (Figure 4). Furthermore, the drop in the probability from c1 to >c4 was abruptin low-frequency bands (1–8 Hz) (Figure 4a, b) and smoother in the high-frequency bands (8–80 Hz) (Figure 4c–f). Comparing the de-synchronisation events duration between the groups, we observed that the rotenone decreased the probability of short desynchroni- sation events (c1) and increased the probability of long desynchro-nisation events (>c4).
We also calculated the desynchronisation duration ratio in which higher values indicated a lower probability of having long desynchronisation events. The low-frequency bands (1–8 Hz) had an increased desynchronisation duration ratio compared to high-frequency bands. In fact, there was a decrease in this ratio as the frequency of bands increased in both groups (Figure 5a). To investigate the effect of rotenone, we computed the desynchro-nisation duration ratio values of both groups. The results showed values greater than one for all frequency bands, i.e. rotenone re- duced this ratio, indicating a higher probability of having long de-synchronisation events. This effect was prominent in the 4–12 Hz frequency bands (Figure 5b).
3.6 | Object recognition memory
The ORT was performed to observe the effects of rotenone on mem-ory consolidation (Figure S4). Rotenone administration impaired the object recognition memory, considering that the rotenone group did F I G U R E 3 Synchronisation dynamics: transition rates. The figure represents the SWS transition rates (r1, r2, r3, r4) obtained from EEG data using first return map for six frequency bands: (a) delta (1–4 Hz); (b) theta (4–8 Hz); (c) alpha (8–12 Hz); (d) low beta (13–20 Hz); (e) high beta (20–30 Hz); and (f) gamma (40–80 Hz). Values are expressed as median (interquartile range). *p ≤ .05; **p ≤ .01; ***p ≤ .001. Kolmogorov–Smirnov test. n = 5 rats/group. The arrows were placed at r1 and r4 as an eye guide only when a significant change in the mean probability values was observed between groups
not differentiate the familiar and new objects, while the sham group was capable of accomplishing this task. This resulted in a positive index for the sham group that was statistically different from the rotenone group (p ≤ .05).
4 | DISCUSSION
We demonstrated that a lesion within the SNpc induced by rote-none administration decreased the time spent in SWS and altered the power spectrum within different frequency bands. Aiming to investigate whether this outcome was associated with an alteration in the synchronisation dynamics within the neocortex, we employed a powerful, recently developed technique, to characterise the tem-poral structure of intermittent phase-locking in oscillatory systems (Ahn et al., 2011; Park et al., 2010). Different from the expected inter-hemispheric synchronisation during SWS, rotenone administration increased the transition rate from a synchronised to a desynchro-nised state in this context. In addition, once in the desynchronised condition, the rotenone group had a lower probability of returning to synchronisation. Furthermore, we demonstrated that this neuro-toxin increased the probability of longer and decreased the prob-ability of shorter desynchronisation events. These outcomes were parallel to impairment in object recognition memory.
The role of SNpc and dopaminergic system in sleep regulation has gained considerable attention in recent decades (Lima, 2013;
Lima, Andersen, Reksidler, Vital, & Tufik, 2007). We previously demonstrated that rotenone not only reduces the time spent in SWS, but also prevents the sleep rebound after REM sleep depriva- tion (Targa et al., 2016). In addition, the decrease in SWS is not eas-ily reversed, at least not by L-DOPA administration (García-García, Ponce, Brown, Cussen, & Krueger, 2005). In the present study, we investigated whether the reduction in SWS was due to a decrease in the number and/or duration of the episodes, but no significant differences between the groups were observed. Also, we demon-strated that rotenone administration altered the power spectrum within different frequency bands, which was also observed with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) adminis-tration (Mizrahi-Kliger, Kaplan, Israel, Deffains, & Bergman, 2020), but not in the 6-hydroxydopamine (6-OHDA) animal model (Cavelli et al., 2019).
Previous studies demonstrated that improperly organised corti-cal oscillatory activity is associated with the appearance of symptoms of different diseases (Helfrich et al., 2018; Nimmrich et al., 2015). In Parkinson’s disease, there is an increase in the oscillatory activity be- tween STN, GPi and GPe, which is related to the incidence of trem-ors and impairment of movement execution (Ahn et al., 2015). Also, lesions or high-frequency stimulation in either GPi or STN improve motor symptoms of Parkinson’s disease due to a decrease in the ab-normal oscillatory activity within the basal ganglia (Ahn et al., 2015; Schnitzler & Gross, 2005). In the present study, we did not investi-gate the effect of rotenone-induced desynchronisation on the motor F I G U R E 4 Synchronisation dynamics: duration of events. The figure represents the desynchronisation duration cycles (c1, c2, c3, c4 and >c4) obtained from EEG data using first return map for six frequency bands: (a) delta (1–4 Hz); (b) theta (4–8 Hz); (c) alpha (8–12 Hz); (d) low beta (13–20 Hz); (e) high beta (20–30 Hz); and (f) gamma (40–80 Hz). Values are expressed as median (interquartile range). *p ≤ .05; **p ≤ .01; ***p ≤ .001. Kolmogorov–Smirnov test. n = 5 rats/group. The arrows were placed at c1 and >c4 as an eye guide only when a significant change in the mean probability values was observed between groups
activity of the rats. In fact, at the chosen dose and protocol, rotenone does not affect motor function, effectively mimicking an early stage of the disease (Fagotti et al., 2019; Targa et al., 2018). In turn, we ob-served that the rotenone-induced desynchronisation was associated with a decrease in SWS and impairment in object recognition mem-ory. Accordingly, the organised oscillatory activity among different brain areas is an important event for memory consolidation. Various studies have shown that temporally ordered firing sequences related to a recent experience are replayed in both the hippocampus and neocortex during SWS (Mehta, 2007). Also, top-down cortico-corti- cal long-range circuits during SWS, as well as inter-hemispheric con-nectivity, may play a relevant role in this context (Geiger, O’Gorman Tuura, & Klaver, 2016; Miyamoto et al., 2017). Based on that, it is possible to speculate that rotenone, by altering the transition rates (increasing the probability to escape from the synchronised to the desynchronised state and decreasing the probability to return to synchronisation) and the desynchronisation events duration (dimin-ishing the probability of shorter and augmenting the probability of longer desynchronisation events) disrupted the typical oscillatory activity between neocortical areas, interfering with the homeostatic maintenance of local synapses, which ultimately led to object recog-nition memory impairment.
Further investigations will be necessary to address some points that could not be established in the present study. First, it will be important to elucidate the physiological and clinical relevance
associated with the rotenone-induced alteration of synchronisa-tion dynamics. In fact, due to our methodological approach, it was not possible to infer whether the decreased synchronisation or the decreased time in SWS effectively led to object recognition mem-ory impairment. The rotenone administration might have affected sleep regulation and cognitive function by independent mecha-nisms. Also, detailed investigations should address the effects of this neurotoxin in the organised oscillatory activity between hip-pocampal sharp wave-ripples, cortical delta waves, and spindles during SWS, which are important players in memory consolida-tion. Furthermore, different methodological approaches will allow the investigation of rotenone-induced desynchronisation effects on specific phases of memory processing as acquisition, consoli-dation, and retrieval.
In summary, our present findings demonstrate an electrophys-iological fingerprint represented by a disruption in the typical os-cillatory activity within the neocortex in the rotenone model of Parkinson’s disease, concomitant with a decrease in the time spent in SWS and impairment in recognition memory. Clinical studies will be necessary to confirm the existence of such disruption in humans, shedding light on the potential role of oscillatory activity in predict- ing the disease at the early stages. Furthermore, if the proper cau-sality relationships are established, this feature could be a promising target to decrease non-motor signs or avert the appearance of motor symptoms.
F I G U R E 5 Desynchronisation duration ratio in the different frequency bands. Panel (a) represents the desynchronisation duration ratios,
following the c1/>c4 ratio rule, in each frequency band for the sham and rotenone groups. The panel inside (a) shows an example of the desynchronisation rate rule c1/>c4 using theta frequency band (4–8 Hz) for the sham group (as pointed by the arrows). Panel (b) shows the ratio of sham and rotenone desynchronisation duration ratios. Each bar in (a) and (b) plots the mean ± SEM of the given distribution
ACKNOWLEDGEMENTS
We would like to express our gratitude to Juliane Fagotti (Physiology Department, Federal University of Paraná) for the concept and design of Figure 1 and to Dunga Aires Leite (Psychobiology Department, Federal University of São Paulo) for the technical assistance.
CONFLIC T OF INTEREST
The authors declare that no conflict of interests exists.
AUTHOR CONTRIBUTION
Gustavo Zampier dos Santos Lima and Adriano D. S. Targa: Conceptualization, Methodology, Formal analysis, Investigation, Writing (original draft, review and editing); Samantha de Freitas Cavalcante and Lais S. Rodrigues: Investigation, Writing (review and editing); John Fontenele-Araújo and Pablo Torterolo: Writing (re-view and editing); Monica L. Andersen: Resources, Writing (review and editing); Marcelo M. S. Lima: Conceptualization, Methodology, Resources, Writing (review and editing), Funding acquisition. ORCID
Gustavo Zampier dos Santos Lima http://orcid. org/0000-0002-4462-0522
Adriano D. S. Targa https://orcid.org/0000-0002-7577-7661
Lais S. Rodrigues https://orcid.org/0000-0002-7965-1361
John Fontenele-Araújo http://orcid.org/0000-0002-8022-2425
Pablo Torterolo https://orcid.org/0000-0002-3531-5008
Monica L. Andersen https://orcid.org/0000-0002-1894-6748
Marcelo M. S. Lima https://orcid.org/0000-0001-9602-4880 REFERENCES
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SUPPORTING INFORMATION
Additional supporting information may be found online in the Supporting Information section.
How to cite this article: dos Santos Lima GZ, Targa ADS, de
Freitas Cavalcante S, et al. Disruption of neocortical synchronisation during slow-wave sleep in the rotenone model of Parkinson’s disease. J Sleep Res. 2020;00:e13170.
