Decreased electrocortical temporal complexity distinguishes sleep from wakefulness

Characterising the power spectrum dynamics of the non-REM to REM sleep transition

The transition from non-rapid eye movement (NREM) to rapid eye movement (REM) sleep is considered a transitional or intermediate stage (IS), characterised by high amplitude spindles in the frontal cortex and theta activity in the occipital cortex. Early reports in rats showed an IS lasting from 1 to 5 s, but recent studies suggested a longer duration of this stage of up to 20 s. To further characterise the IS, we analysed its spectral characteristics on electrocorticogram (ECoG) recordings of the olfactory bulb (OB), primary motor (M1), primary somatosensory (S1), and secondary visual cortex (V2) in 12 Wistar male adult rats. By comparing the IS with consolidated NREM/REM epochs, our results reveal that the IS has specific power spectral patterns that fall out of the NREM and REM sleep state power distribution. Specifically, the main findings were that sigma (11-16 Hz) power in OB, M1, S1, and V2 increased during the IS compared with NREM and REM sleep, which started first in the frontal part of the brain (OB -54 s, M1 -53 s) prior to the last spindle occurrence. The beta band (17-30 Hz) power showed a similar pattern to that of the sigma band, starting -54 s before the last spindle occurrence in the M1 cortex. Notably, sigma infraslow coupling (~0.02 Hz) increased during the IS but occurred at a slower frequency (~0.01 Hz) compared with NREM sleep. Thus, we argue that the NREM to REM transition contains its own local spectral profile, in accordance with previous reports, and is more extended than described previously.

Altered sleep onset transition in depression: Evidence from EEG activity and EEG functional connectivity analyses

OBJECTIVE

Sleep disorders constitute a principal diagnostic criterion for depression, potentially reflecting the abnormal persistence of brain activity during the sleep onset (SO) transition. Here, we sought to explore the differences in the dynamic changes in the EEG activity and the EEG functional connectivity (FC) during the SO transition in depressed patients.

METHODS

Overnight polysomnography recordings were obtained from thirty-two depressed patients and thirty-three healthy controls. The multiscale permutation entropy (MSPE) and EEG relative power were extracted to characterize EEG activity, and weighted phase lag index (WPLI) was calculated to characterize EEG FC.

RESULTS

The intergroup differences in EEG activity of relative power and MSPE were reversed near SO, which attributed to slower rates of change among depressed patients. Regarding the characteristics of the EEG FC network, depressed patients exhibited significantly higher inter-hemispheric and interregional WPLI values in both delta and alpha bands throughout the SO transition, concomitant with different dynamic properties in the delta band FC. During the process after SO, patients exhibited increased inter-hemispheric long-range links, whereas controls showed more intra-hemispheric ones. Finally, we found significant correlations in the dynamic changes between different EEG measures.

CONCLUSIONS

Our research revealed that the abnormal changes during the SO transition in depressed patients were manifested in both homeostatic and dynamic aspects, which were reflected in EEG FC and EEG activity, respectively.

SIGNIFICANCE

These findings may elucidate the mechanism underlying sleep disorders in depression from the perspective of neural activity.

Noribogaine acute administration in rats promotes wakefulness and suppresses REM sleep

Ibogaine is a potent atypical psychedelic that has gained considerable attention due to its antiaddictive and antidepressant properties in preclinical and clinical studies. Previous research from our group showed that ibogaine suppresses sleep and produces an altered wakefulness state, which resembles natural REM sleep. However, after systemic administration, ibogaine is rapidly metabolized to noribogaine, which also shows antiaddictive effects but with a distinct pharmacological profile, making this drug a promising therapeutic candidate. Therefore, we still ignore whether the sleep/wake alterations depend on ibogaine or its principal metabolite noribogaine. To answer this question, we conducted polysomnographic recordings in rats following the administration of pure noribogaine. Our results show that noribogaine promotes wakefulness while reducing slow-wave sleep and blocking REM sleep, similar to our previous results reported for ibogaine administration. Thus, we shed new evidence on the mechanisms by which iboga alkaloids work in the brain.

Dorsal and median raphe neuronal firing dynamics characterized by nonlinear measures

The dorsal (DRN) and median (MRN) raphe are important nuclei involved in similar functions, including mood and sleep, but playing distinct roles. These nuclei have a different composition of neuronal types and set of neuronal connections, which among other factors, determine their neuronal dynamics. Most works characterize the neuronal dynamics using classic measures, such as using the average spiking frequency (FR), the coefficient of variation (CV), and action potential duration (APD). In the current study, to refine the characterization of neuronal firing profiles, we examined the neurons within the raphe nuclei. Through the utilization of nonlinear measures, our objective was to discern the redundancy and complementarity of these measures, particularly in comparison with classic methods. To do this, we analyzed the neuronal basal firing profile in both nuclei of urethane-anesthetized rats using the Shannon entropy (Bins Entropy) of the inter-spike intervals, permutation entropy of ordinal patterns (OP Entropy), and Permutation Lempel-Ziv Complexity (PLZC). Firstly, we found that classic (i.e., FR, CV, and APD) and nonlinear measures fail to distinguish between the dynamics of DRN and MRN neurons, except for the OP Entropy. We also found strong relationships between measures, including the CV with FR, CV with Bins entropy, and FR with PLZC, which imply redundant information. However, APD and OP Entropy have either a weak or no relationship with the rest of the measures tested, suggesting that they provide complementary information to the characterization of the neuronal firing profiles. Secondly, we studied how these measures are affected by the oscillatory properties of the firing patterns, including rhythmicity, bursting patterns, and clock-like behavior. We found that all measures are sensitive to rhythmicity, except for the OP Entropy. Overall, our work highlights OP Entropy as a powerful and useful quantity for the characterization of neuronal discharge patterns.

Wavelet Entropy Analysis of Electroencephalogram Signals During Wake and Different Sleep Stages in Patients with Insomnia Disorder

Objective

To investigate the changes in the wavelet entropy during wake and different sleep stages in patients with insomnia disorder.

Methods

Sixteen patients with insomnia disorder and sixteen normal controls were enrolled. They underwent scale assessment and two consecutive nights of polysomnography (PSG). Wavelet entropy analysis of electroencephalogram (EEG) signals recorded from all participants in the two groups was performed. The changes in the integral wavelet entropy (En) and individual-scale wavelet entropy (En(a)) during wake and different sleep stages in the two groups were observed, and the differences between the two groups were compared.

Results

The insomnia disorder group exhibited lower En during the wake stage, and higher En during the N3 stage compared with the normal control group (all P < 0.001). In terms of En(a), patients with insomnia disorder exhibited lower En(a) in the β and α frequency bands during the wake stage compared with normal controls (β band, P < 0.01; α band, P < 0.001), whereas they showed higher En(a) in the β and α frequency bands during the N3 stage than normal controls (β band, P < 0.001; α band, P < 0.001).

Conclusion

Wavelet entropy can reflect the changes in the complexity of EEG signals during wake and different sleep stages in patients with insomnia disorder, which provides a new method and insights about understanding of pathophysiological mechanisms of insomnia disorder. Wavelet entropy provides an objective indicator for assessing sleep quality.

Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine

AIMS

Sleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time-consuming and labor-intensive. In this study, we performed an automatic sleep-wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats.

METHODS

The study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep-wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD.

RESULTS

Features extracted from occipital ECoG signals were more suitable for constructing sleep-wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep-wake states of rats (average Cohen's kappa: 0.79).

CONCLUSION

This study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep-wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably.

Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches

Developing reliable methodologies to decode brain state information from electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG-based brain-computer interfaces (BCIs). For example, signal processing methods that identify brain states could allow motor-impaired patients to communicate via non-invasive, EEG-based BCIs. In this work, we focus on the problem of distinguishing between the states of eyes closed (EC) and eyes open (EO), employing quantities based on permutation entropy (PE). An advantage of PE analysis is that it uses symbols (ordinal patterns) defined by the ordering of the data points (disregarding the actual values), hence providing robustness to noise and outliers due to motion artifacts. However, we show that for the analysis of multichannel EEG recordings, the performance of PE in discriminating the EO and EC states depends on the symbols' definition and how their probabilities are estimated. Here, we study the performance of PE-based features for EC/EO state classification in a dataset of N=107 subjects with one-minute 64-channel EEG recordings in each state. We analyze features obtained from patterns encoding temporal or spatial information, and we compare different approaches to estimate their probabilities (by averaging over time, over channels, or by "pooling"). We find that some PE-based features provide about 75% classification accuracy, comparable to the performance of features extracted with other statistical analysis techniques. Our work highlights the limitations of PE methods in distinguishing the eyes' state, but, at the same time, it points to the possibility that subject-specific training could overcome these limitations.

Urethane anaesthesia exhibits neurophysiological correlates of unconsciousness and is distinct from sleep

Urethane is a general anaesthetic widely used in animal research. The state of urethane anaesthesia is unique because it alternates between macroscopically distinct electrographic states: a slow-wave state that resembles non-rapid eye movement (NREM) sleep and an activated state with features of both REM sleep and wakefulness. Although it is assumed that urethane produces unconsciousness, this has been questioned because of states of cortical activation during drug exposure. Furthermore, the similarities and differences between urethane anaesthesia and physiological sleep are still unclear. In this study, we recorded the electroencephalogram (EEG) and electromyogram in chronically prepared rats during natural sleep-wake states and during urethane anaesthesia. We subsequently analysed the power, coherence, directed connectivity and complexity of brain oscillations and found that EEG under urethane anaesthesia has clear signatures of unconsciousness, with similarities to other general anaesthetics. In addition, the EEG profile under urethane is different in comparison with natural sleep states. These results suggest that consciousness is disrupted during urethane. Furthermore, despite similarities that have led others to conclude that urethane is a model of sleep, the electrocortical traits of depressed and activated states during urethane anaesthesia differ from physiological sleep states.

Sleep disrupts complex spiking dynamics in the neocortex and hippocampus

Neuronal interactions give rise to complex dynamics in cortical networks, often described in terms of the diversity of activity patterns observed in a neural signal. Interestingly, the complexity of spontaneous electroencephalographic signals decreases during slow-wave sleep (SWS); however, the underlying neural mechanisms remain elusive. Here, we analyse in-vivo recordings from neocortical and hippocampal neuronal populations in rats and show that the complexity decrease is due to the emergence of synchronous neuronal DOWN states. Namely, we find that DOWN states during SWS force the population activity to be more recurrent, deterministic, and less random than during REM sleep or wakefulness, which, in turn, leads to less complex field recordings. Importantly, when we exclude DOWN states from the analysis, the recordings during wakefulness and sleep become indistinguishable: the spiking activity in all the states collapses to a common scaling. We complement these results by implementing a critical branching model of the cortex, which shows that inducing DOWN states to only a percentage of neurons is enough to generate a decrease in complexity that replicates SWS.

Discriminating rapid eye movement sleep from wakefulness by analyzing high frequencies from single-channel EEG recordings in mice

Rapid eye movement sleep (REMS) is characterized by the appearance of fast, desynchronized rhythms in the cortical electroencephalogram (EEG), similar to wakefulness. The low electromyogram (EMG) amplitude during REMS distinguishes it from wakefulness; therefore, recording EMG signal seems to be imperative for discriminating between the two states. The present study evaluated the high frequency components of the EEG signal from mice (80-500 Hz) to support REMS detection during sleep scoring without an EMG signal and found a strong positive correlation between waking and the average power of 80-120 Hz, 120-200 Hz, 200-350 Hz and 350-500 Hz. A highly negative correlation was observed with REMS. Furthermore, our machine learning approach demonstrated that simple EEG time-series features are enough to discriminate REMS from wakefulness with sensitivity of roughly 98 percent and specificity of around 92 percent. Interestingly, assessing only the higher frequency bands (200-350 Hz as well as 350-500 Hz) gives significantly greater predictive power than assessing only the lower end of the EEG frequency spectrum. This paper proposes an approach that can detect subtle changes in REMS reliably, and future unsupervised sleep-scoring approaches could greatly benefit from it.

Ordinal methods for a characterization of evolving functional brain networks

Ordinal time series analysis is based on the idea to map time series to ordinal patterns, i.e., order relations between the values of a time series and not the values themselves, as introduced in 2002 by C. Bandt and B. Pompe. Despite a resulting loss of information, this approach captures meaningful information about the temporal structure of the underlying system dynamics as well as about properties of interactions between coupled systems. This-together with its conceptual simplicity and robustness against measurement noise-makes ordinal time series analysis well suited to improve characterization of the still poorly understood spatiotemporal dynamics of the human brain. This minireview briefly summarizes the state-of-the-art of uni- and bivariate ordinal time-series-analysis techniques together with applications in the neurosciences. It will highlight current limitations to stimulate further developments, which would be necessary to advance characterization of evolving functional brain networks.

Dynamic Characteristics of State Transitions Composed of Neural Activity in the Brain by Circadian Rhythms

In recent years, as a treatment for mental disorders in addition to drug treatment, a non-drug treatment called chronotherapy has been attracting attention. However, the achievement of optimized chronotherapy for each subject's condition requires that the disturbance of the patient's circadian rhythm must be captured over a long duration. Therefore, it is necessary to develop biomarkers that are easy to measure, quantitative, and continuously measured. Complexity analysis of electroencephalograms revealed specific patterns related to circadian rhythms. However, such complexity analysis cannot capture variability in spatial patterns, although moment-to-moment temporal dynamic characteristics can be captured. Therefore, it is necessary to evaluate the dynamic characteristics of the interaction of neural activity throughout the brain. To evaluate the dynamic whole-brain interaction, we proposed a new microstate approach based on the instantaneous frequency distribution. In this context, we hypothesized that it would be possible to detect circadian rhythms using the microstate approach. In this study, to clarify the dynamic interactions of the entire neural network of the brain by circadian rhythms, we measured EEG data at day and night, and detected dynamic state transitions based on the instantaneous frequency distribution of the whole brain from EEG. The results showed the probability of transition among region-specific phase-leading states related to circadian rhythms. This finding might be widely utilized to detect circadian rhythms in healthy and pathological conditions.

Different Frequency Bands in Various Regions of the Brain Play Different Roles in the Onset and Wake-Sleep Stages of Infantile Spasms

OBJECTIVE

The study aimed to identify the signatures of brain networks using electroencephalogram (EEG) in patients with infantile spasms (IS).

METHODS

Scalp EEGs of subjects with IS were prospectively collected in the first year of life (n = 8; age range 4-8 months; 3 males, 5 females). Ten minutes of ictal and interictal EEGs were clipped and filtered into different EEG frequency bands. The values of each pair of EEG channels were directly compared between ictal with interictal onsets and the sleep-wake phase to calculate IS brain network attributes: characteristic path length (CPL), node degree (ND), clustering coefficient (CC), and betweenness centrality (BC).

RESULTS

CPL, ND, and CC of the fast waves decreased while BC increased. CPL and BC of the slow waves decreased, while ND and CC increased during the IS ictal onset (P < 0.05). CPL of the alpha decreased, and BC increased during the waking time (P < 0.05).

CONCLUSION

The transmission capability of the fast waves, the local connectivity, and the defense capability of the slow waves during the IS ictal onset were enhanced. The alpha band played the most important role in both the global and local networks during the waking time. These may represent the brain network signatures of IS.

Changes in EEG permutation entropy in the evening and in the transition from wake to sleep

Quantifying the complexity of the EEG signal during prolonged wakefulness and during sleep is gaining interest as an additional mean to characterize the mechanisms associated with sleep and wakefulness regulation. Here, we characterized how EEG complexity, as indexed by Multiscale Permutation Entropy (MSPE), changed progressively in the evening prior to light off and during the transition from wakefulness to sleep. We further explored whether MSPE was able to discriminate between wakefulness and sleep around sleep onset and whether MSPE changes were correlated with spectral measures of the EEG related to sleep need during concomitant wakefulness (theta power-Ptheta: 4-8 Hz). To address these questions, we took advantage of large datasets of several hundred of ambulatory EEG recordings of individual of both sexes aged 25-101 years. Results show that MSPE significantly decreases before light off (i.e. before sleep time) and in the transition from wakefulness to sleep onset. Furthermore, MSPE allows for an excellent discrimination between pre-sleep wakefulness and early sleep. Finally, we show that MSPE is correlated with concomitant Ptheta. Yet, the direction of the latter correlation changed from before light-off to the transition to sleep. Given the association between EEG complexity and consciousness, MSPE may track efficiently putative changes in consciousness preceding sleep onset. An MSPE stands as a comprehensive measure that is not limited to a given frequency band and reflects a progressive change brain state associated with sleep and wakefulness regulation. It may be an effective mean to detect when the brain is in a state close to sleep onset.

Glutamatergic Neurons in the Preoptic Hypothalamus Promote Wakefulness, Destabilize NREM Sleep, Suppress REM Sleep, and Regulate Cortical Dynamics

Clinical and experimental data from the last nine decades indicate that the preoptic area of the hypothalamus is a critical node in a brain network that controls sleep onset and homeostasis. By contrast, we recently reported that a group of glutamatergic neurons in the lateral and medial preoptic area increases wakefulness, challenging the long-standing notion in sleep neurobiology that the preoptic area is exclusively somnogenic.

Clinical and experimental data from the last nine decades indicate that the preoptic area of the hypothalamus is a critical node in a brain network that controls sleep onset and homeostasis. By contrast, we recently reported that a group of glutamatergic neurons in the lateral and medial preoptic area increases wakefulness, challenging the long-standing notion in sleep neurobiology that the preoptic area is exclusively somnogenic. However, the precise role of these subcortical neurons in the control of behavioral state transitions and cortical dynamics remains unknown. Therefore, in this study, we used conditional expression of excitatory hM3Dq receptors in these preoptic glutamatergic (Vglut2⁺) neurons and show that their activation initiates wakefulness, decreases non-rapid eye movement (NREM) sleep, and causes a persistent suppression of rapid eye movement (REM) sleep. We also demonstrate, for the first time, that activation of these preoptic glutamatergic neurons causes a high degree of NREM sleep fragmentation, promotes state instability with frequent arousals from sleep, decreases body temperature, and shifts cortical dynamics (including oscillations, connectivity, and complexity) to a more wake-like state. We conclude that a subset of preoptic glutamatergic neurons can initiate, but not maintain, arousals from sleep, and their inactivation may be required for NREM stability and REM sleep generation. Further, these data provide novel empirical evidence supporting the hypothesis that the preoptic area causally contributes to the regulation of both sleep and wakefulness.

SIGNIFICANCE STATEMENT Historically, the preoptic area of the hypothalamus has been considered a key site for sleep generation. However, emerging modeling and empirical data suggest that this region might play a dual role in sleep-wake control. We demonstrate that chemogenetic stimulation of preoptic glutamatergic neurons produces brief arousals that fragment sleep, persistently suppresses REM sleep, causes hypothermia, and shifts EEG patterns toward a “lighter” NREM sleep state. We propose that preoptic glutamatergic neurons can initiate, but not maintain, arousal from sleep and gate REM sleep generation, possibly to block REM-like intrusions during NREM-to-wake transitions. In contrast to the long-standing notion in sleep neurobiology that the preoptic area is exclusively somnogenic, we provide further evidence that preoptic neurons also generate wakefulness.

EEG Gamma Band Alterations and REM-like Traits Underpin the Acute Effect of the Atypical Psychedelic Ibogaine in the Rat

Ibogaine is a psychedelic alkaloid that has attracted large scientific interest because of its antiaddictive properties in observational studies in humans as well as in animal models. Its subjective effect has been described as intense, vivid dream-like experiences occurring while awake; hence, ibogaine is often referred to as an oneirogenic psychedelic. While this unique dream-like profile has been hypothesized to aid the antiaddictive effects, the electrophysiological signatures of this psychedelic state remain unknown. We previously showed in rats that ibogaine promotes a waking state with abnormal motor behavior along with a decrease in NREM and REM sleep. Here, we performed an in-depth analysis of the intracranial electroencephalogram during "ibogaine wakefulness". We found that ibogaine induces gamma oscillations that, despite having larger power than control levels, are less coherent and less complex. Further analysis revealed that this profile of gamma activity compares to that of natural REM sleep. Thus, our results provide novel biological evidence for the association between the psychedelic state and REM sleep, contributing to the understanding of the brain mechanisms associated with the oneirogenic psychedelic effect of ibogaine.

Microsleep disturbances are associated with noradrenergic dysfunction in Parkinson's disease

STUDY OBJECTIVES

Parkinson's disease (PD) commonly involves degeneration of sleep-wake regulating brainstem nuclei; likewise, sleep-wake disturbances are highly prevalent in PD patients. As polysomnography macroparameters typically show only minor changes in PD, we investigated sleep microstructure, particularly cyclic alternating pattern (CAP), and its relation to alterations of the noradrenergic system in these patients.

METHODS

We analysed 27 PD patients and 13 healthy control (HC) subjects who underwent over-night polysomnography and 11C-MeNER positron emission tomography for evaluation of noradrenaline transporter density. Sleep macroparameters as well as CAP metrics were evaluated according to the consensus statement from 2001. Statistical analysis comprised group comparisons and correlation analysis of CAP metrics with clinical characteristics of PD patients as well as noradrenaline transporter density.

RESULTS

PD patients and HC subjects were comparable in demographic characteristics (age, sex, body mass index) and polysomnography macroparameters. CAP rate as well as A index differed significantly between groups, with PD patients having a lower CAP rate (46.7 ± 6.6% versus 38.0 ± 11.6%, p = 0.015) and lower A index (49.0 ± 8.7/hour versus 40.1 ± 15.4/hour, p = 0.042). In PD patients, both CAP metrics correlated significantly with diminished noradrenaline transporter density in arousal prompting brainstem nuclei (locus coeruleus, raphe nuclei) as well as arousal propagating brain structures like thalamus and bitemporal cortex.

CONCLUSIONS

Sleep microstructure is more severely altered than sleep macrostructure in PD patients and is associated with widespread dysfunction of the noradrenergic arousal system.

Power and Coherence in the EEG of the Rat: Impact of Behavioral States, Cortical Area, Lateralization and Light/Dark Phases

The sleep-wake cycle is constituted by three behavioral states: wakefulness (W), non-REM (NREM) and REM sleep. These states are associated with drastic changes in cognitive capacities, mostly determined by the function of the thalamo-cortical system, whose activity can be examined by means of intra-cranial electroencephalogram (iEEG). With the purpose to study in depth the basal activity of the iEEG in adult rats, we analyzed the spectral power and coherence of the iEEG during W and sleep in the paleocortex (olfactory bulb), and in neocortical areas. We also analyzed the laterality of the signals, as well as the influence of the light and dark phases. We found that the iEEG power and coherence of the whole spectrum were largely affected by behavioral states and highly dependent on the cortical areas recorded. We also determined that there are night/day differences in power and coherence during sleep, but not in W. Finally, we observed that, during REM sleep, intra-hemispheric coherence differs between right and left hemispheres. We conclude that the iEEG dynamics are highly dependent on the cortical area and behavioral states. Moreover, there are light/dark phases disparities in the iEEG during sleep, and intra-hemispheric connectivity differs between both hemispheres during REM sleep.

Transition of Neural Activity From the Chaotic Bipolar-Disorder State to the Periodic Healthy State Using External Feedback Signals

Chronotherapy is a treatment for mood disorders, including major depressive disorder, mania, and bipolar disorder (BD). Neurotransmitters associated with the pathology of mood disorders exhibit circadian rhythms. A functional deficit in the neural circuits related to mood disorders disturbs the circadian rhythm; chronotherapy is an intervention that helps resynchronize the patient's biological clock with the periodic daily cycle, leading to amelioration of symptoms. In previous reports, Hadaeghi et al. proposed a non-linear dynamic model composed of the frontal and sensory cortical neural networks and the hypothalamus to explain the relationship between deficits in neural function in the frontal cortex and the disturbed circadian rhythm/mood transitions in BD (hereinafter referred to as the Hadaeghi model). In this model, neural activity in the frontal and sensory lobes exhibits periodic behavior in the healthy state; while in BD, this neural activity is in a state of chaos-chaos intermittency; this temporal departure from the healthy periodic state disturbs the circadian pacemaker in the hypothalamus. In this study, we propose an intervention based on a feedback method called the "reduced region of orbit" (RRO) method to facilitate the transition of the disturbed frontal cortical neural activity underlying BD to healthy periodic activity. Our simulation was based on the Hadaeghi model. We used an RRO feedback signal based on the return-map structure of the simulated frontal and sensory lobes to induce synchronization with a relatively weak periodic signal corresponding to the healthy condition by applying feedback of appropriate strength. The RRO feedback signal induces chaotic resonance, which facilitates the transition to healthy, periodic frontal neural activity, although this synchronization is restricted to a relatively low frequency of the periodic input signal. Additionally, applying an appropriate strength of the RRO feedback signal lowered the amplitude of the periodic input signal required to induce a synchronous state compared with the periodic signal applied alone. In conclusion, through a chaotic-resonance effect induced by the RRO feedback method, the state of the disturbed frontal neural activity characteristic of BD was transformed into a state close to healthy periodic activity by relatively weak periodic perturbations. Thus, RRO feedback-modulated chronotherapy might be an innovative new type of minimally invasive chronotherapy.