Association of circadian properties of temporal processing with rapid antidepressant response to wake and light therapy in bipolar disorder

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Association of circadian properties of temporal processing with rapid antidepressant response to wake and light therapy in bipolar disorder Takuya Yoshiike , Sara Dallaspezia , Kenichi Kuriyama , Naoto Yamada , Cristina Colombo , Francesco Benedetti PII: DOI: Reference:

S0165-0327(19)31890-7 https://doi.org/10.1016/j.jad.2019.11.132 JAD 11379

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Journal of Affective Disorders

Received date: Revised date: Accepted date:

19 July 2019 22 October 2019 29 November 2019

Please cite this article as: Takuya Yoshiike , Sara Dallaspezia , Kenichi Kuriyama , Naoto Yamada , Cristina Colombo , Francesco Benedetti , Association of circadian properties of temporal processing with rapid antidepressant response to wake and light therapy in bipolar disorder, Journal of Affective Disorders (2019), doi: https://doi.org/10.1016/j.jad.2019.11.132

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Highlights 

Altered circadian physiology and behavior are a hallmark of bipolar disorder.

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Production of 10s synchronously oscillates with mood during chronotherapeutics.

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An early circadian pattern of 10s production predicts treatment response.

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Changes in daily variation of 10s production and mood differ according to response.

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Association of circadian properties of temporal processing with rapid antidepressant response to wake and light therapy in bipolar disorder

Takuya Yoshiike1-4*, Sara Dallaspezia1, Kenichi Kuriyama3,4, Naoto Yamada3, Cristina Colombo1,2, Francesco Benedetti1,2

1

Psychiatry and Clinical Psychobiology, Division of Neuroscience, Scientific Institute Ospedale

San Raffaele, Milan, Italy 2

University Vita-Salute San Raffaele, Milan, Italy

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Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan

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Department of Sleep-Wake Disorders, National Institute of Mental Health, National Center of

Neurology and Psychiatry, Kodaira, Japan

*Correspondence Takuya Yoshiike, MD, PhD, Department of Sleep-Wake Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry

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4-1-1 Ogawahigashi, Kodaira, Tokyo 187-8553, Japan Tel: +81-42-346-2071 Fax: +81-42-346-2072 E-mail: [email protected]

Abbreviations ANOVA, analysis of variance; BD, bipolar disorder; CBT, core body temperature; CV, coefficient of variation; GZLM, generalized linear model ; HDRS, Hamilton Depression Rating Scale; HSD, honestly significance difference; LR, likelihood ratio; LT, light therapy; SCN, suprachiasmatic nucleus; TP, time perception; PTI, produced time interval; TSD, total sleep deprivation; VAS, visual analogue scale

Acknowledgements This study was supported by the SENSHIN Medical Research Foundation and by the Italian MoH grant RF-2011-02350980. We thank Sara Poletti, Veronica Aggio, Elena Mazza, Federica Rossi, and Lorenzo Parenti for assistance with data collection.

Conflict of interest

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None.

Author contributions TY and FB designed research. TY, SD, CC, and FB performed research and acquired data. TY and FB analyzed data. TY, FB, KK, and NY interpreted data. TY wrote the first draft of the paper. TY, FB, KK, SD, NY, and CC revised the paper.

Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Abstract Background: Temporal processing, crucial to guide behavior toward a goal, may have a role in forming a depressive episode, yet it remains unclear which properties of temporal processing are central to antidepressant response. Production of a short duration oscillates in a circadian manner. Altered circadian organization of physiology and behavior are a hallmark of bipolar disorder. We thus tested whether circadian dynamics of time production associate with treatment response in bipolar disorder. Methods: Over the three cycles of total sleep deprivation combined with light therapy (chronotherapeutics) in one week, 20 inpatients with a major depressive episode in the course of bipolar disorder produced 10 seconds and rated their subjective mood and vigilance levels repeatedly. Results: Eleven patients (58%) among 19 completers achieved remission. Produced time intervals (PTIs) fluctuated more synchronously with mood levels (r = –0.77) than vigilance levels (r = –0.59) during treatment. A higher degree of shortening of PTIs, but not changes in mood or vigilance levels, during the initial 24-h period of treatment predicted better response (LR χ2 = 4.58, P = 0.032). Strong opposite daily changes for PTIs and mood levels observed at baseline were both attenuated after treatment only in remitters (F = 7.25, P = 0.015). Limitations: Potential external confounders that affect time perception were not controlled.

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Conclusions: The results are the first to demonstrate an association of the circadian properties of time perception with antidepressant effects of chronotherapeutics and suggest the potential utility of time production in predicting clinical outcome of bipolar depression.

Keywords: Antidepressant chronotherapeutics; Bipolar disorder; Circadian dynamics, Sleep deprivation, Time perception

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Introduction Time perception (TP) refers to a brain function that subserves timekeeping across species (Buhusi and Meck, 2005). Humans can explicitly estimate the passage of time by applying one’s internal standard of time unit (subjective time) to a given length of objective time (i.e., clock) (Tysk, 1983). However, it is likely that TP has been designated to function implicitly, so that it can provide timekeeping without sacrificing the other ongoing cognitive processes (Kuriyama et al., 2003). The pacemaker function of the suprachiasmatic nucleus (SCN) helps cyclic behaviors (e.g., sleep/wake) to entrain to the 24-hour light-dark cycle (circadian timing). By contrast, temporal cognition, especially perception of a short duration in the seconds range, may play a fundamental role in guiding non-cyclic behaviors in time within varying timeframes, which is typically required for social synchronization with conspecifics (interval timing) (Bloch et al., 2013; Buhusi and Meck, 2005). For instance, ring dove males use interval, but not circadian, timing strategies to effectively communicate with females toward a goal of coordinating egg incubation (Gibbon et al., 1984). Therefore, human goal-directed behaviors may require this implicit timekeeping operation, even after clock devices well developed. TP fluctuates diurnally in humans (Kuriyama et al., 2005; Pöppel and Giedke, 1970). Among the behavioral measures of TP, “time-interval production” has widely been employed to determine subjective time in healthy individuals and depressed patients (Thönes and Oberfeld,

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2015; Wiener et al., 2010). In this task, a longer produced time-interval (PTI) (overproduction), relative to a given length of time, implies slower subjective time, while a shorter PTI (underproduction) implies faster subjective time. Similarly, compared with a PTI measured at a reference timepoint, a longer or shorter PTI at another timepoint also implies relatively slower or faster subjective time, respectively. Circadian oscillators (e.g., the SCN) have been hypothesized to influence the production of short time-intervals. An inverse correlation of PTIs in short time production tasks (e.g., 10 s) with core body temperature (CBT) (Aschoff, 1998; Kuriyama et al., 2005), and positive correlations of those with serum melatonin concentration (Kuriyama et al., 2005) and with light intensity (Aschoff and Daan, 1997), have been noted. In contrast, perception of longer timescales (hours) is homeostatically influenced (Aschoff and Daan, 1997). These findings suggest clinical utility of short time production as a behavioral circadian marker. Bipolar disorder (BD) is characterized by a higher level of dependence of behaviors on the circadian clock (Bollettini et al., 2017; Dallaspezia et al., 2016; Frank et al., 2000; Wehr et al., 1979). Altered circadian patterns of clock gene expression (Bunney et al., 2015), endocrine secretion (Steiger et al., 1989), neuropeptide synthesis (Salomon et al., 2003; Zhou et al., 2001), rest–activity cycle (Duncan et al., 2017), and thermoregulation (Avery et al., 1999) have been documented as a hallmark of mood disorders including BD. Given that thermoregulation has a

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critical role in both circadian gene expression (Saini et al., 2012) and antidepressant response (Avery et al., 1999; Elsenga and Van den Hoofdakker, 1988; Souetre et al., 1988), the circadian variation of TP would be associated with antidepressant response in patients with BD. Considering the circadian aspects of TP and BD, it appears necessary to control for the effects of time of day on temporal cognition in BD. Clinical observations that manic and depressed patients perceive time flow as either too fast or too slow have led to the assumption that synchronization between inner and outer time is disturbed by acceleration or retardation of inner time in accord with mood alterations (i.e., a manic or depressive episode) (Fuchs, 2013; Northoff et al., 2018). Despite the long history of research on TP in mood disorders (Wyrick and Wyrick, 1977), few studies have addressed the circadian characteristics of TP (Edelstein, 1974), which could subsequently result in negative conclusions about TP in depressed patients in a recent meta-analysis (Thönes and Oberfeld, 2015). Therefore, how TP is altered in mood disorders may still be worth pursuing. However, our goal is not to present data that give an answer to the question of how accurately patients with BD perceive the passage of time during treatment (Edelstein, 1974; Thönes and Oberfeld, 2015), but to study the circadian properties of TP and their association with antidepressant response in those with BD, by employing a short time-interval production method. We hypothesized that TP would fluctuate similarly with mood in response to

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antidepressant chronotherapeutics, because the circadian aspects of TP could reflect a basic neuronal component of BD (Northoff et al., 2018). Among antidepressant modalities, we employed antidepressant chronotherapeutics, total sleep deprivation (TSD) combined with light therapy (LT), because it can act promptly within hours with sustainable antidepressant effects (Wirz-Justice and Benedetti, 2019), possibly related to robust modulation of circadian patterns of TP. Given the rapidity of the onset of antidepressant effects of combined TSD + LT treatment, key circadian alterations would occur in the earliest treatment phase. Therefore, (1) the early circadian variation of TP would predict treatment response, and (2) the diurnal variation of TP, as well as that of mood (e.g., worsening of depression in the morning), would be attenuated through treatment in BD. To test our hypotheses, we evaluated TP and associated behavior (mood and vigilance) repeatedly at different times of day during TSD + LT treatment in BD. Here, we report a phenotypical proximity between TP and mood, suggesting a potential clinical utility of TP in predicting clinical outcome of bipolar depression.

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Methods Participants We studied 20 consecutively admitted inpatients, who were affected by a major depressive episode without psychotic features, in the course of BD (DSM-5 criteria, American Psychiatric Association, 2013). Inclusion criteria were: to be willing to participate; a baseline Hamilton Depression Rating Scale (HDRS) (Hamilton, 1967) score of 16 or higher; absence of other psychiatric diagnoses and of mental retardation, pregnancy, history of epilepsy, major medical and neurological disorders; no treatment with long-acting neuroleptic drugs in the last three months before admission; and absence of a history of drug or alcohol dependency or abuse within the last six months. After a complete description of the study to the subjects, a written informed consent was obtained. All the research activities were approved by the local ethics committee.

Treatment All patients were treated with a combined wake and light therapy. Each patient was administered three consecutive TSD cycles over a week; each cycle was composed of a period of 36 hours awake. On days 0, 2, and 4 patients were totally sleep deprived from 7 AM until 7 AM of the following day. They were then allowed to sleep during the nights of days 1, 3, and 5.

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Patients were administered LT (exposure for 30 min to a 10,000-lux bright white light, color temperature 4,600 K) at 3 AM during the TSD night and in the morning after recovery sleep, half an hour after awakening, between 8 AM and 9 AM. LT in the morning was then continued for two weeks. All patients were medicated with mood stabilizer and/or antidepressant. The majority of patients were either taking lithium at admission, and continued it, or started lithium together with the chronotherapeutic procedure to enhance its effect and prevent relapse. Patients were followed up for one month after the acute chronotherapeutic treatment. Nonresponders were treated by the psychiatrists in charge upon clinical need. Severity of depression was rated (days 0, 1, 2, and 6) by the psychiatrists in charge of the patients according to a modified version of the 21-item HDRS (HDRS-NOW) (Leibenluft et al., 1993), the primary outcome measure, from which items that could not be meaningfully rated due to the TSD procedure were excluded (i.e., weight changes and insomnia). Treatment response was defined as a ≥ 50% reduction of HDRS scores on day 6. Remission was defined as a HDRS score of ≤ 7 on day 6.

Time perception We evaluated the circadian patterns of TP over three TSD + LT cycles in one week. We employed a time production task (Aschoff and Daan, 1997; Kuriyama et al., 2005; Pöppel and

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Giedke, 1970) to determine circadian properties of TP influenced by the chronotherapeutic procedures. The time production task was run on a PC using E-Prime experimental software (Psychology Software Tools, Pittsburgh, PA). On each trial, participants were asked to produce an interval of 10 s without seeing any outer clock signals, by pressing a button to mark the beginning and end of the interval. Each session comprised five trials. Among the five produced time values for each session, both maximum and minimum values were omitted, and the remaining three values were entered into analysis. Participants did not receive any feedback on the accuracy of their performance. Patients performed the time production trials at 12 timepoints over the week in a psychiatric ward in our hospital, according to the schedule as follows (Figure 1): every four hours during the first circadian period (from 9 AM on day 0 to 9 AM on day 1; sessions 1–7); before and after the first recovery sleep (at 5 PM on day 1 and at 9 AM on day 2; sessions 8 and 9); and every four hours during daytime after treatment (from 9 AM to 5 PM on day 6; sessions 10–12). On day –1, patients were trained on the task to control for learning effects on their performance.

Mood and vigilance At the respective timepoints of time production sessions, participants’ mood levels were rated

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by a self-administered 12.5-cm visual analogue scale (VAS). Raw data were converted to a 0– 100 rating scale, with 0 and 100 denoting extreme depression and euphoria, respectively. Accordingly, participants’ vigilance levels were rated by another VAS and converted to the rating scale, with 0 and 100 denoting extremely sleepy and aroused states, respectively.

General cognition Cognitive alterations during a depressive episode that can last after successful treatment (Poletti et al., 2014) could affect TP before, during, and after treatment. Therefore, we measured baseline neurocognitive functions that are not directly related to temporal cognition with the Brief Assessment of Cognition in Schizophrenia (BACS; Keefe et al., 2004) on day –1. This battery consists of the following subtests covering a broad range of neurocognitive functions: verbal memory, working memory, motor function, verbal fluency, speed of processing, and executive function. Normative Italian adjusted scores were used for the subtests (Anselmetti et al., 2008).

Data analysis Given the stability of TP against cognitive interference (Kuriyama et al., 2003), TP is likely stable within individuals even if it can vary between individuals. To test this assumption, we

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compared the coefficient of variation (CV, defined as the ratio of the standard deviation to the mean) of PTIs averaged within patients with that of PTIs averaged across patients. The effects of time of day (morning vs evening) and treatment (day 0 vs day 6) on PTIs were tested by means of analysis of variance (ANOVA), followed by Tukey’s honestly significance difference (HSD) test. Given the variability of PTIs, mood, and vigilance across patients, these values collected at 12 timepoints were z-normalized to obtain their common fluctuation patterns across patients during treatment. Successive changes over time of these measures were tested by means of one-way ANOVAs of these measures with Tukey’s HSD test. The temporal association of TP with mood and vigilance over time during treatment were tested by cross-correlation analyses between PTIs and mood and between PTIs and vigilance, by entering 12 z-normalized values averaged at every timepoint for each behavioral measure. The possible predictive effect of the early circadian patterns of TP on antidepressant response was tested in the context of the generalized linear model (GZLM) with an identity link function, by entering the changes in z-normalized PTIs during the initial 24-h period of the protocol (9 AM on day 0 > 9 AM on day 1) as a continuous predictor and delta HDRS scores (day 0 > day 6) as a dependent variable. Parameter estimates were obtained with iterative re-weighted least squares maximum likelihood procedures. The significance of the effects was

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calculated with the likelihood ratio (LR) statistic, which provides the most asymptotically efficient test known. Furthermore, the possible effects of treatment response (remitter vs nonremitter), treatment (day 0 vs day 6), and behavior (TP vs mood) on the morning-evening variations in TP and mood were tested by a three-way repeated measures ANOVA with Tukey’s HSD test, by entering the daily changes in z-normalized PTIs and mood levels (5 PM > 9 AM on days 0 and 6) as dependent variables. This analysis was performed in the context of the general linear model. Given that lithium can affect TP (Elsass et al., 1979), current lithium use, as well as sex, both coded as a binary variable, were treated as a nuisance covariate. All analyses were performed using a commercially available software (StatSoft Statistica 12, Tulsa, OK) and following standard computational procedures.

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Results One patient withdrew from the protocol due to manic switch. Clinical and demographic characteristics of the remaining 19 study completers are summarized in Table 1. Confirming previous results, 68% (n = 13) of the patients responded to treatment and 58% (n = 11) achieved remission. Remitters did not significantly differ from nonremitters on any baseline variables except sex distribution. Among these baseline variables, the only significant correlate of baseline PTIs was the speed of processing on the BACS (ρ = 0.70, P = 0.002), suggesting that the higher performance on the speed of processing, the longer period of time patients produced. Given this, the speed of processing scores were also treated as an additional nuisance covariate.

Within-subject stability and morning-evening variation of time perception The overall mean ± SD PTI was 9.8 ± 3.7 s across patients and sessions at 12 timepoints and did not differ between remitters (9.9 ± 4.4 s) and nonremitters (9.7 ± 2.4 s) (Z = 0.25, P = 0.80). The mean PTI showed a marked variability across patients (range: 3.2–17.6 s). Nine patients overproduced, while 10 patients underproduced 10 s (Figure 2A). However, the within-individual variation of PTIs (0.13 ± 0.07, mean ± SD) was significantly smaller than the between-individual variation of PTIs (0.38 ± 0.05) (P < 0.00001). The morning-evening variation of TP, as confirmed by a shortening of PTIs from morning

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to evening, was sustained before and after treatment (main effect of time of day: F1, 14 = 6.77, P = 0.0209). The PTIs were shorter after treatment compared with those at baseline (main effect of treatment: F1, 14 = 8.41, P = 0.0116). Treatment showed a marginal effect on morning-evening variation of PTIs (interaction of time of day × day: F1, 14 = 3.80, P = 0.0714) (Figure 2B).

Fluctuation patterns of time perception, mood, and vigilance during treatment One-way ANOVAs showed a main effect of session on TP (F11, 198 = 4.53; P = 0.000004), mood (F11, 198 = 3.91; P = 0.00004), and vigilance (F11, 198 = 2.58; P = 0.0045). Over the treatment, PTIs were finally shortened, while mood and vigilance levels were finally elevated (Figures 3A–3C). A cross-correlation analysis confirmed that PTIs were negatively correlated with mood levels at lag 0 (r = –0.77, P < 0.05) (Figure 4A). The correlation data plots at lag 0 showed a progressive shortening of PTIs with a progressive elevation of mood levels across time (Figure 4B). PTIs were also correlated negatively with vigilance levels at lag 0 (r = –0.59, P < 0.05).

Early circadian patterns of time perception predict treatment response A GZLM regression showed a significant positive effect of the shortening of PTIs during the initial 24-hour period of treatment (9 AM on day 0 > 9 AM on day 1) on the improvement of

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depression as measured by the HDRS (day 0 > day 6) (parameter estimate = 1.50, LR χ2 = 4.58, P = 0.032: Figure 5A). The predictive effect remained significant while controlling for the effects of current lithium use, sex, and baseline speed of processing (parameter estimate = 1.78, LR χ2 = 6.48, P = 0.011). A bivariate correlation also showed that the early shortening of PTIs are significantly associated with the clinical outcome (ρ = 0.50, P = 0.028) (Figure S1). In contrast, neither changes in mood levels (parameter estimate = 0.25, LR χ2 = 0.38, P = 0.54) nor those in vigilance levels (parameter estimate = 0.16, LR χ2 = 0.15, P = 0.70) during this period of time predicted treatment response.

Diurnal variation of time perception and mood associates with treatment response A three-way ANOVA showed differential patterns over time of morning-evening variation in TP and mood between remitters and nonremitters (behavior × treatment × response interaction: F1,

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= 7.25, P = 0.0154) (Figure 5B). This interaction persisted after controlling for the effects of

current lithium use, sex, and baseline speed of processing (F1, 12 = 5.73, P = 0.0339). At baseline, strong opposite daily changes were observed for normalized PTIs (negative changes) and mood levels (positive changes) in remitters (P = 0.0002) and nonremitters (P = 0.0035). After treatment, the diurnal variations in both measures were significantly attenuated in remitters (TP: P = 0.0321; mood: P = 0.0175) and showed a weak diurnal variation. By contrast, there were no

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significant attenuations of these measures in nonremitters.

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Discussion The present study is the first to show markedly shared circadian dynamics between TP and mood and the potential utility of the circadian fluctuation of TP for early prediction of response to antidepressant chronotherapeutics in BD. We observed that a greater shortening of PTIs, which implies inner time acceleration, during the earliest phase of treatment associated with better treatment response and that morning-evening fluctuations of TP and mood were differentially influenced by treatment between remitters and nonremitters. In agreement with the previous observations in healthy subjects, PTIs, representing the explicitly expressed durations of inner time, showed a clear morning-evening decline both at baseline and after treatment (Kuriyama et al., 2005, 2003; Pöppel and Giedke, 1970). PTIs widely distributed with both directions (over- and under-production), relative to a given timespan (10 s), even when controlling for time of day. This contrasts with prior studies only reporting overproduction (underestimation) of short durations in depressed patients (Thönes and Oberfeld, 2015). However, despite the large variation of PTIs across patients, PTIs were stable within patients across time. Such characteristics of TP support an assumption that the individuals’ internal standards of time unit are rigidly conserved within individuals (Tysk, 1983). Our findings highlight the association of the circadian properties of temporal processing with antidepressant treatment in three ways: a shortening of PTIs after treatment; a higher

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degree of shortening of PTIs during the first circadian period with better treatment response; and marked suppression of diurnal variation of PTIs in remitters. These results suggest that antidepressant treatment, as well as the diagnosis of BD, influences the conserved internal reference of timescale. This appears to be in line with an assumption that the inner clock is slowed down during depression, relative to that during euthymia (Northoff et al., 2018; Wyrick and Wyrick, 1977). Conversely, antidepressant response could be associated with inner time acceleration and might modify such state-dependent alterations in temporal cognition. Despite the lack of healthy controls in the present study, a night of TSD seemed to differentially influence PTIs between BD patients and healthy subjects: we observed a progressive shortening of PTIs, whereas PTIs tend to return to its baseline level in healthy young adults (Kuriyama et al., 2005) during the comparable 24-h period from the first morning to the next. We therefore speculate the differential effects of TSD on circadian organization of physiology and behavior, including thermoregulation, temporal processing, and mood regulation, between BD patients and healthy subjects (Avery et al., 1999; Elsenga and Van den Hoofdakker, 1988; Souetre et al., 1988). Our study does not allow us to draw definitive inferences about mechanisms underlying circadian regulation of TP that differ according to treatment response. A depressive episode has been hypothesized to associate with altered circadian organization of physiology and behavior

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attributable to clock gene machinery that can be modified by chronotherapeutics (Bunney and Bunney, 2013). Our findings highlight a distinctive role of circadian modulation in eliciting rapid antidepressant effects of wake therapy in BD (Bunney et al., 2015; Dallaspezia et al., 2016). Further, it is plausible that molecular mechanisms of sleep deprivation, as well as those of ketamine, another fast-acting antidepressant, involving clock genes (Orozco-Solis et al., 2017), glutamatergic neurotransmission (Cirelli and Tononi, 2000), mammalian target of rapamycin (Li et al., 2010), and glycogen synthase kinase 3β (Benedetti et al., 2004; Beurel et al., 2011), have a role in modulating temporal processing. The clinical presentation of depression is typically worse in the morning during the acute phase of depression (American Psychiatric Association, 2013), which can then be flattened after treatment. In agreement with this well-known phenomenon, the morning-evening variation of TP was attenuated after treatment in remitters, together with that of mood. However, it is unlikely that this attenuation of daily changes in TP merely reflects the whole flattening of circadian oscillation of TP through treatment, rather it is likely that it can then be linked with the recovery of nocturnal, instead of diurnal, oscillations of neurophysiological correlates of mood disorders (Avery et al., 1999; Bunney et al., 2015; Duncan et al., 2017; Salomon et al., 2003; Steiger et al., 1989; Zhou et al., 2001). By contrast, flattened diurnal patterns of TP and mood suggest the lack of these circadian oscillations, irrespective of treatment, as seen in

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nonremitters. How does perceived time relate to neuronal activity eventually in mood disorders? Altered temporal processing per se appears to be nonspecific to mood disorders, because such alterations are observable in a variety of neuropsychiatric conditions (Castellanos and Tannock, 2002; Shea-Brown et al., 2006). However, our findings emphasize the importance of circadian variation of inner time as a possible marker of BD pathophysiology, which can be abolished by averaging PTIs across time, as seen in similar mean PTIs across sessions between remitters and nonremitters in our sample. A meta-analysis of neuroimaging of TP (Wiener et al., 2010) provides strong support for the notion that neuronal activity in the somatomotor regions, such as supplementary motor area and prefrontal cortex, serves as the neural basis for constituting the speed of inner time, while the sensory network may be relevant to the processing of outer time in the external environment (Northoff et al., 2018). Direct evidence linking inner time speed and neuronal activity in mood disorders has been scarce (Alústiza et al., 2017); however, this potential mechanistic link is supported by demonstration of contrasting variability patterns of intrinsic neuronal oscillations in the somatomotor and sensory networks balance in BD mania and depression (Northoff et al., 2018). This would be more plausible if we consider an imbalance between self- and environment-focus as a hallmark of mood disorders. Furthermore, the observed circadian changes in the representation of inner time and their relationship to

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depressive symptoms in the present study, further support the idea of a temporal basis of psychopathological symptoms rather than a cognitive or affective basis, which has recently been conceptualized as “spatiotemporal psychopathology” to fulfill an unmet need to integrate brain, experience, and behavior altogether (Northoff, 2016a, 2016b). From this perspective, future studies may address the association of temporal processing with spatiotemporal characteristics of the brain’s resting state activity (Martino et al., 2016) and cortical excitability (Canali et al., 2014; Ly et al., 2016) in mood disorders. This study has several limitations, including the rather small sample size, patients being non-drug-naive, and the lack of healthy controls. Patients did not live in isolation that controls for potential external confounders, such as time cues, ambient light and temperature, but stayed in an ordinary psychiatric ward. It is plausible that bright light provided in the morning (3 AM or 8 AM) influences the subsequent temporal processing (Aschoff and Daan, 1997). However, the prior findings indicate the light-dependent lengthening of PTIs, and thus without LT even more shortening of PTIs can be expected. Although taking lithium can affect TP in humans (Elsass et al., 1979), our main findings persisted after controlling for current lithium use. No patients took antipsychotics or dopamine agonists/antagonists that can affect TP (Meck, 1996). However, given the potential effects of serotonergic antidepressants on dopaminergic neurotransmission, current antidepressant use can also affect TP in our sample. Nevertheless,

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our findings may directly reflect what medicated patients with BD perceive during a major depressive episode and its treatment. Moreover, the measured general cognition, as represented by the BACS in the present study, is not necessarily comprehensive or discernable from temporal cognition, because these may not be mutually exclusive. Therefore, we cannot exclude the possibility that some other baseline cognitive performance that differ according to treatment response confounded our findings.

Conclusions The present study examined the association of circadian properties of temporal processing with rapid antidepressant response to total sleep deprivation combined with light therapy in bipolar depression, using production of short duration of time as a behavioral circadian marker. We demonstrated not only a predictive value of the early circadian pattern of TP for remission, but also a suppression of morning-evening fluctuation of TP and mood associated with remission. Our findings highlight a role for early circadian alterations in antidepressant response to chronotherapeutics and may represent an important missing link between the processing of temporal information and mood regulation (Northoff et al., 2018; Wyrick and Wyrick, 1977).

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Author contributions TY and FB designed research. TY, SD, CC, and FB performed research and acquired data. TY and FB analyzed data. TY, FB, KK, and NY interpreted data. TY wrote the first draft of the paper. TY, FB, KK, SD, NY, and CC revised the paper. All authors have approved the final version of the paper. Role of the Funding Source The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgements This study was supported by the SENSHIN Medical Research Foundation and by the Italian MoH grant RF-2011-02350980. We thank Sara Poletti, Veronica Aggio, Elena Mazza, Federica Rossi, and Lorenzo Parenti for assistance with data collection.

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Table 1. Characteristics of 19 patients who completed the chronotherapeutic procedures Remitters

Nonremitters

(n = 11)

(n = 8)

Z / χ2

P

53.8 ± 13.3

46.8 ± 12.0

1.28

0.20

2/9

5/3

3.91

0.048

Age at onset, years

31.2 ± 13.5

27.9 ± 15.5

0.78

0.43

Duration of illness, years

24.1 ± 12.7

17.0 ± 10.1

1.17

0.24

Previous depressive episodes, n

10.6 ± 6.2

4.4 ± 3.9

1.91

0.06

Previous manic episodes, n

6.1 ± 5.8

3.3 ± 4.8

1.65

0.10

Duration of current episode, weeks

65 ± 82

18 ± 19

1.95

0.051

8

5

0.22

0.64

10.3 ± 3.7

10.4 ± 2.3

0.17

0.87

Verbal memory

47.7 ± 6.1

39.9 ± 9.9

1.36

0.17

Working memory

19.6 ± 6.7

19.5 ± 4.8

0.23

0.82

Motor function

65.5 ± 16.2

61.3 ± 25.7

–0.06

0.95

Verbal fluency

43.8 ± 11.1

46.8 ± 14.3

–0.54

0.59

Speed of processing

41.5 ± 13.9

44.5 ± 7.2

–0.05

0.96

Executive function

14.6 ± 3.3

15.5 ± 3.8

–0.93

0.35

21-item HDRS baseline score

19.6 ± 2.4

22.4 ± 4.8

–0.57

0.56

HDRS-NOW day 0 (baseline)

16.3 ± 2.9

20.6 ± 4.7

–1.94

0.052

HDRS-NOW day 1

9.9 ± 5.8

13.8 ± 4.4

–1.44

0.15

HDRS-NOW day 2

6.7 ± 3.3

18.0 ± 5.7

–3.39

0.0007

HDRS-NOW day 6 (after treatment)

3.3 ± 2.6

14.6 ± 5.9

–3.59

0.0003

Age, years Men / Women, n

Current lithium use, n Produced time intervals, s

a

BACS, subscale scores

Values shown are mean ± SD. a Values averaged across sessions 1–3 are shown as baseline performance. BACS, Brief Assessment of Cognition in Schizophrenia; HDRS, Hamilton Depression Rating Scale; HDRS-NOW, a modified version of the 21-item HDRS.

38

Figure 1. Schedule of treatment and data collection. Patients were administered three total sleep deprivation (TSD; white bars) + light therapy (L; gray bars) cycles. During wakefulness, patients performed a time-production task at 12 timepoints, as shown by the black triangles. The timepoints were determined according to clock time. On day –1, patients were trained on the task procedure. At every timepoint, immediately before performing the time-production task, patients rated their subjective mood and vigilance levels using a visual analogue scale. The data collected at the first seven timepoints (the first circadian cycle during treatment) were analyzed to test the early prediction of treatment response. The data collected at morning and evening on days 0 and 6 (sessions 1, 3, 10, and 12) were analyzed to test attenuation of morning-evening variation of time production. RS, recovery sleep; S, nocturnal sleep; W, daytime wake.

39

Figure 2. Characteristics of produced time intervals (PTIs). (A) Stability and variation of time production. Plots represent the mean of PTIs at each timepoint for each patient. For a visual purpose, the patients were rearranged from left to right in the ascending order considering their performance. (B) Morning-evening variation and acceleration of time production. A significant shortening of PTIs from morning to evening of the day was observed on both days 0 and 6 (at baseline and after treatment). The PTIs were significantly shortened from day 0 to day 6 irrespective of times of day. Error bars indicate SEM.

40

Figure 3. Normalized fluctuation patterns of time perception (A), mood (B), and vigilance (C) across total sleep deprivation cycles. Asterisks denote significant differences in post hoc comparisons of the values at corresponding timepoints for each measure from the values at session 1 (9 AM on day 0) (* P < 0.05; ** P < 0.001; *** P < 0.0001). Zero on vertical axis of each measure represents the mean of the values across 12 timepoints of each patient. Error bars denote SEM. RS, recovery sleep; TSD, total sleep deprivation. 41

Figure 4. A temporal relationship between time perception and mood. (A) Normalized produced time intervals (PTIs) were lagged by normalized mood levels in a cross-correlation analysis. PTIs are correlated negatively with mood levels at lag 0, indicating that time production and mood fluctuate simultaneously in opposite directions. (B) Plots of the cross-correlation with lag of 0 indicate a successive shortening of PTIs with a successive elevation of mood levels, which are notable during the first circadian period from session 1 to session 7. Numbers denote timepoints for data collection. CI, confidence interval; CL, confidence limit.

42

Figure 5. Circadian properties of time production are related to antidepressant response. (A) The shortening of produced time intervals (PTIs) during the first circadian period (PTIs 9 AM on day 0 minus 9 AM on day 1) positively predicted the improvement of depression (HDRS scores day 0 minus day 6). (B) Differential diurnal patterns over time of time production and mood between remitters and nonremitters. Morning-evening variations of PTIs and mood levels (values 5 PM minus 9 AM) found at baseline with strong opposite directions were significantly attenuated after treatment only in remitters, but not in nonremitters. Error bars denote SEM. * P < 0.05.

43