Anticipating manic and depressive shifts in patients with bipolar disorder using early warning signals

Background. In bipolar disorder treatment, accurate prediction of manic and depressive episodes is paramount but remains difficult. A novel idiographic approach to prediction is to monitor generic early warning signals (EWS), which may manifest in symptom dynamics. EWS could thus form personalized alerts in clinical care. The present study investigated whether EWS can anticipate mood shifts in individual patients with bipolar disorder. Methods. Twenty bipolar type I/II patients participated in ecological momentary assessment (EMA), completing five questionnaires a day for four months (Mean=491 observations per person). Weekly completed symptom questionnaires on depressive (Quick Inventory for Depressive Symptomatology Self-Report) and manic (Altman Self-Rating Mania Scale) symptoms were used to determine transitions. EWS (rises in autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective and symptomatic EMA items. Positive and negative predictive values were calculated to determine clinical utility. Results. Eleven (of the twenty) patients reported 1-2 manic or depressive transitions. The presence of EWS increased the probability of impending transitions towards depression and mania from 32-36% to 46-48% (autocorrelation) and 29-41% (standard deviation). However, the absence of EWS could not be taken as a sign that no transition would occur in the near future. The momentary states that indicated nearby transitions most accurately (predictive values: 65-100%) were cheerfulness, focusing ability, full of ideas, worry, racing thoughts, agitation, energy, and tiredness. Large individual differences in the utility of EWS were found.Conclusions. EWS may improve anticipating manic and depressive transitions in bipolar disorder, but await further empirical testing.

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Efficacy of early warning signals and spectral periodicity for predicting transitions in bipolar patients: An actigraphy study

Translational Psychiatry ◽

10.1038/s41398-021-01465-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yoram K. Kunkels ◽

Harriëtte Riese ◽

Stefan E. Knapen ◽

Rixt F. Riemersma - van der Lek ◽

Sandip V. George ◽

...

Keyword(s):

Spectral Analysis ◽

Early Warning ◽

Type I ◽

Warning Signals ◽

Early Warning Signals ◽

Research Fields ◽

Mood Episode ◽

Final Sample ◽

Bipolar Patients ◽

Direction Opposite

AbstractEarly-warning signals (EWS) have been successfully employed to predict transitions in research fields such as biology, ecology, and psychiatry. The predictive properties of EWS might aid in foreseeing transitions in mood episodes (i.e. recurrent episodes of mania and depression) in bipolar disorder (BD) patients. We analyzed actigraphy data assessed during normal daily life to investigate the feasibility of using EWS to predict mood transitions in bipolar patients. Actigraphy data of 15 patients diagnosed with BD Type I collected continuously for 180 days were used. Our final sample included eight patients that experienced a mood episode, three manic episodes and five depressed episodes. Actigraphy data derived generic EWS (variance and kurtosis) and context-driven EWS (autocorrelation at lag-720) were used to determine if these were associated to upcoming bipolar episodes. Spectral analysis was used to predict changes in the periodicity of the sleep/wake cycle. The study procedures were pre-registered. Results indicated that in seven out of eight patients at least one of the EWS did show a significant change-up till four weeks before episode onset. For the generic EWS the direction of change was always in the expected direction, whereas for the context-driven EWS the observed effect was often in the direction opposite of what was expected. The actigraphy data derived EWS and spectral analysis showed promise for the prediction of upcoming transitions in mood episodes in bipolar patients. Further studies into false positive rates are suggested to improve effectiveness for EWS to identify upcoming bipolar episode onsets.

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Early-Warning Signals For Cenozoic Climate Transitions

10.5194/egusphere-egu21-2520 ◽

2021 ◽

Author(s):

Georg Klinghammer ◽

Christopher Böttner

Keyword(s):

Standard Deviation ◽

Early Warning ◽

Large Scale ◽

Basin Of Attraction ◽

Warning Signals ◽

Climate History ◽

Early Warning Signals ◽

Critical Transitions ◽

Carbon And Oxygen Isotope ◽

Climate Transitions

Paleoclimatic records document large-scale shifts in the Earth&#8217;s climate history. Among other possibilities, these transitions might have been caused by bifurcations in the leading dynamical modes. Such bifurcation-induced critical transitions are typically preceded by characteristic early-warning signals (EWS), for example in terms of rising standard deviation and lag-one autocorrelation. These EWS are caused by the phenomenon of critical slowing down (CSD) in response to a widening of the underlying basin of attraction as the bifurcation is approached. The presence of EWS prior to an observed transition therefore provides evidence that the transition is caused by a bifurcation. We reveal significant EWS prior to several critical transitions within a paleoclimate record spanning the Cenozoic Era, i.e., the last 67M years. We employed the CENOzoic Global Reference benthic foraminifer carbon and oxygen Isotope Dataset (CENOGRID), comprising two time series of isotope variations of &#948;18O and &#948;13C. The standard deviation and lag-one autocorrelation are estimated in sliding windows for both records, to reveal whether CSD occurs ahead of the major abrupt transitions in these records. Specifically, we detect significant EWS for five out of nine previously identified transitions in at least one of the two available records. EWS are recognized for significant increases in both CSD indicators prior to the transition. Our results hence suggest that at least five major climate transitions of the last 67 Ma were triggered by bifurcations in leading modes of variability, indicating bifurcations have likely played a key role in the deep-time evolution of the Earth's climate system.

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Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach

Psychological Medicine ◽

10.1017/s0033291721005183 ◽

2021 ◽

pp. 1-9

Author(s):

Joshua E. Curtiss ◽

David Mischoulon ◽

Lauren B. Fisher ◽

Cristina Cusin ◽

Szymon Fedor ◽

...

Keyword(s):

Time Series ◽

Standard Deviation ◽

Dynamical Systems ◽

Early Warning ◽

Network Connectivity ◽

Depression Symptoms ◽

Warning Signals ◽

Early Warning Signals ◽

Auto Correlation ◽

Temporal Standard Deviation

Abstract Background Predicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain ‘early warning signals’ (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether rises in these EWSs over time are associated with future changes in disorder severity among a group of patients with major depressive disorder (MDD). Methods Thirty-one patients with MDD completed the study, which consisted of daily smartphone-delivered surveys over 8 weeks. Daily positive and negative affect were collected for the time-series analyses. A rolling window approach was used to determine whether rises in auto-correlation of total affect, temporal standard deviation of total affect, and overall network connectivity in individual affect items were predictive of increases in depression symptoms. Results Results suggested that rises in auto-correlation were significantly associated with worsening in depression symptoms (r = 0.41, p = 0.02). Results indicated that neither rises in temporal standard deviation (r = −0.23, p = 0.23) nor in network connectivity (r = −0.12, p = 0.59) were associated with changes in depression symptoms. Conclusions This study more rigorously examines whether rises in EWSs were associated with future depression symptoms in a larger group of patients with MDD. Results indicated that rises in auto-correlation were the only EWS that was associated with worsening future changes in depression.

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