Data-driven classification of bipolar I disorder from longitudinal course of mood

Characterizing the longitudinal course of symptoms and functioning in bipolar disorder

BACKGROUND

The course of Bipolar Disorder (BD) is highly variable, with marked inter and intra-individual differences in symptoms and functioning. In this study, we identified illness trajectories across major clinical domains that could have etiological, prognostic, and therapeutic relevance.

METHODS

Using the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study, we performed univariate and multivariate trajectory modeling of depressive symptoms, manic symptoms, and psychosocial functioning. Multinomial regression was performed to identify baseline variables associated with poor outcome trajectories.

RESULTS

Depressive symptoms predominated, with most subjects being found in trajectories characterized by various degrees of depressive symptoms and 13% of subjects being classified in a poor outcome 'persistently depressed' trajectory. Most subjects experienced few manic symptoms, although approximately 10% of subjects followed a trajectory of persistently manic symptoms. Trajectory analysis of psychosocial functioning showed impairment in most of the sample, with little improvement during follow up. Multi-trajectory analyses highlighted significant impairment in subjects with persistently mixed and persistently depressed trajectories of illness. In general, poor outcome trajectories were marked by lower educational attainment, higher unemployment and disability, and a greater likelihood of adverse clinical features (rapid cycling and suicide attempts) and comorbid diagnoses (anxiety disorders, PTSD, and substance abuse/dependence disorders).

CONCLUSIONS

Subjects with BD can be classified into several trajectories of clinically relevant domains that are prognostically relevant and show differing degrees of associations with a broad range of negative clinical risk factors. The highest level of psychosocial disability was found in subjects with chronic mixed and depressive symptoms, who show limited improvement despite guideline-based treatment.

Specificity of affective dynamics of bipolar and major depressive disorder

OBJECTIVE

Here, we examine whether the dynamics of the four dimensions of the circumplex model of affect assessed by ecological momentary assessment (EMA) differ among those with bipolar disorder (BD) and major depressive disorder (MDD).

METHODS

Participants aged 11-85 years (n = 362) reported momentary sad, anxious, active, and energetic dimensional states four times per day for 2 weeks. Individuals with lifetime mood disorder subtypes of bipolar-I, bipolar-II, and MDD derived from a semistructured clinical interview were compared to each other and to controls without a lifetime history of psychiatric disorders. Random effects from individual means, inertias, innovation (residual) variances, and cross-lags across the four affective dimensions simultaneously were derived from multivariate dynamic structural equation models.

RESULTS

All mood disorder subtypes were associated with higher levels of sad and anxious mood and lower energy than controls. Those with bipolar-I had lower average activation, and lower energy that was independent of activation, compared to MDD or controls. However, increases in activation were more likely to perpetuate in those with bipolar-I. Bipolar-II was characterized by higher lability of sad and anxious mood compared to bipolar-I and controls but not MDD. Compared to BD and controls, those with MDD exhibited cross-augmentation of sadness and anxiety, and sadness blunted energy.

CONCLUSION

Bipolar-I is more strongly characterized by activation and energy than sad and anxious mood. This distinction has potential implications for both specificity of intervention targets and differential pathways underlying these dynamic affective systems. Confirmation of the longer term stability and generalizability of these findings in future studies is necessary.

Latent subtypes of manic and/or irritable episode symptoms in two population-based cohorts

BACKGROUND

Mood disorders are characterised by pronounced symptom heterogeneity, which presents a substantial challenge both to clinical practice and research. Identification of subgroups of individuals with homogeneous symptom profiles that cut across current diagnostic categories could provide insights in to the transdiagnostic relevance of individual symptoms, which current categorical diagnostic systems cannot impart.

AIMS

To identify groups of people with homogeneous clinical characteristics, using symptoms of manic and/or irritable mood, and explore differences between groups in diagnoses, functional outcomes and genetic liability.

METHOD

We used latent class analysis on eight binary self-reported symptoms of manic and irritable mood in the UK Biobank and PROTECT studies, to investigate how individuals formed latent subgroups. We tested associations between the latent classes and diagnoses of psychiatric disorders, sociodemographic characteristics and polygenic risk scores.

RESULTS

Five latent classes were derived in UK Biobank (N = 42 183) and were replicated in the independent PROTECT cohort (N = 4445), including 'minimally affected', 'inactive restless', active restless', 'focused creative' and 'extensively affected' individuals. These classes differed in disorder risk, polygenic risk score and functional outcomes. One class that experienced disruptive episodes of mostly irritable mood largely comprised cases of depression/anxiety, and a class of individuals with increased confidence/creativity reported comparatively lower disruptiveness and functional impairment.

CONCLUSIONS

Findings suggest that data-driven investigations of psychopathological symptoms that include sub-diagnostic threshold conditions can complement research of clinical diagnoses. Improved classification systems of psychopathology could investigate a weighted approach to symptoms, toward a more dimensional classification of mood disorders.

Limited time-specific and longitudinal effects of depressive and manic symptoms on cognition in bipolar spectrum disorders

OBJECTIVES

Previous research suggests that cognitive performance worsens during manic and depressed states in bipolar disorder (BD). However, studies have often relied upon between-subject, cross-sectional analyses and smaller sample sizes. The current study examined the relationship between mood symptoms and cognition in a within-subject, longitudinal study with a large sample.

METHODS

Seven hundred and seventy-three individuals with BD completed a neuropsychological battery and mood assessments at baseline and 1-year follow-up. The battery captured eight domains of cognition: fine motor dexterity, visual memory, auditory memory, emotion processing, and four aspects of executive functioning: verbal fluency and processing speed; conceptual reasoning and set shifting; processing speed with influence resolution; and inhibitory control. Structural equation modeling was conducted to examine the cross-sectional and longitudinal relationships between depressive symptoms, manic symptoms, and cognitive performance. Age and education were included as covariates. Eight models were run with the respective cognitive domains.

RESULTS

Baseline mood positively predicted 1-year mood, and baseline cognition positively predicted 1-year cognition. Mood and cognition were generally not related for the eight cognitive domains. Baseline mania was predictive in one of eight baseline domains (conceptual reasoning and set shifting); baseline cognition predicted 1-year symptoms (inhibitory control-depression symptoms, visual memory-manic symptoms).

CONCLUSIONS

In a large community sample of patients with bipolar spectrum disorder, cognitive performance appears to be largely unrelated to depressive and manic symptoms, suggesting that cognitive dysfunction is stable in BD and is not dependent on mood state in BD. Future work could examine how treatment affects relationship between cognition and mood.

SIGNIFICANT OUTCOMES

Cognitive dysfunction appears to be largely independent of mood symptoms in bipolar disorder.

LIMITATIONS

The sample was generally highly educated (M = 15.22), the majority of the subsample with elevated manic symptoms generally presented with concurrent depressive elevated symptoms, and the study did not stratify recruitment based on mood state.

A critical evaluation of dynamical systems models of bipolar disorder

Bipolar disorder (BD) is a mood disorder involving recurring (hypo)manic and depressive episodes. The inherently temporal nature of BD has inspired its conceptualization using dynamical systems theory, which is a mathematical framework for understanding systems that evolve over time. In this paper, we provide a critical review of the dynamical systems models of BD. Owing to the heterogeneity of methodological and experimental designs in computational modeling, we designed a structured approach that parallels the appraisal of animal models by their face, predictive, and construct validity. This tool, the validity appraisal guide for computational models (VAG-CM), is not an absolute measure of validity, but rather a guide for a more objective appraisal of models in this review. We identified 26 studies published before November 18, 2021 that proposed generative dynamical systems models of time-varying signals in BD. Two raters independently applied the VAG-CM to the included studies, obtaining a mean Cohen's κ of 0.55 (95% CI [0.45, 0.64]) prior to establishing consensus ratings. Consensus VAG-CM ratings revealed three model/study clusters: data-driven models with face validity, theory-driven models with predictive validity, and theory-driven models lacking all forms of validity. We conclude that future modeling studies should employ a hybrid approach that first operationalizes BD features of interest using empirical data to achieve face validity, followed by explanations of those features using generative models with components that are homologous to physiological or psychological systems involved in BD, to achieve construct validity. Such models would be best developed alongside long-term prospective cohort studies involving a collection of multimodal time-series data. We also encourage future studies to extend, modify, and evaluate the VAG-CM approach for a wider breadth of computational modeling studies and psychiatric disorders.

Strategies and foundations for scientific discovery in longitudinal studies of bipolar disorder

Bipolar disorder (BD) is a complex and dynamic condition with a typical onset in late adolescence or early adulthood followed by an episodic course with intervening periods of subthreshold symptoms or euthymia. It is complicated by the accumulation of comorbid medical and psychiatric disorders. The etiology of BD remains unknown and no reliable biological markers have yet been identified. This is likely due to lack of comprehensive ontological framework and, most importantly, the fact that most studies have been based on small nonrepresentative clinical samples with cross-sectional designs. We propose to establish large, global longitudinal cohorts of BD studied consistently in a multidimensional and multidisciplinary manner to determine etiology and help improve treatment. Herein we propose collection of a broad range of data that reflect the heterogenic phenotypic manifestations of BD that include dimensional and categorical measures of mood, neurocognitive, personality, behavior, sleep and circadian, life-story, and outcomes domains. In combination with genetic and biological information such an approach promotes the integrating and harmonizing of data within and across current ontology systems while supporting a paradigm shift that will facilitate discovery and become the basis for novel hypotheses.

A digital self-report survey of mood for bipolar disorder

OBJECTIVES

Bipolar disorder (BP) is commonly researched in digital settings. As a result, standardized digital tools are needed to measure mood. We sought to validate a new survey that is brief, validated in digital form, and able to separately measure manic and depressive severity.

METHODS

We introduce a 6-item digital survey, called digiBP, for measuring mood in BP. It has three depressive items (depressed mood, fidgeting, fatigue), two manic items (increased energy, rapid speech), and one mixed item (irritability); and recovers two scores (m and d) to measure manic and depressive severity. In a secondary analysis of individuals with BP who monitored their symptoms over 6 weeks (n = 43), we perform a series of analyses to validate the digiBP survey internally, externally, and as a longitudinal measure.

RESULTS

We first verify a conceptual model for the survey in which items load onto two factors ("manic" and "depressive"). We then show weekly averages of m and d scores from digiBP can explain significant variation in weekly scores from the Young Mania Rating Scale (R²  = 0.47) and SIGH-D (R²  = 0.58). Lastly, we examine the utility of the survey as a longitudinal measure by predicting an individual's future m and d scores from their past m and d scores.

CONCLUSIONS

While further validation is warranted in larger, diverse populations, these validation analyses should encourage researchers to consider digiBP for their next digital study of BP.

Dynamics of data-driven microstates in bipolar disorder

Many of the existing models of mood in bipolar disorder can largely be divided into two camps, tracking mood as either a discrete or continuous variable. Both groups rely upon certain assumptions, with most considering only aggregate scores on clinical instruments. In this study, we propose a novel framework that combines elements from both discrete and continuous mood models, using a machine learning pipeline to detect subtle patterns across individuals. Latent factors are constructed from assessments at the item level, then clustered into groups referred to as microstates. Transitions between microstates are captured via a discrete-time Markov chain, allowing for characterization of mood's dynamic nature. Key findings include a factor mapping heavily onto irritability and aggression, as well as a hierarchical pattern of microstates within depression and mania. Validity of these results is confirmed by reproduction in an unseen data set from a separate subject cohort.

Conceptualizing the later stage of bipolar disorder: Descriptive analyses from the ORBIT trial

OBJECTIVES

This study aimed to investigate the features of later stage bipolar disorder (BD) and specifically, factors underlying later stage BD and potential subgroups within this stage, to understand more about the later stage group and contribute to the measurement of stage.

METHODS

An exploratory factor analysis was conducted using variables relating to current phenomenological aspects of illness, followed by cluster analyses based on the identified factors. Finally, the resultant clusters were compared based on course of illness variables.

RESULTS

Fourteen extracted factors explained 57 percent of the variance. Latent structures aligned with current depressive symptoms, energy and interest, independence, occupational functioning, symptoms of anxiety, pain, elevated symptoms, interpersonal functioning, anger, perceptions of social connections, and perceptions of current medication effectiveness, cognitive issues, sleep issues, and sense of isolation. Two clusters were identified which differed significantly on each of these factors, and on a range of course of illness features including lifetime number of episodes, duration of illness and number of depressive hospitalizations.

CONCLUSIONS

Latent phenomenological features relevant to individuals in the later stage of BD were identified. Two clusters of individuals in later stage BD differ based on these features as well as course of illness, suggesting that there are distinct subgroups of individuals in the later stage of BD, distinguishable based on current phenomenology and illness history. However, findings are exploratory and therefore require confirmation before they can be applied clinically.

A Lifespan Model of Interference Resolution and Inhibitory Control: Risk for Depression and Changes with Illness Progression

The cognitive processes involved in inhibitory control accuracy (IC) and interference resolution speed (IR) or broadly - inhibition - are discussed in this review, and both are described within the context of a lifespan model of mood disorders. Inhibitory control (IC) is a binary outcome (success or no for response selection and inhibition of unwanted responses) for any given event that is influenced to an extent by IR. IR refers to the process of inhibition, which can be manipulated by task design in earlier and later stages through use of distractors and timing, and manipulation of individual differences in response proclivity. We describe the development of these two processes across the lifespan, noting factors that influence this development (e.g., environment, adversity and stress) as well as inherent difficulties in assessing IC/IR prior to adulthood (e.g., cross-informant reports). We use mood disorders as an illustrative example of how this multidimensional construct can be informative to state, trait, vulnerability and neuroprogression of disease. We present aggregated data across numerous studies and methodologies to examine the lifelong development and degradation of this subconstruct of executive function, particularly in mood disorders. We highlight the challenges in identifying and measuring IC/IR in late life, including specificity to complex, comorbid disease processes. Finally, we discuss some potential avenues for treatment and accommodation of these difficulties across the lifespan, including newer treatments using cognitive remediation training and neuromodulation.

Digital solutions for shaping mood and behavior among individuals with mood disorders

Mood disorders present on-going challenges to the medical field, with difficulties ranging from establishing effective treatments to understanding complexities of one's mood. One solution is the use of mobile apps and wearables for measuring physiological symptoms and real-time mood in order to shape mood and behavior. Current digital research is focused on increasing engagement in monitoring mood, uncovering mood dynamics, predicting mood, and providing digital microinterventions. This review discusses the importance and risks of user engagement, as well as barriers to improving it. Research on mood dynamics highlights the possibility to reveal data-driven computational phenotypes that could guide treatment. Mobile apps are being used to track voice patterns, GPS, and phone usage for predicting mood and treatment response. Future directions include utilizing mobile apps to deliver and evaluate microinterventions. To continue these advances, standardized reporting and study designs should be considered to improve digital solutions for mood disorders.

The Emerging Neurobiology of Bipolar Disorder

(Reprinted with permission from Trends in Neurosciences, January 2018, Vol. 41, No. 1 ).

A clinical staging model for bipolar disorder: longitudinal approach

Bipolar disorder (BD) has been identified as a life-course illness with different clinical manifestations from an at-risk to a late stage, supporting the assumption that it would benefit from a staging model. In a previous study, we used a clustering approach to stratify 224 patients with a diagnosis of BD into five clusters based on clinical characteristics, functioning, cognition, general health, and health-related quality of life. This study was design to test the construct validity of our previously developed k-means clustering model and to confirm its longitudinal validity over a span of 3 years. Of the 224 patients included at baseline who were used to develop our model, 129 (57.6%) reached the 3-year follow-up. All life domains except mental health-related quality of life (QoL) showed significant worsening in stages (p < 0.001), suggesting construct validity. Furthermore, as patients progressed through stages, functional decline (p < 0.001) and more complex treatment patterns (p = 0.002) were observed. As expected, at 3 years, the majority of patients remained at the same stage (49.6%), or progressed (20.9%) or regressed (23.3%) one stage. Furthermore, 85% of patients who stayed euthymic during that period remained at the same stage or regressed to previous stages, supporting its longitudinal validity. For that reason, this study provides evidence of the construct and longitudinal validity of an empirically developed, comprehensive staging model for patients with BD. Thus, it may help clinicians and researchers to better understand the disorder and, at the same time, to design more accurate and personalized treatment plans.

Emotion dynamics concurrently and prospectively predict mood psychopathology

INTRODUCTION

Altered emotion dynamics may represent a transdiagnostic risk factor for mood psychopathology. The present study examined whether altered emotion dynamics were associated with bipolar and depressive psychopathology concurrently and at a three-year follow-up.

METHODS

At baseline (n = 138), participants completed diagnostic interviews, questionnaires, and seven days of experience sampling assessments. Four emotion dynamics were computed for negative affect (NA) and positive affect (PA) - within-person variance (variability), mean square of successive differences and probability of acute change (instability), and autocorrelation (inertia). At the three-year follow-up, participants (n = 108) were re-assessed via interviews and questionnaires.

RESULTS

NA variability was associated with bipolar spectrum disorders at baseline and follow-up. NA instability predicted depressive symptoms and hypomanic personality at baseline, and bipolar spectrum disorders at the follow-up. NA inertia did not predict diagnoses or symptoms at either assessment. PA inertia predicted hyperthymic temperament at baseline but not follow-up. Notably, NA variability and instability predicted the development of new bipolar spectrum disorders at the follow-up.

LIMITATIONS

Consistent with the recruitment strategy and young age of the participants, only 50% had developed diagnosable psychopathology by the time of the follow-up assessment.

CONCLUSIONS

The present study provided a unique demonstration that altered emotion dynamics differentially predicted bipolar and depressive psychopathology concurrently and prospectively. Emotion dynamics are important to both digital phenotyping and mobile-based interventions as emotional instability offers a measurable risk factor that is identifiable prior to illness onset.

Touchscreen typing pattern analysis for remote detection of the depressive tendency

Depressive disorder (DD) is a mental illness affecting more than 300 million people worldwide, whereas social stigma and subtle, variant symptoms impede diagnosis. Psychomotor retardation is a common component of DD with a negative impact on motor function, usually reflected on patients' routine activities, including, nowadays, their interaction with mobile devices. Therefore, such interactions constitute an enticing source of information towards unsupervised screening for DD symptoms in daily life. In this vein, this paper proposes a machine learning-based method for discriminating between subjects with depressive tendency and healthy controls, as denoted by self-reported Patient Health Questionnaire-9 (PHQ-9) compound scores, based on typing patterns captured in-the-wild. The latter consisted of keystroke timing sequences and typing metadata, passively collected during natural typing on touchscreen smartphones by 11/14 subjects with/without depressive tendency. Statistical features were extracted and tested in univariate and multivariate classification pipelines to reach a decision on subjects' status. The best-performing pipeline achieved an AUC = 0.89 (0.72-1.00; 95% Confidence Interval) and 0.82/0.86 sensitivity/specificity, with the outputted probabilities significantly correlating (>0.60) with the respective PHQ-9 scores. This work adds to the findings of previous research associating typing patterns with psycho-motor impairment and contributes to the development of an unobtrusive, high-frequency monitoring of depressive tendency in everyday living.

Identification and initial validation of empirically derived bipolar symptom states from a large longitudinal dataset: an application of hidden Markov modeling to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study

BACKGROUND

Although bipolar disorder (BD) is a fundamentally cyclical illness, a divided model of BD that emphasizes polarity over cyclicity has dominated modern psychiatric diagnostic systems since their advent in the 1980s. However, there has been a gradual return to conceptualizations of BD which focus on longitudinal course in the research community due to emerging supportive data. Advances in longitudinal statistical methods promise to further progress the field.

METHODS

The current study employed hidden Markov modeling to uncover empirically derived manic and depressive states from longitudinal data [i.e. Young Mania Rating Scale and Montgomery-Asberg Depression Rating Scale responses across five occasions from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study], estimate participants' probabilities of transitioning between these states over time (n = 3918), and evaluate whether clinical variables (e.g. rapid cycling and substance dependence) predict participants' state transitions (n = 3229).

RESULTS

Analyses identified three empirically derived mood states ('euthymic,' 'depressed,' and 'mixed'). Relative to the euthymic and depressed states, the mixed state was less commonly experienced, more temporally unstable, and uniquely associated with rapid cycling, substance use, and psychosis. Individuals assigned to the mixed state at baseline were relatively less likely to be diagnosed with BD-II (v. BD-I), more likely to present with a mixed or (hypo)manic episode, and reported experiencing irritable and elevated mood more frequently.

CONCLUSIONS

The results from the current study represent an important step in defining, and characterizing the longitudinal course of, empirically derived mood states that can be used to form the foundation of objective, empirical attempts to define meaningful subtypes of affective illness defined by clinical course.

Pathways to Neuroprediction: Opportunities and challenges to prediction of treatment response in depression

PURPOSE OF REVIEW

We set out to review the current state of science in neuroprediction, using biological measures of brain function, with task based fMRI to prospectively predict response to a variety of treatments.

RECENT FINDINGS

Task-based fMRI neuroprediction studies are balanced between whole brain and ROI specific analyses. The predominant tasks are emotion processing, with ROIs based upon amygdala and subgenual anterior cingulate gyrus, both within the salience and emotion network. A rapidly emerging new area of neuroprediction is of disease course and illness recurrence. Concerns include use of open-label and single arm studies, lack of consideration of placebo effects, unbalanced adjustments for multiple comparisons (over focus on type I error), small sample sizes, unreported effect sizes, overreliance on ROI studies.

SUMMARY

There is a need to adjust neuroprediction study reporting so that greater coherence can facilitate meta analyses, and increased funding for more multiarm studies in neuroprediction.

Testing frameworks for personalizing bipolar disorder

The hallmark of bipolar disorder is a clinical course of recurrent manic and depressive symptoms of varying severity and duration. Mathematical modeling of bipolar disorder holds the promise of an ability to personalize diagnoses, to predict future mood episodes, to directly compare diverse datasets, and to link basic mechanisms to behavioral data. Several modeling frameworks have been proposed for bipolar disorder, which represent competing hypothesis about the basic framework of the disorder. Here, we test these hypotheses with self-report assessments of mania and depression symptoms from 178 bipolar patients followed prospectively for 4 or more years. Statistical analysis of the data did not support the hypotheses that mood arises from a rhythmic process or multiple stable states (e.g., mania or depression) or that manic and depressive symptoms are highly anti-correlated. Alternatively, it is shown that bipolar disorder could arise from an inability for mood to quickly return to normal when perturbed. This latter concept is embodied by an affective instability model that can be personalized to the clinical course of any individual with chronic disorders that have an affective component.

The Emerging Neurobiology of Bipolar Disorder

Bipolar disorder (BD) is a leading cause of global disability. Its biological basis is unknown, and its treatment unsatisfactory. Here, we review two recent areas of progress. First, the discovery of risk genes and their implications, with a focus on voltage-gated calcium channels as part of the disease process and as a drug target. Second, facilitated by new technologies, it is increasingly apparent that the bipolar phenotype is more complex and nuanced than simply one of recurring manic and depressive episodes. One such feature is persistent mood instability, and efforts are underway to understand its mechanisms and its therapeutic potential. BD illustrates how psychiatry is being transformed by contemporary neuroscience, genomics, and digital approaches.

The clinical course of late-life bipolar disorder, looking back and forward

OBJECTIVES

Little is known about the course of late-life bipolar disorder (LLBD). First, we studied patients with LLBD retrospectively with regard to age at first mood episode, onset polarity, predominant polarity and episode density and its associations with other clinical variables. Next, we examined prospectively the clinical course and its associated factors.

METHODS

Data were used from a dynamic cohort (Dutch Older Bipolars [DOBi]) including 101 patients with LLBD (mean age of 68.9 years) at baseline in 2012, with 3-year follow-up measurements available for 64 of these patients. Retrospective course was assessed by diagnostic interviews, and at follow-up polarity and duration for each consecutive episode were noted. Linear and logistic analyses were performed to assess associations between relevant factors and outcome.

RESULTS

The mean age at the first episode was 33.0 years. Onset polarity was depression in 44.6% of patients, with a predominant polarity of depression in 47.5%. At 3-year follow-up, 37.5% of patients reported at least one mood episode, mainly depression. Life events, somatic illness, use of lithium and other factors were not associated with recurrence during the 3-year follow-up.

DISCUSSION

A relapse rate of 37.5% in 3 years is high, considering that LLBD patients generally have a longer history of disease and were receiving care and medication. The course of LLBD can provide important information on which clinical factors are associated with recurrence. Further phenotyping may reveal unique predictors for outcome, and both course specifiers and clinical variables should be included.