Background. Bipolar disorder (BD) is a chronic psychiatric condition characterized by large shifts in mood, energy, and cognitive functioning. Recently, the conceptualization of BD has shifted from alternating discrete episodes to a chronic cyclical mood instability model. Recog-nizing and quantifying this mood instability may improve care and calls for high-frequency measures coupled with advanced statistical models. Methods. To uncover empirically derived mood states, a multilevel hidden Markov model (HMM) was applied to 4-month ecological momentary assessment (EMA) data in twenty pa-tients with BD, yielding ~9.820 assessments in total. EMA data comprised self-reported ques-tionnaires (5 per day) measuring manic and depressive constructs using 12 items. Manic and depressive symptoms were further assessed by weekly self-reported questionnaires (i.e., Alt-man Self-Rating Mania Scale and Quick Inventory for Depressive Symptomatology Self-Report). Alignment between uncovered mood states and weekly questionnaires was assessed with a multilevel linear model.Results. HMM uncovered four mood states (euthymic, manic, mixed, and depressive) that aligned with weekly symptom scores. States switched more frequently than weekly data sug-gested. The average state duration was 17.1h (SD=78.0) for euthymic, 16.3h (SD=70.8) for depressive, 9.1h (SD=11.6) for manic, and 8.8h (SD=10.9) for mixed. In almost half of the patients, significant mood instability was observed. Large individual differences were observed in state duration and switching.Conclusions. The results indicate that mood instability is a key feature of BD, which should be considered in theoretical and clinical conceptualizations of the disorder.

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Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analysing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity but also because of changes in the signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analysing such data in terms of discrete latent states, but previous approaches have not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modelled condition‐specific differences. We present a multilevel Bayesian HMM addresses these shortcomings by incorporating multivariate Poisson log‐normal emission probability distributions, multilevel parameter estimation and trial‐specific condition covariates. We applied this framework to multi‐unit neural spiking data recorded using chronically implanted multi‐electrode arrays from macaque primary motor cortex during a cued reaching, grasping and placing task. We show that, in line with previous work, the model identifies latent neural population states which are tightly linked to behavioural events, despite the model being trained without any information about event timing. The association between these states and corresponding behaviour is consistent across multiple days of recording. Notably, this consistency is not observed in the case of a single‐level HMM, which fails to generalise across distinct recording sessions. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long‐term plasticity in neural populations.

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Spurred in part by the ever-growing number of sensors and web-based methods of collecting data, the use of Intensive Longitudinal Data (ILD) is becoming more common in the social and behavioural sciences. The ILD collected in this field are often hypothesised to be the result of latent states (e.g. behaviour, emotions), and the promise of ILD lies in its ability to capture the dynamics of these states as they unfold in time. In particular, by collecting data for multiple subjects, researchers can observe how such dynamics differ between subjects. The Bayesian Multilevel Hidden Markov Model (mHMM) is a relatively novel model that is suited to model the ILD of this kind while taking into account heterogeneity between subjects. While the mHMM has been applied in a variety of settings, large-scale studies that examine the required sample size for this model are lacking. In this paper, we address this research gap by conducting a simulation study to evaluate the effect of changing (1) the number of subjects, (2) the number of occasions, and (3) the between subjects variability on parameter estimates obtained by the mHMM. We frame this simulation study in the context of sleep research, which consists of multivariate continuous data that displays considerable overlap in the state dependent component distributions. In addition, we generate a set of baseline scenarios with more general data properties. Overall, the number of subjects has the largest effect on model performance. However, the number of occasions is important to adequately model latent state transitions. We discuss how the characteristics of the data influence parameter estimation and provide recommendations to researchers seeking to apply the mHMM to their own data.